Wireless Powered Communication Networks Research Articles

Long-Term Throughput Maximization in Wireless Powered Communication Networks: A Multitask DRL Approach

Long-Term Throughput Maximization in Wireless Powered Communication Networks: A Multitask DRL Approach

Enhancing Throughput in IoT Networks: The Impact of Active RIS on Wireless Powered Communication Systems

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity.

Multiobjective Optimization of Wireless Powered Communication Networks Assisted by Intelligent Reflecting Surface Based on Multiagent Reinforcement Learning

Multiobjective Optimization of Wireless Powered Communication Networks Assisted by Intelligent Reflecting Surface Based on Multiagent Reinforcement Learning

Delay Minimization of Federated Learning Over Wireless Powered Communication Networks

Delay Minimization of Federated Learning Over Wireless Powered Communication Networks

Joint Optimization of Trajectory and Resource Allocation for Multi-UAV-Enabled Wireless-Powered Communication Networks

Joint Optimization of Trajectory and Resource Allocation for Multi-UAV-Enabled Wireless-Powered Communication Networks

User-Adaptive IRS Beamforming Design in Hybrid Wireless Powered Communication Networks

User-Adaptive IRS Beamforming Design in Hybrid Wireless Powered Communication Networks

Sum rate maximization for multiuser full-duplex wireless powered communication networks

Sum rate maximization for multiuser full-duplex wireless powered communication networks

Fairness-aware transmitter design for OFDMA-based full-duplex wireless powered communication networks

Fairness-aware transmitter design for OFDMA-based full-duplex wireless powered communication networks

Exploiting Intelligent Reflecting Surface for Enhancing Full-Duplex Wireless-Powered Communication Networks

Exploiting Intelligent Reflecting Surface for Enhancing Full-Duplex Wireless-Powered Communication Networks

Joint Resource Allocation for Maximizing Energy Efficiency in MmWave-Based Wireless-Powered Communication Networks

Joint Resource Allocation for Maximizing Energy Efficiency in MmWave-Based Wireless-Powered Communication Networks

Performance Analysis of a WPCN-Based Underwater Acoustic Communication System

Underwater acoustic communication (UWAC) has a wide range of applications, including marine environment monitoring, disaster warning, seabed terrain exploration, and oil extraction. It plays an indispensable and increasingly important role in marine resource exploration and marine economic development. In current UWAC systems, the terminal nodes are usually powered by energy-limited batteries. Due to the harshness of the underwater environment, especially in the ocean environment, it is very costly and difficult, even impossible, to replace the batteries for the terminal nodes in UWACs, which results in the short lifetime and unreliability of the terminal nodes and the systems. In this paper, we present the application of a wireless powered communication network (WPCN) to the UWAC systems to provide an auxiliary and convenient energy supplement for solving the energy-limited problem of the terminal nodes, where the hybrid access point (H-AP) transfers energy to the terminal nodes in the downlink. In contrast, the terminal nodes use the harvested energy to transmit the information to the H-AP in the uplink. To evaluate the proposed WPCN-based UWAC systems, we investigate the performance of the average bit error rate (BER), outage probability, and achievable information rate for the systems in a frequency-selective sparse channel and non-white noise environment. We derive the closed-form expression for the probability density function (PDF) of the received signal-to-noise ratio (SNR). Based on this, we further derive novel closed-form expressions for the average BER and the outage probability of the systems. Numerical results confirm the validity of the proposed analytical results. It is shown that there exists an optimal signal frequency and time allocation factor for the systems to achieve optimal performance, and a larger optimal time allocation factor is preferred for a smaller hybrid access point (H-AP) transmit power or a larger transmission distance, while a smaller optimal signal frequency is required for a larger transmission distance.

Minimal Throughput Maximization of UAV-Enabled Wireless Powered Communication Network in Cuboid Building Perimeter Scenario

Minimal Throughput Maximization of UAV-Enabled Wireless Powered Communication Network in Cuboid Building Perimeter Scenario

Multiantenna Clustering Collaboration for WPCNs Based on Nonlinear EH

This article considers a wireless-powered communication network (WPCN) composed of a multiantenna hybrid access point (HAP) based on nonlinear energy harvesting (EH). To improve some distant WDs’ throughput performance, one of them is allowed to be selected as a cluster head (CH) to help transfer information from other cluster members (CMs). Nevertheless, the proposed clustering collaboration’s performance is essentially restricted by the CH’s energy-intensive consumption (EC), which requires to transfer every WDs’ information, covering its own. In order to figure out the question, the HAP’s energy beamforming (EB) capability with multiple antennas is utilized that can concentrate greater transmission power into the CH to equilibrate its EC to assist other WDs. To be specific, each WD’s throughput performance is firstly derived under the proposed approach. A high-efficiency optimization algorithm for addressing cooperative optimization problem is put forward. In addition, the simulations are carried out in the actual network environment, and the results demonstrate that our proposed clustering collaboration with multiple antennas can validly enhance the WPCN’s throughput fairness based on nonlinear EH.

Smart Energy Borrowing and Relaying in Wireless-Powered Networks: A Deep Reinforcement Learning Approach

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of a wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy, by using energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex, as it involves non-convex variables and requires combinatorial relay selection indicator optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimization techniques would have difficulty solving in complex problem environments. The proposed algorithm significantly improved the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%, 27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

Multiplexing eMBB and URLLC in Wireless Powered Communication Networks: A Deep Reinforcement Learning-Based Approach

Multiplexing eMBB and URLLC in Wireless Powered Communication Networks: A Deep Reinforcement Learning-Based Approach

An Adaptive Mode Selection Mechanism for Hybrid Active and Passive Communications

In this paper, we propose an adaptive mode selection algorithm for a hybrid active and passive communication system, where the communication paradigm of the wireless-powered communication network (WPCN) is referred to as the active mode, while ambient backscatter (AB) is applied for the passive mode. Specifically, the proposed algorithm switches between the active and passive modes by simultaneously considering both energy harvesting and signal-to-noise ratio (SNR) conditions. Besides, to further improve the system's reliability, we adopt the rate-adaptive technique in passive mode, allowing devices to choose an appropriate rate for data transfer when performing backscattering. We also introduce analytical models to characterize the performance of the proposed mode selection algorithm. Simulation results validate the analysis and indicate that the proposed mode selection algorithm has the advantage in terms of both success probability and average throughput.

Gain Without Pain: Recycling Reflected Energy From Wireless-Powered RIS-Aided Communications

In this paper, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching-and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are respectively formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we respectively develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding sub-optimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.

Throughput Maximization of Wireless-Powered Communication Network With Mobile Access Points

In order to mitigate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">double near-far effect</i> , we focus on a mobile wireless-powered communication network (WPCN), where sensor nodes harvest energy from the radio frequency (RF) signal of the mobile energy access point (EAP), and transmit data to the mobile data access point (DAP) by using the harvested energy. Only the sensor nodes with energy larger than a threshold, which is mainly determined by the energy consumption of one transmission, have opportunities to transmit data. Due to the mobility of the EAP and DAP, the distance between the EAP and DAP changes over time. When the DAP moves into the operation region of the EAP, the EAP and DAP could not work simultaneously due to the severe interference, and an energy harvesting probability is employed to denote the probability that the EAP works in this scenario. The purpose of this paper is to identify the optimal transmission policy, i.e., the optimal pairing of the energy consumption of one transmission and the energy harvesting probability, that maximizes the throughput of the WPCN under an energy causality constraint. By analyzing the energy causality constraint, we show that the WPCN could be divided into an energy-sufficient state and an energy-limited state by the pairing of the energy consumption of one transmission and the energy harvesting probability. Since the energy consumption of one transmission and the energy harvesting probability are jointly intertwined with the energy causality constraint, making the joint optimization problem intractable, we divide the throughput maximization problem into two layers. In the inner problem, we investigate the optimal energy consumption of one transmission with a given energy harvesting probability. In the outer problem, we derive the optimal energy harvesting probability based on the obtained optimal energy consumption of one transmission. According to the aforementioned investigations, we propose a two-layer algorithm to obtain the specific solution. Numerical results are conducted to validate the theoretical results and the efficiency of the proposed two-layer algorithm.

On Performance of NOMA-Based Wireless Powered Communication Networks Assisted With Power Beacons and PPP Distributed IN

This work studies the performance of power beacon (PB) assisted downlink cooperative non-orthogonal multiple access (NOMA) in a wireless powered communication network (WPCN) with realistic non-linear energy harvesting (EH) model. The paper proposes an analytical methodology for improving performance at blocked user at the edge of network via an intermediate node (IN). IN deployed in the coverage area of the base station (BS), acts as decode-and-forward relay to perform cooperative transmission. IN assists the BS in communicating with the blocked Internet of Things (IoT) device after harvesting energy from PB mounted on the BS. IN employs NOMA to serve IoT devices with heterogeneous quality of service requirements in order to achieve higher spectral and energy efficiency. A practical non-linear EH model is employed at IN to power the cooperative transmission to the IoT devices. The performance of the proposed scheme is compared with the system employing orthogonal multiple access and it is shown that the proposed scheme attains improved performance in terms of outage probability, system throughput, and energy efficiency.

Optimal Energy Signal Design for Multiuser MISO WPCNs With Non-Linear Energy Harvesting Circuits

The optimal energy signal design for wireless powered communication networks (WPCNs) enabling energy-sustainable communication for a large number of low-power devices is still an open problem in practical systems. In this work, we study a multi-user WPCN, where a multi-antenna base station (BS) sends an energy signal to multiple single-antenna users, which, in turn, harvest energy from the received signal and utilize it for information transmission in the uplink. In contrast to the existing works on multiple-input single-output (MISO) WPCN design, in this paper, we jointly optimize the energy signal waveform and downlink beamforming at the BS for energy harvesting (EH) devices described by non-linear circuit-based models. To this end, we assume that the BS broadcasts a pulse-modulated signal employing multiple energy signal vectors and we formulate an optimization problem for the joint design of the downlink transmit energy signal vectors, their number, the durations of the transmit pulses, and the time allocation policy for minimization of the average transmit power at the BS. We show that for single-user WPCNs, a single energy signal vector, which is collinear with the maximum ratio transmission (MRT) vector and drives the EH circuit at the user device into saturation, is optimal. Next, for the general multi-user case, we show that the optimal signal design requires a maximum number of energy signal vectors that exceeds the number of users by one and propose an algorithm to obtain the optimal energy signal vectors. Since the complexity of the optimal design is high, we also propose two suboptimal schemes for WPCN design. First, for asymptotic massive WPCNs, where the ratio of the number of users to the number of BS antennas, i.e., the system load, tends to zero, we show that the optimal downlink transmit signal can be obtained in closed-form and comprises a sequence of weighted sums of MRT vectors. Next, based on this result, for general WPCNs with finite system loads, we propose a suboptimal closed-form MRT-based design and a suboptimal semidefinite relaxation (SDR)-based scheme. Our simulation results reveal that the proposed optimal scheme and suboptimal SDR-based design achieve nearly identical performance and outperform two baseline schemes, which are based on linear and sigmoidal EH models. Furthermore, we show that, if the system load of the WPCN is low, the performance gap between the proposed suboptimal solutions is small and becomes negligible as the number of BS antennas tends to infinity.