Energy Harvesting Receiver Research Articles

Adaptive ON/OFF Scheduling to Minimize Age of Information in an Energy-Harvesting Receiver

Adaptive ON/OFF Scheduling to Minimize Age of Information in an Energy-Harvesting Receiver

Information Energy Capacity Region for SWIPT Systems Over Rayleigh-Fading Channels

In this paper, we study the fundamental limits of simultaneous information and power transfer over a Rayleigh-fading channel, where the channel input is constrained to peak-power (PP) constraints that vary in each channel use by taking into account high-power amplifier (HPA) nonlinearities. In particular, a three-party communication system is considered, where a transmitter aims simultaneously conveying information to an information receiver and delivering energy to an energy harvesting receiver. For the special case of static PP constraints, we study the information-energy capacity region and the associated input distribution under: a) average-power and PP constraints at the transmitter, b) an HPA nonlinearity at the transmitter, and c) nonlinearity of the energy harvesting circuit at the energy receiver. By extending Smith’s mathematical framework, we show that the optimal input distribution under those constraints is discrete with a finite number of mass points. We show that HPA significantly reduces the information energy capacity region. In addition, we derive a closed-form expression of the capacity-achieving distribution for the low PP regime, where there is no trade-off between information and energy transfer. For the case with time-varying PP constraints, we characterize the optimal input distribution of this channel by using Shannon’s coding scheme. Specifically, we numerically study a particular scenario for the time-varing PP constraints, where the PP constraint probabilistically is either zero or equal to a non-zero constant.

On the Performance of Coded Cooperative Communication with Multiple Energy-Harvesting Relays and Error-Prone Forwarding

In this paper, we consider a coded cooperative communication network with multiple energy-harvesting (EH) relays. In order to adequately address the problem of error propagation due to the erroneous decoding at the relays, as in the case of conventional decode and forward (DF) relaying protocol, we propose coded cooperative schemes with hard information relaying (HIR) and soft information relaying (SIR) strategies. The performance of the relayed communication with EH relay depends crucially on the channel decoding capability at the relay, channel gains at the source–relay and relay–destination links, and ultimately on the power-splitting ratio of the relay EH receiver. The exact closed-form expression for the outage probability performance of the coded cooperative scheme with HIR strategy and relay selection (CC-HIR-RS) is derived for both cases, namely for constant and optimal power-splitting ratios. Concerning the coded cooperative scheme with SIR strategy, a Rayleigh Gaussian log likelihood ratio-based model is used to describe the soft estimated symbols at the output of the relay soft encoder. Directives are provided to determine the model parameters, and, accordingly, the signal-to-noise ratio (SNR) of the equivalent one-hop relaying channel is derived. A closed-form expression for the outage probability performance of the proposed coded cooperative scheme with SIR and relay selection (CC-SIR-RS) is derived. In addition, a fuzzy logic-based power-splitting scheme in EH relay applying SIR is proposed. The fading coefficients of the source–relay and relay–destination links and distance between source and relay node are considered as input parameters of the fuzzy logic system to obtain an appropriate power-splitting ratio that leads to a quasi-optimal SNR of the equivalent end-to-end channel. Monte Carlo simulations are presented to demonstrate the validity of the analytical results, and a comparison between the performance of the CC-HIR-RS scheme with constant and optimized power-splitting ratios and that of the CC-SIR-RS scheme with constant and fuzzy logic-based power-splitting ratios is provided.

Simultaneous Wireless Information and Power Transfer Over KG Fading Channels

In this paper, we proposed a novel expression for the probability density function (PDF) and the cumulative density function (CDF) of the output SNR for the M-branch Selection Combining (M-SC) receiver over Generalized-K (KG) fading channels. Unlike conventional energy sources, the proposed scheme uses a Power splitter (PS) scheme at the receiver side. The received signal is split into information decoding and energy harvesting receiver with adjustable power levels. The expressions of our system consider arbitrary channel parameters and diversity branches. We analyze the proposed model and derive the closed-form expression for average SNR and average bit error rate (ABER) to evaluate the system performance. The obtained results are corroborated with the help of Monte-Carlo simulations. The effects of system parameters, such as power splitter ratio, modulation order, and shaping parameters of KGfading channels, are studied. This type of system will benefit reliable data transmission in an energy-limited scenario.

Deep Learning-Based Transmit Power Control for Wireless-Powered Secure Communications With Heterogeneous Channel Uncertainty

In this paper, we investigate a method of transmit power control (TPC) for wireless-powered secure communications with untrusted energy harvesting receivers (EH-Rxs), which are allowed to harvest energy but not to decode information from the signals sent by transmitters. In practice, channel state information (CSI) is sometimes inaccurate due to the time-varying nature of wireless channels and quantization errors in the CSI feedback. Furthermore, the CSI on the EH link between transmitters and EH-Rxs is likely to be more inaccurate than the CSI on the signal link when the EH-Rxs act deceitfully, so the level of uncertainty in the CSI can be different for the signal link and the EH link. In the presence of this heterogeneity of channel uncertainty, a deep learning (DL)-based TPC strategy is proposed to maintain the confidentiality of information at the untrusted EH-Rxs whilst guaranteeing that the required energy can still be harvested. In particular, the modified CSI for each signal and EH link, which is generated from the estimated CSI through the addition of random noise, is taken into account in the training of a deep neural network (DNN) to compensate appropriately for the heterogeneous errors in the CSI. Simulation results confirm that the proposed scheme provides a good approximation to the optimal TPC strategy, even in the presence of severely heterogeneous channel errors, such that it outperforms conventional baseline schemes and shows a near-optimal secrecy performance whilst achieving a significantly lower computation time.

Joint transmission design for IRS-assisted MISO SWIPT systems

In this paper, we investigate the transmission design for an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, where an IRS adjusts its reflection phase shifts to facilitate information transfer and energy harvesting. Two types of input signals, i.e., Gaussian signals and finite-alphabet signals, are both considered. Our goal is to maximize the mutual information between the access point (AP) and the information receiver (IR) by jointly optimizing the transmit beamforming at the AP and the phase shifts of the IRS under the constraints of transmit power at the AP and required harvested energy at the energy harvesting receiver (ER). We show that the transmission designs for these two types of signals actually can be unified into a common optimization problem. Although the formulated problem is an intractable non-convex problem, the solving frameworks in cases of perfect and imperfect channel state information (CSI) are provided, respectively. For the perfect case, an efficient algorithm, which combines semidefinite relaxation (SDR) and penalty-based manifold optimization (PMO), is proposed. For the imperfect case, we study the worst-case transmission design and develop an algorithm based on SDR. Numerical results show the superiority of our proposed algorithms.

Power-Efficient Passive Beamforming and Resource Allocation for IRS-Aided WPCNs

This paper studies an intelligent reflecting surface (IRS)-assisted wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. In particular, we propose three types of IRS beamforming configurations, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully dynamic IRS beamforming (FDBF)</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially dynamic IRS beamforming (PDBF)</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static IRS beamforming (SBF)</i> , to strike a balance between the system performance and signaling overhead as well as implementation complexity. Moreover, we adopt a practical non-linear energy harvesting (EH) model, and leverage a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio frequency power to get stuck into the saturation regime. We aim to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problems, a successive convex approximation (SCA) based alternating optimization algorithm is proposed. Moreover, we study the case with the ideal linear EH model and two algorithms, namely SCA-based algorithm and semidefinite relaxation (SDR) algorithm, are proposed. Simulation results demonstrate the effectiveness of our proposed designs over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice.

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.

Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model.

In this paper, the maximization of the achievable information rate is proposed for the multi-relay amplify-and-forward cooperative simultaneous wireless information and power transfer communication systems, where the nonlinear characteristic of the energy harvesting (EH) circuits is taken into account for the receivers of the relay nodes. The time switching (TS) and power splitting (PS) schemes are considered for the EH receivers and the achievable rate maximization problems are formulated as convex and non-convex optimization problems, respectively. The optimal TS and PS ratios for the relay nodes along with the maximum achievable rates for the system are obtained, respectively, by solving the optimal problems with efficient algorithms. The asymptotic maximum achievable rates at low and high input signal-to-noise ratios (SNRs) for both the PS and TS schemes are also analyzed. It is demonstrated that the PS scheme is more susceptible to the variation of the relays’ location and the channel parameters than TS scheme, whereas the TS scheme is more susceptible to the mismatch of the resource allocation than PS scheme. Specifically, compared to the linear EH model, the nonlinear EH model achieves significant performance gain for the TS scheme, whereas inconspicuous performance improvement is achieved for the PS scheme.

IRS-aided SWIPT systems with power splitting and artificial noise

Intelligent reflecting surface (IRS) is regarded as a promising technology because it can achieve higher passive beamforming gain. In particular, the IRS assisted simultaneous wireless information and power transfer (SWIPT) system can make the information decoding receivers (IDRs) have a higher signal-to-noise ratio (SNR), and the energy harvesting receivers (EHRs) have the guarantee of minimum harvested energy threshold. Motivated by the above, in this paper, we use the power splitting (PS) at the user and introduce artificial noise (AN) into the access point (AP), so that the user in system can harvest energy and decode information simultaneously, further improve the security of user. We jointly optimize the beamforming matrix at AP, the reflection phase shift at IRS and the PS ratio, in order to maximize the user's achievable secrecy rate, subject to the user's minimum harvested energy threshold and AP's transmission power. Due to the introduction of PS ratio, the coupling between variables is increased, and the complexity of the problem is significantly increased. Furthermore, the problem is non-convex, so we propose an efficient algorithm based on Taylor Formula, semi-definite relaxation (SDR) and alternating optimization (AO) to get the solution. Numerical results show that the proposed IRS-SWIPT system with PS and AN achieves significant performance improvement compared with other benchmark scheme.

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.

Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A Scalable Optimization Framework

In this paper, we study the optimal resource allocation algorithm design for large intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. To facilitate efficient system design for large IRSs, instead of jointly optimizing all the IRS elements, we partition the IRS into several tiles and employ a scalable optimization framework comprising an offline design stage and an online optimization stage. In the offline stage, the IRS elements of each tile are jointly designed to support a set of different phase shift configurations, referred to as transmission modes, while the best transmission mode is selected from the set for each tile in the online stage. Given a transmission mode set, we aim to minimize the total base station (BS) transmit power by jointly optimizing the beamforming and the transmission mode selection policy taking into account the quality-of-service requirements of information decoding and non-linear energy harvesting receivers, respectively. Although the resource allocation algorithm design is formulated as a non-convex combinatorial optimization problem, we solve it optimally by applying the branch-and-bound (BnB) approach which entails a high computational complexity. To strike a balance between optimality and computational complexity, we also develop an efficient suboptimal algorithm capitalizing on the penalty method and successive convex approximation. Our simulation results show that the proposed designs enable considerable power savings compared to several baseline schemes. Moreover, our results reveal that by properly adjusting the numbers of tiles and transmission modes, the proposed scalable optimization framework indeed facilitates online design for large IRSs. Besides, our results confirm that the advocated physics-based model and scalable optimization framework enable a flexible trade-off between performance and complexity, which is vital for realizing the performance gains promised by large IRS-assisted communication systems in practice.

Nonlinear EH-Based UAV-Assisted FD IoT Networks: Infinite and Finite Blocklength Analysis

In this article, we investigate the nonlinear energy harvesting (EH)-based unmanned aerial vehicle (UAV)-assisted full-duplex (FD) Internet of Things (IoT) network with infinite and finite blocklength (FBL) codes. The reliability performance of the considered network, having two half-duplex UAVs and an FD IoT device, is analyzed in terms of the block error rate (BLER) with given ultrareliable and low-latency communication constraints. With the assumption of the combined effect of fading and shadowing, the closed-form expressions for BLER and network goodput are obtained over the Rician shadowed fading channels considering various shadowing scenarios, EH receiver architecture, IoT device mobility, inter-UAV interference, and self-interference (SI) cancelation capabilities at FD IoT device. The obtained results over the Rician shadowed fading for the nonlinear EH receiver architecture are also compared with the linear EH and over the Rician fading channels. The numerical results reveal important observations related to the impact of time-selective fading channels with imperfect channel state information, shadowing severity in the suburban areas, SI cancelation capabilities, blocklength, and the number of channel uses on the reliability performance of the UAV-assisted FD IoT network. Furthermore, the tightness of the approximation presented is verified through the Monte-Carlo simulations.

Secrecy Energy Efficiency in Full-Duplex AF Relay Systems With Untrusted Energy Harvesters

In this letter, we investigate an artificial noise (AN) aided beamforming scheme for full-duplex amplify-and-forward relay network in the presence of multiple untrusted energy harvesting receivers (EHRs). We establish an optimization problem to maximize the secrecy energy efficiency (SEE), while meeting the self-interference nulling and transmit power constraints at relay as well as maintaining the energy harvesting threshold at the EHRs by jointly designing the relay beamforming (BF) and AN covariance matrices. Due to the non-convex structure of the SEE maximization problem, we first design a null-space BF scheme at relay to eliminate the SI and simplify the joint optimization and then propose an iterative algorithm to achieve a local optimum based on the penalty function and the successive convex approximation methods. Numerical results are provided to validate the effectiveness of our proposed design.

Non-Linear Energy Harvesting in RIS-Assisted URLLC Networks for Industry Automation

A reconfigurable intelligent surface (RIS)-assisted wireless communication system with non-linear energy harvesting (EH) and ultra-reliable low-latency constraints is considered for its possible applications in industrial automation. A distant data-center (DC) communicates with the multiple destination machines with the help of a full-duplex (FD) server machine (SM) and RIS. Assuming the deficiency of enough transmission power at the FD-SM, the SM is considered in the near vicinity of the destinations in the industry to forward the data received from the distant DC. The reception at SM is assisted by the RIS and a non-linear hybrid power-time splitting (PTS) based EH receiver architecture is adopted to extend the lifespan of SM, thus increasing network lifetime. The scheduling of multiple destinations is done by SM based on the considered selection criteria namely, random (RND) scheduling, absolute (ABS) channel-power-based (CPB) scheduling and normalized (NRM) CPB scheduling. The end-to-end performance of the considered FD RIS-assisted network is analyzed, and the expressions for the block error rate (BLER) for all scheduling schemes are derived. Moreover, the effects of number of RIS elements, packet size, channel uses on the system performance are analyzed for the considered ultra-reliable and low-latency communication (URLLC) network. The scheduling fairness of all the scheduling schemes is also analyzed to study the performance-fairness trade-off. The derived analytical results are verified through Monte-Carlo simulations.

Optimal Beamforming for IRS-Assisted SWIPT System with an Energy-Harvesting Eavesdropper

In this paper, we study a simultaneous wireless information and power transfer (SWIPT) system aided by the intelligent reflecting surface (IRS) technology, where an AP transmits confidential information to the legitimate information receiver (IR) in the presence of an energy harvesting (EH) receiver that could be a potential eavesdropper. We aim to maximize the secrecy rate at the legitimate IR by jointly optimizing the information beamforming vector and the energy transfer beamforming vector at the access point (AP), and the phase shift matrix at the IRS, subject to the minimum harvested power required by the EH receiver. The semi-definite relaxation (SDR) approach and the alternating optimization (AO) method are proposed to convert the original non-convex optimization problem to a series of semi-definite programs (SDPs), which are solved iteratively. Numerical results show that the achievable secrecy rate of the proposed IRS-assisted SWIPT system is higher than that of the SWIPT system without the assistance of the IRS.

Deep Learning Framework for Secure Communication With an Energy Harvesting Receiver

In this paper, we consider wireless-powered secure communication with an energy harvesting receiver, which is allowed to harvest energy from the transmitted signals but not to decode information, and there is thus a requirement to keep the information secret from this potential eavesdropper. Considering co-channel interference among signal links, we find the optimal transmit power to maximize the sum rate of the signal links, while ensuring the requirements of information secrecy and energy harvesting. Due to the non-convexity of the optimization problem formulated here, we first derive suboptimal solutions using an iterative algorithm based on a dual method. In order to address the limitations caused by the use of the iterative algorithm, i.e., long computation time and suboptimality, we design an efficient deep neural network (DNN) framework and a novel training strategy as a means of combining supervised and unsupervised training. Specifically, the DNN is first pre-trained using labeled training data with the suboptimal solutions obtained from the iterative algorithm in a supervised manner; further training is then applied to the DNN using a well designed loss function in an unsupervised manner to enhance the training performance. Simulation results reveal that the proposed scheme achieves a near-optimal performance with a lower computation time than existing schemes. We also verify that the pre-training and the new loss function are effective in improving the speed of training of the DNN.

Multi-Objective Resource Allocation for IRS-Aided SWIPT

In this letter, we study the resource allocation for a multiuser intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, a multi-antenna base station (BS) transmits energy and information signals simultaneously to multiple energy harvesting receivers (EHRs) and information decoding receivers (IDRs) assisted by an IRS. Under this setup, we introduce a multi-objective optimization (MOOP) framework to investigate the fundamental trade-off between the data sum-rate maximization and the total harvested energy maximization, by jointly optimizing the energy/information beamforming vectors at the BS and the phase shifts at the IRS. This MOOP problem is first converted to a single-objective optimization problem (SOOP) via the $\epsilon $ -constraint method and then solved by majorization minimization (MM) and inner approximation (IA) techniques. Simulation results unveil a non-trivial trade-off between the considered competing objectives, as well as the superior performance of the proposed scheme as compared to various baseline schemes.

Waveforms and End-to-End Efficiency in RF Wireless Power Transfer Using Digital Radio Transmitter

We study radio frequency (RF) wireless power transfer (WPT) using a digital radio transmitter for applications where alternative analog transmit circuits are impractical. An important parameter for assessing the viability of an RF WPT system is its end-to-end efficiency. In this regard, we present a prototype test bed comprising a software-defined radio (SDR) transmitter and an energy-harvesting receiver with a low resistive load; employing an SDR makes our research meaningful for simultaneous wireless information and power transfer (SWIPT). We analyze the effect of clipping and nonlinear amplification at the SDR on multisine waveforms. Our experiments suggest that when the direct current (dc) input power at the transmitter is constant, high peak-to-average power ratio (PAPR) multisines are unsuitable for RF WPT over a flat-fading channel, due to their low average radiated power. The results indicate that the end-to-end efficiency is positively correlated with the average RF power of the waveform and that it reduces with increasing PAPR. Consequently, digital modulations, such as phase shift keying (PSK) and quadrature amplitude modulation (QAM), yield better end-to-end efficiency than multisines. Moreover, the end-to-end efficiency of PSK and QAM signals is invariant to the transmission bit rate. An in-depth analysis of the end-to-end efficiency of WPT reveals that the transmitter efficiency is lower than the receiver efficiency. Furthermore, we study the impact of a reflecting surface on the end-to-end efficiency of WPT and assess the transmission quality of the information signals by evaluating their error vector magnitude (EVM) for SWIPT. Overall, the experimental observations of end-to-end efficiency and EVM suggest that, while employing an SDR transmitter with fixed dc input power, a baseband quadrature PSK signal is most suitable for SWIPT at large, among PSK and QAM signals.

Enhanced Secure Wireless Information and Power Transfer via Intelligent Reflecting Surface

In this letter, an intelligent reflecting surface (IRS)-aided secure wireless information and power transfer system is studied. To maximize the harvested power of energy harvesting receiver (EHR), we optimize the secure transmit beamforming at the access point (AP) and phase shifts at the IRS subject to the secrecy rate (SR) and the reflecting phase shifts at the IRS constraints. Due to the non-convexity of optimization problem and coupled optimization variables, we convert the optimization problem into a semidefinite relaxation (SDR) problem and a sub-optimal solution is obtained. To reduce the high-complexity of the proposed SDR method, a low-complexity alternating optimization (LC-AO) algorithm is proposed. Simulation results show that the harvested power of the proposed SDR and LC-AO methods approximately double that of the existing method without IRS with the same SR. In particular, the proposed LC-AO achieves almost the same performance as the proposed SDR but with a much lower complexity.