Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer

Wireless Powered Relaying Networks Under Imperfect Channel State Information: System Performance and Optimal Policy for Instantaneous Rate

In this investigation, we consider wireless powered relaying systems, where energy is scavenged by a relay via radio frequency (RF) signals. We explore hybrid time switching-based and power splitting-based relaying protocol (HTPSR) and compare performance of Amplify-and-Forward (AF) with Decode-and-Forward (DF) scheme under imperfect channel state information (CSI). Most importantly, the instantaneous rate, achievable bit error rate (BER) are determined in the closed-form expressions under the impact of imperfect CSI. Through numerical analysis, we evaluate system insights via different parameters such as power splitting (PS) and time switching (TS) ratio of the considered HTPSR which affect outage performance and BER. It is noted that DF relaying networks outperform AF relaying networks. Besides that, the numerical results are given to prove the optimization problems of PS and TS ratio to obtain optimal instantaneous rate

Resource allocation in future green wireless networks : applications and challenges

Over the past few years, green radio communication has been an emerging topic since the footprint from the Information and Communication Technologies (ICT) is predicted to increase 7.3% annually and then exceed 14% of the global footprint by 2040. Moreover, the explosive progress of ICT, e.g., the fifth generation (5G) networks, has resulted in expectations of achieving 10-fold longer device battery lifetime, and 1000-fold higher global mobile data traffic over the fourth generation (4G) networks. Therefore, the demands for increasing the data rate and the lifetime while reducing the footprint in the next-generation wireless networks call for more efficient utilization of energy and other resources. To overcome this challenge, the concepts of small-cell, energy harvesting, and wireless information and power transfer networks can be evaluated as promising solutions for re-greening the world. In this dissertation, the technical contributions in terms of saving economical cost, protecting the environment, and guaranteeing human health are provided. More specifically, novel communication scenarios are proposed to minimize energy consumption and hence save economic costs. Further, energy harvesting (EH) techniques are applied to exploit available green resources in order to reduce carbon footprint and then protect the environment. In locations where implemented user devices might not harvest energy directly from natural resources, base stations could harvest-and-store green energy and then use such energy to power the devices wirelessly. However, wireless power transfer (WPT) techniques should be used in a wise manner to avoid electromagnetic pollution and then guarantee human health. To achieve all these aspects simultaneously, this thesis proposes promising schemes to optimally manage and allocate resources in future networks. Given this direction, in the first part, Chapter 2 mainly studies a transmission power minimization scheme for a two-tier heterogeneous network (HetNet) over frequency selective fading channels. In addition, the HetNet backhaul connection is unable to support a sufficient throughput for signaling an information exchange between two tiers. A novel idea is introduced in which the time reversal (TR) beamforming technique is used at a femtocell while zero-forcing-based beamforming is deployed at a macrocell. Thus, a downlink power minimizationscheme is proposed, and optimal closed-form solutions are provided. In the second part, Chapters 3, 4, and 5 concentrate on EH and wireless information and power transfer (WIPT) using RF signals. More specifically, Chapter 3 presents an overview of the recent progress in green radio communications and discusses potential technologies for some emerging topics on the platforms of EH and WPT. Chapter 4 develops a new integrated information and energy receiver architecture based on the direct use of alternating current (AC) for computation. It is shown that the proposed approach enhances not only the computational ability but also the energy efficiency over the conventional one. Furthermore, Chapter 5 proposes a novel resource allocation scheme in simultaneous wireless information and power transfer (SWIPT) networks where three crucial issues: power-efficient improvement, user-fairness guarantee, and non-ideal channel reciprocity effect mitigation, are jointly addressed. Hence, novel methods to derive optimal and suboptimal solutions are provided. In the third part, Chapters 6, 7, and 8 focus on simultaneous lightwave information and power transfer (SLIPT) for indoor applications, as a complementary technology to RF SWIPT. In this research, Chapter 6 investigates a hybrid RF/visible light communication (VLC) ultrasmall cell network where optical transmitters deliver information and power using the visible light, whereas an RF access point works as a complementary power transfer system. Thus, a novel resource allocation scheme exploiting RF and visible light for power transfer is devised. Chapter 7 proposes the use of lightwave power transfer to enable future sustainable Federated Learning (FL)-based wireless networks. FL is a new data privacy protection technique for training shared machine learning models in a distributed approach. However, the involvement of energy-constrained mobile devices in the construction of the shared learning models may significantly reduce their lifetime. The proposed approach can support the FL-based wireless network to overcome the issue of limited energy at mobile devices. Chapter 8 introduces a novel framework for collaborative RF and lightwave power transfer for wireless communication networks. The constraints on the transmission power set by safety regulations result in significant challenges to enhance the power transfer performance. Thus, the study of technologies complementary to conventional RF SWIPT is essential. To cope with this isue, this chapter proposes a novel collaborative RF and lightwave power transfer technology for next-generation wireless networks

Adaptive Resource Allocation Algorithms For Data And Energy Integrated Networks Supporting Internet of Things

According to the forecast, there are around 2.1 billion IoT devices connected to the network by 2022. The rapidly increased IoT devices bring enormous pressure to the energy management work as most of them are battery-powered gadgets. What’s more, in some specific scenarios, the IoT nodes are fitted in some extreme environment. For example, a large-scale IoT pressure sensor system is deployed underneath the floor to detect people moving across the floor. A density-viscosity sensor is deployed inside the fermenting vat to discriminate variations in density and viscosity for monitoring the wine fermentation. A strain distribution wireless sensor for detecting the crack formation of the bridge is deployed underneath the bridge and attached near the welded part of the steel. It is difficult for people to have an access to the extreme environment. Hence, the energy management work, namely, replacing batteries for the rapidly increased IoT sensors in the extreme environment brings more challenges. In order to reduce the frequency of changing batteries, the thesis proposes a self-management Data and Energy Integrated Network (DEIN) system, which designs a stable and controllable ambient RF resource to charge the battery-less IoT wireless devices. It embraces an adaptive energy management mechanism for automatically maintaining the energy level of the battery-less IoT wireless devices, which always keeps the devices within a workable voltage range that is from 2.9 to 4.0 volts. Based on the DEIN system, RF energy transmission is achieved by transmitting the designed packets with enhanced transmission power. However, it partly occupies the bandwidth which was only used for wireless information transmission. Hence, a scheduling cycle mechanism is proposed in the thesis for organizing the RF energy and wireless information transmission in separate time slots. In addition, a bandwidth allocation algorithm is proposed to minimize the bandwidth for RF energy transmission in order to maximize the throughput of wireless information. To harvest the RF energy, the RF-to-DC energy conversion is essential at the receiver side. According to the existing technologies, the hardware design of the RF-to-DC energy converter is normally realized by the voltage rectifier which is structured by multiple Schottky diodes and capacitors. Research proves that a maximum of 84% RF-to-DC conversion efficiency is obtained by comparing a variety of different wireless band for transmitting RF energy. Furthermore, there is energy loss in the air during transmitting the RF energy to the receiver. Moreover, the circuital loss happens when the harvested energy is utilized by electronic components. Hence, how to improve the efficiency of RF energy utilization is considered in the thesis. According to the scenario proposed in the thesis, the harvested energy is mainly consumed for uplink transmission. a resource allocation algorithm is proposed to minimize the system’s energy consumption per bit of uplink data. It works out the optimal transmission power for RF energy as well as the bandwidth allocated for RF energy and wireless information transmission. Referring to the existing RF energy transmission and harvesting application on the market, the Powercast uses the supercapacitor to preserve the harvested RF energy. Due to the lack of self-control energy management mechanism for the embedded sensor, the harvested energy is consumed quickly, and the system has to keep transmitting RF energy. Existing jobs have proposed energy-saving methods for IoT wireless devices such as how to put them in sleep mode and how to reduce transmission power. However,they are not adaptive, and that would be an issue for a practical application. In the thesis, an energy-saving algorithm is designed to adaptively manage the transmission power of the device for uplink data transmission. The algorithm balances the trade-off between the transmission power and the packet loss rate. It finds the optimal transmission power to minimize the average energy cost for uplink data transmission, which saves the harvested energy to reduce the frequency of RF energy transmission to free more bandwidth for wireless information

Multiple Access in Aerial Networks: From Orthogonal and Non-Orthogonal to Rate-Splitting

Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to satisfy the high requirements of wireless aerial networks, several multiple access techniques have been investigated. In particular, space-division multiple access(SDMA) and power-domain non-orthogonal multiple access (NOMA) present promising multiplexing gains for aerial downlink and uplink. Nevertheless, these gains are limited as they depend on the conditions of the environment. Hence, a generalized scheme has been recently proposed, called rate-splitting multiple access (RSMA), which is capable of achieving better spectral efficiency gains compared to SDMA and NOMA. In this paper, we present a comprehensive survey of key multiple access technologies adopted for aerial networks, where aBSs are deployed to serve ground users. Since there have been only sporadic results reported on the use of RSMA in aerial systems, we aim to extend the discussion on this topic by modelling and analyzing the weighted sum-rate performance of a two-user downlink network served by an RSMA-based aBS. Finally, related open issues and future research directions are exposed.Comment: 16 pages, 6 figures, submitted to IEEE Journa

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure

Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components