Search Results (147)

The sharp increase in Internet of Things (IoT) terminal numbers imposes significant pressure on energy and wireless spectrum resources. Battery-free IoT technology has become an effective solution to address the high power consumption and cost issues of traditional IoT systems. While leveraging backscatter communication, battery-free IoT faces challenges such as low throughput and poor fairness among wireless links. To tackle these problems, this study proposes a low-power hybrid communication mechanism for terminals. Within this mechanism, a time-frame partitioning method for hybrid communication strategies is designed based on sensing results of licensed spectrum channels. Considering terminal power constraints, quality of service (QoS) requirements of primary communication links, and time resource limitations, a hybrid communication strategy model is established to jointly optimize fairness and maximize throughput. To resolve the non-convexity in the Multi-objective Lexicographical Optimization Problem (MLOP), the Block Coordinate Descent (BCD) method and auxiliary variables are introduced. Simulation results demonstrate that, compared to the baseline scheme, the proposed approach reduces the throughput gap between links from 85.4% to 0.32% when the channel gain differences are small, while the total system throughput decreases by only 8.81%. As the channel gain disparity increases, the baseline scheme exhibits a more pronounced disadvantage in terms of throughput fairness, while the proposed approach still reduces the throughput gap between the best and worst links from 91.02% to 0.684% at the cost of a 9.18% decrease in total system throughput. These results demonstrate that the proposed scheme effectively balances fairness and throughput performance across diverse channel conditions, ensuring relatively equitable quality of service for all users in the IoT network. Full article

In this paper, an energy efficiency (EE)–spectral efficiency (SE) trade-off scheme is investigated for the distributed reconfigurable intelligent surface (RIS)-assisted backscatter vehicle-to-infrastructure (V2I) communication system. Firstly, a multi-objective optimization framework balancing EE and SE is established using the linear weighting method, and the quadratic transformation is utilized to recast the optimization problem as a strictly convex problem. Secondly, an alternating optimization (AO) approach is applied to partition the original problem into two independent subproblems of the BS and RIS beamforming, which are, respectively, designed by the weighted minimization mean-square error (WMMSE) and the Riemannian conjugate gradient (RCG) algorithms. Finally, according to the trade-off factor, the power reflection coefficients of backscatter devices (BDs) are dynamically optimized with the BS beamforming vectors and RIS phase shift matrices, considering their activation requirements and the vehicle minimum quality of service (QoS). The simulation results verify the effectiveness of the proposed algorithm in simultaneously improving SE and the EE in practical V2I applications through rational optimization of the BD power reflection coefficient. Full article

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

Sixth-generation (6G) communication systems, with ultra-wide bands, energy-autonomous end nodes, and dense connectivity, challenge existing network designs. Optimizing time resources with energy harvesting, backscatter communication, and relays is essential to maximize the total bit rate in multi-user symbiotic radio networks (SRNs) with blocked direct paths. The literature lacks a unified optimization treatment that explicitly accounts for imperfect successive interference cancellation (SIC). This study addresses this gap by proposing the first optimization framework to maximize total bit rate for energy-harvesting TDMA/PD–NOMA-based multi-cluster and relay-assisted peer-assisted SR networks. The two-phase architecture defines a tractable constrained optimization problem that jointly adjusts cluster-specific time slots ($\mathit{\tau }$ and $\lambda$). Incorporating QoS, signal power, and reflection coefficient constraints, it provides a compact formulation and numerical solutions for both perfect and imperfect SIC. Detailed simulations performed under typical 6G power levels, bandwidths, and energy-harvesting efficiencies demonstrate graphically that imperfect SIC significantly limits total throughput due to residual interference, while perfect SIC completely eliminates this ceiling under the same conditions, providing a significant capacity advantage. Furthermore, the gap between the two scenarios rapidly closes with increasing relay time margin. The findings demonstrate that network capacity is primarily determined by the triad of base station output power, channel noise, and SIC accuracy, and that the proposed framework achieves strong performance across the explored parameter space. Full article

In the field of the Internet of Things (IoT), technical solutions that enable information transmission with minimal energy consumption are of particular interest. Common solutions frequently used in the field of radio frequency identification (RFID) involve utilizing electromagnetic waves to power tags and employing backscattering for communication. Detecting the received signal in a coherent manner enables increased reliability in tag reading. This paper proposes a method for coherent signal detection in a bistatic backscatter communication system (BBCS), which includes coarse carrier frequency offset (CFO) from preamble and fine phase correction from data symbols. The proposed method outperforms the detection approach based on maximum likelihood estimation (MLE) of CFO from the preamble, particularly in scenarios with higher CFO values. The proposed detection method is well suited for implementation in software-defined radios, particularly in low-cost devices characterized by less stable oscillators. It is also shown that a preamble of six symbols is sufficient to perform a coarse CFO estimation. Since the analyzed system is equivalent to binary frequency-shift keying (FSK) modulation, the performance of FSK is presented as the theoretical upper bound in the results. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

The pervasive scale of networks brought about by smart city applications has created infeasible energy footprints and necessitates the inclusion of sensing sustained operations with minimal human intervention. Consequently, integrated sensing and communication (ISAC) is emerging as a key technology for 6G systems. ISAC systems realize dual functions using shared spectrum, which complicates interference management. This motivates the development of advanced signal processing and multiplexing techniques. In this context, extremely large antenna arrays (ELAAs) have emerged as a promising solution. ELAAs offer substantial gains in spatial resolution, enabling precise beamforming and higher multiplexing gains by operating in the near-field (NF) region. Despite these advantages, the use of ELAAs increases energy consumption and exacerbates carbon emissions. To address this, NF multiple-input multiple-output (NF-MIMO) systems must incorporate sustainable architectures and scalable solutions. This paper provides a comprehensive review of the various methodologies utilized in the design of energy-efficient NF-MIMO-based ISAC systems. It introduces the foundational principles of the latest research while identifying the strengths and limitations of green NF-MIMO-based ISAC systems. Furthermore, this work provides an in-depth analysis of the open challenges associated with these systems. Finally, it offers a detailed overview of emerging opportunities for sustainable designs, encompassing backscatter communication, dynamic spectrum access, fluid antenna systems, reconfigurable intelligent surfaces, and energy harvesting technologies. Full article

Ambient backscatter communication (AmBC) has been regarded as an energy- and spectrum-efficient backscatter scheme for the massive Internet of Things (IoT). However, most existing AmBC systems are non-adaptive end-to-end systems, which cannot fully accommodate the forthcoming massive communications of the sixth-generation (6G) wireless communication systems. Adaptive backscatter communication has emerged as a research hotspot in AmBC in recent years. In this paper, we propose a novel adaptive backscatter technique on passive backscatter devices (BDs) in massive IoT scenarios. We first design a low-power adaptive strategy for the AmBC system where the backscatter receiver (BR) assigns a decision threshold to the passive BDs for the local adaptive backscatter mode chosen. Then, we propose the decision threshold design method by solving a joint sum rate maximization problem where the reflection coefficients (RCs) and transmit time allocation (TA) of different backscatter modes are also jointly optimized. Finally, simulations are provided to verify the efficiency of the proposed adaptive backscatter technique in terms of sum rate and outage probability performances. The results show that our proposed adaptive multi-source AmBC system can achieve a 34.8% average sum rate performance improvement compared with traditional AmBC systems under a common setup, and it performs better than other existing adaptive backscatter systems. Moreover, the numeric results confirm the accuracy and tightness of our derivation of outage probabilities. Full article

Fluid Antenna Systems (FASs) have recently emerged as a promising solution to address the demanding performance indicators (KPIs) and scalability challenges of future 6G mobile communications. By enabling agile control over both radiating position and antenna shape, FAS can significantly improve diversity gain and reduce outage probability through dynamic selection of the optimal radiation point, also known as port. Numerous theoretical studies have explored novel FAS concepts, often in conjunction with other wireless communication technologies such as multiple-input multiple-output (MIMO), Non-Orthogonal Multiple Access (NOMA), Reconfigurable Intelligent Surfaces (RIS), Integrated Sensing and Communication (ISAC), backscatter communication, and Semantic communication. To validate these theoretical concepts, several early-stage FAS hardware prototypes have been developed, including liquid–metal fluid antennas, mechanically movable antennas, pixel-reconfigurable antennas, and meta-fluid antennas. Initial measurements have demonstrated the potential advantages of FAS. This article provides a brief review of these early FAS hardware technologies. Furthermore, we share our vision for future hardware development and the corresponding challenges, aiming to fully release the potential of FAS and stimulate further research and development within the antenna research community. Full article

Wireless passive temperature sensors are essential in environments where wired connections are impractical, such as rotating machinery and harsh conditions. A key advantage of these sensors is their ability to operate without a local power source. This study employs the antenna backscattering method, which relies on the wireless interaction between the interrogator antenna and the sensor antenna’s resonant frequency, implemented in the far-field region to support long communication distances. To evaluate the impact of antenna shape on sensor performance, three microstrip patch antenna shapes—rectangular, circular, and equilateral triangular—were designed to operate in the fundamental mode at 2.4 GHz. These designs were simulated using HFSS in Ansys Electromagnetic Suite^® 2023 R1 (Ansys Inc., Canonsburg, PA, USA), fabricated on alumina substrates, and assessed for performance metrics, including communication distance and sensitivity. Results indicated that the equilateral triangular patch outperformed the others, achieving a maximum communication distance of 16.5 cm, a sensitivity of 0.129 MHz/°C over a temperature range of 25 °C to 500 °C, and a simulated gain of 5.84 dBi. These findings underscore the importance of antenna shape selection and optimization for robust, wireless temperature sensing in demanding environments. Full article

The last few decades have witnessed the rapid development of passive backscatter technologies, which envision promising cost-efficient ambient Internet of Things (IoT) for various applications, such as distributed solar sensor networks. However, limited by the harmonic interference caused by the conventional frequency-shifting-based backscatter control methods, existing backscatter communication technologies cannot support the growing scale of the network. To tackle this issue, we propose a harmonic interference resilient frequency-shifting technique to compress the harmonics during backscatter communication. Different from conventional backscatter tags that shift the frequency with square waves with a constant pulse width, we dynamically modify the pulse width of the square wave to compress different parts of the harmonic waves. Furthermore, we propose a lightweight communication coding algorithm to enhance the compatibility of our system with backscatter applications. We implement the system with off-the-shelf components and conduct comprehensive experiments to evaluate the performance. The results demonstrate our harmonic interference resilient backscatter system can compress the harmonic interference and reduce the BER (bit error rate) by 70%. Full article

The widespread application of terminal sensors in power pipe galleries (PPGs) has significantly improved our ability to monitor power equipment status. However, the difficulties in battery replacement caused by confined space and energy loss caused by communication conflicts between sensors due to existing low-power communication strategies results in a lack of reliable energy supply for terminal sensors. In this context, a low-power communication strategy for terminal sensors is proposed. Firstly, a demand analysis is conducted on the status monitoring of PPGs, and a technical framework for low-power communication of terminal sensors is proposed. Afterward, a system model for the low-power communication of terminal sensors is established based on cognitive backscatter technology. Subsequently, key technologies, such as RF energy harvesting of terminal sensors and distance–energy level coupling analysis, are proposed to achieve continuous energy supply and avoid communication conflicts in the system model. Finally, a wireless communication simulation environment for PPGs is established to simulate the status monitoring process, based on terminal sensors, in order to verify the effectiveness of the proposed method. Full article

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. Full article

Integrating Non-Orthogonal Multiple Access (NOMA) backscatter with Cognitive Radio (CR) can effectively alleviate the pressure of large-scale Internet of Things (IoT) device connections while reducing power consumption. In the downlink of NOMA backscatter-based CR networks (NB-CR), most researchers only consider the case of a single backscatter device (BD), ignoring the fact that multiple BDs can prolong device usage and enhance system robustness. The resource allocation (RA) problem is crucial in the downlink of NB-CR networks. Most existing RA algorithms focus on system throughput but rarely consider energy efficiency (EE). In this paper, we propose the RA problem for downlink communication (NBCR-RA) in NB-CR networks with multiple BDs. We jointly optimize power allocation coefficients and reflection coefficients (RCs) to maximize EE. We model the NBCR-RA problem as a non-convex problem and divide it into two subproblems: power allocation optimization and RC optimization. Firstly, we propose a Lagrange-based power allocation optimization (L-PA) algorithm to obtain the optimal power allocation coefficients. Secondly, we design an RC optimization algorithm, PS-RC, based on a particle swarm algorithm to determine the optimal RCs. Finally, we validate the superiority of L-PA and PS-RC algorithms in terms of EE. Through multiple experiments, we obtained a $95\%$ confidence interval of [10.5552, 10.6465]. Full article

Indoor Internet of Things (IoT) is considered as a crucial component of Industry 4.0, enabling devices and machine to communicate and share sensed data leading to increased efficiency, productivity, and automation. Increased energy efficiency is a significant focus within Industry 4.0, as it offers numerous benefits. To support this focus, we developed a hybrid switching mechanism to switch between energy harvesting techniques, ambient backscattering and Simultaneous Wireless Information and Power Transfer (SWIPT), which can be utilized within cooperative communications. To implement the proposed switching mechanism, we consider an indoor warehouse environment, where the moving sensor node transmits sensed data to the fixed relay located on the roof, which is then transmitted to an IoT gateway. The relay is equipped with the proposed switch to energize its communication capabilities while maintaining the expected quality of service at the IoT gateway. Simulation results illustrate the improved energy efficiency within the indoor communication setup while maintaining QoS at varying signal-to-noise (SNR) conditions. Full article