Users Harvest Energy Research Articles

Low-Complexity Beamforming Designs and Channel Estimation for Passive-Intelligent-Surface-Assisted MISO Energy Transfer

The usage of passive intelligent surface (PIS) is emerging as a low-cost green alternative to massive antenna systems for realizing high energy beamforming (EB) gains. Considering the limited computational capability and constant-envelope precoding for PIS, we propose three novel low-complexity passive EB designs for optimizing the efficacy of PIS-assisted energy transfer (PET) from a multiantenna power beacon (PB) to a single-antenna energy harvesting (EH) user. The first EB design involves solving a univariate equation, and closed forms expressed are presented for the other two. Further, to maximize the practical utility of PET, we introduce a novel channel estimation (CE) protocol for obtaining least-squares estimators for the channels as required for EB designing. Using them, we also derive closed-form expressions for optimal PIS location and optimal time allocation between CE and PET within each coherence block to maximize the user’s net harvested energy. Numerical results verify the CE analysis and validate the novel analytical bound derived for received power during PET and proposed PIS designs’ quality against existing benchmarks. We show that the proposed jointly optimal design for PET can yield a significant improvement of about 15dB, and a reduced active array size at PB can achieve the desired EB gain with sufficient passive elements at PIS. Lastly, we also briefly discuss how the proposed CE and EB designs can be extended to the multiuser settings.

Resource allocation method for Mobility‐Aware and Multi‐UAV‐Assisted mobile edge computing systems with energy harvesting

AbstractDue to the limited coverage of base station (BS) and battery capacity of mobile users, the resource allocation strategy in multiple unmanned aerial vehicles (UAVs)‐assisted edge computing system with nonlinear energy harvesting is investigated in this paper. The cooperation between BS and multi‐UAV is considered, which can provide extensive coverage for users with mobility. Mobile users can simultaneously offload computation bits to the BS and the UAV, and mobile users harvest energy from BS and UAV. Meanwhile, the mobility of users is taken into account. Moreover, an echo state network (ESN)‐based prediction algorithm is utilized for predicting the future positions of mobile users. Therefore, the UAV can reach the predicted users' positions in advance to ensure the continuity of communication. The objective of the resource allocation strategy is to maximize the energy efficiency by jointly optimizing bandwidth allocation, computation resources, the trajectory of UAV, and transmitting power of mobile users. Then, the resource allocation problem is formulated as a mixed‐integer nonlinear programming problem. The quantum‐behaved particle swarm optimization (QPSO) algorithm is used to solve the problem. Simulation results demonstrate that the proposed strategy can achieve higher energy efficiency than other benchmark strategies. In addition, QPSO algorithm outperforms the standard particle swarm optimization algorithm and genetic algorithm in terms of energy efficiency.

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.

Performance enhancement in the cell-free massive MIMO SWIPT system with active eavesdropping

This paper investigates the secure transmission for simultaneous wireless information and power transfer (SWIPT) in the cell-free massive multiple-input multiple-output (MIMO) system. To develop green communication, legitimate users harvest energy by the hybrid time switching (TS) and power splitting (PS) strategy in the downlink phase, and the harvested energy can provide power to send uplink pilot sequences for the next time slot. By in-built batteries, the active eavesdropper can send the same pilots with the wiretapped user, which results in undesirable correlations between the channel estimates. Under these scenarios, we derive the closed-form expressions of average harvested energy and achievable rates, and propose an iterative power control (PC) scheme based on max–min fairness algorithm with energy and secrecy constraints (MMF-ESC). This scheme can ensure the uniform good services for all users preserving the distributed architecture advantage of cell-free networks, while meeting the requirements of energy harvested by legitimate users and network security against active eavesdroppers. Besides, continuous approximation, bisection and path tracking are jointly applied to cope with the high-complexity and non-convex optimization. Numerical results demonstrate that MMF-ESC PC scheme can effectively increase the achievable rate and the average harvested energy of each user, and decrease the eavesdropping rate below the threshold. Moreover, the results also reveal that PS strategy is superior in harvesting energy in terms of more stringent network requirements for average achievable rates or security.

Optimal User Cooperation in IRS Assisted WPCN for Practical Non-Linear Phase-Shift Model

This letter is a novel attempt to consider intelligent reflecting surfaces (IRS) assisted user cooperation adopting the practical non-linear phase-shift model in a wireless-powered communication network (WPCN). Specifically, we aim to maximize both max-min fairness and sum throughput among the energy harvesting users while adopting the practical IRS model. We propose an alternating optimization (AO) algorithm exploiting convex relaxation and semidefinite programming to optimize the phase-shift matrices at IRS along with time and power allocation at the hybrid access point serving the users. Simulation results corroborate key analytical claims and show that the proposed jointly-optimal design provides better performance gain for max-min and sum-rate maximization over the existing benchmark schemes.

Information Freshness and Energy Harvesting Tradeoff in Network-Coded Broadcasting

This letter investigates both information freshness and energy harvesting (EH) in downlink information update systems. Information freshness is characterized by age of information (AoI). We consider a scenario in which a large update message is packetized into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> small packets and encoded by random linear network coding (RLNC). A base station continuously broadcasts the RLNC-encoded packets to multiple users until all the users have received the message. When a user successfully receives the update message while at least one other user has not received it yet, the user harvests energy from the received signals. A key challenge is to decide the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> in the message packetization in order to achieve low AoI and high EH simultaneously. To this end, we conduct a theoretical analysis of the tradeoff between the AoI and EH performance under different values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> . In particular, the packet error rates of short packets in the message packetization are estimated by the short packet theory, and the closed-form AoI and EH formulas are derived. Our numerical results reveal that there exists a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> (neither too small nor too large) that can achieve both low AoI and high EH.

Binary vs partial offloading in wireless powered mobile edge computing systems with fairness guarantees

Mobile Edge Computing (MEC) has recently emerged as a new communications/computing concept that amends the limited computing of IoT devices by completely or partially offloading the computational tasks to the MEC servers at the network edge (typically co-located with the base stations). Because IoT devices are typically power limited, the potential of the MEC is further enhanced by its integration with wireless power transfer technology, especially for those IoT devices with a high duty cycle that requires frequent battery replacement. This paper develops fairness-aware resource allocation schemes for a WPT-assisted MEC system whose Energy Harvesting Users (EHUs) employ either binary or partial offloading. Specifically, the proposed schemes optimize the computational speeds and the energy harvesting and offloading durations of the EHUs with the aim to maximize the minimum of their computed bits (sum of locally and remotely processed bits of each EHU), subject to the RF energy harvested from the base station. When EHUs are concentrated closer to the base station, remote processing is preferred over local processing, as local processing consumes more energy than the Radio Frequency (RF) power for offloading data to the MEC server, but this effect diminishes for lower values of the computational effort needed for the processing of a single bit. Interestingly, in terms of the sum computation rate, the partial offloading scheme only slightly outperforms the binary offloading scheme, but only when the EHUs are moderately away from the base station.

Reinforcement-Learning-Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks

This article proposes a novel approach to improve the energy efficiency (EE) of an energy-harvesting (EH)-enabled IoT network supported by simultaneous wireless information and power transfer (SWIPT). More specifically, the device-to-device (D2D) users harvest ambient energy throughout their communication with the time switching (TS) technique, while the Internet of Things (IoT) users harvest energy from the base station (BS) based on the power splitting (PS) method. We study the EE optimization problem that takes into account transmit power feasibility conditions for D2D users and IoT users, the minimum data rate requirements for D2D and IoT users, joint spectrum sharing block, and time allocation for the D2D links. The underlying problem is a highly nonconvex mixed-integer nonlinear problem (MINLP) and the global optimal solution is intractable. To handle it, we decompose the original problem into three subproblems: 1) joint subchannel allocation and PS; 2) power control; and 3) time allocation. Since it is difficult to find an exact state model approach in a dynamic environment with a large state space, we exploit a <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> -learning method based on reinforcement learning (RL) to solve the first subproblem. To solve the second subproblem, we apply a conventional convex optimization technique based on the majorization–minimization (MM) approach and Dinkelbach method. Simulation results not only demonstrate the superiority of our proposed algorithm as compared to other methods in the literature but also confirm impressive EE gains through spectrum sharing and harvested energy from D2D and IoT users.

Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks.

The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user harvests energy from the primary users’ transmissions and opportunistically accesses the primary users’ licensed spectrum to deliver the status-update data pack. We aim to minimize the AoI subject to the energy causality and spectrum constraints by optimizing the sensing and update decisions. We formulate the AoI minimization problem as a partially observable Markov decision process and solve it via dynamic programming. Simulation results verify that our proposed policy is significantly superior to the myopic policy under different parameter settings.

Optimal downlink and uplink design in a wireless powered two‐user indoor communication system

This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H-AP), due to the short device-to-device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H-AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non-convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device-to-device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum-throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non-convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless-powered communication system.

Worst-Case Energy Efficiency in Secure SWIPT Networks With Rate-Splitting ID and Power-Splitting EH Receivers

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

Low-Complexity PSO-Based Resource Allocation Scheme for Cooperative Non-Linear SWIPT-Enabled NOMA

Cooperative networks integrating non-orthogonal multiple access (NOMA) and simultaneous wireless information power transfer (SWIPT) are emerging technologies that have been investigated as potential techniques to support the proliferation of the Internet of Things (IoT) and to obtain greener communications. In this sense, the optimization of resource allocation schemes is crucial to improve the performance of future cooperative wireless networks. However, conventional optimization methods that attempt to find the optimal solution may entail high computational complexity. Therefore, we propose a low-complexity particle swarm optimization (PSO)-based scheme to solve the resource allocation problem in a cooperative non-linear SWIPT-enabled NOMA system with a non-linear energy harvesting (EH) user. Specifically, we consider two optimization problems. First, we minimize transmission power, and second, we maximize energy efficiency subject to meeting quality-of-service (QoS) constraints. The problems are non-convex and challenging to solve. Furthermore, we develop the optimal solution based on convex optimization and the exhaustive search (ES) method to validate the results of the proposed PSO-based framework. Afterward, we investigate the performance of five swarm intelligence-based baseline schemes and evaluate an additional low-complexity solution based on the cuckoo search (CS) technique. For comparison purposes, we use orthogonal multiple access (OMA), equal power splitting (EPS), and time fixed (TF) baseline schemes. To our satisfaction, the proposed SWIPT NOMA network outperforms the benchmark schemes, and the proposed PSO-based framework achieves the nearest performance to the optimal scheme with lower complexity than obtained by the comparative swarm intelligence techniques and from convex optimization with the ES method.

Hybrid LSTM‐PCA‐Powered Renewable Energy‐Based Battery Life Prediction and Management for IoT Applications

To ensure self‐sustainable and long‐term requirement of Internet of Things (IoT) operation, energy harvesting (EH) is a promising technique. In this work, battery prediction problems and joint access control are studied in a IoT cell system with a base station (BS) and EH user equipment (UE) with limited uplink access channels. Every UE present holds a limited capacity rechargeable battery. In order to address this issue, we take into consideration the uplink wireless system with “N” EH user equipment (UE) along with the base station. To build the UE uplink access control, the first step involves the application of hybrid Q network (HQN) with long short‐term memory (LSTM) to tackle the access control issue. This technique maximizes the uplink transmission sum rate. The LSTM DQN network designed has two layers with battery prediction and joint access control solution. The first layer is developed to predict battery levels, while the next layer generates the access control information based on channel information along with the predicted values. This network is trained to decrease the discounted prediction loss and increase long‐term discounted sum rate, at the same time by training the two layers jointly. In recent years, IoT sensors have played a major role in collecting and monitoring data gathered from real‐world applications such as environmental monitoring, transportation, urban security, smart energy management, agriculture, and health care. Several of these appliances function with batteries. Hence, the primary task would be to predict the batter’s lifespan and provide a timeframe to replace or recharge the battery. The battery life used in an IoT network is predicted with the help of a hybrid random forest‐principal component analysis (PCA) regression algorithm with machine learning.

Deep Reinforcement Learning for Energy-Efficient Federated Learning in UAV-Enabled Wireless Powered Networks

Federated learning (FL) is a promising solution to privacy preservation for data-driven deep learning approaches. However, enabling FL in unmanned aerial vehicle (UAV)-assisted wireless networks is still challenging due to limited resources and battery capacity in the UAV and user devices. In this regard, we propose a deep reinforcement learning (DRL)-based framework for joint UAV placement and resource allocation to enable sustainable FL with energy harvesting user devices. We aim to maximize the long-term FL performance considering the limited resources in the network, such as harvested energy, bandwidth resources, and UAV’s energy budget. To reduce the complexity of the original problem, we leverage the Lyapunov optimization technique to transform a long-term energy constraint into a deterministic problem. We reformulate the optimization problem as the framework of a Markov decision process (MDP) and design a DRL-based algorithm to solve the MDP. The proposed solution can guarantee the sustainable operation of UAV-aided wireless networks by improving energy conservation of the network in the long run.

Achievable Information Rate in Hybrid VLC-RF Networks With Lighting Energy Harvesting

This paper investigates the relay-assisted wireless information and power transfer enabled hybrid visible light communication (VLC)-radio frequency (RF) network, where a light emitting diode (LED) access point (AP) serves multiple information users (IUs) and multiple energy harvesting users (EHUs). IUs are allowed to receive information from the LED AP through time-division-multiple-access (TDMA) manner by either the single-hop VLC-ONLY mode or the relay-assisted dual-hop VLC-RF mode, while EHUs harvest energy via the VLC links. An optimization problem is formulated to maximize the achievable information rate of IUs by jointly optimizing the access mode selection, the direct current (DC) offset at the LED AP, the peak amplitude of the alternating current (AC) component at the LED AP, the electrical power allocated to the LED AP and the power allocation at relay, subject to the energy harvesting (EH) requirement constraints of EHUs. To tackle the non-convex problem with binary variables, we first decompose it into two subproblems in terms of the two access modes. Then, the subproblems are equivalently transformed and solved by the proposed successive convex approximation (SCA)-based algorithms. Simulation results show that significant performance gain can be achieved by optimizing the DC offset. It is also observed that the area where the VLC-ONLY mode is superior to the VLC-RF mode is enlarged with the decrease of the minimal EH requirement. Besides, the achievable information rate of IUs by the VLC-RF mode first increases and then decreases with the increment of the distance between the relay and the LED AP.

Designing Wireless Powered Networks Assisted by Intelligent Reflecting Surfaces With Mechanical Tilt

In this paper, we propose a fairness-aware rate maximization scheme for a wireless powered communications network (WPCN) assisted by an intelligent reflecting surface (IRS). The proposed scheme combines user scheduling based on time division multiple access (TDMA) and (mechanical) angular displacement of the IRS. Each energy harvesting user (EHU) has dedicated time slots with optimized durations for energy harvesting and information transmission whereas, the phase matrix of the IRS is adjusted to focus its beam to a particular EHU. The proposed scheme exploits the fundamental dependence of the IRS channel path-loss on the angle between the IRS and the node's line-of-sight, which is often overlooked in the literature. Additionally, the network design can be optimized for large number of IRS unit cells, which is not the case with the computationally intensive state-of-the-art schemes. In fact, the EHUs can achieve significant rates at practical distances of several tens of meters to the base station (BS) only if the number of IRS unit cells is at least a few thousand.

Multi-User Energy Beamforming for Different Energy Requests

Energy beamforming takes center stage in radio frequency (RF)-enabled wireless energy transfer (WET) systems. In this letter, we study multi-user energy beamforming to minimize the overall charging time for energy harvesting users (EHUs) with different energy requirements. In order to lessen the cost and power consumption by reducing the required RF chains, it is of consequence to figure out low-rank optimal energy beamforming. By analyzing an upper bound on the rank of a minimum-rank solution, we develop a method to figure out low-rank optimal energy beamforming from the formulated semi-definite programming problem. Our simulation results exhibit the proposed energy beamforming considerably reduces the charging time required until all the requests are met. We also reveal that the capacity achieving precoder for multicasting transmission is a special case of the proposed energy beamforming solution.

Feasible Point Pursuit and Successive Convex Approximation for Transmit Power Minimization in SWIPT-Multigroup Multicasting Systems

We consider three wireless multi-group (MG) multicasting (MC) systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures, energy harvesting (EH) only users with non-linear EH module, and users with joint ID and EH capabilities having separate units for the two operations, respectively. Each user is categorized under unique group(s), which can be of MC type specifically meant for ID users, and/or an energy group consisting of EH explicit users. The joint ID and EH users are a part of both EH group and single MC group. We formulate an optimization problem to minimize the total transmit power with optimal precoder designs for the three aforementioned scenarios, under certain quality-of-service constraints. The problem may be adapted to the well-known semi-definite program and solved via relaxation of rank-1 constraint. However, this process leads to performance degradation in some cases, which increases with the rank of solution obtained from the relaxed problem. Hence, we develop a novel technique motivated by the feasible-point pursuit successive convex approximation method in order to address the rank-related issue. The benefits of proposed method are illustrated under various operating conditions and parameter values, with comparison between the three above-mentioned scenarios.

Sum-Throughput Maximization in NOMA-Based WPCN: A Cluster-Specific Beamforming Approach

Wireless power transfer is a promising solution for wireless networks composed of Internet-of-Things (IoT) devices that may suffer from insufficient battery capacity. A wireless powered communication network (WPCN) is a framework to design energy-constrained networks such as IoT networks. In this article, we consider WPCNs consisting of a hybrid access point (H-AP) and energy harvesting users, all equipped with multiple antennas. A H-AP transfers energy to the users using energy beamforming in the downlink, and the users transmit information using the harvested energy in the uplink, where nonorthogonal multiple access (NOMA) transmission is employed. For the uplink NOMA transmission, users are grouped into multiple clusters by cluster-specific beamforming. In particular, signal alignment is exploited for the beamforming so that the channels of users in a cluster are aligned in the same direction. By signal alignment, the number of messages decoded by successive interference cancellation (SIC) is reduced, which can be effective at lowering the decoding complexity and SIC error propagation. Due to the difficulty of jointly optimizing cluster-specific beamforming and time/energy resources for sum-throughput maximization, we determine the beamforming relying on signal alignment first, and then the resources are optimized for given beamforming. To be more specific, we propose a novel iterative algorithm for cluster-specific beamforming design followed by the sum-throughput maximization algorithm. Numerical results show the sum-throughput performance of the proposed scheme and its robustness toward SIC error propagation compared to existing schemes.

Joint Beamforming and Power Control for Throughput Maximization in IRS-Assisted MISO WPCNs

Intelligent reflecting surface (IRS) is an emerging technology to enhance the energy efficiency and spectrum efficiency of wireless-powered communication networks (WPCNs). In this article, we investigate an IRS-assisted multiuser multiple-input single-output (MISO) WPCN, where the single-antenna wireless devices (WDs) harvest wireless energy in the downlink (DL) and transmit their information simultaneously in the uplink (UL) to a common hybrid access point (HAP) equipped with multiple antennas. Our goal is to maximize the weighted sum rate (WSR) of all the energy-harvesting users. To make full use of the beamforming gain provided by both the HAP and the IRS, we jointly optimize the active beamforming of the HAP and the reflecting coefficients (passive beamforming) of the IRS in both DL and UL transmissions, as well as the transmit power of the WDs to mitigate the interuser interference at the HAP. To tackle the challenging optimization problem, we first consider fixing the passive beamforming, and converting the remaining joint active beamforming and user transmit power control problem into an equivalent weighted minimum mean-square error problem, where we solve it using an efficient block-coordinate descent method. Then, we fix the active beamforming and user transmit power, and optimize the passive beamforming coefficients of the IRS in both the DL and UL using a semidefinite relaxation method. Accordingly, we apply a block-structured optimization method to update the two sets of variables alternately. The numerical results show that the proposed joint optimization achieves significant performance gain over other representative benchmark methods and effectively improves the throughput performance in multiuser MISO WPCNs.