Power Wireless Communication Research Articles

STAR-RIS Assisted Reliable and Secure Transmissions in Wireless-Powered Communications

STAR-RIS Assisted Reliable and Secure Transmissions in Wireless-Powered Communications

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

Toward a Wireless Image Sensor for Real-Time Fluorescence Microscopy in Cancer Therapy.

We present a mm-sized, ultrasonically powered lensless CMOS image sensor as a progress towards wireless fluorescence microscopy. Access to biological information within the tissue has the potential to provide insights guiding diagnosis and treatment across numerous medical conditions including cancer therapy. This information, in conjunction with current clinical imaging techniques that have limitations in obtaining images continuously and lack wireless compatibility, can improve continual detection of multicell clusters deep within tissue. The proposed platform incorporates a 2.4 × 4.7 mm2 integrated circuit (IC) fabricated in TSMC 0.18 µm, a micro laser diode (µLD), a single piezoceramic and off-chip storage capacitors. The IC consists of a 36 × 40 array of capacitive trans-impedance amplifier-based pixels, wireless power management and communication via ultrasound and a laser driver all controlled by a Finite State Machine. The piezoceramic harvests energy from the acoustic waves at a depth of 2 cm to power up the IC and transfer 11.5 kbits/frame via backscattering. During Charge-Up, the off-chip capacitor stores charge to later supply a high-power 78 mW µLD during Imaging. Proof of concept of the imaging front end is shown by imaging distributions of CD8 T-cells, an indicator of the immune response to cancer, ex vivo, in the lymph nodes of a functional immune system (BL6 mice) against colorectal cancer consistent with the results of a fluorescence microscope. The overall system performance is verified by detecting 140 µm features on a USAF resolution target with 32 ms exposure time and 389 ms ultrasound backscattering.

Seamless Connectivity: The Power of Integrating Power Line and Wireless Communications

Seamless Connectivity: The Power of Integrating Power Line and Wireless Communications

Delay Minimization of Federated Learning Over Wireless Powered Communication Networks

Delay Minimization of Federated Learning Over Wireless Powered Communication Networks

Enhancing User Fairness in Wireless Powered Communication Networks With STAR-RIS

Enhancing User Fairness in Wireless Powered Communication Networks With STAR-RIS

Learning Decentralized and Scalable Resource Management for Wireless Powered Communication Networks

Learning Decentralized and Scalable Resource Management for 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

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.

Physical-Layer Security for Intelligent-Reflecting-Surface-Aided Wireless-Powered Communication Systems

Physical-Layer Security for Intelligent-Reflecting-Surface-Aided Wireless-Powered Communication Systems

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.

Multi-channel LFP recording data compression scheme using Cooperative PCA and Kalman Filter

The power consumption and wireless communication bandwidth restrict the channel count and amount of high-resolution intracortical Brain–Computer Interfaces(iBCIs). So, it is necessary to compress the captured data before transmission. Here, we propose a multi-channel data compression method on the correlation between channels for Local Field Potential(LFPs) recorded through Utah microelectrode arrays. The method improves the compression performance of the Stepwise Expectation-Maximization Principal Component Analysis(SEM-PCA) algorithm by implementing the Kalman filter. The multi-channel correlation is sampled with high resolution while the uniqueness of each channel is retained at a low rate. Then, the two parts of information are fused by the Kalman filter, and finally, the data is reconstructed with high precision and high rate. Our testing results verified the effectiveness of the proposed compression scheme. Compared to the traditional SEM-PCA, this algorithm significantly improved neural LFP reconstruction accuracy from 79.61% to 88%.

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.

Wireless-Powered Communication Assisted by Two-Way Relay with Interference Alignment Underlaying Cognitive Radio Network

Wireless-Powered Communication Assisted by Two-Way Relay with Interference Alignment Underlaying Cognitive Radio Network

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.

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.

Resource Scheduling for Intelligent Reflecting Surface-Assisted Full-Duplex Wireless-Powered Communication Networks With Phase Errors

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless powered communication networks (FD-WPCNs). This paper investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of formulated non-convex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method, and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

Multicast Spatial Filter Beamforming with Resource Allocation Using Joint Multi-objective Optimization Approaches in Wireless Powered Communication Networks

Multicast Spatial Filter Beamforming with Resource Allocation Using Joint Multi-objective Optimization Approaches in Wireless Powered Communication Networks