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Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (10 October 2019) | Viewed by 31229

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Special Issue Editors

Prof. Dr. Bang Chul Jung

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Guest Editor

Department of Electronics Engineering, Chungnam National University, Daejeon 34143, Korea
Interests: B5G/6G wireless communication networks; interference management(interference alignment); radio resource management; information theory & communication theory; cognitive radios & spectrum sharing; wireless LAN systems; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Won-Yong Shin

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Guest Editor

Yonsei University, Department of Computational Science & Engineering, Seoul 03722, Korea
Interests: Information Theory; Communications; Mobile Computing; Big Data Analytics; Social Network Analysis

Prof. Dr. Howon Lee

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Guest Editor

School of Electronic and Electrical Engineering, Hankyong National University, Anseong 17579, Korea
Interests: B5G/6G wireless communication networks; reinforcement learning based radio resource management; UAV communications; long-range low-power communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy efficiency is considered one of the most important performance metrics for beyond-5G wireless communication systems, which are expected to support tremendous mobile data traffic from/to massive mobile devices including smartphones, various sensors, Internet-of-Things (IoT) devices, etc. Thus, advanced green radio techniques are being proposed to reduce the overall power consumption of wireless communication systems, including energy efficient transmission/reception design, medium access control, scheduling algorithms, network operation methods, etc. Energy harvesting wireless networks refer to wireless networks deploying energy harvesting devices, where various natural sources such as solar/indoor lightening, vibrational, thermal, biological, chemical, and electromagnetic sources can be utilized for energy harvesting. Many promising techniques are currently being proposed to improve the performance of energy harvesting wireless networks. In particular, wireless-powered communication techniques have recently been emerging as a promising energy harvesting technique from electromagnetic wireless signals. Simultaneously wireless information and power transfer (SWIPT) has also received much attention from both academia and industry.

The goal of this Special Issue is to disseminate the recent theoretical and practical results in green radio technologies, energy harvesting wireless networks, and wireless-powered communication techniques for beyond-5G wireless communication systems. Review papers on these topics are also welcome.

Potential topics include, but are not limited to, the following:

Green communications
Green radio techniques for 5G/beyond-5G wireless networks
Energy-efficient wireless communication techniques
Energy efficiency and spectrum efficiency trade-offs in wireless networks
Energy-efficient IoT networks
Wireless protocols for energy saving in wireless networks
Green computing and communication technologies
Green applications for wireless systems
Energy harvesting techniques for wireless networks
Energy harvesting wireless communications
Simultaneous wireless information and power transfer
Wireless-powered communication protocols
Wireless-powered IoT
Scheduling and resource management for wireless-powered networks
Green energy management systems
Smart grid

Prof. Bang Chul Jung
Prof. Won-Yong Shin
Prof. Howon Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Green radio techniques
Energy-efficient wireless communications
Energy harvesting (EH) techniques
Energy harvesting wireless communications
Power transfer
Wireless-powered communication networks
Simultaneous wireless information and power transfer (SWIPT)
Energy management systems

Published Papers (11 papers)

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Research

18 pages, 874 KiB

Open AccessArticle

Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

by Hoon Lee, Han Seung Jang and Bang Chul Jung

Energies 2019, 12(22), 4300; https://doi.org/10.3390/en12224300 - 11 Nov 2019

Cited by 9 | Viewed by 2118

Abstract

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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13 pages, 3596 KiB

Open AccessArticle

Dynamic Duty-Cycle MAC Protocol for IoT Environments and Wireless Sensor Networks

by Gayoung Kim, Jin-Gu Kang and Minjoong Rim

Energies 2019, 12(21), 4069; https://doi.org/10.3390/en12214069 - 25 Oct 2019

Cited by 17 | Viewed by 2990

Abstract

This paper proposes a new protocol that can be used to reduce transmission delay and energy consumption effectively. This will be done by adjusting the duty-cycle (DC) ratio of the receiver node and the contention window size of the sender node according to the traffic congestion for various devices in the Internet of Things (IoT). In the conventional duty-cycle MAC protocol, the data transmission delay latency and unnecessary energy consumption are caused by a high collision rate. This is because the receiver node cannot sufficiently process the data of the transmitting node during the traffic peak time when the transmission and reception have the same duty-cycle ratio. To solve this problem, this paper proposes an algorithm that changes the duty-cycle ratio of the receiver and broadcasts the contention window size of the senders through Early Acknowledgment (E-ACK) at peak time and off/peak time. The proposed algorithm, according to peak and off/peak time, can transmit data with fewer delays and minimizes energy consumption. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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22 pages, 2811 KiB

Open AccessFeature PaperArticle

Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks

by Rony Kumer Saha

Energies 2019, 12(20), 3825; https://doi.org/10.3390/en12203825 - 10 Oct 2019

Cited by 7 | Viewed by 2290

Abstract

In this paper, we propose a technique to share the licensed spectrums of all mobile network operators (MNOs) of a country with in-building small cells per MNO by exploiting the external wall penetration loss of a building and introducing the time-domain eICIC technique. The proposed technique considers allocating the dedicated spectrum B_op per MNO only its to outdoor macro UEs, whereas the total spectrum of all MNOs of the country B_co to its small cells indoor per building such that technically any small indoor cell of an MNO can have access to B_co instead of merely B_op assigned only to the MNO itself. We develop an interference management strategy as well as an algorithm for the proposed technique. System-level capacity, spectral efficiency, and energy efficiency performance metrics are derived, and a generic model for energy efficiency is presented. An optimal amount of small indoor cell density in terms of the number of buildings L carrying these small cells per MNO to trade-off the spectral efficiency and the energy efficiency is derived. With the system-level numerical and simulation results, we define an optimal value of L for a dense deployment of small indoor cells of an MNO and show that the proposed spectrum sharing technique can achieve massive indoor capacity, spectral efficiency, and energy efficiency for the MNO. Finally, we demonstrate that the proposed spectrum sharing technique could meet both the spectral efficiency and the energy efficiency requirements for 5G mobile networks for numerous traffic arrival rates to small indoor cells per building of an MNO. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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13 pages, 853 KiB

Open AccessArticle

Multi-User AF Relay Networks with Power Allocation and Transfer: A Joint Approach

by Ramnaresh Yadav, Keshav Singh, Sudip Biswas and Ashwani Kumar

Energies 2019, 12(16), 3157; https://doi.org/10.3390/en12163157 - 16 Aug 2019

Cited by 1 | Viewed by 1965

Abstract

The Internet-of-Things (IoT) framework has been considered as an enabler of the smart world where all devices will be deployed with extra-sensory power in order to sense the world as well as communicate with other sensor nodes. As a result, smart devices require more energy. Therefore, energy harvesting (EH) and wireless power transfer (WPT) emerge as a remedy for relieving the battery limitations of wireless devices. In this work, we consider a multi-user amplify-and-forward (AF)-assisted network, wherein multiple source nodes communicate with destination nodes with the help of a relay node. All the source nodes and the relay node have the capability of EH. In addition, to cope with a single point of failure i.e., failure of the relay node due to the lack of transmit power, we consider the WPT from the source nodes to the relay node. For WPT, a dedicated energy control channel is utilized by the source nodes. To maximize the sum rate using a deadline, we adopt a joint approach of power allocation and WPT and formulate an optimization problem under the constraints of the battery as well as energy causality. The formulated problem is non-convex and intractable. In order to make the problem solvable, we utilize a successive convex approximation method. Furthermore, an iterative algorithm based on the dual decomposition technique is investigated to get the optimal power allocation and transfer. Numerical examples are used to illustrate the performance of the proposed iterative algorithm. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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21 pages, 2920 KiB

Open AccessArticle

An Energy-Efficient Cross-Layer Routing Protocol for Cognitive Radio Networks Using Apprenticeship Deep Reinforcement Learning

by Yihang Du, Ying Xu, Lei Xue, Lijia Wang and Fan Zhang

Energies 2019, 12(14), 2829; https://doi.org/10.3390/en12142829 - 22 Jul 2019

Cited by 10 | Viewed by 3084

Abstract

Deep reinforcement learning (DRL) has been successfully used for the joint routing and resource management in large-scale cognitive radio networks. However, it needs lots of interactions with the environment through trial and error, which results in large energy consumption and transmission delay. In this paper, an apprenticeship learning scheme is proposed for the energy-efficient cross-layer routing design. Firstly, to guarantee energy efficiency and compress huge action space, a novel concept called dynamic adjustment rating is introduced, which regulates transmit power efficiently with multi-level transition mechanism. On top of this, the Prioritized Memories Deep Q-learning from Demonstrations (PM-DQfD) is presented to speed up the convergence and reduce the memory occupation. Then the PM-DQfD is applied to the cross-layer routing design for power efficiency improvement and routing latency reduction. Simulation results confirm that the proposed method achieves higher energy efficiency, shorter routing latency and larger packet delivery ratio compared to traditional algorithms such as Cognitive Radio Q-routing (CRQ-routing), Prioritized Memories Deep Q-Network (PM-DQN), and Conjecture Based Multi-agent Q-learning Scheme (CBMQ). Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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Graphical abstract

28 pages, 3506 KiB

Open AccessArticle

Realization of Licensed/Unlicensed Spectrum Sharing Using eICIC in Indoor Small Cells for High Spectral and Energy Efficiencies of 5G Networks

by Rony Kumer Saha

Energies 2019, 12(14), 2828; https://doi.org/10.3390/en12142828 - 22 Jul 2019

Cited by 17 | Viewed by 3469

Abstract

In this paper, we show how to realize numerous spectrum licensing policies by means of time-domain enhanced inter-cell interference coordination (eICIC) technique to share both the licensed and unlicensed spectrums with small cells in order to address the increasing demand of capacity, spectral efficiency, and energy efficiency of future mobile networks. Small cells are deployed only in 3-dimensional (3D) buildings within a macrocell coverage of a mobile network operator (MNO). We exploit the external wall penetration loss of each building to realize traditional dedicated access, co-primary shared access (CoPSA), and licensed shared access (LSA) techniques for the licensed spectrum access, whereas, for the unlicensed spectrum access, the licensed assisted access (LAA) technique operating in the 60 GHz unlicensed band is realized. We consider that small cells are facilitated with dual-band, and derive the average capacity, spectral efficiency, and energy efficiency metrics for each technique. We perform extensive evaluation of various performance metrics and show that LAA outperforms considerably all other techniques concerning particularly spectral and energy efficiencies. Finally, we define an optimal density of small cells satisfying both the spectral efficiency and energy efficiency requirements for the fifth-generation (5G) mobile networks. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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9 pages, 773 KiB

Open AccessArticle

Exact Performance Analysis of Amplify-and-Forward Bidirectional Relaying over Nakagami-m Fading Channels with Arbitrary Parameters

by Dong Qin, Yuhao Wang and Tianqing Zhou

Energies 2019, 12(7), 1277; https://doi.org/10.3390/en12071277 - 03 Apr 2019

Viewed by 2328

Abstract

The exact performance of amplify-and-forward (AF) bidirectional relay systems is studied in generalized and versatile Nakagami-m fading channels, where the parameter m is an arbitrary positive number. We consider three relaying modes: two, three, and four time slot bidirectional relaying. Closed form expressions of the moment generating function (MGF), higher order moments of signal-to-noise ratio (SNR), ergodic capacity, and average signal error probability (SEP) are derived, which are different from previous works. The obtained expressions are very concise, easy to calculate, and evaluated instantaneously without a complex summation operation, in contrast to the nested multifold numerical integrals and truncated infinite series expansions used in previous work, which lead to computational inefficiency, especially when the fading parameter m increases. Simulation results corroborate the correctness and tightness of the theoretical analysis. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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20 pages, 970 KiB

Open AccessArticle

Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems

by Kuk Yeol Bae, Han Seung Jang, Bang Chul Jung and Dan Keun Sung

Energies 2019, 12(7), 1249; https://doi.org/10.3390/en12071249 - 01 Apr 2019

Cited by 17 | Viewed by 3370

Abstract

Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer’s profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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11 pages, 804 KiB

Open AccessArticle

Energy-Efficient Multicast Precoding for Massive MIMO Transmission with Statistical CSI

by Li You, Wenjin Wang and Xiqi Gao

Energies 2018, 11(11), 3175; https://doi.org/10.3390/en11113175 - 15 Nov 2018

Cited by 7 | Viewed by 2359

Abstract

In this paper, we investigate energy-efficient multicast precoding for massive multiple-input multiple-output (MIMO) transmission. In contrast with most previous work, where instantaneous channel state information (CSI) is exploited to facilitate energy-efficient wireless transmission design, we assume that the base station can only exploit statistical CSI of the user terminals for downlink multicast precoding. First, in terms of maximizing the system energy efficiency, the eigenvectors of the optimal energy-efficient multicast transmit covariance matrix are identified in closed form, which indicates that optimal energy-efficient multicast precoding should be performed in the beam domain in massive MIMO. Then, the large-dimensional matrix-valued precoding design is simplified into an energy-efficient power allocation problem in the beam domain with significantly reduced optimization variables. Using Dinkelbach’s transform, we further propose a sequential beam domain power allocation algorithm which is guaranteed to converge to the global optimum. In addition, we use the large-dimensional random matrix theory to derive the deterministic equivalent of the objective to reduce the computational complexity involved in sample averaging. We present numerical results to illustrate the near-optimal performance of our proposed energy-efficient multicast precoding for massive MIMO. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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17 pages, 912 KiB

Open AccessArticle

Energy-Efficient Path Selection Using SNR Correlation for Wireless Multi-Hop Cooperative Communications

by In-Ho Lee and Haejoon Jung

Energies 2018, 11(11), 3004; https://doi.org/10.3390/en11113004 - 01 Nov 2018

Cited by 1 | Viewed by 2172

Abstract

In this paper, we consider partial path selection (PPS) for a multi-hop decode-and-forward cooperative system with limited channel state information (CSI) feedback, where the PPS utilizes local CSI only for a subset of hops on each of all independent paths between a source and a destination to reduce the energy consumption for CSI feedback. To enhance the end-to-end performance of the PPS, we propose a novel PPS method with local CSI chosen by the correlation between the end-to-end signal-to-noise ratios (SNRs) based on global and local CSI under Nakagami-m fading channels. For each path, we pick a subset of hops to report CSI with the highest correlation for a given CSI feedback overhead requirement, which can achieve the similar end-to-end outage probability to the best path selection with global CSI. We provide an exact and closed-form expression for the SNR correlation coefficient and present an impact of the SNR correlation on the end-to-end outage probability. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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23 pages, 2452 KiB

Open AccessArticle

Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks

by Tae Yeong Kim and Dong In Kim

Energies 2018, 11(10), 2855; https://doi.org/10.3390/en11102855 - 22 Oct 2018

Cited by 1 | Viewed by 3273

Abstract

In this paper, we propose a multi-dimensional sparse-coded ambient backscatter communication (MSC-AmBC) system for long-range and high-rate massive Internet of things (IoT) networks. We utilize the characteristics of the ambient sources employing orthogonal frequency division multiplexing (OFDM) modulation to mitigate strong direct-link interference and improve signal detection of AmBC at the reader. Also, utilization of the sparsity originated from the duty-cycling operation of batteryless RF tags is proposed to increase the dimension of signal space of backscatter signals to achieve either diversity or multiplexing gains in AmBC. We propose optimal constellation mapping and reflection coefficient projection and expansion methods to effectively construct multi-dimensional constellation for high-order backscatter modulation while guaranteeing sufficient energy harvesting opportunities at these tags. Simulation results confirm the feasibility of the long-range and high-rate AmBC in massive IoT networks where a huge number of active ambient sources and passive RF tags coexist. Full article

(This article belongs to the Special Issue Green Radio, Energy Harvesting, and Wireless-Powered Communications for Beyond-5G Wireless Systems)

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