Power Transfer Networks Research Articles

Performance analysis of shared relay CR-NOMA network based on SWIPT

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

A sum‐rate maximization for IRS‐aided broadcast SWIPT with time‐switching structure and nonlinear energy harvester

AbstractThis study evaluates the sum‐rate gain of the intelligent reflecting surface (IRS) in a broadcast simultaneous wireless information and power transfer network. Users are equipped with a time‐switching (TS) structure for an alternating information decoder and a nonlinear energy harvester. The sum‐rate maximization (SRM) problem is formulated subject to the constraints of the energy‐harvesting threshold required by users and limited transmission power at the base station (BS). To address the SRM problem, we jointly designed a broadcast beamforming precoding vector at the BS, reflective phase shifters at the IRS, and TS factors at the users. Subsequently, after certain manipulations, we propose efficient iterative algorithms that combine alternating optimization, successive convex approximation, semidefinite relaxation, and rank‐one penalty function methods to obtain a suboptimal solution. Finally, the numerical results show the promising sum‐rate gain of the proposed scheme compared with the conventional schemes.

A Dynamic Power Transfer Route Construction and Optimization Method Considering Random Node Distribution for Wireless Power Transfer Network

A Dynamic Power Transfer Route Construction and Optimization Method Considering Random Node Distribution for Wireless Power Transfer Network

Construction and Optimization of Power Transfer Route in Array Wireless Power Transfer Network With Multiple Load Nodes

Construction and Optimization of Power Transfer Route in Array Wireless Power Transfer Network With Multiple Load Nodes

Energy Minimization for UAV-Enabled Wireless Power Transfer and Relay Networks

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the quality of service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is non-convex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, simulation results confirm the superiority of our proposed algorithm.

Outage Performance of Asymmetrical Cognitive Simultaneous Wireless Information and Power Transfer Networks Based on Non-Orthogonal Multiple Access with an Incremental Cooperation Relay and Hardware Impairments

This work aims to investigate an asymmetrical NOMA transmission network based on cognitive radio, where both the source and relay nodes of the secondary user are energy limited, and the energy could be harvested from the transmission signal of the power beacon. Considering some particular hardware impairments and asymmetry in the NOMA transmission, the closed-form expressions of outage probability for relay and destination nodes were derived using incremental relay methods. Furthermore, built on the expressions of the outage probability, the throughput performance was analyzed. Finally, the correctness of our theoretical analysis was verified using simulations.

Joint optimization of trajectory, beamforming, and power allocation in UAV-enabled WPT networks using DRL combined with water-filling algorithm

In this work, we study a wireless power transfer (WPT) network supported by an unmanned aerial vehicle (UAV), in which a UAV equipped with an array of antennas supplies wireless energy to charge ground user (GU) devices. With this configuration, we aim to maximize the lowest GU energy by jointly optimizing the UAV's trajectory, beamforming pattern, and transmitting power. Because the proposed maximization of the lowest GU energy is a non-convex optimization problem that is difficult to be solved, we transform the problem into a grid world problem in discrete-time space and propose a design of a deep reinforcement learning (DRL) approach that optimizes the loss function by determining the UAV's moving direction, beamforming angle, and transmit power level. Furthermore, we combine the water-filling algorithm with DRL, which can assist DRL in determining the hovering duration. Simulation results verify that our proposed design increases the minimum energy of GUs received from the UAV compared to the successive hover-and-fly algorithm while exploring the most appropriate path with low computational complexity.

Joint Trajectories and Resource Allocation Design for Multi-UAV-Assisted Wireless Power Transfer with Nonlinear Energy Harvesting

In this work, we explore a multi-UAV-assisted wireless power transfer (WPT) network, where multiple UAVs are deployed to provide WPT services to multiple ground devices (GDs) in order to extend their lifespan. To enhance the WPT efficiency while considering fairness, we investigate the joint trajectories and transmit power design. For fairness-aware consideration, our objective is to maximize the harvested energy of the GD with the worst condition, taking into account UAV mobility, anti-collision, and power budget constraints. Unlike previous works that focus on the simplified linear energy harvesting (EH) model, a more accurate multi-source nonlinear EH model is, for the first time, adopted to formulate the problem. Given the highly non-convex nature of the original problem due to the presence of coupled variables, we leverage the convexity of the multi-source nonlinear EH model and introduce a convex approximation method, which enables us to construct a tightly convex problem in each iteration for the original joint design problem, thereby obtaining a high-quality solution. Finally, we present numerical results to showcase the convergence of our algorithm and validate the performance advantages of the proposed multi-UAV WPT scheme with a nonlinear EH model versus benchmarks.

Energy Efficiency Maximization in RIS-Assisted SWIPT Networks With RSMA: A PPO-Based Approach

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks

In this article, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the nonlinear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is nonconvex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the nonconvex original problem. In detail, the whole problem is divided into three subproblems to solve. For the nonconvexity of the objective function, successive convex approximation (SCA) is used to transform it, and the penalty function method and difference-of-convex (DC) programming are applied to deal with the nonconvex constraints. Finally, we alternately solve the three subproblems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.

Optimizing the Age of Information in RIS-Aided SWIPT Networks

In this letter, a reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) network is investigated. To quantify the freshness of the data packets at the information receiver, the age of information (AoI) is considered. To minimize the sum AoI of the information users while ensuring that the power transferred to energy harvesting users is greater than the demanded value, we formulate a scheduling scheme, and a joint transmit beamforming and phase shift optimization at the base station (BS) and RIS, respectively. The alternating optimization (AO) algorithm is proposed to handle the coupling between active beamforming and passive RIS phase shifts, and the successive convex approximation (SCA) algorithm is utilized to tackle the non-convexity of the formulated problems. The improvement in terms of AoI provided by the proposed algorithm and the trade-off between the age of information and energy harvesting is quantified by the numerical simulation results.

Joint Communication and Trajectory Design for Intelligent Reflecting Surface Empowered UAV SWIPT Networks

Aiming at the limited battery capacity of widely deployed low-power smart devices in the Internet-of-things (IoT), this paper proposes a novel intelligent reflecting surface (IRS) empowered unmanned aerial vehicle (UAV) simultaneous wireless information and power transfer (SWIPT) network framework, in which IRS is used to reconstruct the wireless channel to enhance the wireless energy transmission efficiency and coverage area of the UAV SWIPT networks. In this paper, we formulate an achievable sum-rate maximization problem by jointly optimizing UAV trajectory, successive interference cancellation (SIC) decoding order, UAV transmit power allocation, power splitting (PS) ratio and IRS reflection coefficient while taking account of user non-orthogonal multiple access (NOMA) and a non-linear energy harvesting model. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. We first transform the problem, and then apply the alternating optimization (AO) algorithm framework to divide the transformed problem into four sub-problems to solve it. Specifically, by applying successive convex approximation (SCA), penalty function method and difference-convex (DC) programming, UAV trajectory, SIC decoding order, UAV transmit power allocation, PS ratio and IRS reflection coefficient are alternately optimized until the convergence is achieved. Numerical simulation results verify the effectiveness of our proposed algorithm compared to other algorithms.

System and methods for wireless power transfer scheduling in a wireless power transfer network

A transmitter in a wireless power transfer network (WPTN) needs to deliver wireless power to a number of wireless power receiver. There can be diverse set of wireless power receivers, for example, Internet of Things devices, smart phones, laptops, etc. The wireless power requirements for receivers vary depending upon their type and state. Commercially deployed WPTNs may also have different subscription plans for users, hence each user’s device should receive wireless power transfer (WPT) service based on the subscribed plan. Usually, a transmitter has to provide service to a number of receivers. Hence, the transmitter needs to have a WPT scheduling method for WPT service delivery. The challenges associated with WPT delivery in a WPTN require a scheduling method that possess the following characteristics: a generalized scheduling method that can work in a setting comprising of heterogeneous receivers, and a scheduling method that can deliver prioritise WPT service, however at the same time it should avoid starvation of lower priority WPT requests. Here, a system and methods for WPT service scheduling are presented. The presented methods possess the above stated characteristics. One of the methods focuses on enhancing a WPT system’s throughput and the other focuses on a receiver device’s remaining charging time. Moreover, both methods avoid starvation of low-priority requests. The presented methods are compared with an existing method, and simulation results demonstrate effectiveness and usefulness of the presented methods.

Coverage Performance of UAV-Assisted SWIPT Networks With Directional Antennas

This article studies the coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios, including smart farming and smart ranching, where the None-Line-of-Sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for the TS-based system than that for PS-based one.

On the Coverage of UAV-Assisted SWIPT Networks With Nonlinear EH Model

Unmanned aerial vehicles (UAVs) with huge-capacity batteries could be employed to wirelessly charge the ground sensor users (GSUs) and enhance the coverage of aerial wireless networks in outdoor Internet of Things (IoT). This paper investigates the information and energy coverage of UAV-enabled simultaneous wireless information and power transfer (SWIPT) networks. Both power splitting (PS) and time switching (TS) receiver architectures are considered. By using stochastic geometry approach, the general and explicit expressions of the information coverage probability (ICP), the energy coverage probability (ECP) and the joint information and energy coverage probability (JIECP) are derived under the nonlinear and linear energy harvesting (EH) models, respectively. Particularly, the Laplace transform and the probability generating functional (PGFL) are used to derive the ICP. And, Campbell’s theorem and the maximum function are applied to obtain the ECP and the JIECP, respectively. To achieve the optimal UAVs’ deployment density, the maximization optimization problems are formulated for the PS-based and TS-based systems, respectively. By using the series expansion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q(x)$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -function) with large <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> , the closed-form approximating optimal solutions to the formulated problems are obtained. Monte Carlo simulations validate the correction of our obtained theoretical results, and numerical results show that the performance of the PS-based system is superior to that of the TS-based one. Moreover, when the energy requirement of GSUs or the transmit power of UAVs is relatively large, or when the information requirement of GSUs or the UAV deployment density is relatively small, compared with the nonlinear EH model, the analysis bias caused by traditional linear EH model is relatively large and in these cases, traditional linear EH model cannot be used to replace the nonlinear EH one for the system performance analysis or optimal system design.

Energy‐efficient alternating current computing‐enabled receiver design for K‐user interference channel simultaneous wireless information and power transfer networks

SummaryEnergy efficiency is one of the key challenges in fifth generation (5G) and beyond wireless communication systems. Recently, simultaneous wireless information and power transfer (SWIPT) systems are usually adopted to enhance energy efficiency. The harvested energy through power transfer is first rectified to generate the direct current (DC) voltage that is used to power the communication devices. This rectification reduces the energy efficiency due to losses in conversion to DC voltages. In this work, we consider integrated information decoding and energy harvesting receiver architecture through power splitting SWIPT network that enables alternating current (AC) computational logic. The AC computing method is used to supply the computing block of receivers to further enhance the energy efficiency. Multiuser multiple input single output (MISO) interference channel (IC) is considered, where receivers are required to compromise between quality of service (QoS) and energy consumption. The system sum rate is maximized under sum harvested energy and total transmit power constraints through joint beamforming and power splitting factors design. For the problem tractability in the proposed AC computing‐enabled receiver, standard beamforming schemes are deployed, namely, zero forcing (ZF), regularized zero forcing (RZF), maximum ratio transmission (MRT), and a hybrid scheme between MRT and ZF beamforming (MRT‐ZF). Moreover, the power splitting ratios design problem is transformed into a one dimension search problem. Simulation results show that different beamforming designs exhibit a great trade‐off between the achieved rate and the harvested energy.

Deep learning driven physical layer security for a simultaneously wireless information and power transfer network

Artificial intelligent approaches have been considered as the promising techniques to enable smart communications in future wireless networks. In this paper, we investigate the deep learning based resource allocation approach for secure transmission in a simultaneously wireless information and power transfer (SWIPT) network. In particular, we design the resource allocations to maximize the minimum achievable secrecy rate of the legitimate user under the constraints of energy harvesting requirements of the energy receivers (ERs). Conventionally, the optimal or suboptimal solutions of resource allocation problems can be obtained by exploiting convex optimization approaches, which are often developed based on iterative algorithms, and always result in long computational time. To satisfy ultra low latency demands and achieve physical layer security for future SWIPT systems, we develop a DNN based approach that has the capability to optimize the power allocations for a SWIPT network, where the computational time and complexity have been significantly cut down. Numerical results are provided to illustrate that the effectiveness of our proposed DNN based approach, which is capable to achieve near optimal secrecy rate performances in comparing with convex optimization approach.

A Pricing-Based Approach for Energy-Efficiency Maximization in RIS-Aided Multi-User MIMO SWIPT-Enabled Wireless Networks

In this work, we investigate the performance of a reconfigurable intelligent surface (RIS)-aided multi-user simultaneous wireless information and power transfer (SWIPT) network, where a multiple-input multiple-output (MIMO) base station (BS) serves multiple MIMO information receivers (IRs) while ensuring a minimum harvested power at multiple MIMO energy receivers (ERs). In order to improve the energy efficiency (EE) of the network, we consider a pricing-based performance metric called network utility. We then establish an optimization framework to jointly optimize the transmit precoding matrix (TPM) and phase shift matrix (PSM) to maximize the network utility function with constraints on the available transmit power at BS, minimum harvested power required at each ER, and unit modulus phase shift condition at RIS. Due to the non-convex nature of this problem, we divide it into two sub-problems where a sub-optimal solution of TPM and PSM are obtained separately using successive convex approximation and bisection search-based algorithms. Further, we propose an EE maximization (EEM) algorithm based on the block coordinate descent method to achieve the optimal solution of the master problem by iteratively obtaining the sub-optimal TPM, PSM, and network price using their respective algorithms. Moreover, we also prove that the solution obtained for each problem using their respective algorithm converges to the Karush-Kuhn-Tucker (KKT) optimum point of that problem. We also show the efficacy of the proposed algorithm using simulation results. In particular, we highlight the importance of using RIS in a multi-user MIMO SWIPT network and demonstrate the effect of various parameters on the network’s EE performance.

A 2-D Inductive Power Transfer Network for Powering Massive Neighboring IoT Devices

Due to the wide distribution of Internet of Things (IoT) devices, reliable and convenient power solutions for these devices have been a continuous pursuit recently. In this paper, a two-dimensional (2-D) inductive power transfer (IPT) design based on multiple coupled resonators is proposed for powering IoT devices in a reconfigurable neighboring scenario. There are two types of cells in this system: constant voltage (CV) and constant current (CC) cells. These two types of cells are alternately arranged. In this IPT system, the coupling electromagnetic field is mostly constrained in a small region, so that the radiation leakage to the free space can be relieved without introducing extra complex control. The wireless power makes the whole system convenient and reconfigurable according to the user’s requirements. The circuit model is mathematically formulated. A nine-cell prototype is implemented and tested. Experimental results approve the CC or CV properties in each cell. The power conversion efficiency of the whole system reaches 75%.

Evolution Strategies for Lightwave Power Transfer Networks

This work revolves around lightwave power transfer networks in which we aim to maximize the number of users served while simultaneously minimizing the transmit power. By formulating the problem as a reinforcement learning (RL) problem, we propose the use of the evolution strategies (ES) method as a novel solution. In this context, ES is a heuristic search method inspired from the biological evolution of nature and it is used to solve complex machine learning problems. Hence, a learning scenario and an ES-based algorithm are devised to solve the RL problem. The results demonstrate that the proposed approach can achieve considerable performance gains compared to the conventional Q-learning method.