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The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm. [ABSTRACT FROM AUTHOR]

Computation offloading is an effective method in mobile edge computing (MEC) to relieve user equipment (UE) from the limited computation resource and battery capacity. Meanwhile, simultaneous wireless information and power transmission (SWIPT) can be applied to MEC to extend the operating time of the equipment. However, in multi-user network environment, diverse computation task requirements and changeable network channel states make it challenging to obtain offloading strategy timely and accurately. To address the issue, we propose an intelligent computation offloading scheme (iCOS) based on enhanced priority deep deterministic policy gradient (EPDDPG) algorithm to minimize the energy consumption of all the UEs by jointly optimizing the offloading decision, the central processing unit (CPU) frequency and the power split ratio in a dynamic SWIPT-MEC network. In particular, we improve the traditional fully-connected network structure to obtain both discrete and continuous action outputs, and accelerate neural network parameter updates by using prioritized experience tuples. Furthermore, we use dynamic voltage and frequency scaling (DVFS) technology to dynamically adjust the CPU frequency of local computing, and employ SWIPT technology to balance the charging and communication according to the obtained strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the energy cost of UEs, and complete more computation tasks within the delay limit. [ABSTRACT FROM AUTHOR]

Simultaneous vibration control and energy harvesting of vehicle suspensions have attracted significant research attention over the past decades. However, existing energy harvesting shock absorbers (EHSAs) compromise suspension performance for high-efficiency energy harvesting and being only responsive to narrow-bandwidth vibrations. In this paper, we propose a new ball-screw-based EHSA design – inerter pendulum vibration absorber (IPVA) – that integrates an electromagnetic rotary EHSA with a nonlinear pendulum vibration absorber.We show that this design simultaneously improves ride comfort and energy harvesting efficiency by exploiting the nonlinear effects of pendulum inertia. To further improve the performance, we develop a novel stochastic linearization model predictive control (SL-MPC) approach in which we employ stochastic linearization to approximate the nonlinear dynamics of EHSA that has superior accuracy compared to standard linearization. In particular, we develop a new stochastic linearization method with guaranteed stabilizability, which is a prerequisite for control designs. This leads to an MPC problem that is much more computationally efficient than the nonlinear MPC counterpart with no major performance degradation. Also, the effect of different road preview configurations on control performance is investigated, which is shown to have a significant impact on the control performance. Extensive simulations are performed to show the superiority of the proposed new nonlinear EHSA and to demonstrate the efficacy of the proposed SL-MPC. [ABSTRACT FROM AUTHOR]

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source’s signals to the destination. Whereas the source S can act as a wireless charging station or a base station to supply power or transmit information to the UB using the dynamic time splitting (DTS) method. The UAV utilizes its harvested energy for backscattering (i.e., passive communication) and transmit information (i.e., active communication) to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the DTS ratio and the UB’s trajectory with caching capability at the UB. The formulation is troublesome to solve since it is a non-convex problem. To find solutions, we decompose the original problem into two sub-problems, whereas we first optimize the DTS ratio for a given UB’s trajectory and the UB’s trajectory optimization for a given DTS ratio. By using the KKT conditions, a closed-form expression for the optimal value of the DTS ratio is obtained, greatly reducing the computation time. Moreover, the solution of the second sub-problem can be acquired by adopting the successive convex approximation (SCA) technique. Consequently, an efficient alternating algorithm is proposed by leveraging the block coordinate descent (BCD) method. To show the advantages of the proposed BCD-based algorithm, we also provide the solution of the original problem applying the inner approximation (IA) method. Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes. [ABSTRACT FROM AUTHOR]

Disguised tailing and visual monitoring of suspicious mobile targets is a promising application of security unmanned aerial vehicles (UAVs). But trajectory planning is non-trivial, especially for fixed-wing UAVs with more constrained maneuverability and dynamic models. This paper proposes a new framework to optimize collectively the propulsion power and the three-dimensional (3D) trajectory of a solar-powered, fixed-wing UAV on a disguised tailing and video surveillance mission. The multi-objective optimization strikes a balance between distance keeping, elevation variance, and power efficiency. A key aspect is that we develop a new propulsion power model of the fixed-wing UAV by analyzing the forces undergone while the UAV is ascending or descending. Another important aspect is a series of non-trivial reformulations, which convexify the multi-objective problem progressively with increasingly tightening linear approximation and solve the problem with a polynomial time-complexity. Our algorithm can control the trajectory of the UAV on-the-fly. Simulations confirm that the algorithm outperforms existing schemes in terms of visual disguise and power efficiency. The fixed-wing UAV also demonstrates its advantage of energy efficiency and sustainability to elongate the surveillance mission, over its rotary-wing counterpart. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is considered as a multiple access technique for supporting massive number of devices with high spectral efficiency. Simultaneous wireless information and power transfer (SWIPT) is an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in networks employing cooperative NOMA (CNOMA) is investigated with imperfect channel state information (CSI). Performance of the CNOMA network with SWIPT is investigated in a multi-tier downlink scenario with macro-base stations (MBSs) and femto-base stations (FBSs). The distribution of MBSs and FBSs follow Poisson point process model. Unlike solution in the existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating node/user employs EH using interference from the adjacent base stations. Expressions for outage probability and throughput are derived at the user pair (comprising a cell-center user and a cell-edge user) served using NOMA. Comparison of the proposed EH system with the existing solutions is highlighted. Monte Carlo simulations are carried out to validate the analytical results. [ABSTRACT FROM AUTHOR]

With the development of 6G wireless communication technologies, various resource-intensive and delay-sensitive vehicle application tasks are generated. These application tasks can be offloaded to Mobile Edge Computing (MEC) which deploys computing resources at the edge of networks. Besides, the recent proposed Cybertwin, as the digital representation of the complicated physical end-systems, can help the terminals obtain the required services from networks. Vehicles enabled by Cybertwin can offload their tasks to MEC and achieve better performance. In this paper, we focus on the study of a hybrid energy-powered multi-server MEC system with Cybertwin. Vehicles enabled by Cybertwin and edge servers send the current network status and unprocessed vehicle application tasks to the macro base station (MBS) to achieve the better allocation of resources. Energy harvesting (EH) devices are deployed on edge servers to form a “green energy-grid” hybrid energy supply model. We formulate a stochastic offloading optimization problem, and the goal is to minimize the system cost. The stochastic optimization problem is decomposed into three sub-problems. Then, we design an efficient multi-vehicle task offloading (EMT) algorithm to achieve the trade-off between system cost and task queue length. Theoretical analysis shows that EMT algorithm can optimize the total cost of the MEC system and guarantee the system performance. According to experimental evaluation, we verify the performance of the EMT algorithm. [ABSTRACT FROM AUTHOR]

This paper studies a joint design of resource allocation and task offloading in a wireless powered mobile edge computing network involving different types of computation tasks. To deal with diverse computation tasks, we explore a mixed-offloading paradigm to support the coexistence of partial and binary offloading modes. Specifically, devices harvest energy from an access point (AP) via wireless power transfer (WPT) and utilize the harvested energy to execute their computation tasks using partial or binary offloading. Based on a practical non-linear energy harvesting model, a residual energy maximization problem is formulated by jointly optimizing the transmit power of the AP, the offloading power of devices, the time allocation on WPT and task offloading, and the task partitions and the binary offloading decisions of devices, which turn out to be a non-convex mixed-integer non-linear programming problem. Thus, we develop an efficient dual-layer optimization algorithm by decomposing the optimization problem into an inner and outer layer structure that aims to obtain resource allocation and offloading decisions. Simulation results show that our proposed scheme achieves residual energy gains compared to existing schemes. [ABSTRACT FROM AUTHOR]

The ongoing growth of Internet of Things (IoT) traffic increasesthe emergence of Multi-Access Edge Computing (MEC), which can serve IoT devices to process edge missions properly. The power supply for IoT devices is addressed by energy harvesting (EH) technology. However, the energy arrival model obeys the unknown energy arrival process. In addition, due to the dynamical changes of network state, it is crucial to study the access control in the MEC-based green IoT system. In this paper, we propose an MEC-based access control strategy to maximize the system utility for the green IoT system, considering the spectral efficiency and the successful access ratio from the perspective of operators and users respectively. Specifically, we model the access control problem as a Constrained Markov Decision Process (CMDP), and develop a novel centralized reward-based experience replay deep convolutional Q network algorithm (RCQN) to attain the optimal access control for EH IoT devices. Additionally, to enrich the prior knowledge of access control strategy, we design a Long Short-Term Memory (LSTM) based energy prediction module to predict the energy state of IoT devices by learning the historical energy arrival information, whose output is taken as the state information of RCQN. Simulation results demonstrate that the proposed LSTM-based RCQN access control algorithm significantly improves the system utility with a proper reward design and training mechanism. [ABSTRACT FROM AUTHOR]

In this paper, we develop centralized and distributed dynamic resource allocation schemes for an intelligent reflecting surface (IRS) aided energy harvesting (EH) system that optimize transmit power of a source node and phase-shift of passive reflecting elements of IRS. The source node randomly harvests renewable energy from the surrounding environment and performs data transmission with the harvested energy while satisfying statistical packet delay constraints in terms of maximum acceptable delay-outage probability. Our developed schemes do not require the statistical distributions of channel and energy profiles to be known and apply deep reinforcement learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed resource control scheme for IRS-aided EH system in different channel and energy conditions. [ABSTRACT FROM AUTHOR]

Nowadays, wireless communication has become a fabric of our daily life, and it has been a universal demand for higher capacity and longer battery lifetime. For the sake of solving these problems, this paper considers taking the advantage of cooperative communication with the assistance of green relays. So, in this paper, we design an auction market that is composed of one base station (acts as the auctioneer), multiple green relays (act as sellers), and multiple mobile terminals (act as buyers). The terminals need to pay for the cooperative service, and the relays sell it for revenue. In addition, the terminals can raise their bid according to their residual energy; thus the terminals that lack energy have more opportunities to get cooperative service to avoid energy exhaustion. At the same time, the relays can reduce the price based on their instantaneous energy harvesting amount, and, hence, the relays can serve more terminals when they can harvest more energy from the environment. This paper also proposes three different auction rules to show the effect of relay selection. Furthermore, power allocation among relays and terminals is adopted to minimize the power consumption of terminals under the SNR requirement. Simulation results show that the three relay selection rules perform diversely and have their own advantages and disadvantages, but all of them can improve system capacity and prolong the lifetime of the mobile terminals effectively. And, with power allocation, the terminal can utilize the least energy to achieve its SNR requirement. [ABSTRACT FROM AUTHOR]

In this paper, the physical-layer security (PLS) for an underlay cognitive radio network (CRN) with multiple non-colluding eavesdroppers is studied. We assume that a secondary user (SU) transmitter communicates with an SU receiver over a cascaded $\kappa$ - $\mu$ fading channel and under the threat of eavesdropping. A cooperative jammer exists to improve the SUs’ security by harvesting energy from the SU transmitter using power splitting technique. This harvested energy is utilized to generate jamming signals to deteriorate the eavesdroppers’ reception quality. Moreover, the eavesdroppers are assumed to be randomly distributed according to a homogeneous Poisson point process (HPPP), in which the $k^{th}$ closest eavesdropper to the SU transmitter will be selected in our analysis. Additionally, the legitimate receiver is assumed to harvest energy from the SU transmitter using the power splitting technique. In this context, we propose an optimum power splitting factor at the legitimate receiver that achieves the best privacy while constraining the amount of the harvested energy. Furthermore, on enhancing system security, we investigate the effectiveness of the jamming technique. The impact of distances over security is also examined. The results reveal an improvement in privacy as the legitimate receiver utilizes the adaptable power splitting factor. PLS is evaluated in terms of the probability of non-zero secrecy capacity and the intercept probability. [ABSTRACT FROM AUTHOR]

In 5G wireless networks, cooperative non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) are efficient ways to improve the spectral efficiency (SE) and energy efficiency (EE). In this paper, a new cooperative NOMA scheme with WPT is proposed, where EE optimization with a constrained maximum transmit power and minimum required SE is considered for the user grouping and transmit power allocation of users. We obtain a sub-optimal solution by decoupling the original problem in two sub-problems: an iterative algorithm is considered for the user grouping, while, in addition, we utilize the Bat Algorithm (BA) for solving the power allocation problem, where BA was proved to be able to achieve a higher accuracy and efficiency with respect to other meta-heuristic algorithms. Furthermore, to validate the performance of the proposed system, analytical expressions for the energy outage probability and outage probability of users are derived, confirming the effectiveness of the simulation results. It is demonstrated that the proposed cooperative NOMA with WPT offers a considerable improvement in terms of SE and EE of the network compared to other methods. Finally, the effectiveness of BA in solving the EE optimization problem is demonstrated through a high convergence speed by comparing it with other methods. [ABSTRACT FROM AUTHOR]

The performance of an unmanned aerial vehicle (UAV) assisted cooperative cognitive radio (CR) network is studied in this paper. The study is carried out for line-of-sight (LOS) links between UAV and a ground user and UAV aerodynamic power consumption of UAV. The UAV consists of an RF energy harvester and it is also a CR enabled device. It harvests energy from RF signal of source nodes. It rotates periodically over a circular region of interest (CROI) for finding the vacant primary user (PU) band. It uses a PU band to set a link between ground nodes, while the direct links between them suffer from severe fading. The decision about the status of a PU band is made cooperatively. In cooperative sensing, all the sources along with the UAV take sensing decisions and send them to fusion centre (FC). A two stage fusion scheme is proposed to take the overall decision. The considered network model uses non-orthogonal multiple access (NOMA) scheme to forward the information of a source to its corresponding destination. Analytical expressions of harvested energy, overall sensing decision and throughput are developed. The analytical expressions capture the effect of fading as well as change in distances (source-UAV and UAV-destination) due to the circular flight of the UAV. Impact of imperfect successive interference cancellation (SIC) on the system performance is also studied. Power allocation in the second hop link is optimized to maximize the network throughput. Energy-efficiency of the considered network is evaluated. For a given UAV flight period, radius of the circular trajectory, and the velocity of the UAV is optimized to maximize the energy-efficiency. Sensing performance of the considered system model is studied in terms of false alarm. Network throughput is investigated for several network parameters such as fading parameter, the number of sources, the number of UAV's sensing slots, the velocity of UAV, the radius of the UAV trajectory etc. Sensing angle and harvesting angle are optimized to maximize the throughput. Results show that the value of fading parameter, radius of UAV trajectory, and UAV velocity has significant impact on throughput. The performance of NOMA is compared with orthogonal multiple access (OMA). [ABSTRACT FROM AUTHOR]

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless powered communication network, where a UAV serves multiple energy constrained wireless sensor nodes (WSNs). In such a network, a UAV transmits wireless power to a group of WSNs which exploits the harvested power to transfer their own information towards the UAV in the uplink. For practical scenarios, a nonlinear energy harvesting saturation model is considered at WSNs. We aim to design resource allocation which maximizes the achievable sum rate in the uplink and minimizes the downlink transmit power simultaneously. The design is formulated as a multi-objective optimization problem (MOOP) which optimizes the UAV's 3D position, its transmit power, the time splitting ratio, the uplink transmission time and the beamwidth angle of the UAV. The formulated problem is a non-convex problem which is generally intractable. To address the MOOP, a weighted Tchebycheff method is proposed. The numerical results show a trade-off between the minimum power in the downlink and the maximum rate as well as energy efficiency in the uplink. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has been recently emerged as an effective way for improving the performance of wireless networks by reconfiguring the propagation environment through a large number of passive reflecting elements. This game-changing technology is especially important for stepping into the Internet of Everything (IoE) era, where high performance is demanded with very limited available resources. In this paper, we study a backscatter-assisted wireless powered communication network (BS-WPCN), in which a number of energy-constrained users, powered by a power station (PS), transmit information to an access point (AP) via backscatter and active wireless information transfer, with their communication being aided by an IRS. Using a practical energy harvesting (EH) model which is able to capture the characteristics of realistic energy harvesters, we investigate the maximization of total network throughput. Specifically, IRS reflection coefficients, PS transmit and AP receive beamforming vectors, power and time allocation are designed through a two-stage algorithm, assuming minimum mean square error (MMSE) receiver at the AP. The effectiveness of the proposed algorithm is confirmed via extensive numerical simulations. We also show that our proposed scheme is readily applicable to practical IRS-aided networks with discrete phase shift values. [ABSTRACT FROM AUTHOR]

In this paper, novel ergodic capacity (EC) performance evaluation results of a power beacon (PB)-assisted multi-input multi-output (MIMO) wireless powered communication network are presented. In the considered system, the energy harvesting node harvests energy from the radio-frequency signals sent by the dedicated PB and uses this energy to communicate with the destination node. To accurately model the combined effect of multi-path fading and shadowing, it is assumed that the energy transfer link is subject to $\kappa$ - $\mu$ shadowed fading. Performance evaluation results are presented for two cases, depending upon the availability of channel state information (CSI) at the PB, namely, $no$ CSI and $full$ CSI. In the former case, equal power allocation is assumed, whereas, in the later case, energy beamforming is employed to increase energy transfer efficiency. For the performance evaluation of EC under $full$ CSI, a closed-form approximation for the probability density function of the maximum eigenvalue of a $\kappa$ - $\mu$ shadowed distributed random matrix is derived. For both $no$ CSI and $full$ CSI cases, lower and upper bounds on the achievable EC are derived in closed-form. Moreover, in order to obtain further insights on the impact of key parameters on the system performance, asymptotic EC expressions which become very tight at low- and high-signal-to-noise ratio regimes, are obtained. Using the proposed EC lower bound as well as these asymptotic results, simple closed-form expressions for the optimal time split that maximize the achievable EC are derived. Numerically evaluated results accompanied with Monte-Carlo simulations are further presented to corroborate the theoretical analysis. [ABSTRACT FROM AUTHOR]

The development of unmanned aerial vehicles (UAVs) is one of the driving forces for revolutionizing wireless communications in the modern era. Although there are many unique features of UAV networks, their widespread use is still hampered by the short battery life. As a result, most of the research efforts in this domain mainly have focused on the efficient use of energy through trajectory planning and cooperative communication. While there have been a few proposals to use energy harvesting techniques for UAV networks, a complete understanding of the performance limits of such networks is still missing in the literature and needs further investigations. Within this context, our paper provides a comprehensive performance evaluation of UAV relay networks (UAVRNs) employing non-orthogonal multiple access (NOMA) technique. Specifically, a cooperative communication system, where a UAV serves as a mobile relay, is fully considered. The UAV relay is considered to be wirelessly-powered and harvests energy from the radio signals received from a nearby base station. For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic capacities are provided for each UAV relaying protocol. Extensive simulations are performed to verify the accuracy of the derived closed-form expressions. The results provided in this work characterize the outage and ergodic capacity performance of NOMA-enabled UAVRNs. [ABSTRACT FROM AUTHOR]

This paper investigates the outage and throughput performance of full-duplex (FD) cooperative relaying (CR) based non-orthogonal multiple access system (NOMA) with energy harvesting (EH) i.e., FD-CR-EH-NOMA system. We consider a network scenario, where the base station (BS) serves two downlink users, with the near user configured as a FD relay to deliver messages to the far user. We derive analytical expressions for the outage probabilities experienced by the users and the system outage probability of FD-CR-EH-NOMA network under time switching relaying (TSR) protocol in the presence/absence of direct link from BS to the far user. Assuming imperfect successive interference cancellation (i-SIC), we derive analytical expressions for (i) optimal power allocation (OPA) factor at the BS and (ii) optimal time switching (OTS) factor at the relay that independently minimizes the system outage probability. Further, we use the alternating optimization technique to determine the jointly optimal power allocation and time switching factors that minimize the system outage probability. Furthermore, we determine the OTS factor that maximizes the asymptotic throughput of the system in the delay-limited transmission mode. With the help of numerical and simulation investigations, we show that the system outage and throughput of the considered network improve significantly under the proposed schemes. [ABSTRACT FROM AUTHOR]

The capability of Non-Orthogonal Multiple Access (NOMA) for minimizing interference at the receiver using successive interference cancellation (SIC) makes it as a promising solution for 5G mobile communication. Device-to-device (D2D) communication is a part of the 5G network which provides frequency reuse in cellular networks and provides a better quality of service (QoS) to co-channel users. In this paper, we investigate the resource allocation and time scheduling algorithms for a NOMA based D2D communication system. In addition, to overcome the problem of energy at D2D transmitter and receivers, wireless powered communication network (WPCN) is used. Considering these issues, the objective of the proposed scheme is to maintain energy efficient communication among D2D transmitters and receivers without compromising energy efficiency (EE) of cellular users. To solve the fractional form of an optimization problem, we employ the Dinkelbach method. A many-to-many matching game approach is used for channel allocation and successive convex approximation for low complexity (SCALE) is used to control the power of the D2D transmitter. The simulation results demonstrate that by maximizing the EE and optimizing the time and transmission power of D2D groups, the energy efficiency of the network can be significantly improved in comparison to the other competitive schemes in the existing literature. [ABSTRACT FROM AUTHOR]

Energy Harvesting Wireless Body Area Networks (EH-WBANs) where sensor nodes can harvest energy from their ambient environment are of great potential for low-power medical monitoring systems. The availability of energy in EH-WBANs, however, is challenged by uncertainty of harvested energy sources and limited charging efficiency, and thus the energy saving is still of great importance. To improve energy efficiency, this paper studies the sleep scheduling problem in EH-WBANs, which is of fundamental importance for network lifetime and connectivity while being not systematically addressed. Technically, this problem is proved to be NP-Complete, and thus needs non-trivial efforts to deal with. To that end, we present a series of approximation algorithms with proven performance from perspective of constructing minimum dominating set (DS), where energy saving and energy harvesting techniques are combined to prolong the network lifetime while guaranteeing real-time requirement of EH-WBANs. Specifically, we first propose a centralized algorithm that can construct DS with minimum size and discover the maximum number of DSs. We then design two distributed algorithms independent of prior global knowledge to improve network scalability, namely EEU and Improved EEU respectively. Theoretical analysis and extensive simulations are conducted to confirm the superiority of the proposed sleep scheduling algorithms. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is considered as a potential multiple access technique for supporting massive number of devices with high spectral efficiency for the 5 G and beyond networks. Simultaneous wireless information and power transfer (SWIPT) is considered as an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in heterogeneous networks (HetNets) employing cooperative NOMA (CNOMA) is investigated. The HetNets consists of macro-base stations (MBS) under-laid by small-base stations (SBS). The distribution of MBSs follow Poisson point process model, while the SBS tier supports NOMA and carrier sensing for its transmission. Carrier sensing helps in reducing interference by blocking the base stations within a specific range from transmitting. In this work, the analysis at SBS tier is carried out in two phases. In the direct phase transmission, the SBS transmits the superimposed signal to a user pair, comprising of a cell-centre user (CCU) and a cell-edge user (CEU), using NOMA principle. The CCU acts as a cooperative relay to forward information to the CEU in the cooperative phase. Unlike existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating user (i.e. CCU) employs EH using interference from the adjacent base stations along with the EH from the superimposed signal. Expressions for outage probability and throughput are derived at the user pair served using NOMA. Comparison of the proposed EH system with existing solutions are highlighted and useful insights are drawn. Monte Carlo simulations are carried out to validate the analytical results. [ABSTRACT FROM AUTHOR]

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate. [ABSTRACT FROM AUTHOR]

In this paper, we consider a coexisting network of cellular transmission and device-to-device (D2D) communication, in which two users need to receive downlink signals from base station (BS), meanwhile, they want to exchange information with each other. For such a network, a hybrid cellular and bidirectional D2D cooperative non-orthogonal multiple access (HCBD-CNOMA) scheme is proposed. To characterize the performance of our proposed scheme, we derive the closed-form ergodic rate and outage probability by considering both perfect and imperfect successive interference cancellation (SIC). To acquire more insights, their corresponding asymptotic expressions are obtained as well. Moreover, considering the fact that the nodes in Internet of Things (IoT) network are usually powered by battery and thus their power is limited, we further consider the scenario that two users are energy-constraint. In this scenario, users first need to harvest energy from the transmission of BS based on time splitting (TS) or power splitting (PS) energy harvesting (EH) protocol and then use these energy to accomplish subsequent communications. Similarly, the exact ergodic rate and outage probability as well as their asymptotic expressions for this scenario are analyzed. Simulation results validate the accuracy of analytical results, and reveal the performance gain of our schemes over the benchmark schemes. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance analysis of point-to-point communication, where the source node is powered with a fixed-power battery as well as a harvest-use battery. A time-switching scheme is implemented such that in each time frame the source either harvests energy or sends information to the destination node. Since switching takes place within a communication time frame, energy harvesting may not necessarily be beneficial in terms of improving the maximum achievable spectral efficiency or the energy efficiency of system. Here, we investigate and provide conditions under which harvesting can improve the system performance. We consider a scenario where the transmission power from the fixed battery is constant. Closed-form expressions for the conditions under which energy harvesting is proven beneficial are provided. The effects of important parameters, namely, time switching parameter, circuit powers, and harvested energy, on the system performance and on the condition for harvesting beneficialness, are investigated and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we consider wireless-powered secure communication with an energy harvesting receiver, which is allowed to harvest energy from the transmitted signals but not to decode information, and there is thus a requirement to keep the information secret from this potential eavesdropper. Considering co-channel interference among signal links, we find the optimal transmit power to maximize the sum rate of the signal links, while ensuring the requirements of information secrecy and energy harvesting. Due to the non-convexity of the optimization problem formulated here, we first derive suboptimal solutions using an iterative algorithm based on a dual method. In order to address the limitations caused by the use of the iterative algorithm, i.e., long computation time and suboptimality, we design an efficient deep neural network (DNN) framework and a novel training strategy as a means of combining supervised and unsupervised training. Specifically, the DNN is first pre-trained using labeled training data with the suboptimal solutions obtained from the iterative algorithm in a supervised manner; further training is then applied to the DNN using a well designed loss function in an unsupervised manner to enhance the training performance. Simulation results reveal that the proposed scheme achieves a near-optimal performance with a lower computation time than existing schemes. We also verify that the pre-training and the new loss function are effective in improving the speed of training of the DNN. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the performance of a hybrid receiver-based simultaneous wireless information and power transfer (SWIPT) two-way relay network over Nakagami-m faded channels. A hybrid receiver is adopted at the relay node that utilizes both the time switching (TS) and power splitting (PS) protocols for energy harvesting and information transmission. Amplify-and-forward relaying protocol is utilized at the relay node to process the information in half-duplex mode. In the considered network, impact of a practical non-linear power amplifier (NLPA) at the relay node is considered. Selection combining is performed at the destination node to utilize the direct-link and relay assisted signals. For the considered system, the analytical expressions of outage probability (OP) and asymptotic OP are derived over Nakagami-m faded channels for both the integer and non-integer value of m. The analytical expression of the system throughput and energy efficiency of the considered network are also derived. Further, the analytical expression of ergodic capacity is derived in terms of the Meijer-G function. By utilizing a cumulative distribution function based approach, analytical expressions of average symbol error rate (ASER) for general order hexagonal quadrature amplitude modulation (QAM), general order rectangular QAM, and 32-cross QAM schemes are derived. For different QAM constellations, a comparative ASER analysis is also illustrated. Furthermore, the impact of TS factor, PS ratio, energy conversion efficiency, NLPA, and threshold data-rates on the considered network is also highlighted. Finally, Monte-Carlo simulations are performed for validation of the derived analytical expressions. [ABSTRACT FROM AUTHOR]

This paper investigates a novel joint content caching and energy harvesting scheme to enhance unmanned aerial vehicle (UAV) communications in Internet of Things non-orthogonal multiple access (NOMA) network, where a UAV is acted as an aerial relay to serve users on demand. In particular, a neoteric gravitational search-based multi-constraint optimization algorithm is designed to maximize the throughput of the served users by jointly optimizing power allocation, energy harvesting, and time scheduling schemes. The algorithm engages two main characteristics: 1) classification of multiple constraints, which is for reducing the complexity of the problem solving process due to multiple iterations, and 2) design of force sets, which aims to enhance the global search for the optimal solution at the feasible region boundary. Finally, simulations are provided to show the effective convergence and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large. [ABSTRACT FROM AUTHOR]

This paper studies a novel design of user pairing and resource allocation in a simultaneous wireless information and power transfer (SWIPT)-enabled cooperative non-orthogonal multiple access (NOMA) system with coexisting power-splitting (PS) and time-switching (TS) users. In this system, near NOMA users (NUs) that are close to a source can assist the communications between the source and far NOMA users (FUs) using the energy harvested by either PS or TS strategy. The design aims to maximize the sum-rate of the system by optimizing user pairing and power allocation between NUs and FUs as well as performing PS and TS control at NUs, while guaranteeing the target data rates and the minimum harvested energy requirements of users. Since the joint design is formulated as a non-convex mixed-integer non-linear programming (MINLP) problem that is challenging to tackle, we propose a two-step user pairing and resource allocation algorithm. Specifically, we decouple the original problem into an inner resource allocation problem and an outer user pairing problem. For the inner problem, we propose successive convex approximation (SCA) and block coordinate descent (BCD)-based algorithms to perform power allocation as well as PS and TS control iteratively, with the algorithms’ convergence being theoretically proved. By exploiting the characteristics of the outer problem of user pairing, this problem is well tackled by the Hungarian algorithm. Simulation results demonstrate the proposed joint design yields better performance gains than the existing schemes. [ABSTRACT FROM AUTHOR]

Simultaneous lightwave information and power transfer (SLIPT) is considered as an effective method of transferring information and energy via illuminated lightwaves. In this paper, we investigate the effect of non-linear responsivity on the SLIPT/radio frequency (RF) performance. We develop a tractable mathematical model for the non-linearity of responsivity and use it to study the end-to-end signal-to-interference-plus-noise ratio (SINR). Based on that, we derive the closed-form expressions for the probability density function and cumulative distribution function of the output SINR at the receiver. These statistical distributions are then used to derive the closed-form expressions of the outage probability of SLIPT/RF system over Nakagami- $m$ fading channels. Numerical results indicate that the simplified model used in previous studies leads to inaccurate performance evaluation results, and that our model provides more accurate evaluation. We also verify the analytical findings by simulation. [ABSTRACT FROM AUTHOR]

Using a practical relay model, in which an energy transmitter (ET) transmits energy to a sensory node S and an information receiver D, information freshness or age of information (AoI) under outdated-channel-state-information (oCSI) severity and line-of-sight (LoS) power is examined in this paper. The sensory node—equipped with a finite-size-battery capacitor—harvests energy from the ET to sustain its operation. Rician fading under non-oCSI and oCSI environments is employed to study the AoI, in which benefits and severity of LoS power and oCSI will be elaborated. The proposed AoI results exactly reduce to existing ones under Rayleigh fading, which validates this work. Simulation results are additionally deployed to validate the proposed AoI results. Impact of parameters such as capacitor capacity, system incoherence coefficient and spectral efficiency on AoI under specific scenarios is also discussed. [ABSTRACT FROM AUTHOR]

This paper considers an intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) network, where multiple users decode data and harvest energy from the transmitted signal of a transmitter. The proposed design framework exploits the cost-effective IRS to establish favorable communication environment to improve the fair energy efficient. In particular, we study the max-min energy efficiency (EE) of the system by jointly designing the transmit information and energy beamforming at the base station (BS), phase shifts at the IRS, as well as the power splitting (PS) ratio at all users subject to the minimum rate, minimum harvested energy, and transmit power constraints. The formulated problem is non-convex and thus challenging to be solved. We propose two algorithms namely penalty-based and inner approximation (IA)-based to handle the non-convexity of the optimization problem. As such, we divide the original problem into two sub-problems and apply the alternating optimization (AO) algorithm for both proposed algorithms to handle it iteratively. In particular, in the penalty-based algorithm for the first sub-problem, the semi-definite relaxation (SDR) technique, difference of convex functions (DC) programming, majorization-minimization (MM) approach, and fractional programming theory are exploited to transform the non-convex optimization problem into a convex form that can be addressed efficiently. For the second sub-problem, a penalty-based approach is proposed to handle the optimization on the phase shifts introduced by the IRS with the proposed algorithms. For the IA-based method, we jointly optimize beamforming vectors and phase shifts while the PS ratio is solved optimally in the first sub-problem. Simulation results verify the effectiveness of the IRS, which can significantly improve the system EE as compared to conventional benchmark schemes and also unveil a trade-off between convergence and performance gain for the two proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a cooperative spatial modulation (SM) based non-orthogonal multiple access (NOMA) scheme with simultaneous wireless information and power transfer is proposed. In particular, NOMA is applied at the base station (BS) and the near user (U1) acts as an energy harvesting full-duplex relay to assist the far user (U2). The data bits of U1 and U2 are mapped into SM symbols and amplitude/phase modulation symbols, respectively. Furthermore, the rate of U1 is maximized while satisfying the target rate of U2 by jointly designing the power allocation coefficient, power-splitting ratio, and transmit power of U1. To solve the non-convex problem, a successive convex approximation-based iterative algorithm is developed. Finally, numerical results reveal that the proposed scheme can achieve the superior performance compared to existing schemes. [ABSTRACT FROM AUTHOR]

This paper studies the joint beamforming design and time allocation for a two-way energy harvesting (EH) relay network, where a single-antenna base station and a single-antenna user communicate with each other through multiple-single antenna EH relay nodes, and an external multiantenna power beacon wirelessly power the relays. Unlike the existing studies that assumed the symmetric two-way relaying scenario, we assume a more practical scenario of asymmetric two-way relaying considering different traffic demands on the downlink and uplink. In this paper, we design the optimal beamforming vectors at the relays and at the power beacon jointly with the time allocation between information and EH transmissions, in the sense of maximizing the sum rate of the asymmetric information transmission under the EH constraints at the relays, and the power constraint at the power beacon. The formulated optimization problem is nonconvex, and thus, it is difficult to solve. To solve this challenging problem, we propose an iterative algorithm based on the semidefinite relaxation and the successive convex approximation techniques. Also, to reduce the computational complexity, we propose an efficient noniterative scheme based on the maximum-ratio transmission. The performance of the proposed schemes is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

In this paper, we study short-packet communications in wireless-powered cognitive Internet-of-Things (IoT) networks with multiple primary receivers (PRs). The considered system can be applied for small factory automations, where a source and multiple relays harvest energy from a multi-antenna dedicated power beacon (PB) to send short packets to a robot destination for controlling purposes under cognitive radio constraint imposed by PRs. We propose an opportunistic relay selection (ORS) scheme to maximize the end-to-end signal-to-noise ratio in cognitive IoT systems. Closed-form expressions for the average block error rate (BLER) of the proposed system are obtained, based on which the performance floor analysis, goodput, and energy efficiency (EE) are also carried out. Relying on analytical results, we develop a deep learning framework for the BLER prediction with high accuracy and short execution time. Simulation results show the BLER, goodput, and EE improvements of the ORS scheme over conventional relay selection schemes. Moreover, the developed deep learning-based evaluation model achieves the equivalent performance as the ORS scheme in terms of BLER, goodput, and EE, while remarkably reducing the execution time in cognitive IoT systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a full-duplex (FD) relaying system with multi-input multi-output (MIMO) with simultaneous wireless information and power transfer (SWIPT), where the communication from source to destination is assisted by a full-duplex decode-and-forward (DF) relay. The time switching (TS) protocol is used at the relay to harvest the energy of radio-frequency (RF) signals transmitted from the source. To improve both system performance and the amount of harvested energy, multiple antennas are used at source and destination. Transmit antenna selection (TAS) is employed at the source with an assumption that the feedback of channel state information (CSI) is outdated. Meanwhile, both selection combining (SC) and maximal ratio combining (MRC) techniques are applied at the destination. The closed-form expressions of the outage probability (OP), optimal throughput, and symbol error probability (SEP) are derived subject to the outdated CSI over Rayleigh fading channels. We also make a comparison between the performance of the proposed MIMO-FD relaying system with SWIPT and that of MIMO half-duplex (HD) relaying systems with and without SWIPT. The validity of derived mathematical expressions is verified by Monte-Carlo simulations. Numerical results show that the TAS/MRC scheme gives better system performance than the TAS/SC scheme. Moreover, higher energy harvesting time is needed to maximize the throughput than to minimize the OP. [ABSTRACT FROM AUTHOR]

This paper considers a cooperative cognitive radio network with two primary users (PUs), and two secondary users (SUs) that enables two-way communications of primary, and secondary systems in conjunction with non-linear energy harvesting based simultaneous wireless information, and power transfer (SWIPT). With the considered network, SUs are able to realize their communications over the licensed spectrum while extending relay assistance to the PUs. The overall bidirectional end-to-end transmission takes place in four phases, which include both energy harvesting (EH), and information transfer. A non-linear energy harvester with a hybrid SWIPT scheme is adopted in which both power-splitting, and time-switching EH techniques are used. The SUs aid in relay cooperation by performing an amplify-and-forward operation, whereas selection combining technique is adopted at the PUs to extract the intended signal from multiple received signals broadcasted by the SUs. Accurate outage probability expressions for the primary, and secondary links are derived under the Nakagami- $m$ fading environment. Further, the system behavior is analyzed with respect to achievable system throughput, and energy efficiency. Since the performance of the considered system is strongly affected by the spectrum sharing factor, and hybrid SWIPT parameters, particle swarm optimization is implemented to optimize the system parameters so as to maximize the system throughput, and energy efficiency. Simulation results are provided to corroborate the performance analysis, and give useful insights into the system behavior concerning various system/channel parameters. [ABSTRACT FROM AUTHOR]

This paper characterizes the structure of the Age of Information (AoI)-optimal policy in wireless powered communication systems while accounting for the time and energy costs of generating status updates at the source nodes. In particular, for a single source-destination pair in which a radio frequency (RF)-powered source sends status updates about some physical process to a destination node, we minimize the long-term average AoI at the destination node. The problem is modeled as an average cost Markov Decision Process (MDP) in which, the generation times of status updates at the source, the transmissions of status updates from the source to the destination, and the wireless energy transfer (WET) are jointly optimized. After proving the monotonicity property of the value function associated with the MDP, we analytically demonstrate that the AoI-optimal policy has a threshold-based structure w.r.t. the state variables. Our numerical results verify the analytical findings and reveal the impact of state variables on the structure of the AoI-optimal policy. Our results also demonstrate the impact of system design parameters on the optimal achievable average AoI as well as the superiority of our proposed joint sampling and updating policy w.r.t. the generate-at-will policy. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a wireless powered communication network where a hybrid access point can wirelessly activate the power of source nodes, and receive their information. Considering the time-switching protocol that implies energy harvesting, and energy usage are separated by time, a novel graphics-based multi-epoch power adjustment (GB-MPA) strategy is proposed to maximize the sum-throughput over a finite time horizon consisting of multiple transmission epochs. The proposed strategy relies on graphical constraint conditions, and allows the elimination of restrictions corresponding to the objective of throughput maximization. Moreover, the proposed GB-MPA strategy can be combined with the time-switching factor optimization by employing an alternating optimization procedure for performance enhancement. Simulation results show the accuracy of the proposed strategy, reduced complexity compared with the conventional convex optimization method, and the existing multi-epoch power adjustment strategy, and the compatibility with the alternating optimization procedure for developing the joint power, and time allocation scheme. [ABSTRACT FROM AUTHOR]

The transmit time-switching (transmit-TS) approach, under which the energy and information are transferred over different fractions of a time slot has proved its supremacy over the power splitting (PS) approach of simultaneous wireless information and power transfer, where PS splits the power of the received signal for energy harvesting and information decoding. For integrating data and energy transfer, this paper develops new classes of beamforming that are suitable for improper Gaussian signaling which is capable of network throughput improvements while maintaining high computational efficiency in its design. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a non-orthogonal multiple access (NOMA) network with simultaneous wireless information, and power transfer (SWIPT) based battery-assisted energy harvesting (EH) full-duplex relay (FDR). In each signalling interval, the base-station communicates directly to a near user (NU), and through an EH FDR to a far user (FU). Considering the power-splitting (PS) protocol with a non-linear EH model, we derive closed-form expressions for the NU, and FU outage probabilities. We also demonstrate how the battery energy, and PS parameter can be chosen to maximize the FU throughput while ensuring a desired NU throughput when there is imperfect interference cancellation at the NU. Computer simulation results validate the accuracy of the derived expressions, and demonstrate that augmenting the harvested energy with a small quantum of the relay's battery energy can considerably improve the FU performance. [ABSTRACT FROM AUTHOR]

The increasing number of low power communication devices in the era of Internet of Things (IoT) has resulted in challenges in maintenance and continuous energy supply. In this paper, the ambient energy harvesting is integrated with wireless local area network (WLAN), where low-power users adopt a distributed coordination strategy for their random access, namely ambient energy harvesting carrier-sense-multiple-access with collision avoidance (AEH-CSMA/CA). All users harvest ambient energy to power their data uploading. In order to increase the uplink throughput and to reduce the energy outage probability, a classic deep reinforcement learning (DRL) algorithm, namely deep Q-learning (DQL), is adopted by a smart user for intelligently adjusting its initial backoff window size during the backoff process. Simulation results demonstrate that the proposed DQL algorithm is capable of substantially improving the throughput performance, while gauranteeing the energy outage probability of the smart user lower than a certain threshold. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) is a promising charging technology for battery-limited sensors. In this paper, we study the use of an unmanned aerial vehicle (UAV) as a charger for WPT. Unlike the previous works, our study takes into account the power consumption of the UAV (power consumption during hovering and flight), the charging process from a base station (BS) to the UAV and the conversion loss of the energy harvester. Both one-dimensional (1D) and two-dimensional (2D) WPT systems are considered. The sum-energy received by all sensors is maximized to find the optimal strategy for UAV deployment. Two different charging schemes are proposed. Numerical results show that the sum-energy received by all sensors is determined by sensors’ topology, the flight speed of the UAV and the transmit power. They also show that, when the BS charging process and the UAV power consumption are considered in the optimization, the optimal location of the UAV in the 1D and 2D WPT systems is closer to the BS than in the previous works that ignore these two practical factors. [ABSTRACT FROM AUTHOR]

Backscatter radio is an intriguing technology for Internet of Things applications, which enables ultra-low-power devices to communicate with each other. Most of the existing works on the combination of the backscatter communications and the “harvest-then-transfer” (HTT) protocol focus on the throughput maximization, yet the study on the energy efficiency (EE) maximization is still lacking. Driven by this observation, this paper studies the EE maximization for an RF-powered backscatter network with the HTT protocol. Multiple hybrid devices (HDs) can either use the backscatter mode by backscattering energy signals from the power beacon (PB) to the reader or use the HTT protocol to first harvest energy from the PB, and then to transmit data to the reader. The proposed scheme aims to optimize multiple parameters including PB's transmit power and time, HDs’ transmit power and time allocation between the backscatter mode and the HTT mode for maximizing the EE of all the HDs. An iterative algorithm is developed to find the optimal solution for the proposed resource allocation scheme. Simulation results demonstrate the superiority of the proposed scheme in terms of EE of all the HDs. [ABSTRACT FROM AUTHOR]

This paper focuses on the secure transmission of wireless-powered full-duplex (FD) relay systems, where a multiantenna source communicates with a single-antenna destination with the help of a FD relay in the presence of a single-antenna eavesdropper. It is assumed that the FD relay is wireless energy harvesting-enabled, adopting both transmit and receive antennas to harvest energy in a time switching (TS) mode. As the objective of this paper is to maximize the system secrecy rate through jointly designing the energy beamforming vector, the information beamforming vector and the TS coefficient, an optimization problem is formulated. The formulated problem is proved to be nonconvex and the challenge to solve which is to concurrently solve out the three variables. To cope with this difficulty, an iterative algorithm is proposed to convert the formulated optimization problem into three convex subproblems, based on which the closed form solutions for the beamforming vectors are derived and the TS coefficient is obtained. Convergence property of the iterative method is analyzed. Simulations are also done to verify the theoretical derivations in terms of the convergence speed and the secrecy rate. Results reveal that the secrecy rate performance of exploiting transmit antenna together with receive antenna for energy harvesting at the FD relay outperforms only receive antenna case. Moreover, although there exists loopback interference between antennas, the FD relaying can always substantially boost the secrecy rate compared with half-duplex relaying architecture. [ABSTRACT FROM PUBLISHER]

This paper investigates the energy consumption of distributed large-scale MIMO systems made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Hence, the problem of grid power consumption minimization has to be solved by efficiently managing the energy delivered from different sources while satisfying the system requirements in terms of users’ quality of service demands. First, this paper solves the optimal offline version of the problem using linear programming. In fact, the main problem is decomposed and heuristically solved in order to deal with the large number of constraints and variables. Since sometimes all the users cannot be served, an iterative link removal algorithm is devised ensuring the feasibility of the problem. Next, we investigate the online energy management problem. We first propose a dynamic programming approach to obtain the optimal online solution but with high complexity. Then, we develop and propose a low complexity heuristic solution based on using the maximal available energy at the batteries. Taking benefits of the characteristics of large-scale MIMO and the modeling of the harvested energy as Markov chains, we devise an efficient online energy management algorithm based on energy prediction. Finally, we propose a heuristic solution for RRH on/off operation with the objective of improving the system energy efficiency. The performance of the proposed algorithms is evaluated by simulations and it is shown that the proposed energy management approaches offer efficient use of nonrenewable energy to compensate the variability of renewable energy in large-scale MIMO systems. [ABSTRACT FROM PUBLISHER]