A Survey on Green Designs for Energy Harvesting Backscatter Communications to Enable Sustainable IoT

Abstract

The majority of Internet of Things (IoT) devices operate with limited energy resources, making it essential to prioritize sustainable carbon emissions and the adoption of energy-efficient IoT solutions. For this reason, backscatter communication (BackCom) devices are widely deployed because they are mostly passive devices that harvest energy from RF signals and modulate the information onto reflected signals by adjusting the impedance of the load. BackCom devices have a simple structure, low cost, and easy deployment. Although BackCom plays a positive role in improving energy efficiency, IoT systems that deploy many EH BackCom devices and connect numerous peripherals still face difficulties in terms of power limitations because the energy required for their operation is almost all harvested from the outside. This paper comprehensively reviews the approaches to solving the energy efficiency issues in energy harvesting (EH) BackCom-enabled IoT systems, which mainly include high-efficiency EH and energy conversion designs for the BackCom tag, renewable energy harvesting, waveform design, and resource allocation for readers. We also investigate various green designs for cooperative EH BackCom systems. Finally, we indicate the new applications and open challenges of green BackCom IoT systems, as well as future research directions.

1. Introduction

1.1. Background

The rapid development of Internet of Things (IoT) technology opens up many possibilities for the intelligent application in industry and in life [1], including smart cities [2], smart agriculture [3], smart healthcare [4], smart education [5], and environmental monitoring [6]. The large-scale deployment of IoT devices has triggered a transformation that allows greater convenience and efficiency in all aspects of human life [7]. These devices have recently exploded and are expected to increase from 50 billion in 2020 [8] to 500 billion in 2030 [9]. It is also estimated that by 2025, the global energy consumption of edge devices of the IoT will reach 46 Terawatt-hours [10]. A vast network of devices not only fosters innovation in various applications, but also creates a significant challenge in providing reliable and continuous energy support for the growing number of IoT devices.

In this context, green and sustainable IoT solutions are gradually gaining attention [11,12]. Backscatter communication (BackCom) technology is widely regarded as a potential technological path to solving this problem due to its advantages of low power consumption and low cost [13]. In particular, BackCom’s energy harvesting (EH) can collect and convert energy from RF signal [14], solar energy [15], wind energy [13], and other renewable energy sources [16] to power the communication and operation of IoT devices. This technology opens up new opportunities for green and sustainable IoT development, reducing dependence on traditional power sources and offering the possibility of battery-free or low-battery operation of devices.

However, EH BackCom technologies face several challenges in practice. One of them is the low efficiency of EH [17], which cannot always meet the demand for continuous equipment operation. In addition, as a result of the instability of the energy sources in the environment [15], it can lead to fluctuations in the performance of the device, which in turn affects the stability and effectiveness of communication. The trade-off between communication power consumption and data throughput is also one of the key issues [18]. Since the energy that EH can provide is unstable and limited, optimizing the allocation and distribution of resources in practical applications is also a challenge that must be solved to ensure the best performance of devices in different scenarios and the efficient operation of the network [19].

To address these challenges, we provide a comprehensive review of various approaches to improving the energy efficiency (EE) of EH BackCom. First, we summarize and analyze the current state of research on existing BackCom technologies. Second, we present the source of EH and its working principle, as well as the system architecture and features of BackCom, and introduce the framework of EH BackCom. We then review the green design of the BackCom tag and the green design of the reader and energy source. Furthermore, we also summarize the relevant literature on cooperative EH BackCom green designs and explore the practical effects of its trade-off strategies. Finally, we look forward to the challenges, potential applications, and future research directions of EH BackCom in green and sustainable development and commit to contributing to the building of a greener, smarter, and more efficient IoT ecosystem.

1.2. Overview of Related Works on EH BackCom

For years, researchers have been working on energy-efficient and low-cost green IoT systems, particularly BackCom that captures energy. First, a detailed literature summary is presented for the basic concepts, operational methods, mechanisms [15], and application background of Ambient BackCom Systems (ABCS), including summaries of advanced design techniques and discussions of the challenges faced. Note that ABCS is a technology that harvests energy from the environment and uses this energy for BackCom. Next, a more comprehensive review of the literature [18] on BackCom is presented, which investigates not only ambient BackCom but also bistatic and monostatic BackCom. Data and power transmission strategies are considered to improve the EE of the BackCom network, as well as the reliability, security, and extension of range [20]. In addition, Ref. [14] summarizes the existing work and applications of wireless-powered BackCom, which is more stable than ambient energy sources. Related work [21] investigates ambient excitation signals based on existing research on ambient backscattering. In addition, Ref. [13] reviewed existing battery-free IoT BackCom prototypes and explored four key issues: link performance improvement, multidevice concurrent transmission, security assurance, and interaction with environmental RF communication systems. However, the above works failed to summarize sustainable EH BackCom, especially the summary of EH efficiency and BackCom EE.

To provide a clearer overview of the existing literature, we summarize the main contributions and limitations of key EH BackCom studies below in

Table 1. List of related reviews.

Reference	Main Contributions	Limitations	Year
[13]	The content reviews existing BackCom for battery-free IoT prototypes, addressing key issues including link performance enhancement, multidevice concurrent transmission, and security issues.	Overlooked the EE and sustainability of battery-free BackCom IoT.	2023
[21]	This content introduces the fundamentals of ambient BackCom, including its architecture, techniques, and ambient signal basics. It then presents a new classification system based on signal types, reviews various systems in the literature under this taxonomy, and explores potential applications.	The EH efficiency and sustainability of BackCom have not been highlighted in this paper.	2022
[14]	This content shows key aspects of wireless-powered networks and BackCom, including their principles, architectures, advancements, and challenges, with how they enable energy-efficient, sustainable wireless communication and IoT systems.	Only summarized wireless-powered BackCom, did not focus on the EE of EH BackCom, lack of literature review on the structure of EH tags.	2020
[18]	This paper highlights the progress made over the past 70 years and explores emerging technologies like quantum backscatter.	Although BackCom is summarized, it failed to provide a more detailed summary and description of EH.	2019
[20]	This content discusses BackCom communication modes, modulation schemes, and multiple access techniques for accommodating maximum users with high throughput. It also reviews the strategies for data and power transfer to enhance network EE, along with considerations for reliability, security, and range extension.	Although the problem of limited energy is emphasized, it does not consider EH BackCom EE.	2019
[15]	This article provides a background of ABCS on the basic concepts, operation methods, mechanisms, and applications, summarizes advanced design techniques, and discusses challenges, open issues, and future research directions.	Lack of summary of ABCS energy efficiency.	2018

. This table highlights research gaps, particularly the lack of a comprehensive focus on EE and sustainability in EH BackCom systems, which our survey aims to address.

1.3. Motivation and Contribution

Based on our survey of existing work, most existing studies have ignored error designs for EH BackCom, which causes readers to be unable to access the key information about EH BackCom’s energy-saving and energy-efficient technologies. In addition, the applications of EH BackCom emphasized in the existing literature are not perfect, and the latest review of the related literature is from 2023, and the subject is time-sensitive. To fill this gap, we aim to comprehensively review energy-saving technologies in green EH BackCom IoT and related applications, challenges, and future trends. Our contributions are threefold:

(a) We review existing green designs for EH in BackCom in IoT. We further demonstrate the structure of the EH tag and its design to improve EH efficiency and communication efficiency.

(b) We also review the energy-efficient designs of the readers and the RF energy source that can utilize renewable energy, and ambient RF energy is also reviewed.

(c) We summarize the high EE techniques of cooperative systems based on EH BackCom. This includes UAV-assisted BackCom, IRS-assisted BackCom, and a relaying protocol for BackCom. We review the application of EH BackCom in these systems and the green designs that improve the overall energy efficiency of these systems.

(d) We also point out various new applications in EH BackCom and demonstrate the open challenges in this rapidly developing research field. Finally, we also predict the future research direction of the green design of EH BackCom to improve a sustainable IoT.

1.4. Paper Organization

The rest of this review article is organized as follows: Section 2 provides comprehensive reviews of the EH technology and the BackCom technology and introduces the structure and scope of EH BackCom. Section 3 and Section 4 introduce the green design of the tags and the readers based on the structure of EH BackCom. Section 5 summarizes the EE and green designs of cooperative systems based on EH BackCom. Finally, in Section 6, we present the new applications and the open challenges of EH BackCom. In Section 6.3, we projected the future research direction of the EH BackCom Green designs. Finally, we summarize and conclude our work in Section 7. Overall, the structure of this review article is illustrated in Figure 1, which provides a visual representation of the organization and flow of this paper.

2. Principles of EH in BackCom Systems

In this section, we will introduce the basic principles and structure of the EH BackCom, discuss its advantages and disadvantages, and provide a basic understanding for the reader to understand the subsequent chapters.

2.1. EH

To extend the service life of equipment, EH brings many new opportunities and promising developments for energy-constrained networks [22]. EH is a method of powering electronic devices by capturing various forms of energy from the environment and converting them into electricity to power device operation [23]. EH technology is promising, as network devices require longer life spans and less maintenance [24]. Based on different energy sources and application scenarios, EH technologies can be subdivided into the following categories: wireless power transfer, the wireless-powered communication network, simultaneous wireless information and power transfer, and ambient energy-powered wireless communication networks.

2.1.1. Radio Frequency EH

Radio frequency EH (RF-EH) technology powers devices by capturing radio frequency waves and converting them into electrical energy. There are four main application models for RF-EH, among which the three most common energy supply methods are illustrated in Figure 2, providing an intuitive comparison of their principles and applications:

• Wireless power transfer (WPT): Transmits energy through a dedicated power transmitter without carrying information [25,26]. Commonly used in home electronics [27], medical implants [28], and wireless charging of electric vehicles [29,30,31].
• Wireless-powered communication network (WPCN): A two-way communication system that transmits energy in the downlink and information in the uplink [32]. The typical protocol is “Harvest-Then-Transmit (HTT)” [33], which is widely used in IoT and WSN applications [34,35].
• Simultaneous wireless information and power transfer (SWIPT): Transmits energy and information through a common signal [36,37], where energy and data are synchronized on the same channel.
• Ambient energy-powered wireless communication networks: A two-way communication system where wireless devices harvest RF energy from the environment to provide the energy needed for communication [38,39].

The advantage of the first three RF energy supply methods is that they provide a controlled and stable energy source, which ensures the timeliness of recharge and communication. However, this requires additional energy consumption and reduces the system’s overall energy efficiency. EH using ambient energy does not require the provision of additional energy, but the instability of the energy that the ambient energy source supplies can lead to an increase in the EH time.

2.1.2. Renewable EH

In addition to RF energy, other renewable environmental energy sources can be harvested and used in a green and sustainable IoT. Figure 3 categorizes key renewable energy harvesting methods, including solar, piezoelectric, thermoelectric, and wind energy, providing a clear visualization of their applications in green and sustainable IoT systems. We next discuss them one by one.

• Solar EH: The use of solar cells (photovoltaic cells) to convert light energy into electricity is one of the most widely used EH technologies [40]. It is suitable for outdoor environmental sensors, monitoring devices, and embedded IoT devices. Solar systems often use maximum power point tracking (MPPT) algorithms to improve conversion efficiency.
• Piezoelectric EH: The generation of electrical energy through mechanical vibration or pressure changes [41]. Piezoelectric materials generate an electrical charge when subjected to stress or deformation and are widely used in mechanical vibration sensors and industrial environmental monitoring equipment. IoT devices require small, sustainable power sources, and piezoelectric materials are considered the most promising and widespread solution for powering these devices by converting mechanical energy into electrical energy [42].
• Thermoelectric EH: A technology that converts thermal energy to electrical energy by using a temperature difference, often used to recover waste heat in factories, automobile engines, and other environments [43]. Thermoelectric generators (TEGs) are the core components of this technology and are suitable for scenarios with large temperature differences.
• Wind EH: Wind power generation is a renewable energy technology that uses the kinetic energy of the wind to convert it into electrical energy through a wind turbine. The wind drives the generator blades to rotate, and the blades transfer the rotational kinetic energy to the generator rotor through the main shaft connected to it. Under the interaction of the coil and the magnetic field, electrical energy is generated according to the principle of electromagnetic induction [44].

Current discussions on renewable energy sources in the IoT focus on two key issues [45]. First, existing renewable EH technologies are inefficient and require additional components (solar panels, piezoelectric components, etc.) compared to RF-EH through antennas, which will undoubtedly increase the overall size of the device. In IoT applications, most devices (e.g., backscatter tags and wireless sensors) are highly integrated and miniaturized, whereas renewable energy EH devices typically require a larger physical space to ensure sufficient efficiency. This conflicts with the portability and compactness of IoT devices.

Secondly, the availability of energy sources is also one of the challenges. Most IoT devices rely on permanent energy storage devices such as batteries or capacitors, which are limited sources of energy. Furthermore, it is critical for IoT devices to be able to access energy when it is needed, but solar energy can only be obtained during the daytime, wind energy is dependent on the climate and topography, piezoelectricity relies on the occurrence of vibrations and motion, and thermal energy relies on the difference in temperatures. This instability brings limitations to these sources’ applications to the IoT. Nonetheless, the relevant literature points out that compared to other renewable energy sources, RF and solar energy are the most easily accessible and efficient energy sources for EH BackCom systems [45,46,47].

2.2. Fundamentals of BackCom

BackCom is an emerging ultralow power communication technology that transmits data by modulating and reflecting incident RF signals without actively generating energy itself [13]. The core of this technology is the use of the interaction between the backscatter tag and the reader [48]. The tag has two loads, which modulate the signal by adjusting the impedance of the antenna load [49], and whose impedances are intentionally matched and mismatched to the antenna, respectively [50]. The data information is modulated into binary “0” and “1” bits, which are then transmitted to the reader in the form of changes in the reflection and harvesting states [51]. We can classify tags into two categories: (i) active or semi-passive tags and (ii) passive tags. Active tags have RF circuitry and a battery. Semi-passive tags include a battery but no active RF modulation. Due to their higher power, active or semi-passive tags can communicate at longer distances than passive tags [20]. The reader obtains the data by decoding the modulation information carried in the reflected signal [52].

Compared with traditional wireless systems, BackCom tags do not require active RF components, such as RF synthesizers and analog-to-digital converters. They collect energy from the incident signal to support their operation, thus greatly reducing the power consumption and cost of the device. Usually, the power consumption of the tag is only at the microwave level, and the cost is also very low. The price of the tag can be as low as a few cents [53]. This cost-effectiveness and ultra-low power consumption make backscatter technology particularly suitable for the Internet of Things, wireless sensor networks, and energy-constrained applications. Specifically, it can be used in wearable devices, smart homes, industrial monitoring, environmental monitoring, smart agriculture, and asset tracking. However, EH BackCom also faces some challenges. The first challenge is that it obtains energy through RF signals, which are subject to channel conditions, and RF sources, so the stability of the EH is limited. In addition, BackCom devices face double fading when communicating and the transmit power is low, which makes its transmission rate lower than that of traditional communication methods. In addition, the EH BackCom device has a simple structure and almost no encryption capability, which increases the security risk of the EH BackCom system. In general, BackCom systems are categorized into three types: the monostatic BackCom system (MBCS), bistatic BackCom system (BBCS), and ambient BackCom system (ABCS). Figure 4 provides a detailed description of each type, highlighting their unique architectures, operational principles, and application scenarios to enhance the audience’s understanding.

• Monostatic BackCom system: In an MBCS (e.g., an RFID system), there are two main components: the backscatter tag and the reader. The reader consists of an RF source and a backscatter receiver. The reader emits a carrier signal to activate the tag, and then the tag modulates its information to the carrier signal and reflects it to the reader receiver [48]. Since the reader’s RF source and the backscatter receiver are on the same device, the loss of the round-trip path will affect the reflected signal during transmission [54]. This loss will increase the probability of system energy interruption and weaken the strength of the modulated backscatter signal.
• Bistatic BackCom systems: In a BBCS, the RF source (e.g., a carrier transmitter) and the backscatter receiver are independent of each other, compared to a MBCS, thus avoiding the loss of the round-trip path in the MBCS [14]. By optimizing the placement of the carrier transmitters, the performance of the BBCS can be significantly improved. Specifically, a centralized backscatter receiver is usually placed in a central location, and multiple carrier transmitters are arranged around the backscatter transmitter to achieve wider coverage. In addition, the BBCS effectively alleviates the problem of double-path loss. The backscatter transmitter can obtain unmodulated RF signals from the adjacent carrier transmitter for EH and data transmission, improving signal quality and stability. Although the carrier transmitter is larger and may be more expensive to deploy, the carrier transmitter and backscatter receiver of the BBCS are cheaper to manufacture than the MBCS due to their simplified design.
• Ambient BackCom system: An ABCS is a BackCom system that utilizes existing environmental RF sources such as TV towers, cellular base stations, and WiFi access points as carrier transmitters [15]. Unlike traditional BackCom systems, it does not require a dedicated RF source. Thus, the need for dedicated spectrum is reduced, costs are significantly reduced, and the system’s spectrum resource utilization is improved. This feature gives the ABCS some unique advantages. For example, there is no need to deploy dedicated high-power readers to transmit RF signals, making it more suitable for outdoor or large-scale environments, further reducing additional energy consumption.

However, since the ambient RF signals on which the ABCS relies are often dynamically changing and modulated, this unpredictability and signal interference may limit its performance, especially in backscatter signal receiving, which is more complex than that of the BBCS and MBCS. In addition, due to the uncontrollable transmitting power and the location of the ambient RF source, a more complicated design and deployment are required for the ABCS to achieve optimal performance.

2.3. Structure of EH BackCom System

EH BackCom systems can be understood as a special kind of BackCom. They tend to have the following characteristics: the energy source for backscatter may be a dedicated energy source, a dedicated energy source capable of harvesting energy from the environment to replenish the energy source, or a source of RF energy from the environment. Furthermore, the backscatter tags in the system are generally semi-passive or passive, with an EH circuit. This means that the communication energy of the tags is mainly dependent on the EH. The structure is shown below.

We can divide the green designs of EH BackCom into three parts and introduce them separately. One is the green design of BackCom readers, which will include renewable energy harvesting, utilization of ambient RF sources, waveform and modulation design, resource allocation algorithms, coding and multi-access techniques, etc. Next, we look at the green design of BackCom tags, which will include high-efficiency antennas and matching circuits, high-efficiency RF-to-DC rectification circuits, high-efficiency energy storage and load design, and tag modulator design. The last part is the green design of the cooperative BackCom system, which includes many specific systems that utilize BackCom technology, for example, UAV-assisted BackCom, IRS-assisted BackCom, efficient relaying works in BackCom, etc. The structure of green design for EH BackCom is illustrated in Figure 5, which provides a comprehensive overview of key design aspects, including renewable energy harvesting, ambient RF source utilization, waveform design, and multi-access techniques for readers, as well as high-efficiency design for EH tags and cooperative systems.

3. Green Designs for BackCom Tags

After discussing the principles of EH BackCom, we present the high-efficiency EH design for BackCom tags, as illustrated in Figure 6. This figure outlines the key components and mechanisms involved in the energy harvesting process, providing a clear visualization of the structure and its functionality.

According to the structure, the BackCom tag has four parts: (1) the antenna and matching circuit; (2) the rectifier; (3) the final energy storage and load, which is generally the identity chip in RFID; and (4) the modulation method design of the tag [55], which is one of the key factors affecting EE. These components are crucial for the tag’s EH efficiency. We define the efficiency of these four parts separately as ${\eta }_{\mathrm{ant}}$, ${\eta }_{\mathrm{rec}}$, ${\eta }_{\mathrm{load}}$, and ${\eta }_{\mathrm{mod}}$. The first three parts are the efficiency of EH, while the final part is the energy utilization rate when modulating the information. The overall efficiency of the RF-EH system can be defined as

$$\begin{array}{c}\hfill {\eta }_{\mathrm{overall}}={\eta }_{\mathrm{ant}}{\eta }_{\mathrm{rec}}{\eta }_{\mathrm{load}}{\eta }_{\mathrm{mod}}.\end{array}$$

(1)

Figure 7 illustrates the detailed breakdown of each variable in (1) and highlights their interconnections. The color scheme aligns with that of Figure 6, where the corresponding components are consistently represented, providing a coherent understanding of the system architecture. Notably, Equation (1) and Figure 7 show the relationship between the parts of the EH tag. It can be seen that they are multiplicative, which means that a decrease in the efficiency of any part will result in a decrease in the efficiency of the EH as a whole.

3.1. Antenna and Matching Load Design

In this section, we discuss the impact of antennas on the performance of RF-EH systems. Furthermore, we examine how highly efficient antenna designs influence the overall efficiency of RF-EH systems [17].

First, antenna types can be classified according to frequency band, antenna gain, radiation pattern, polarization, physical size, or application field [56]. For example, some antennas can be used in specific frequency bands, such as VHF and UHF [57]. The dipole antenna, loop antenna, array antenna, horn antenna, aperture antenna, microstrip antenna, and periodic log antenna are some common antennas [58]. Antenna gain is an important parameter that measures the antenna’s ability to focus RF signals in a specific direction and convert them into electrical signals, and is usually expressed in decibels (dB). Antennas with higher gain are highly prized for their ability to significantly increase the conversion efficiency of RF signals [59]. This feature directly increases the total amount of energy collected, making it particularly effective in specific application scenarios. The radiation characteristics of antennas can be divided into two types: isotropic and directional [60]. When the specific location of the source of the RF signal is known, a directional antenna can be adopted to improve the EE of the EH [61]. The physical size of the antenna may vary depending on the specific application requirements, especially those closely related to the circuit power requirements.

The electromagnetic energy around the harvesting antenna is distributed in different spectrum bands. Single-band, multiband, or broadband antennas can be designed according to specific needs. The design and manufacture of single-band antennas are relatively simple, but the energy they harvest is limited [62], which is a disadvantage compared to multiband antennas. Multiband antennas can cover multiple spectrum bands at the same time and have higher EH efficiency [63]. However, using multiple antennas will increase the size and cost of the circuit.

Table 2. Type of antennas that have been applied in RF energy harvesting systems.

Reference	Design/Type	Frequency Bands	Year
[64]	Lens Antenna Arrays	30 GHz	2024
[65]	Microstrip Patch Antenna	890–960 MHz, GSM-900, GSM-1800, UMTS-2100, (NB-IoT), EGSM-900, GSM-1800, UMTS	2021
[66]	Bowtie Antenna	845 MHz, 3.5 GHz, EGSM-1800	2020
[67]	Planar Inverted-F Antenna	GSM-900, GSM-1800	2019
[68]	Log-Periodic Dipole Antenna	1800 MHz, 2100 MHz	2017
[69]	Loop Antenna	600 MHz–1500 MHz, GSM-1800	2017
[70]	Dielectric Resonator Antenna	GSM-900, GSM-1800	2017
[71]	Monopole Antenna	902 MHz–928 MHz	2015
[72]	Dipole Antenna	650 MHz–2500 MHz	2015

summarizes the types of antennas used in RF-EH systems, providing an overview of their applications and characteristics.

The load matching (mismatching) between the impedance and the antenna impedance is required to ensure that most of the RF signal is utilized efficiently in the harvesting state (reflection) [73]. Therefore, finding the appropriate antenna impedance and matching load impedance value is crucial to antenna design. Simple matching circuits can be composed of a combination of resistors, inductors, or capacitors. Resistors are the real part of impedance, while inductors and capacitors are the reactive parts. Using only resistors for impedance matching will cause power loss.

3.2. Rectifier Design

In the process of converting RF power to DC power, rectification is the key step in converting the AC voltage in the RF signal into DC voltage. Commonly used rectification methods include half-wave rectification, full-wave rectification, and bridge rectification [74]. The half-wave rectifier circuit has a simple structure and requires only one diode. Its working principle is to allow only the positive half-cycle of the alternating current to pass through, while the negative half-cycle is blocked [75]. Since only half of the AC waveform can be utilized, half-wave rectification has low efficiency and is suitable for scenarios with small energy requirements. In the full-wave rectification circuit, by cooperating with two diodes and two capacitors, positive and negative half-cycles of the alternating current can be utilized simultaneously, thereby significantly improving the energy conversion efficiency [76]. However, this method requires a center-tapped transformer, which increases the complexity of the circuit. Bridge rectification uses four diodes to form a bridge structure, which eliminates the need for a center-tapped transformer [77]. At the same time, it can make full use of the positive and negative half-cycles of the AC signal to output a more continuous and stable DC voltage. Although the bridge rectifier requires a larger number of components and the power loss is slightly higher, its conversion efficiency is higher and is suitable for radio frequency EH and higher-power application scenarios [78]. In addition, the voltage doubler rectifier uses multistage capacitors and diodes to increase the output DC voltage, which is suitable for applications that require a high output voltage, but the energy conversion efficiency may be reduced [79]. Transistor rectifiers use active devices instead of diodes to reduce losses and improve conversion efficiency, especially under low-power and low-voltage conditions [80]. In practical applications, the selection of rectification methods must take into account factors such as efficiency, cost, and circuit complexity.

The ability of a rectifier to convert RF power to DC power is affected by nonlinear component characteristics and input signal strength. Commonly used components include Schottky diodes [81], GaAs/GaN devices [82], and low-threshold transistors [83]. Schottky diodes are suitable for low-power RF energy rectification because of their low forward voltage drop and fast high-frequency response, but their efficiency is limited at extremely low powers. GaAs/GaN devices have the advantages of high efficiency, fast high-frequency response, and high power density, making them suitable for high-frequency and high-power applications, but their high cost and complexity limit their use in scenarios with higher economic requirements. Low-threshold transistors perform well in low-power environments due to their low-threshold voltage characteristics, but due to the complexity of the driving circuit, their applications are mostly concentrated in specific areas, such as micro-power sensors, IoT devices, and BackCom equipment. In addition, an N-type metal oxide semiconductor (NMOS) rectifier is proposed based on an innovative diode design [84], which improves the efficiency of radio frequency EH in UHF RFID tags by reducing the threshold voltage and achieving 63% power conversion.

Table 3. The efficiency of different rectifier circuits.

Reference	Design/Type	Diodes	Frequency	Efficiency (${\mathit{\eta }}_{\mathbf{rec}}$)	Year
[84]	CMOS Rectifier	NMOS transistor	900 MHz	63%	2024
[77]	Half-Wave and Multistage NMOS Rectifier	NMOS transistor	1.05 GHz	45%	2022
[85]	CMOS Reconfigurable System	-	915 MHz	99.8%	2019
[86]	Voltage Multiplier	Schottky diodes	0.1 GHz to 2.5 GHz	75%	2019
[87]	3-Stage Voltage Multiplier	Schottky diodes	915 MHz	80%	2018
[88]	CMOS Rectifier	transistor	900 MHz	86%	2017
[89]	Voltage Doubler	Schottky diodes	868 MHz	81.65%	2016
[90]	Voltage Quadrupler	Schottky diodes	2.4 GHz, 5.8 GHz	75.108%	2016
[91]	Greinacher Rectifier with Rat-Race Coupler	Schottky diodes	Schottky Diodes	71%	2013

compares high-efficiency rectifiers in RF energy harvesting systems, detailing their designs, diode technologies, operating frequencies, and efficiencies ${\eta }_{\mathrm{rec}}$.

3.3. Energy Storage and Load Design

In EH BackCom, the EH circuit is designed to generate a certain amount of electrical energy. If the average collected electrical energy is greater than the power consumption of the load, the EH circuits can continuously power the load; therefore, no energy storage device is needed; this characterizes the passive backscatter tag. Otherwise, the collected electrical energy should be stored in an energy storage device and used to drive the device when the energy is sufficient, which is the semi-passive tag.

Existing work [17] shows that EH BackCom tag energy storage devices are generally supercapacitors or batteries. In the past five years, significant progress has been made in the field of supercapacitor materials, giving new impetus to the development of energy storage technologies. Research has focused on the optimization of electrode materials to enhance capacitance performance, cycling stability, and lifetime, improvements that are critical to increasing the energy density and overall reliability of the devices. The work that has been done has deeply explored the potential of conducting polymers, metal oxides, carbon-based nanomaterials, and their composites to achieve superior electrochemical performance by changing the structure and chemical properties of the materials. These advances broaden the applications of supercapacitors in fast charging/discharge and long-life energy storage and lay the foundation for high-efficiency energy storage in EH devices [92].

Batteries can be used as a rechargeable power source in EH circuits. There are many different types of batteries, such as nickel–cadmium (NiCd), sealed lead acid (SLA), nickel–metal hydride (NiMH), lithium (Li), and lithium-ion (Li-ion) [93]. Lithium and lithium alloy batteries have an advantage over other batteries because they are more efficient. Batteries have an advantage over supercapacitors and conventional capacitors because they have a higher energy density. However, batteries have a lower power density than ultracapacitors and a shorter useful life.

The load is usually a switching circuit load or an IC chip used for modulation, and the efficiency of the load is directly related to the impedance of the load. The impedance-matching efficiency ${\eta }_{\mathrm{load}}$ can be calculated according to the following formula:

$$\begin{array}{c}\hfill {\eta }_{\mathrm{load}}=1-{\left|{\mathrm{\Gamma }}_{\mathrm{load}}\right|}^2,\end{array}$$

(2)

where the reflection coefficient ${\mathrm{\Gamma }}_{\mathrm{load}}$ describes the mismatch between the load impedance $Z_{\mathrm{load}}$ and the circuit characteristic impedance. The calculation formula is [17]

$$\begin{array}{c}\hfill {\mathrm{\Gamma }}_{\mathrm{load}}={\displaystyle \frac{Z_{\mathrm{load}}-Z_0}{Z_{\mathrm{load}}+Z_0}},\end{array}$$

(3)

where $Z_{\mathrm{load}}$ denotes the impedance of the load. $Z_0$ is the characteristic impedance of the transmission line or system. This formula shows how the difference between the load impedance $Z_{\mathrm{load}}$ and the system impedance $Z_0$ affects reflection. The more severe the impedance mismatch, the greater the reflection coefficient, which means a decrease in EE. Therefore, choosing the right load impedance is an important part of the green design of a BackCom tag.

3.4. Tag Modulation Design

The design of the tag modulator is an important part that affects the EE of the tag communication. In EH BackCom, the ratio of power to transmission rate is generally used as a measure of EE. This can be expressed as

$$\begin{array}{c}\hfill \mathrm{Modulator}\mathrm{Energy}\mathrm{efficiency}\left({\eta }_{\mathrm{mod}}\right)(\mathrm{J}/\mathrm{bit})={\displaystyle \frac{\mathrm{Power}(\mathrm{J}/\mathrm{s})}{\mathrm{Rate}(\mathrm{bit}/\mathrm{s})}}={\displaystyle \frac{P_{\mathrm{m}}}{R_{\mathrm{m}}}}.\end{array}$$

(4)

Boyer et al. [94] introduced the concept of normalized per-bit power consumption (modulator EE) for such BackCom systems to capture the resulting trade-offs. Next, a 16 QAM UHF BackCom modulator was studied, achieving a performance of 96 Mbit/s, $15.5$ pJ/bit [95]. Ref. [96] demonstrated the generation of channelized communication signals similar to those of traditional wireless devices by modulating the backscattered signals. Taking the Bluetooth 4.0 standard as an example, it was verified that the prototype tag can generate three-channel FSK data packets that are not different from BLE advertising packets at a rate of 1 Mbps, and the modulation EE reaches $28.4$ pJ/bit. Ricardo et al. introduced a 16 QAM modulator with WPT capability. The circuit uses two different frequencies, one for WPT communication and the other for BackCom. For a data rate of 960 Mb/s, the modulator achieves an energy per bit as low as $61.5$ fJ and an EVM of 8.37% [97]. Furthermore, in the same year, they also investigated a modulator employing a Wilkinson power divider with a phase shift in one of the design branches and two transistors as switches to produce M quadrature amplitude modulation backscatter modulation, achieving $6.7$ pJ/bit for a data rate of 120 Mb/s with an error vector magnitude (EVM) of 16.7% [98]. A high-efficiency modulator operating at $1.76$ GHz is proposed. The modulator is composed of a series combination of a variable resistor and a varactor diode, and uses gallium nitride (GaN) on a silicon carbide (SiC) high-electron-mobility transistor (HEMT) to implement the variable resistor. The modulation efficiency is $21.4$ fJ/bit at a transmission rate of 96 Mbps, and the EVM is as low as 1.73% [99]. The MFM modulator has also been investigated because it performs well in the time-varying multi-path BackCom channel. James et al. adopted five sub-carriers of binary phase-shift keying (BPSK) modulation with a symbol rate of 250 kSymbols/s and a throughput of $1.25$ Mbit/s, achieving a modulator EE of 160 pJ/bit [100] in 2021. In the end, Junliang et al. describe a versatile modulator that enables tags to communicate with transceivers at megabit rates using versatile modulation that supports binary and higher-order modulation [101]. Experimental results show that the versatile modulation system is capable of transmitting QPSK symbols at rates up to 3 Mbit/s while consuming only $9.63$ µW ($3.21$ pJ/bit).

Table 4. Summary for tag modulation EE.

Literature	Modulation Type	Modulation EE (${\mathit{\eta }}_{\mathbf{mod}}$)	Years
[101]	3 Mbps Versatile Modulation	$9.63$ µW ($3.21$ pJ/bit)	2024
[100]	1.25 Mbps MFM	160 pJ/bit	2021
[99]	96 Mbps 64 QAM	21.4 fJ/bit with an EVM of 1.73%	2021
[98]	120 Mbps M-quadrature Amplitude Modulation	6.7 pJ/bit with an EVM of 16.7%	2017
[97]	960 Mbps 16 QAM	61.5 fJ with an EVM of 8.37%	2017
[96]	1 Mbps FSK	28.4 pJ/bit	2015
[95]	96 Mbps 16 QAM	1.49 mW (15.5 pJ/bit)	2012

provides a detailed comparison of existing works on tag modulation EE across different modulation types.

According to Table 4, 64 QAM achieves the best EE and only needs an energy consumption of $21.4$ fJ/bit to transmit in 2021. Modulation methods with higher information transmission efficiency have higher EE, but the EVM will also be higher, which needs to be considered as a trade-off in practical applications.

In summary, the antenna and matching circuit, as well as the rectifier, mainly affect the conversion efficiency of RF energy to DC. In addition, energy storage components and loads affect the utilization of the DC energy. The modulation designs can improve the EE of information modulation, that is, more bits of information can be modulated per joule. The collective impact of each part will determine the overall efficiency of the RF energy applied to the modulation. We refer to this as the overall EE of the tag, i.e., ${\eta }_{\mathrm{overall}}$.

4. Green Design for Readers and RF Sources in BackCom

Green design for the reader and the RF source in BackCom is key to achieving an energy-efficient and sustainable system. Compared to the tags, the reader and the RF source typically account for a much higher proportion of energy consumption. In an MBCS, where the reader handles both signal transmission and reception, it is characterized by high energy consumption, necessitating optimized resource allocation and carrier waveform design. A BBCS separates the carrier emitter and receiver, introducing unique challenges in coordinating energy use and designing efficient coding techniques. An ABCS leverages existing RF sources, such as TV towers or WiFi signals, which reduces the reliance on dedicated RF sources but requires advanced strategies for energy harvesting and integration to ensure sustainability. This section delves into green design strategies tailored to these scenarios, focusing on five key aspects: energy harvesting from renewable sources, utilization of ambient RF sources, resource allocation, carrier waveform design for BackCom, and coding and multiple access techniques. These aspects address the critical trade-offs between EE and communication performance, offering comprehensive solutions for the sustainable operation of BackCom systems. An overview of the key strategies for green design in BackCom readers and RF sources is presented in

Table 5. Summary of key strategies in green design for readers and RF sources in BackCom.

Key Design Aspects	Literature	Key Focus and Strategies
EH from Renewable Sources	[102,103,104,105,106,107,108,109,110]	Leverages solar, wind, and mechanical vibration energy to power BackCom readers and RF sources, reducing reliance on non-renewable supplies and lowering the carbon footprint. The harvested energy can be stored in capacitors or batteries to ensure consistent operation in dynamic environments, enabling efficient carrier wave generation for tag communication.
Utilization of Ambient RF Sources	[15,111,112,113,114]	Leverages existing RF signals from TV towers, cellular stations, or WiFi to power tags, thus reducing reliance on dedicated transmissions. Readers function as low-power receivers, tapping into abundant ambient signals via backscatter. By intelligently selecting the strongest or most stable ambient source, energy consumption is minimized without compromising reliability, making this approach sustainable for both urban and resource-constrained environments.
Resource Allocation	[115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132]	Encompasses power, time, frequency, and computing resource allocation strategies to optimize energy usage and communication reliability in BackCom. These include adaptive power control and fairness-aware scheduling, dynamic time slot allocation, frequency selection or reuse to reduce interference, and real-time beamforming for targeted energy delivery, ensuring minimal waste and heightened overall efficiency.
Carrier Waveform Design for BackCom	[133,134,135,136]	Leverages multitone waveforms to distribute energy across multiple frequencies, improving both EH efficiency and communication performance. Techniques such as amplitude-phase optimization, harmonic shaping, and frequency spacing minimize power loss and distortion, ensuring stable, energy-efficient transmissions. By tailoring waveforms to varying device distances and demands, these methods enhance overall BackCom system performance while maintaining low power consumption.
Coding and Multiple Access Techniques	[124,137,138,139,140,141,142,143,144,145,146]	Leverage TDMA, OFDM, CDMA, and NOMA to reduce interference and power consumption and enable scalable green BackCom. By dynamically allocating time slots or sub-carriers, these techniques adapt to real-time channel conditions and device requirements, optimizing EE. Combined with EH and waveform design, they help maintain low carbon footprints while supporting reliable, large-scale deployments.

. This table outlines the main approaches discussed in this section, providing a concise framework for understanding the critical trade-offs between EE and communication performance.

4.1. EH from Renewable Sources

In BackCom, the use of renewable energy sources to power the reader and RF source is a pivotal strategy to achieve greener and more energy-efficient systems. Unlike traditional setups that rely on batteries or direct energy supplies, renewable EH utilizes ambient energy from external sources like solar radiation, wind, or even mechanical vibrations [102]. This reduces dependence on non-renewable resources and minimizes the carbon footprint of the system.

Specifically, readers and RF sources can be designed to incorporate solar panels [103,104,105], piezoelectric materials [106,107,108], or wind turbines [109,110], to sustainably harvest energy. For example, solar energy, which is abundant and easy to harness, can power operations during daylight hours, while piezoelectric devices can convert vibrations into electrical energy, ensuring a steady power supply even in dynamic environments. Wind energy may be a viable option in outdoor deployments where airflow is consistent. These harvested energies can be stored in capacitors or batteries and used to power the RF source, enabling it to emit the carrier waves necessary for tag communication. The strategy of the reader and RF source in the EH BackCom system is to collect as much energy as possible, which will reduce the dependence on power lines and batteries, and we will discuss how to allocate energy to improve utilization in the following Section 4.3.1.

4.2. Utilization of Ambient RF Sources

In addition to harvesting energy from renewable sources and existing RF signals, ambient RF sources provide another pathway to enhance EE in BackCom systems. This is unlike MBCS configurations, where the reader simultaneously serves as both the RF source and receiver, performing dual functions of transmitting and decoding signals. Although it simplifies hardware deployment, it typically results in higher energy consumption. In contrast, a BBCS offers a more energy-efficient alternative; by spatially separating it from the reader, EE is improved by delegating the signal transmission task to an external source. This separation enables the reader to function solely as a low-power receiver, aligning more closely with the principles of green communication.

Building upon the advantages of BBCSs, the utilization of ambient RF sources represents an even more energy-efficient paradigm. Instead of relying solely on dedicated RF signal sources to power the tags, ambient RF energy from existing infrastructures, such as television towers, cellular base stations, or WiFi access points, can be used directly [15]. These ambient RF signals, ubiquitous in modern environments, act as a free and readily available source of carrier waves for the reader to facilitate communication with tags [111]. This approach significantly reduces the power burden on the reader, as it no longer needs to emit dedicated RF signals for communication. Instead, it can modulate or leverage existing ambient signals, aligning seamlessly with the backscatter principle, where tags reflect rather than generate their own signals. Using ambient RF sources, the overall energy consumption of the system can be further minimized without compromising functionality or reliability. For example, in urban settings, where ambient RF signals are abundant due to dense network deployments, the reader can dynamically adapt to utilize the strongest or most stable ambient source available [112]. In scenarios where multiple sources of RF coexist, intelligent algorithms can optimize the selection of the best signal based on factors such as signal strength, frequency, and availability [113]. This adaptive utilization ensures consistent performance while maintaining high EE.

Furthermore, this strategy improves the sustainability of the system by reducing the dependency on additional energy harvesting hardware or large power supplies [114]. It also extends the feasibility of BackCom in remote or resource-constrained environments, where infrastructure such as television towers or cellular networks may already exist, but renewable EE could be challenging. By integrating ambient RF utilization into BackCom system design, the dual goals of EE and sustainability are further realized, complementing the renewable EH approach discussed earlier.

4.3. Resource Allocation

While previous discussions focused on reducing energy consumption through renewable EH and ambient RF utilization, this section shifts the focus to efficient energy usage, introducing key resource allocation strategies in BackCom systems. These strategies, which encompass power, time, frequency, and computing resources, address distinct challenges to ensure optimal EE and reliable communication performance.

4.3.1. Power Allocation

Power allocation (PA) for the transmit power from the reader or RF sources is the most popular resource allocation technique adopted by BackCom systems, enabling efficient energy transfer and reliable communication. Various PA strategies have been proposed to address the unique requirements of BackCom, including adaptive power control, load-aware PA, opportunistic PA, AI-enabled PA, and fairness-aware PA. Among these, adaptive power control exploits dynamic adjustment of the power output of readers or RF sources based on real-time conditions, such as channel state information (CSI) [115], device distance [116], or energy demand [117]. Load-aware PA allocates power according to the energy or communication demands of the devices, prioritizing high-load devices for more efficient utilization of RF energy [118,119]. Opportunistic PA leverages favorable channel conditions to maximize power transfer efficiency during optimal transmission moments [120,121,122]. The PA enabled by AI uses predictive algorithms to forecast energy requirements or channel variations, allowing RF sources to adapt dynamically and maximize efficiency [119]. Finally, fairness-aware PA ensures an equitable energy distribution among devices, preventing underperformance under bad channel conditions or via disadvantaged sensor nodes that suffer from very low throughput [123].

In diverse deployment scenarios, such as large-scale sensor networks or industrial IoT, PA methods prove particularly advantageous as they can flexibly adjust power according to real-time channel or traffic conditions, thereby boosting energy transfer efficiency. However, if the environment is highly dynamic or the system lacks accurate channel estimation and prediction, blind or rigid power allocation may lead to energy wastage and fairness issues. For EH BackCom systems aiming to maximize energy utilization and system sustainability, further optimization of PA can involve robust machine learning frameworks for real-time decision making and a joint design with other resource dimensions, such as time, frequency, bandwidth, and channel and computing resources, to further improve overall system performance. This comprehensive optimization seeks to strike a balance between performance, power efficiency, and fairness across diverse device conditions.

4.3.2. Time Allocation

Time allocation (TA) is another common resource allocation technique in BackCom systems, where efficient TA strategies ensure that energy transfer and communication are scheduled to minimize power consumption and maximize system performance. Common TA approach strategies include time division scheduling, duty cycling, and adaptive time slot allocation. Among these, time division scheduling divides communication and energy transfer into specific time slots, often using a time division multiple access protocol (TDMA) to avoid collisions and maximize system performance [124]. Duty cycling minimizes energy consumption by alternating RF sources and devices between active and sleep states, reducing idle power loss [125]. Adaptive time slot allocation dynamically adjusts the transmission and EH durations based on real-time traffic demands, ensuring balanced resource utilization and efficient energy distribution [126]. These strategies enhance EE and support the sustainable operation of green RF sources and readers in BackCom systems.

In scenarios that demand precise coordination between communication and energy harvesting, such as avoiding inter-device collisions or scheduling priority data traffic, TA can greatly reduce idle power consumption while maintaining acceptable throughput. Its advantages lie in its straightforward implementation and relatively clear scheduling policies. However, over-reliance on fixed time slots can cause inefficiencies when the network load fluctuates, leading to underutilized time segments or communication bottlenecks. In EH BackCom systems, a more adaptive TA mechanism that continuously monitors traffic loads and energy states can improve overall efficiency. Moreover, when TA is co-designed with PA strategies, quick bursts of strong energy signals can be employed to optimize both energy transfer and data transmission in dynamic environments.

4.3.3. Frequency Allocation

Frequency allocation (FA) is also one of the resource allocation techniques utilized to optimize the EE of green RF sources in BackCom systems. Although BackCom readers typically transmit RF signals, selecting appropriate frequencies can reduce energy loss and improve overall system efficiency. FA strategies, such as adaptive frequency selection and frequency reuse, are particularly relevant in green system designs. For instance, adaptive frequency selection dynamically adjusts the carrier frequency to minimize propagation loss based on environmental factors, such as device distance or interference levels [127]. Furthermore, frequency reuse allows multiple readers or RF sources to operate in the same frequency band without interference by leveraging spatial separation, thereby reducing the overall spectral footprint [128].

When multiple readers or RF sources coexist, such as is common in large-scale industrial or multi-tier IoT networks, FA helps mitigate interference and minimize energy loss by adaptively selecting or reusing frequencies. However, in environments with severe multi-path fading or rapidly changing device locations, frequent spectrum switching can add overhead and complexity. Coupling FA with machine learning-based interference prediction or jointly designing it with time and power allocation can address these challenges through multi-dimensional resource optimization.

4.3.4. Computing Resource Allocation

Computing resource allocation is also critical in BackCom systems employing beamforming, as it directly impacts the efficiency of energy transfer and the sustainability of green RF source designs. In such systems, computing resources are used primarily for real-time beamforming optimization, energy scheduling, and low-power system management. Beamforming optimization requires computational power to dynamically adjust the phase and amplitude of antenna arrays, directing RF energy toward specific BackCom devices with minimal waste [129]. This ensures that energy is concentrated where it is needed most, reducing overall transmission power. Furthermore, computing resources can support energy scheduling algorithms, which prioritize energy delivery to high-demand devices while minimizing idle power loss [130]. Low-power system management further leverages computational capabilities to regulate RF source operations, such as entering sleep mode during periods of low activity or adjusting the power output based on real-time demand [131].

Computing resource allocation is most applicable in scenarios requiring precise energy targeting, such as UAV-assisted BackCom [132] or multi-antenna systems in dense urban deployments, where it can significantly enhance both energy efficiency and communication quality. However, in resource-constrained or embedded environments, excessive computational overhead may lead to latency issues or increased energy consumption. For example, the overhead associated with beamforming techniques and scheduling algorithms can strain low-power devices, particularly in distributed systems. To mitigate this, edge computing frameworks and distributed optimization techniques can be leveraged by offloading computational tasks to nearby servers or high-capacity nodes, thereby maintaining performance while reducing local device energy consumption. In addition, when integrated with other resource allocations, such as power, time, and frequency, computing resource management can orchestrate a fully optimized, multi-dimensional control framework that respects system sustainability and latency requirements.

4.4. Carrier Waveform Design for BackCom

Waveform design is essential for achieving EE in BackCom systems, particularly in the environmentally friendly design of RF sources and readers. Traditional BackCom systems rely on RF transmitters that generate sinusoidal continuous waves (CWs), which serve as carriers for tags to backscatter information. Although simple CW signals are inherently energy-efficient, optimizing their waveform characteristics can further enhance EH efficiency and communication performance. Multitone waveforms, which distribute energy across multiple frequency components, have been identified as a promising approach to exploit the nonlinear characteristics of rectifiers and the frequency diversity of the channel. By designing the amplitudes and phases of individual tones, multitone waveforms can maximize the total energy harvested by tags while maintaining efficient spectral utilization. This approach is particularly advantageous in scenarios where tags have varying energy requirements or are located at different distances from the RF source. For instance, multisine waveform designs leveraging CSI have demonstrated the ability to balance the trade-off between the efficiency of EH and the signal-to-noise ratio (SNR) of BackCom, achieving optimal performance for both energy transfer and reliable data transmission [133]. Further advancements include designing waveforms that minimize harmonic distortion, thereby improving both the efficiency of energy transfer and the quality of backscattered signals. Techniques such as frequency spacing optimization and waveform shaping have shown the potential to enhance EH efficiency while ensuring robust system performance in dynamic environments. These methods are particularly relevant in scenarios where consistent power delivery and reliable communication are required [134,135]. Moreover, waveform optimization can minimize power losses and reduce harmonic distortion during energy transmission. Techniques employing harmonic waveform shaping, for instance, can significantly improve the efficiency of wireless power transfer (WPT) while ensuring a high quality backscattered signal [136]. Such optimizations are crucial for maintaining stable system performance, particularly in dynamic environments where energy availability and communication reliability are critical.

These advancements underscore the ability of waveform design to meet the rigorous EE and performance criteria of BackCom systems, especially in the development of environmentally friendly RF sources and readers.

4.5. Coding and Multiple Access Techniques

Multiple access techniques are pivotal in enhancing the EE of BackCom systems by facilitating efficient resource sharing, minimizing interference, and ensuring robust connectivity in large-scale deployments. The literature identifies several techniques applied to the EE of BackCom systems, including Code Division Multiple Access (CDMA) [137], Orthogonal Frequency Division Multiplexing (OFDM) [138,139,140,141], Orthogonal Frequency Division Multiple Access (OFDMA) [138,142], time division multiple access (TDMA) [124,143], Orthogonal Multiple Access (OMA) [144], and Non-Orthogonal Multiple Access (NOMA) [144,145,146]. However, selecting the appropriate multiple access scheme based on system requirements is crucial. For instance, TDMA provides simplicity and EE in scenarios with limited devices, whereas NOMA supports high spectral efficiency and simultaneous multi-user communication in dense environments.

To enable efficient operation, dynamic resource allocation is essential. Techniques such as adaptive TA in TDMA or sub-carrier adjustment in OFDMA ensure resources are distributed effectively based on real-time CSI and energy availability, improving overall system efficiency. Additionally, hybrid multiple access frameworks, such as the combination of TDMA and NOMA, offer a balanced approach to addressing the trade-offs between complexity and efficiency. In [143], a hybrid cognitive radio system was explored that combined TDMA and NOMA for an enhanced spectrum and EE. Among the various techniques, NOMA stands out as a widely studied approach for BackCom. Its integration with cognitive radio not only improves PA, but also significantly improves spectrum utilization and EE [143]. Moreover, Manzoor et al. provided a comprehensive survey in [146], emphasizing the integration of NOMA with BackCom for future energy-efficient 6G networks.

Meanwhile, practical implementations of other multiple access techniques also highlight their potential in various applications. For example, Feng et al. introduced a backscatter–CDMA scheme for ultralow-power communication in medical implantable devices [137]. This method employs EM–lens technology to enhance power transfer and enable energy-efficient multi-node communication, highlighting its application in healthcare-specific BackCom use cases. OFDM has been widely employed to enhance EE in BackCom systems. For example, Gu et al. in [138] optimized resource allocation by dynamically adjusting power and transmission time, achieving significant energy savings in EH and data communication. In massive IoT networks, sparse codes and OFDM carriers have been adopted to improve connectivity and EE [139]. Similarly, Huang et al. utilized ambient OFDM signals to develop a highly energy-efficient WiFi BackCom system for sustainable IoT applications [140]. Furthermore, Yang et al. explored the use of an ultra-wideband ABCS utilizing commercial broadcast signals (e.g., FM, TV, and cellular networks), demonstrating the scalability and energy savings achieved through the utilization of ambient signals based on OFDM [141]. OFDMA, as highlighted in [138], offers significant energy savings potential in wirelessly powered BackCom systems. Moreover, Yang et al. proposed an OFDMA-based authentication mechanism to reduce energy consumption and improve scalability for systems with multiple BDs [142].

5. Green Design for Cooperative BackCom Systems

Expanding on the foundational concepts of EE communication explored in earlier sections, we delve into the multifaceted strategies and technologies enhancing EE in cooperative BackCom systems. Such systems utilize collaboration among unmanned aerial vehicles (UAVs), intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs), and ambient energy sources, presenting a sustainable approach to modern communication challenges. Moreover, this section will cover how relay and EH techniques not only support but also advance the capabilities of BackCom systems, ultimately leading to more EE and scalable solutions for modern communication challenges. To provide a visual and structured summary, Figure 8 offers a comprehensive representation of cooperative BackCom system architectures, emphasizing the integration of UAVs, IRSs, and relaying works. This figure serves as an overarching model, illustrating how these components collaborate to achieve energy-efficient communication. Furthermore,

Table 6. Summary of key cooperative strategies in green BackCom systems.

Main Cooperative Aspects	Literature	Main Focus and Approaches
UAV-Assisted EE BackCom	[147,148,149,150,151,152,153,154,155,156,157,158]	UAVs substantially expand coverage and boost energy efficiency in BackCom, especially in large-scale IoT and data collection scenarios. Key innovations revolve around dynamic trajectory optimization that adapts flight paths and system parameters to minimize power consumption. Hybrid energy harvesting designs further optimize active and passive operations, enabling UAVs to operate efficiently under various conditions. By coordinating resource allocation in real time, these cooperative UAV-based approaches also address fairness, security, and interference challenges, underscoring their potential for robust, sustainable, and wide-ranging BackCom networks.
IRS-Assisted EE BackCom	[151,152,153,159,160,161,162,163,164,165,166,167,168,169,170,171]	Employs IRSs to smartly reflect and shape electromagnetic waves, thereby minimizing path loss and optimizing energy transfer. Innovations include phase-shift and beamforming refinements and ML-driven approaches that adapt to varying channel conditions. This cooperative design is highly scalable in dense or mobile settings, supports partial or fully harvested energy, and reinforces overall system sustainability and throughput.
Efficient Relaying Techniques in BackCom	[159,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202]	Implements AaF, DaF, CaF, and hybrid relay modes to overcome distance, energy, and reliability constraints. Through decoding, compressing, or adaptively switching between backscatter and active transmission, these designs could achieve notable gains in EE and system capacity. ML-based scheduling, FD relaying, and SWIPT integration further enhance coverage and reduce power demands, underscoring the benefits of cooperative relaying for green BackCom systems.

presents a detailed summary of the key strategies discussed in this section. It highlights specific design elements, such as UAV-assisted systems, IRS-enhanced architectures, and efficient relaying techniques, providing a concise reference for understanding these multifaceted approaches.

5.1. UAV-Assisted EE BackCom System

Recent advancements in UAV technologies have significantly enhanced BackCom systems, revolutionizing their extensive coverage and EE capabilities, particularly in large-scale data collection and IoT applications. This section reviews how UAVs have been integrated with BackCom systems, detailing innovative approaches from recent studies focusing on trajectory optimization, energy management, and system implementation to improve EE. The discussion highlights novel methodologies and the substantial performance benefits these integrations offer across various implementations.

In the domain of UAV-assisted BackCom systems, trajectory optimization has been highlighted as a key aspect of EE improvement. Specifically, the "communicate-while-flying" scheme was proposed in [147,148] to dynamically adjust UAV paths and BackCom parameters, achieving significant energy savings compared to traditional hover-and-fly schemes. A deep reinforcement learning (DRL)-based approach was introduced to reformulate the trajectory optimization problem as a Markov decision process, allowing UAVs to adjust their trajectories dynamically and the backscattering devices’ (BD) scheduling to achieve near-optimal energy-efficient solutions without requiring global state information [149]. Extending this, Sun et al. investigated the planning of UAV paths in 3D environments, where advanced techniques ensure smooth UAV movements, balancing energy consumption with mission objectives such as navigation and SAR imaging [150].

Beyond trajectory optimization, energy management strategies play a crucial role in achieving energy-efficient UAV-assisted BackCom systems. Hybrid systems, as explored in [151,152], integrate EH techniques with BackCom to intelligently optimize power allocation between active and passive communication modes, significantly enhancing energy utilization. Moreover, IRS-equipped UAVs, as demonstrated in [152,153], leverage reflection coefficients and beamforming to optimize EE, especially in dense urban or rural environments. Similarly, Yang et al. focus on maximizing system-wide EE while meeting fairness and performance constraints [154]. Fu et al. explore the fairness of energy-efficient computation for hybrid systems, employing advanced optimization algorithms to achieve equitable and efficient resource allocation [155]. In addition, energy-efficient UAV-assisted BackCom systems also address challenges related to secure and fair data collection, especially in IoT networks. Zeng et al. tackle the dual challenges of fairness and secrecy by optimizing the trajectory of the UAV and EE while balancing the fairness of the secrecy rate among the BDs [156].

Furthermore, real-world system implementations validate the effectiveness of these UAV-assisted BackCom advancements. In [157], Goel et al. demonstrate how cluster-based resource allocation and directional beams optimize energy transfer and data collection. In [158], a real-world implementation of an energy-efficient UAV radar imaging system that utilizes external illuminators was also presented to significantly reduce UAV energy demands. Finally, spectrum-sharing applications integrating on an ABCS, also highlighted in [153], exploit existing RF signals for low-power communication, achieving substantial energy savings while managing interference in dense networks.

In terms of application domains, UAV-assisted BackCom systems are particularly relevant in scenarios that require wide-area coverage, such as in agricultural monitoring or disaster response, or dynamic communication topologies. Compared to purely ground-based static BackCom systems, UAV-assisted solutions offer a higher degree of mobility and flexible deployment, establishing a hierarchy where dynamic-based methods are especially advantageous when quick repositioning or opportunistic data collection is essential. Implementation guidelines often focus on trajectory planning, power control, and energy management on board the UAV itself. To evaluate their efficacy, key metrics include energy consumption, coverage area, latency, and throughput under varying channel conditions. By prioritizing these metrics, researchers and practitioners can align UAV flight strategies with BackCom’s low-power objectives, ensuring that the resulting systems are robust, energy-efficient, and able to accommodate diverse application requirements.

5.2. IRS-Assisted EE BackCom System

IRS-assisted design has emerged in the cooperative framework to enhance EE in BackCom systems. Using the reflective and programmable capabilities of the IRS, these systems adaptively control the propagation of electromagnetic waves to reduce power loss and improve system performance. This subsection synthesizes various studies in which IRS-assisted designs have contributed to the energy-efficient operation of cooperative BackCom systems.

IRSs can improve throughput while maintaining high EE. In [159], an IRS is shown to outperform relay-based designs by leveraging spatial diversity to reduce energy consumption. Complementing this, a study in [160] introduces an IRS-assisted relay transmission scheme that optimizes phase shifts and power allocation, achieving significant improvements in performance and EE. In addition to extending this theme, the work in [161,162] explores advanced beamforming refinement and machine learning to achieve superior throughput and EE.

Another area of focus involves integrating IRSs into wireless-powered systems for sustainable operation. Xu et al. introduce an IRS-enabled system that operates entirely on harvested energy, achieving self-sustainability while maximizing throughput [163]. Similarly, Jafari et al. employ time and energy optimization in IRS-NOMA systems to enhance efficient energy utilization for constrained devices [164]. Furthermore, a dual-RIS setup investigated in [165] further advances this concept to maximize the efficiency of EH and data transmission efficiency by optimizing beamforming and resource allocation and overcoming channel uncertainties.

For aerial and mobile implementations, Refs. [152,153,166] demonstrate how IRS-equipped UAVs can extend coverage and improve EE. By optimizing the trajectory and leveraging spectrum sharing techniques, these designs address unique challenges, such as shadowing and a limited line of sight, making them suitable for dense urban environments.

Localization is another domain where RIS-assisted systems have also seen significant advancements. In [167], localization accuracy and EE are improved by utilizing RIS through randomized beamforming, particularly in non-line-of-sight scenarios. However, this is complemented by [168], which addresses the balance between active and passive communication modes, identifying strategies to optimize localization alongside EE.

Hybrid BackCom designs also benefit from IRS integration. Studies in [151,169] integrate UAVs and BackCom in IRS-assisted systems. These systems demonstrate significant improvements in EE and computational performance by optimizing task offloading and UAV trajectory, particularly in mobile edge computing environments.

Furthermore, in the domain of cognitive networks, researchers address imperfect channel state information and nonlinear EH challenges. This study emphasizes robust resource allocation and beamforming, achieving substantial improvements in EE [170].

Finally, computational offloading in the RIS-enhanced system is exemplified in [171], which optimizes task offloading strategies to balance EE computation and task performance in IoT applications. These findings are consistent with the broader trends in leveraging IRSs for both energy sustainability and operational efficiency.

Overall, these studies highlight the versatility of IRS/RIS-assisted designs in the advancement of energy-efficient BackCom systems. By addressing diverse challenges such as throughput maximization, EH, localization, enhanced coverage, and spectrum sharing, these designs underscore the transformative potential of IRSs in shaping sustainable and efficient communication paradigms. Regarding the hierarchy of application fields, IRS-assisted BackCom is particularly effective in dense urban centers, underground facilities, or smart factories that require adaptive coverage and reduced power loss. Compared to UAV-focused solutions, IRS-based methods may be less flexible in terms of mobility but often yield higher beamforming gains and more predictable channel conditions. Implementing IRS-assisted designs typically involves the careful deployment of reflective elements, phase-shift optimization, and real-time feedback mechanisms to adapt to channel variations. Key evaluation metrics include throughput rate, spectral efficiency under programmable reflection, and robustness against blockage or multi-path fading. By systematically quantifying these metrics, we can identify where IRS-driven solutions provide the greatest return on investment in energy savings and coverage enhancements.

5.3. Efficient Relaying Techniques in BackCom

Relay techniques have become integral to the advancement of BackCom, addressing challenges such as limited transmission range, low data rates, and energy constraints. Various relay models, including amplify-and-forward (AaF), decode-and-forward (DaF), compress-and-forward (CaF), as illustrated in Figure 9, along with their hybrid combinations, have been extensively explored to enhance the EE and adaptability of BackCom systems. This section provides a comprehensive review of relay techniques in BackCom, focusing on core relay types and their hybrid implementations designed to meet the unique requirements of BackCom.

AaF-type relay amplifies the received signal, including noise, before it is forwarded to the destination. This approach is simple and suitable for scenarios with low computational resources or latency requirements. The study in [172] proposed an optimization-driven hierarchical framework for AaF relays to improve EE, demonstrating their potential to maximize throughput under varying channel conditions. Similarly, Ref. [173] leveraged bio-inspired optimization techniques for relay selection, achieving substantial EE gains in cooperative transmission networks. Articles [174,175,176] explored half-duplex and full-duplex (FD) AaF relaying in BackCom systems, highlighting constructive interference techniques to enhance relay performance and EE. Furthermore, Ref. [177] characterized the energy performance of AaF-based relaying utilizing BDs, revealing efficiency trade-offs under practical conditions.

DaF relays decode and reencode signals before transmission, providing superior error correction capabilities but at the cost of higher computational complexity. This model is widely applied in BackCom systems to address reliability and efficiency challenges. Studies such as [159,178] analyzed DaF relays in wirelessly-powered and NOMA-enabled networks, showcasing a significant improvement in EE and throughput. Articles [179,180,181,182,183] extended these findings by integrating DaF with an ABCS to optimize power allocation and transmission scheduling. The research in [184,185,186,187] explored outage performance and security enhancements, revealing robust relay designs that minimize energy consumption in dynamic environments. Furthermore, Refs. [188,189,190,191,192,193,194,195,196,197] emphasized adaptive scheduling and cooperative strategies for DaF relays, with significant advancements in EH, throughput maximization, and system reliability.

CaF-type relays focus on compressing the received signal to reduce data transmission overhead while preserving critical information. Although less common in BackCom, study [198] provided a detailed analysis of the efficiency of CaF relays in FD systems, highlighting their potential to optimize spectral efficiency and EE. Despite their benefits, CaF relays face challenges in complexity and signal distortion, limiting their adoption in practical BackCom systems.

In contrast, hybrid relay schemes effectively combine multiple relay modes to exploit their complementary advantages. These schemes are particularly well suited for BackCom systems, where balancing EE and system throughput is critical. Study [199] introduced a hybrid relay model for next-generation wireless systems that integrate SWIPT. The proposed design enables relays to adaptively switch between backscatter and active communication modes based on channel conditions and energy availability. By optimizing relay mode selection and power allocation, the system achieved significant improvements in both throughput and EE, making it a promising candidate for low-power IoT applications. In [200], a comprehensive survey highlighted the integration of FD hybrid relays with advanced BackCom technologies, such as RISs and UAVs. The study emphasized the efficiency gains achieved through FD operation, which allows simultaneous transmission and reception over the same frequency band. Hybrid relay architectures were shown to enhance spectral efficiency and reduce latency, addressing critical requirements for 6G networks. Further, Ref. [201] presented an optimization-driven hierarchical learning framework for hybrid relays, employing machine learning techniques to dynamically adapt relay modes. The framework used deep reinforcement learning to optimize relay strategies, enabling efficient mode switching between active and passive operations. This adaptive mechanism ensured energy-efficient operation under diverse channel conditions, achieving up to 20% higher throughput compared to traditional methods. Finally, Ref. [202] investigated the time-to-recharge performance of energy-relay-assisted systems, comparing backscatter relays and energy harvesting relays. The study analyzed the trade-offs between relay efficiency and recharge time, demonstrating that hybrid relays, combining active transmission with backscatter reflection, significantly reduced energy replenishment delays. This advancement supports real-time updates in sensor networks, where maintaining consistent energy availability is essential.

In practical BackCom deployments, relay-based strategies are especially relevant for moderate-range or multihop scenarios, where direct backscatter links cannot consistently meet coverage or rate requirements. Compared with UAV-x or IRS-focused solutions, relaying is often more cost-effective and easier to implement, but may deliver lower data rates or spatial flexibility. Implementation guidelines for relay methods typically involve selecting the appropriate relay type based on network density, channel conditions, and computational constraints. From the perspective of evaluation, crucial performance metrics include throughput rate, energy efficiency, outage probability, and end-to-end latency across varying interference levels. By quantifying these metrics and matching them to specific relay configurations, we can balance complexity and performance across different application domains.

6. New Applications, Open Challenges, and Future Research Directions in Green EH BackCom

6.1. New Applications

Green EH BackCom has many applications in human daily lives. The most common is RFID, which is currently used for supply chain, commodity, and asset tracking. In the future, because of the low cost and low energy consumption of this technology, it will be more widely used in the following fields.

6.1.1. Urban Management in the Era of Smart Cities

Green BackCom will provide low-cost and low-energy solutions for smart cities [203]. In smart transportation, backscatter technology can monitor traffic flow and deploy low-energy road and bridge sensor networks. By embedding passive backscatter vibration sensors in buildings and bridges, vibration data can be collected for the safety assessment of these buildings. Indoor sensing through backscatter technology can not only monitor the number of people but also monitor security threats such as fire, which will play a positive role in the safety of residents [204]. In the future, this technology will be implemented and connected to the large networks of smart cities in most indoor spaces.

6.1.2. AI-Empowered EH BackCom-Enabled IoT

Thanks to the leaps in AI technology in recent years, AI-driven BackCom has created new opportunities for low-cost and energy-saving IoT. Existing methods include supervised learning, reinforcement learning, and deep learning [205]. Supervised learning (SL) attempts to learn how to map data from input to output using a labeled dataset as a guide. The input and output data are often consistent with each other [206]. Finding this input/output mapping relationship is the main goal of SL. Furthermore, reinforcement learning (RL) has been applied to BC to optimize communication parameters based on performance feedback. The agent learns to maximize the cumulative reward in RL by interacting with the environment [207]. Deep learning has shown promising results in improving the performance of BC systems. By leveraging the power of DNNs to learn complex relationships in data, deep learning can help overcome the limitations of traditional signal processing techniques and enable more efficient and reliable BC [208]. There are many deep learning algorithms, including convolutional neural networks (CNNs), artificial neural networks (ANNs), and recurrent neural networks (RNNs). However, AI methods often face problems such as high complexity, high computing power consumption, and time-consuming model training. The energy and computing power of the EH BackCom system are limited, so the development of low-complexity, energy-efficient AI methods will be needed in the future.

6.1.3. Applications of BackCom in Healthcare

In recent years, wearable and implantable health and management devices have become a hot topic. For example, many manufacturers, such as Apple and Huawei, have launched wearable devices such as watches that can monitor heart rate, blood pressure, and other indicators. However, these devices are large in size, high in energy consumption, and have limited monitoring parameters. Therefore, many related works are studying low-power wearable or implantable devices [209]. However, once these devices are worn or implanted, they cannot be easily removed. Therefore, powering these devices has become a challenge. However, with the development of EH BackCom, this problem will be solved. This technology will power and receive information from these devices to manage human health.

6.1.4. Sustainable BackCom-Enabled Sensor Networks

Green BackCom sensor networks have also become a key technology for future low-power sensor networks [210]. Sensors integrated with BackCom technology can obtain energy through EH from RF signals to complete sensing and data upload without batteries. Such a BackCom sensor network has a wide range of application scenarios, especially in suburban and remote areas where energy is not easily accessible. For example, it can be used in smart agriculture to monitor soil temperature and humidity. In addition, it is used to sense wildfires, railway vibrations, and mountain vibrations in remote areas by working with UAVs.

We summarize all the new applications and their main concerns in

Table 7. Summary of new application.

New Application	Literature	Main Focus
Urban Management in the Era of Smart Cities	[203,204]	Green EH BackCom will provide low-cost and low-energy solutions for smart cities.
AI-Empowered EH BackCom-Enabled IoT	[205,206,207,208]	AI-driven BackCom has created new opportunities for low-cost and energy-saving IoT. After training the AI model, it can allocate resources to the BackCom system to improve energy efficiency.
Applications of BackCom in Healthcare	[209]	EH BackCom enables implantable or wearable healthcare devices to be powered by RF energy instead of batteries.
Sustainable BackCom-Enabled Sensor Networks	[210]	Sensors integrated with BackCom technology can obtain energy through EH from RF signals to complete sensing and data upload without batteries.

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6.2. Open Challenges

Although EH BackCom is currently being used in a wide range of applications in the Industrial Internet of Things, it faces some challenges.

6.2.1. Channel Capacity and Coverage Limitations

In current BackCom applications, the number of backscatter devices is often more than a hundred, and most readers work in TDD mode, which means most tags have to wait a long time before being read. In addition, the current backscatter system has a short communication distance and a low data rate, and precious time resources need to be used to harvest energy. Although some work [152,153,211] adopts UAVs to increase coverage, it is beneficial to resource allocation algorithms and beamforming to improve communication efficiency. However, facing the explosive growth in the number of BackCom devices, this bottleneck still exists.

6.2.2. Ensuring Security in BackCom Systems

Although BackCom offers an ultra-low-power, cost-effective solution crucial for green communication technologies in sustainable 6G networks, ensuring privacy and security within BackCom systems remains a significant challenge, particularly concerning interference and eavesdropping [212]. The fundamental communication mechanism of BackCom requires the signal to traverse two paths: from the source to the tag and then from the tag to the receiver. This dual-path transmission exacerbates path fading, resulting in weak signals at the receiver and heightened vulnerability to interference. Furthermore, the simple architecture of BackCom tags, which includes only energy absorption and information modulation circuits, lacks encryption capabilities, making information leakage a critical threat and eavesdropping the primary security concern.

To address these issues, physical layer security (PLS) has emerged as a promising strategy to enhance data confidentiality and safeguard against eavesdropping and jamming attacks. Advanced techniques, such as multi-antenna systems and PLS strategies, have been explored to mitigate these vulnerabilities. For example, integrating UAVs and IRSs into BackCom networks has been proposed to enhance data collection while maintaining line-of-sight (LoS) connectivity, thus reducing the risk of eavesdropping or jamming attacks. Similarly, PLS techniques, including channel state information (CSI)-based encryption and artificial noise generation, have demonstrated potential in theoretical studies. However, these approaches often prioritize the maximization of throughput or involve computationally intensive methods, compromising the core energy efficiency objectives of green communication in BackCom systems. This trade-off highlights the challenge of achieving both security and energy efficiency in practical implementations.

Despite these advances, a significant gap remains in experimental validation. Although many of these techniques have shown theoretical effectiveness in countering eavesdropping and jamming attacks, their real-world applicability has not been thoroughly demonstrated. Theoretical analyses typically assume ideal conditions, disregarding practical environmental variables like wind, humidity, temperature, and magnetic field variations. Furthermore, hardware limitations in BackCom systems, such as power restrictions, short tag reader ranges, and reduced throughput, hinder their ability to perform effectively in real-world performances. Therefore, there is an urgent need for practical and sustainable solutions that optimize system security and energy efficiency while addressing the inherent vulnerabilities of BackCom systems.

6.2.3. Limitations of BackCom Models with Dynamic Readers

The use of dynamic readers, particularly UAVs, in BackCom systems offers numerous benefits, such as expanded coverage, improved data collection efficiency, and energy-efficient network design. However, several limitations must be addressed for practical implementation. A primary challenge lies in the limitations of current UAV communication models. Most existing studies focus predominantly on theoretical energy-saving techniques, often neglecting practical applicability and real-world reliability. Consequently, the results derived from these theoretical analyses may not be fully guaranteed in actual deployment scenarios. Furthermore, these studies frequently rely on strong assumptions, such as maintaining a fixed altitude of the UAV during operation, perfect CSI at both the transmitter and the receiver, and the absence of environmental factors like weather. Such assumptions fail to account for the complexities of real-world conditions. For instance, the inherently short read range of BackCom tags requires UAVs to operate closer to the tags, leading to inefficiencies in trajectory planning and elevated energy consumption. As a result, maintaining a constant altitude of the UAV throughout operations is impractical. Moreover, the dynamic mobility of UAVs introduces challenges in maintaining stable and reliable communication links, with continuous movement causing time-varying channel conditions and Doppler effects that degrade system performance, particularly for low-power tags with limited processing capabilities. Furthermore, environmental factors such as strong winds impact the speed and stability of the UAV, while extreme temperatures reduce battery performance, both contributing to increased energy consumption. Synchronization represents another significant challenge in UAV-assisted BackCom systems. The mobility of UAVs requires precise coordination with BackCom tags and other network elements to enable seamless data collection and efficient spectrum utilization. However, achieving accurate synchronization is particularly difficult in large-scale deployments or multi-UAV scenarios due to high mobility and environmental variability.

Therefore, to enhance the practical feasibility of UAV-aided BackCom, future research must reduce the reliance on overly idealized assumptions and prioritize the development of realistic communication models validated through empirical testing. For instance, incorporating results from real-world experiments could help refine UAV communication models and improve the reliability of the system. Such efforts will ensure that UAV-aided BackCom systems are better equipped to address the complexities of real-world environments and provide sustainable solutions.

6.2.4. Trade-Off Between Computational Complexity and EE in BackCom Relay Systems

Relays are critical components of BackCom systems that enable efficient energy harvesting and reliable data transmission. However, different types of relays inherently involve trade-offs between computational complexity and EE, which must be carefully managed to optimize system performance. For instance, DaF relays offer robust error correction capabilities through their decode-and-reencode process. While this enhances signal quality and reliability, it comes at the cost of higher computational resource consumption, making them less suitable for energy-constrained environments. In contrast, AaF relays amplify both signal and noise, providing a computationally simpler but less efficient solution, particularly in noisy environments. CaF relays, although capable of reducing data transmission overhead, are less commonly adopted in BackCom systems due to their increased complexity and susceptibility to signal distortion during compression.

Existing studies often focus on optimizing individual relay types. For example, hierarchical frameworks in [172] improved the EE of AaF relays under varying channel conditions, while [159] demonstrated significant throughput gains with DaF relays in wireless-powered and NOMA-enabled networks. Despite these advances, single-relay approaches may struggle to balance the competing demands of low computational complexity and high EE. To address this, hybrid relay schemes that combine multiple relay types are gaining traction. Research such as [199,201] highlighted the benefits of dynamic relay mode switching, which allows systems to adaptively balance complexity and efficiency based on channel and energy conditions. Therefore, balancing computational complexity and EE remains a significant challenge in BackCom relay systems. These approaches enable adaptive relay selection and dynamic operation, ensuring optimal performance in dynamic and resource-constrained environments.

6.2.5. Ensuring Full Compatibility with Existing Standards and Network Protocols

Full compatibility with existing communication systems, such as 5G, WiFi, and other standards, is one of the main challenges BackCom faces today. The key reason is that most of the widely used RFID tags operate in the 860–960 MHZ band, which makes their operating frequency inconsistent with WiFi’s 2.4 and 5 GHz, as well as 5G’s multiple frequency bands. Even in recent years, some work has proposed backscatter devices in different frequency bands, such as WiFi tags or MMWave tags, to try to solve the problem of band inconsistency. However, BackCom’s EPC Gen1 or Gen2 protocols are not compatible with WiFi and 5G protocols, and there are differences in modulation and encoding methods. Therefore, the network protocol is also an obstacle for BackCom to be fully compatible with existing systems.

6.2.6. Channel Estimation

Channel estimation is another challenge in EH BackCom. If the BackCom system can perform accurate channel estimation, beamforming can be constructed from the channel information to maximize the energy gained or to maximize the data rate. However, monostatic and bistatic BackCom face secondary fading during estimation, which reduces the accuracy of channel estimation and requires more time slot resources. With ambient BackCom, it is difficult to estimate the channel between the tag and receiver due to the uncontrollable environmental energy source. Furthermore, in the actual BackCom system deployment environment, there is co-channel interference, and BackCom channel estimation under interference is more challenging.

6.2.7. The Trade-Off on Cost-Effectiveness in EH BackCom

The cost-effectiveness trade-off in EH BackCom is challenging because although the BackCom technology is low-cost, in many applications, additional equipment is often required to form the system to solve the problems of the BackCom transmission distance, low transmission rate, and energy supply. For example, components for renewable energy collection, UAVs and IRSs to assist BackCom, and tags for relays, will all incur additional expenditures. A potential solution is to develop resource allocation algorithms to maximize the usage of already existing devices, thus reducing the need for additional components. Another potential solution is ambient BackCom, as it can rely on ambient RF energy for power to reduce the need for the reader and RF source.

We summarize the open challenges mentioned above, as well as their main problems and potential solutions, in

Table 8. Summary of open challenges.

Open Challenges	Main Problem	Potential Solution
Channel Capacity and Coverage Limitations	The current backscatter system has a short communication distance and a low data rate, and precious time resources need to be used to harvest energy.	UAV-assisted BackCom can increase the coverage, mmWave BackCom can increase the data rate.
Ensuring Security in BackCom Systems	Ensuring privacy and security within BackCom systems remains a significant challenge, particularly concerning interference and eavesdropping.	Physical layer security (PLS) has emerged as a promising strategy to enhance data security and against jamming.
Limitations of BackCom Models with Dynamic Readers	Most existing studies focus on theoretical energy-saving techniques, often neglecting practical applicability and real-world reliability.	Future research must reduce the reliance on overly idealized assumptions and prioritize the development of realistic communication models validated through empirical testing.
Trade-off Between Computational Complexity and EE in BackCom Relay Systems	Different types of relays inherently involve trade-offs between computational complexity and EE, which must be carefully managed to optimize system performance.	Algorithms are developed for adaptive relay selection and dynamic operation to ensure optimal performance.
Ensuring Full Compatibility with Existing Standards and Network Protocols	Full compatibility with existing communication systems, such as 5G, WiFi, and other standards is one of the main challenges BackCom faces today.	Develop network standards and protocols compatible with EH BackCom.
Channel Estimation	Monostatic and bistatic BackCom face secondary fading during estimation, which reduces the accuracy of channel estimation and requires more time slot resources. With ambient BackCom, it is difficult to estimate the channel between the tag and receiver due to the uncontrollable environmental energy source.	Develop a channel estimation protocol dedicated to BackCom and optimize its performance.
The Trade-off on Cost-Effectiveness in EH BackCom	Although BackCom technology is low-cost, in many applications, additional equipment is often required to form the system to solve the problems of BackCom, which incurs additional expenditures.	Develop resource allocation algorithms to maximize the usage of already existing devices, thus reducing the need for additional components. Ambient BackCom can rely on ambient RF energy for power to reduce the need for the reader and RF source.

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6.3. Future Research Directions

Next, we will introduce future research directions for the green design of EH BackCom for a more energy-efficient and sustainable IoT.

6.3.1. EE Channel Estimation for BackCom

In BackCom, channel estimation (CE) is an important method to improve communication performance, as all waveform designs rely on the channel state information (CSI) obtained by the CE. In existing work [213,214], the CE method of the monostatic BackCom system has been studied, and the resulting CSI is adopted for optimal beamforming design; their result shows that the SINR at the receiver will be 5–10 dB higher than when beamforming is not adopted. Bisataic BackCom’s CE method has also been studied [215], which splits the channel estimation into three phases to estimate the channel information from the energy source to the tag and the tag to the receiver. Zhao et al. designed a two-step method to jointly estimate the ambient BackCom system channel gain and direction of arrival (DoA), and then optimized the estimate by angular rotation [216]. They also verified that the outage probability of applying the CE protocol is significantly lower than that of the case without CE. However, these existant works ignore the energy efficiency of the channel estimation; although transmitting the pilot signal with higher power will improve the estimation accuracy, this inevitably brings more energy consumption, so the trade-off between channel estimation accuracy and energy consumption is the direction of future research. Potential solutions are blind estimation, or to compress the pilot sequence to reduce energy consumption.

6.3.2. Trade-Off Between EH BackCom Security and Energy Consumption

Due to the fact that backscatter communication devices are low-power devices with simple structure, they have no encryption capability and their reflected signal is weak, which makes security issues such as eavesdropping and jamming more serious than with traditional communications. Hence, the security of EH BackCom has been noted by several researchers [156,212,217]; some work has utilized artificial noise (AN) against eavesdroppers [217,218], and some work has used the mobility of UAVs to circumvent interference and communicate with them in very close proximity to the EH tag to achieve physical layer security [156,219]. In addition, existing work [220] adopts information from the physical layer to generate key encrypted messages to prevent eavesdropping. However, invariably, these works introduce additional power consumption to implement BackCom, the energy to generate AN, the energy required for the propulsion of the UAV, and the energy to generate the key. Due to the limited energy available for EH BackCom, future work should trade off security performance and energy consumption to achieve secure and sustainable BackCom for the IoT.

6.3.3. EH BackCom-Enabled ISAC

In recent years, the integration of sensing and communication has been a very hot topic, especially the utilization of existing WiFi, 5G, BackCom, and satellite communication to realize the ISAC [221]. EH BackCom devices can be widely deployed in ISAC systems for sensing, communication, or relaying due to their low cost and low power consumption [222]. Existing work [222] integrates BackCom and sensing into the same base station. Under the conditions of 20 dBm and a 60% power split, the user’s communication rate is 11.30 bps/Hz and the BS’s sensing rate is 3.63 bps/Hz. In addition, the ISAC backscattering-enabled IRS-assisted network is also studied, and rate and sensing power maximization problems are formulated to improve its performance [223]. However, many BackCom ISAC systems focus only on communication and sensor performance, ignoring energy efficiency. Therefore, the green design of the EH BackCom-enabled ISAC will be required in the future.

6.3.4. Green Design for Millimeter Wave-Based EH BackCom

Although BackCom is an emerging low-power communication technology that has been integrated into the IoT due to its excellent self-sufficiency, BackCom technology usually encounters data transmission rate limitations. In contrast, millimeter wave (mmWave) BackCom has shown great potential in high-speed data transmission due to its abundant spectrum resources [224]. At present, the mmWave BackCom system is mainly based on the following technologies: over-the-air modulation (OTAM), CSI (mmWave backscatter readers extracting CSI), the Van Atta array, and the Vibration transponder [225]. The main advantage of OTAM-based mmWave BackCom is that it solves the beam searching and NLos channel problems, but it lacks mobility and can only support a single tag. The advantage of the CSI-based mmWave BackCom system is that it is easier to establish on commercial readers, but has a small coverage area of only 6 m [226]. In contrast, the Van Atta array mmWave BackCom can cover a larger area and supports multiple tags, but the BER is high in complex environment. The Vibrating Surface also supports multiple tags and has a coverage area of up to 300 m, but the transmission rate is very low at 1.2 kbps. While mmWave BackCom improves the transmission rate, the power consumption is very high (over 1 watt). Therefore, the mmWave BackCom that combines the two can make a trade-off between energy consumption and throughput to achieve a balance. Therefore, future research on the green design of millimeter wave BackCom to improve energy efficiency is crucial. Potential improvement directions include improving the efficiency of millimeter-wave EH and beamforming to improve the throughput of millimeter-wave communication.

6.3.5. Cell-Free Massive MIMO-Enabled EH BackCom Networks

With cell-free massive MIMO (CF mMIMO), a distributed form of mMIMO, all APs are connected to a CPU, which operates all APs as an mMIMO network without cell boundaries, providing services to all users through coherent transmission and reception. Specifically, the CPU allows all APs to provide services to all users on the same time frequency resources by applying spatial multiplexing technology. The system is set up similarly to the distributed antenna system (DAS) and coordinated multipoint (COMP) system to provide services to users within its coverage area in a cooperative manner, providing users with high diversity gain because each user receives different large-scale fading (LSF) components from different APs [227]. Because of the above advantages, CF mMIMO is considered to be the key technology to improve the performance of EH BackCom. Existing work [228] investigates the AP selection problem under a CF mMIMO bisatatic BackCom system. The channel estimation and performance optimization of this system were studied by Diluka et al. [229]. However, CF mMIMO BackCom still cannot solve the problem that the tag itself has no encryption capability, and the reflected signal is weak. Therefore, security issues such as eavesdropping and jamming are current challenges, and potential solutions are to detect the location of eavesdroppers and jammers, and to use CF mMIMO spatial diversity design beamforming to improve the secrecy rate and reduce interference. Before designing beamforming, the channel estimation between multiple APs to tags is also very challenging, which will be the future research direction.

6.3.6. Toward Practical Experimental Validation in BackCom Systems

Future research should prioritize experimental validation of green design paradigms in BackCom by exploring innovative approaches such as UAV-based communication models, RIS-assisted frameworks, PLS strategies and protocols, and hybrid relay techniques. UAV-based communication models in BackCom offer significant potential for enhancing LoS connectivity and large-scale data collection efficiency in a wider coverage field, yet require empirical studies to optimize energy consumption and network stability under varying environmental conditions. Additionally, integrating IRSs or RISs into BackCom systems has the potential to revolutionize signal propagation and energy efficiency. However, practical implementation requires rigorous testing to assess the impact of IRS or RIS configurations and the scale of deployments on overall system performance. Subsequently, for PLS in BackCom, experimental efforts are essential to validate the effectiveness of theoretical techniques under real-world adversarial conditions. Finally, relay-based, tag-to-tag, and multihop communication models can extend network coverage and improve data reliability, but these designs need further experimental validation to quantify their trade-offs in energy efficiency, latency, and security in dynamic IoT environments. These avenues collectively pave the way for practical and sustainable BackCom systems capable of meeting the demands of next-generation networks.

Subsequently, several commercial off-the-shelf products, as shown in Figure 10, Figure 11 and Figure 12, including UAVs [211,230,231,232], readers [233,234,235,236,237,238,239], and RISs [240], have been summarized for their potential use in the experimental validation of BackCom systems. Specifically, devices (a) to (c) in Figure 11 are IMPINJ readers, which are RFID readers designed for high-performance enterprise-level IoT applications. These readers facilitate wireless bidirectional communication between applications and tagged objects, enabling reading, writing, and authentication. In BackCom experiments, IMPINJ readers act as reliable RF sources, playing a critical role in the evaluation of system performance and energy efficiency. In contrast, devices (d) to (f) in Figure 11 represent NI-USRP SDR equipment. These software-defined radios offer a flexible RF architecture for designing, prototyping, and deploying wireless systems with custom signal processing capabilities. Supporting a wide frequency range and real-time bandwidth, they are ideal for diverse communication experiments. In BackCom research, NI-USRP devices are invaluable for emulating communication scenarios, testing modulation schemes, and validating protocols, thereby facilitating the comprehensive analysis and optimization of BackCom system designs.

7. Conclusions

In this review article, we conduct a comprehensive literature survey on the green design of EH BackCom, which is a key technology to realize a sustainable IoT with low power consumption and low cost. First, we introduce the basic principles and scope of EH BackCom. Next, we focus on the green design for a BackCom tag, which is mainly a highly efficient antenna and matching circuit design, rectifier design, energy storage, and load design. We also review the green channel design and the green BackCom or energy source design, which includes the utilization of renewable energy, the utilization of ambient RF energy sources, the waveform design, and resource allocation to improve energy efficiency. In addition, we comprehensively review the emerging applications of EH BackCom in cooperative systems and the energy-saving or energy-efficient green designs therein. Finally, we discuss the new applications and open challenges of EH BackCom, as well as the future research directions. Our main outcomes are as follows: (1). We review existing green designs for an EH tag and reader or RF source. (2). We summarize the highly energy-efficient techniques of cooperative systems based on EH BackCom. (3). We also point out various new applications in EH BackCom and demonstrate the open challenges as well as the future research directions.