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In this paper, we consider a cooperative simultaneous wireless information and power transfer (SWIPT) network with one source–destination pair and multiple energy harvesting relays. How to efficiently use these relays has been addressed in conventional cooperative networks, and this paper investigates the impact of SWIPT on the performance of such multirelay cooperative networks. In particular, the main contribution of this paper is that a closed-form expression of the outage probability achieved by the multirelay cooperative protocol is obtained, as well as its approximation at high SNR. In addition, the performance of the cooperative network with the SWIPT relays is compared with that in conventional systems with self-powered relays. [ABSTRACT FROM PUBLISHER]

In this paper, we study the average throughput performance of energy beamforming in a multi-antenna wireless-powered communication network (WPCN). The considered network consists of one hybrid access point (AP) with multiple antennas and a single-antenna user. The user has no constant power supply and, thus, needs to harvest energy from the signals broadcast by the AP in the downlink (DL) before sending its data back to the AP with the harvested energy in the uplink (UL). We derive closed-form expressions of the average throughput and their asymptotic expressions at a high SNR for both delay-limited and delay-tolerant transmission modes. The optimal DL energy harvesting time, which maximizes the system throughput, is then obtained for high SNR. All analytical expressions are validated by numerical simulations. The impact of various parameters, such as the AP transmit power, the energy harvesting time, and the number of antennas, on the system throughput is also investigated. [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]

This paper provides radio frequency (RF) energy harvesting and coverage probability analysis using stochastic geometry in a two-hop communication network. In the first hop, terrestrial base stations (TBSs) transmit RF energy and data to the aerial base stations (ABSs) and in the second hop, ABSs forward the data to the user. Numerical results show that the energy harvesting and coverage probabilities can be maximized at almost the same altitude of the ABSs with respect to the TBSs height for low TBSs densities. However, as the density of the backhaul nodes and the energy harvesting sub-timeslot increases, energy harvesting probability improves while coverage probability monotonically decreases by increasing the ABSs height. [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 present a simple analytic method that can be used to predict potential energy depletion in off-grid wireless backbone network nodes serving mobile users. The instantaneous energy depletion of the batteries of the network nodes is determined by random energy arrivals and departures and modeled as a G/G/1 queue. To evaluate the online energy depletion probability (EDP), an integral-free asymptotic approach is typically used by assuming that the prediction horizon approaches infinity. However, in many practical cases, the time required by user connections can be rather short. This indicates the need for proactive resource management decisions over finite horizons so that the transmission opportunities with limited energy and time horizons are not wasted. Using Hölder's inequality, we obtain a novel finite-horizon upper bound for the EDP, and the result is compared with the infinite-horizon method. The accuracy of the proposed bound, which is addressed both analytically and numerically, proves to be better for shorter prediction horizons. The finite- and infinite-horizon methods are then applied for an energy provisioning admission control (EP-AC) framework using off-grid backbone network nodes. The key observation of this paper is that the proposed finite-horizon prediction approach admits significantly more users to the network when the connection times are relatively short, while retaining an integral-free closed-form structure suitable for the online evaluation of the EDP. [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]

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]

Radio-frequency (RF) energy harvesting is one of the promising technologies in wireless networks. This paper considers wireless powered communication networks (WPCNs) where one hybrid access point (H-AP) coordinates RF energy/information transmissions to/from a set of nodes. For the important type of WPCNs where the nodes have throughput requirement, we aim to minimize H-AP's energy provision (EP) via jointly optimizing the time allocation of energy/information transmissions and H-AP's transmission power while satisfying node throughput requirement. More specifically, the half-duplex WPCN is considered first. We prove that H-AP's energy transmission power must be the maximum allowed value to achieve the minimal EP, then show that the problem is convex under this condition and finally design an algorithm applying golden section search and bisection search jointly to efficiently obtain the optimal time allocation. Second, the full-duplex WPCN is considered. Its EP minimization problem is proved convex. However, due to the hardness of solving it directly, we turn to solve the feasibility problem (FP) for a given EP per unit time. We construct a convex optimization problem based on this FP and design a dual subgradient algorithm for finding its optimal solution, which indicates whether this FP is feasible. The minimal EP per unit time and its corresponding optimal solution are found by using the bisection search method. Simulation results show that, the EP-minimized schemes are able to greatly reduce H-AP's EP as compared to the baseline 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]

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]

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, the source estimation problem is investigated in a wireless sensor network (WSN) with energy-harvesting (EH) nodes. We first consider a deterministic EH model where the amount of harvested energy at each node is known prior to transmissions. For such a model, we investigate the average mean-square-error (MSE) minimization problem over a finite horizon of multiple estimation periods and derive the optimal solution. Moreover, a more realistic stochastic EH model is considered, where only the current amount of harvested energy is known. The corresponding MSE minimization problem over an infinite horizon is formulated. The Lyapunov optimization technique is adopted to obtain a solution for the stochastic EH problem. Simulation results are conducted to validate the effectiveness of the proposed solutions for both the deterministic and stochastic EH models. [ABSTRACT FROM AUTHOR]

In an energy harvesting vehicular network, efficient file transfer between two charging stations is challenging due to the lack of communication infrastructure. Naturally, a source station can allocate the packets to a set of vehicles, which serve as relays to opportunistically deliver these packets to the destination station while performing their own tasks. In this paper, we show that, with packet-level erasure coding (e.g., fountain codes), only a few redundant packets are required to achieve a significant reduction in file delay. As more redundant packets are added, however, the file delay improvement diminishes, and the storage cost rises. In both the single and multiple-destination scenarios, the fundamental tradeoffs between coding and file delay are characterized. For the multiple-destination scenario, there exists a unique and optimal coding rate that minimizes the average file delay. We propose a greedy algorithm that can achieve this minimum delay in practice. Finally, the proposed tradeoffs are verified through intensive numerical experiments. [ABSTRACT FROM PUBLISHER]

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

This paper proposes an algorithm to control the suspension dynamics with an energy regeneration capability and high conversion efficiency. Central to the concept is the development of a switched-mode rectifier (SMR) that is capable of providing either an equivalent positive or negative damping and alternating between regenerative and motoring modes. Simulation results are presented to demonstrate power flow in regeneration and motoring modes and verify the equivalent damping characteristic and vibration energy harvesting. The studies are conducted on a small-scale proof-of-concept electromagnetic suspension system excited by emulated International Standard Organization (ISO) 8608 road profiles. An autonomous start/stop algorithm is proposed such that autonomously turns on or off the converter. This mechanism mitigates quiescent power consumption of auxiliary integrated circuits (ICs) such as gate drivers and current transducers, therefore increasing the conversion efficiency of the power electronics throughout an ISO standardized variable-speed driving cycle. The autonomous start/stop algorithm is implemented experimentally to demonstrate its feasibility under typical standardized driving cycles. [ABSTRACT FROM PUBLISHER]

In this paper, a three-node wireless powered communication system is studied, where a power receiver harvests energy from a wireless power transmitter via wireless power transfer in the downlink and then executes information transfer in the uplink. According to this simple harvest-then-transmit protocol, the power receiver cannot harvest energy and send information simultaneously. Thus, it is necessary to investigate the tradeoff between wireless power transfer and information transfer to obtain good performance. The goal of this paper is to achieve the maximum throughput by balancing the time duration between the wireless power transfer phase and the information transfer phase while satisfying the energy causality constraint, the time duration constraint, and the quality-of-service constraint (i.e., the symbol error rate is lower than a target value). By solving the optimization problem, an optimal time allocation can be obtained. Simulation results demonstrate the effectiveness of the solution. [ABSTRACT FROM AUTHOR]

Simultaneous wireless information and power transfer (SWIPT) has recently drawn significant interest for its dual use of radio signals to provide wireless data and energy access at the same time. However, a challenging secrecy communication issue arises as the messages sent to the information receivers (IRs) may be eavesdropped upon by energy receivers (ERs), which are presumed to harvest energy only from received signals. To tackle this problem, we propose in this paper an artificial-noise (AN)-aided transmission scheme to facilitate the secrecy information transmission to IRs and, yet, meet the energy harvesting requirement for ERs, under the assumption that the AN can be canceled at IRs but not at ERs. Specifically, the proposed scheme splits the transmit power into two parts: to send the confidential message to the IR and an AN to interfere with the ER, respectively. Under a simplified three-node wiretap channel setup, the transmit power allocations and power splitting ratios over fading channels are jointly optimized to minimize the outage probability for delay-limited secrecy information transmission or to maximize the average rate for no-delay-limited secrecy information transmission, subject to a combination of average and peak power constraints (APC and PPC) at the transmitter (Tx) and an average energy harvesting constraint at the ER. Both the secrecy outage probability minimization and average rate maximization problems are shown to be nonconvex, and for each, we propose the optimal solution based on the dual decomposition and the suboptimal solution based on the alternating optimization. Furthermore, two benchmark schemes are introduced for comparison where the AN is not used at the Tx and where the AN is used but cannot be canceled by the IR, respectively. Finally, the performances of proposed schemes are evaluated by simulations in terms of various tradeoffs for wireless (secrecy) information versus energy transmissions. [ABSTRACT FROM PUBLISHER]

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]

The rapidly growing mobile traffic calls for higher quality of service (QoS) and lower on-grid energy cost. Integrating energy harvesting to small-cell networks (SCNs) with caching has been regarded as a promising solution. However, considering the limited resources of the small-cell base stations (SBSs), how to cache contents and serve the mobile users (MUs) is a crucial problem. In this paper, we jointly design a green content caching and mobile user–base station (MU-BS) association mechanism in the SCNs, where the number of MUs’ content requests served by the SBSs is maximized. This mechanism maximizes the number of requests handled by the SBSs under the constraints of the available energy and backhaul capacity of the SBSs, while concurrently ensures the QoS of the MUs. First, the joint optimization problem is formulated, which is NP-hard. To decrease the complexity of the problem, we divide it into two subproblems: MU-BS association and content caching. But when the number of SBSs is large, these two subproblems cannot be efficiently solved by the conventional centralized approaches. Thus, we construct a two-dimensional-action weakly acyclic game to optimize these two subproblems distributedly and iteratively, in which the existence of the two-dimensional-action Nash equilibrium has been proved. Finally, we propose a two-dimensional iteration algorithm to solve the problem. The simulation results show that the proposed algorithm can achieve better performance when compared with the other existing algorithms. [ABSTRACT FROM AUTHOR]

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]

In this paper, we consider the energy-bandwidth allocation for a network of multiple users, where the transmitters each powered by both an energy harvester and conventional grid, access the network orthogonally on the assigned frequency band. We assume that the energy harvesting state and channel gain of each transmitter can be predicted for $K$ time slots a priori. The different transmitters can cooperate by donating energy to each other. The tradeoff among the weighted sum throughput, the use of grid energy, and the amount of energy cooperation is studied through an optimization objective, which is a linear combination of these quantities. This leads to an optimization problem with O( $N^2K$) constraints, where $N$ is the total number of transmitter–receiver pairs, and the optimization is over seven sets of variables that denote energy and bandwidth allocation, grid energy utilization, and energy cooperation. To solve the problem efficiently, an iterative algorithm is proposed by using the Proximal Jacobian alternating direction method of multipliers (ADMM). The optimization subproblems corresponding to Proximal Jacobian ADMM steps are solved in the closed form. We show that this algorithm converges to the optimal solution with an overall complexity of O($N^2K^2$). Numerical results show that the proposed algorithms can make efficient use of the harvested energy, grid energy, energy cooperation, and the available bandwidth. [ABSTRACT FROM PUBLISHER]

Existing research on a simultaneous wireless information and power transfer (SWIPT) system is based on the assumption of Gaussian inputs. However, the optimal design on Gaussian inputs may lead to dramatic performance loss for practical systems with finite-alphabet inputs. This paper focuses on the precoder design for a SWIPT system with finite-alphabet inputs and instantaneous channel state information over a multiple-input multiple-output channel. We formulate the optimal precoder design as an optimization problem, in which the objective is to maximize the mutual information over the channel from the transmitter to the information receiver under the constraints of transmit power and harvested energy threshold. The formulated problem is NP-hard, so a global optimal solution cannot be found within the polynomial time. The main contributions of this paper are as follows: 1) By using its structure, the NP-hard problem is relaxed to a semidefinite programming problem. Then, a general solving framework for both co-located and separated receiver cases, based on the semidefinite relaxation (SDR) technique, is developed to achieve a near optimal precoder. 2) For the case of co-located receivers, we show that the optimal precoder design is a concave problem with respect to power allocation; then, a specific algorithm for co-located receivers is proposed. Compared with the general SDR-based method, the specific algorithm for co-located receivers exhibits almost the same performance but much lower complexity. 3) The performance of several practical co-located receiver designs is analyzed in SWIPT systems with finite-alphabet inputs. Finally, we provide simulation results to show the efficacy of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

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 the impact of battery charging on a cognitive radio network (CRN), where a hybrid overlay-underlay strategy enables mobile secondary nodes to access the licensed spectrum, and the secondary transmitter (ST) utilizes the energy harvested from the radio-frequency (RF) signal and the battery charging in the charging area for spectrum sensing and data transmission. We characterize the impact of battery charging in the charging area by designing an optimal spectrum sensing policy. The objective is to maximize the secondary throughput subject to energy and collision constraints. To be specific, a metric defined as a function of the spectrum sensor detection threshold is proposed to divide the CRN into three network states with respect to the relationship among energy consumption, energy harvesting, and battery charging. Accordingly, ST in the charging area decides to charge the battery or to exploit transmission opportunity. In each network state, we optimize the spectrum sensor detection threshold for the objective, which is a challenging problem since the spectrum sensor detection threshold is intertwined with the energy constraint, the collision constraint, and secondary throughput. Then we characterize the impact of battery charging in the charging area on the state classification and optimal spectrum sensing, respectively. Simulations show the effectiveness of the proposed metric, and validate the impact of battery charging. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) assisted transmission can significantly improve the spectrum efficiency and coverage of the wireless communication network. The paper proposed a cooperative secrecy transmission mechanism, which can take advantage of transmitter signal of primary user (PU) as a dedicated radio frequency (RF) source with decode and forward (DF) UAV selection and energy harvesting (EH) under cognitive network. Specifically, the transmitting signal of PU in the cognitive network can be viewed and used as energy to drive secondary user (SU) nodes and multi-relay nodes to send signals. It was worth noting that the DF UAV relay may bring about the unexpected noise and decrease the received signal to noise ratio (SNR), therefore, the multiple UAV assisted relays selected with secrecy capacity maximization criteria under cognitive network was proposed and investigated in detail, where the destination node equipped with multiple antennas and adopted the optimal antenna selection receiver. Moreover, By using the function analysis method, the closed expressions of the system secrecy outage probability (SOP) and the probability of non-zero secrecy capacity were calculated and derived accurately. Furthermore, the asymptotic expressions of the UAV assisted relay system under the large SNR cases were analyzed in detail, where the expressions of secrecy diversity order and secrecy diversity gain of the UAV assisted relay cognitive system were obtained thoroughly. Simulation results verified the effectiveness of the proposed mechanism and the correctness of the calculation. [ABSTRACT FROM AUTHOR]

This paper studies the information-energy ($\mathrm{I}$ - $\mathrm{E}$) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models. [ABSTRACT FROM AUTHOR]

The study of the effects of Nakagami- $m$ fading channels with both integer and non-integer $m$ plays an important role in the design of practical wireless systems. In this paper, we investigate a dual-hop cooperative communication system, where source node communicates with destination node through direct and relaying paths. Partial relay selection (PRS) scheme is used to select the best relay among $N$ relays which are capable of harvesting energy from radio frequency (RF) signals. At the destination, selection combining (SC) technique is employed to combine the signals of the direct path and the relaying path. We consider two relaying protocols, i.e. amplify-and-forward (AF) and decode-and-forward (DF). For both AF and DF protocols, we derive the tight lower-bound closed-form approximate expression and the exact closed-form expression of the outage probability (OP) over Nakagami- $m$ fading channels with both integer and non-integer $m$. Moreover, the optimal energy harvesting duration which minimizes the OP is also determined. Numerical results show that, the OP of DF protocol and its minimum value is lower than those of AF protocol for the same parameter settings. Moreover, the time switching ratio which gives the smallest OP in the case without direct path is larger than the case with direct path. [ABSTRACT FROM AUTHOR]

In this paper, we propose and analyze adaptive passive and active radio mode selection for backscatter-assisted communication, in which the passive radio (PR) mode refers to the backscatter communication (BackCom), and the active radio (AR) mode refers to the traditional battery-assisted communication. This is motivated by the fact that the PR mode can harvest power from the radio frequency (RF) source to support “green communication”, while the AR mode can provide the stable power supply. Besides, opportunistic successive interference cancellation (SIC) is exploited to mitigate the direct link interference (DLI), which is in contrast to previous works assuming ideal SIC. Two adaptive mode selection (AMS) schemes are proposed, i.e., power maximization (PM)-oriented AMS and outage minimization (OM)-oriented AMS. Since exact closed-form expressions for the outage performance of proposed schemes are not attainable, lower bounds are obtained to facilitate our analysis. For comparison, benchmark schemes including the PR and the AR modes are investigated. Simulation results demonstrate the correctness and the tightness of our analysis, and show that significant performance gain can be achieved in the proposed OM-oriented AMS scheme. [ABSTRACT FROM AUTHOR]

This paper investigates secure transmission in a wireless powered cooperative communication network (WPCCN) over Nakagami- $m$ fading channels, where multiple intermediate energy harvesting (EH) nodes with finite energy storage are deployed to assist the secure transmission between source and destination in the presence of an eavesdropper. Specifically, the intermediate EH nodes first harvest and accumulate energy from the source's signal and then forward the confidential signal or emit jamming signal to interfere the eavesdropper according to actual requirements. In order to exploit the intermediate EH nodes for secure improvement, considering both energy storage status and channel gains, three relay-and-jammer selection schemes at the intermediate nodes are proposed, namely: energy threshold based best-relay and random-jammer (ETBR), energy threshold based random-relay and best-jammer (ETRB), and energy threshold based best-relay and best-jammer (ETBB). For understanding the impact of critical parameters on the secrecy performance, we first capture the evolution of the EH nodes’ storages using finite-state Markov chain (FSMC) and then derive the analytical expressions for hybrid outage probability (HOP) and secrecy throughput (ST). Furthermore, numerical results demonstrate that our proposed selection schemes outperform the existing schemes (bR-rJ, rR-bJ, bR-bJ) due to the consideration of energy accumulation and storage status. In addition, there is a suitable value of the energy threshold for each proposed scheme to achieve the optimal secrecy performance. [ABSTRACT FROM AUTHOR]

In wireless power transfer (WPT)-assisted relaying systems, spectral efficiency (SE) of source-relay link plays a dominant role in system SE performance due to the limited transmission power at the WPT-aided relay. In this paper, we propose a novel protocol for a downlink orthogonal frequency division multiple access (OFDMA) system with a WPT-aided relay operating in full-duplex (FD) decode-and-forward (DF) mode, where the time slot durations of the source-relay and relay-users hops are designed to be dynamic, to enhance the utilization of degrees of freedom and hence the system SE. In particular, a multiple-input and signal-output (MISO) source-relay channel is considered to satisfy the stringent sensitivity of the energy harvesting (EH) circuit at the relay, while a single-input and single-output (SISO) relay-user channel is considered to alleviate the power consumption at the relay node. Taking into account the non-linearity of EH efficiency, a near-optimal iteration-based dynamic WPT-aided FD relaying (A-FR) algorithm is developed by jointly optimizing the time slot durations, subcarriers, and transmission power at the source and the relay. Furthermore, self-interference generated at the relay is utilized as a vital energy source rather than being canceled, which increases substantially the total energy harvested at the FD relay. We also reveal some implicit characteristics of the considered WPT-aided FD relaying system through intensive discussions. Simulation results confirm that the proposed A-FR achieves a significant enhancement in terms of SE with different relay's locations and the number of users, compared to the conventional symmetric WPT-aided FD relaying (S-FR) and the time-switching-based WPT-aided FD relaying (TS-FR) benchmarks. [ABSTRACT FROM AUTHOR]

Integration of backscatter communication and wireless powered communication is a promising way to improve the performance of future Internet of Things (IoT) networks. However, this integration needs to be studied in practical scenarios with realistic models for the network elements. In this paper, we investigate optimal resource allocation in a backscatter assisted wireless powered communication network (BS-WPCN), where a piece-wise linear energy harvesting (EH) model is assumed at the users, which accounts for the saturation effect of practical energy harvesters. We study the problem of throughput maximization and apply a block coordinate descent (BCD) method to find the optimal allocation of resources for maximizing the total throughput. Numerical results demonstrate that the proposed scheme considerably improves the performance compared to the baseline scheme, which assumes linear EH model at the users. [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]

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]

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]

Wireless energy harvesting is a promising feature for supplying energy to a wireless device. However, this wireless energy supply to a mobile node is random due to mobility. In this paper, we consider performance analysis and optimization for a mobile node with delay-limited communications and wireless energy harvesting capability. Three major issues are considered: 1) an optimal transmission policy for the mobile node; 2) an energy management strategy; and 3) deployment of wireless power sources. First, a constrained Markov decision process (CMDP) model is formulated and solved to obtain the optimal packet transmission policy for minimizing the loss probability due to delay deadline violation. Second, the distributed energy management strategies of wireless power sources are analyzed. Specifically, the threshold-based strategy is considered, where the wireless power source releases wireless energy only when there are a certain number of mobile nodes in its harvesting coverage. Third, the wireless power sources can be optimally deployed to minimize the long-term cost composed of deployment and power consumption costs. The analysis and optimization are presented in a unified framework, which can be applied to the given issues jointly. [ABSTRACT FROM PUBLISHER]

This paper describes a new function for electrical-power-assisted steering (EPAS) systems applied for hybrid electric trucks (HETs). EPAS systems consume power to provide torque to steer the wheels. The required power must be equal to the power caused by the self-alignment torque that forces the wheels back into the aligned position. This paper describes, for the first time, how self-alignment torque is used to recover energy that is required to turn the wheels. This paper shows that, for a common steering maneuver, a maximum of 20\% of the energy required to complete the steering maneuver can be recovered. This energy saving relates to a fuel gain of 2094.5 gal over the lifetime of a truck of 500 000 mi. This paper also shows that the new EPAS function can be embedded in existing systems without changes to the hardware or the motor configuration. An EPAS model is developed in Dymola to show how steering at various vehicle speeds and various steering wheel angles contribute to the energy savings. An EPAS drive has been designed, constructed, and tested for an HET to validate the simulation results. [ABSTRACT FROM PUBLISHER]

Deploying small cells in cellular networks, as a technique for capacity and coverage enhancement, is an indispensable characteristic of future cellular networks. In this paper, two novel online approaches for enabling energy trading in multitier cellular networks with noncooperative energy-harvesting base stations (BSs) are proposed. The goal is to minimize the nonrenewable energy consumption in a multitier cellular network with an arbitrary number of tiers. In the first approach, a decentralized energy trading framework is established in which BSs are stimulated to compensate their energy shortage with the extra harvested energy from other BSs rather than using the nonrenewable energy. Matching theory is used to assign BSs with energy deficit to the BSs with extra harvested energy. In the second approach, which is centralized, BSs with extra harvested energy and BSs with energy deficit enter a double auction for energy trading. The centralized approach also motivates the BSs with deficient energy to use other BSs extra harvested energy and satisfies a number of properties including truthfulness, individual rationalities, and budget balance. Both approaches achieve Nash equilibrium and motivate noncooperative BSs to share their extra harvested energy. The extra harvested energy is exchanged by the smart grid. We show that the amount of information exchanged in the network to enable BSs to trade energy is reduced in the centralized algorithm compared to the decentralized algorithm at the expense of using a control center. Simulation results verify that the proposed approaches reduce the nonrenewable energy consumption conspicuously. Furthermore, by applying the proposed approaches, BSs gain more profit, and consequently, their utility functions enhance. [ABSTRACT FROM AUTHOR]

In conventional energy harvesting amplify-and-forward relaying, only the relay harvests energy from the source. In this paper, a new energy harvesting relaying protocol is proposed, where the source also harvests energy from the relay, in addition to the energy harvesting relay. The performances of the new protocols using two different strategies are analyzed. Numerical results show that the new protocols have certain gain over the conventional protocol. [ABSTRACT FROM PUBLISHER]

Existing works on energy harvesting (EH) wireless systems often assume constant conversion efficiency for the energy harvester. In practice, conversion efficiency often varies with input power. In this paper, based on a review of existing energy harvesters in the literature, a heuristic expression for the conversion efficiency as a function of the input power is derived by curve fitting. Using this function, two example energy harvesters are used to analyze the realistic performances of wireless relaying and wireless energy transfer. Numerical results show that the realistic performances of the wireless systems could be considerably different from the performances predicted by the existing analysis. [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]

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]