Non-linear Energy Harvesting Model Research Articles

Sum-rate maximization for a distributed space-time block code-aided cooperative NOMA with energy harvesting

AbstractIn this paper, we exploit the spatial and transmission diversities in cooperative non-orthogonal multiple access (C-NOMA) networks to improve the system sum-rate. To achieve this, we propose a user-pairing scheme where near-field user pairs serve as relays for user pairs in the far-field region. Based on this pairing scheme, we incorporate a space-time block code transmission technique at the near-field user pairs to maximize the transmission diversity in the cooperative phase. Moreover, we consider a non-linear energy harvesting model at the near-field user pair to alleviate the problem of energy consumption during the cooperative transmission phase. Further to this, we formulate a sum-rate maximization problem that is addressed from the viewpoint of joint power allocation factor and power splitting ratio optimization. We develop a low-computational iterative algorithm based on the concepts of the Stackelberg game and the Nash bargaining solution. We benchmark our findings with different scenarios, such as energy harvesting C-NOMA with a fixed power allocation factor and power splitting ratio, energy harvesting C-NOMA without STBC, non-cooperative NOMA, and orthogonal multiple access. The results obtained via simulations outperform the benchmark schemes.

Non Orthogonal Multiple Access (NOMA) Using a Nonlinear Energy Harvesting Model

This paper derives and optimizes the throughput of NOMA with nonlinear energy harvesting. The Base Station (BS) harvests energy from the received Radio Frequency signal. Then, the BS uses the nonlinear harvested power to diffuse packets to N users. We suggest to optimize harvesting time and the powers of users to optimize the data rate. The optimization of NOMA powers and the harvesting process were not yet performed with a non linear energy harvesting model. The study is performed for Rayleigh channels in the presence of nonlinear energy harvesting model. Furthermore, the theoretical results were confirmed using MATLAB computer simulations.

Robust transmission design for active IRS-aided multiuser MIMO cognitive radio systems with non-linear energy harvesting models

Robust transmission design for active IRS-aided multiuser MIMO cognitive radio systems with non-linear energy harvesting models

Multi-Objective Optimization for IRS-Aidded Multi-user MIMO SWIPT Systems

In this paper, we investigate an intelligent reflecting surface (IRS) assisted simultaneous wireless information and power transfer (SWIPT) system in which users equipped with multiple antennas exploit power-splitting (PS) strategies for simultaneously information decoding (ID) and energy harvesting (EH). Different from the majority of previous studies which focused on single objective optimization problems (SOOPs) and assumed the linearity of EH models, in this paper, we aim at studying the multi-objective optimization problem (MOOP) of the sum rate (SR) and the totalharvested energy (HE) subject to the maximum transmit power (TP) constraint, the user quality of service (QoS), and HE requirements at each user with taking a practical non-linear EH (NLEH) model into consideration. To investigate insightful tradeoffs between the achievable SR and total HE, we adopt the modified weighted Tchebycheff method to transform the MOOP into a SOOP. However, solving the SOOPs and modified SOOP is mathematically difficult due to the non-convexity of the object functions and the constraints of the coupled variables of the base station (BS) transmit precoding matrices (TPMs), the user PS ratios (PSRs), and the IRS phase shift matrix (PSM). To address these challenges, an alternating optimization (AO) framework is used to decompose the formulated design problem into sub-problems. In addition, we apply the majorization-minimization (MM) approach to transform the sub-problems into convex optimization ones. Finally, numerical simulation results are conducted to verify the tradeoffs between the SR and the total amount of HE. The numerical results also indicate that the considered system using the IRS with optimal phase shifts provides considerable performance improvement in terms of the achievable SR and HE as compared to the counterparts without using the IRS or with the IRS of fixed phase shifts.

Linear/Non-Linear Energy Harvesting Models via Multi-Antenna Relay Cooperation in V2V Communications

Vehicle-to-vehicle (V2V) communications is a part of next-generation wireless networks to create smart cities with the connectivity of intelligent transportation systems. Besides, green communications is considered in V2V communication systems for energy sustainability and carbon neutrality. In this scope, radio-frequency (RF) energy harvesting (EH) provides a battery-free energy source as a solution for the future of V2V communications. Herein, the employment of RF-EH in V2V communications is considered where the bit error probability (BEP) of a dual-hop decode-and-forward relaying system is obtained depending on the utilization of antennas at the relay. The multiple antenna power-constraint relay harvests its power by applying dedicated antenna (DA)/power splitting (PS) EH modes and linear (L)/nonlinear (NL) EH models. Moreover, the links between nodes are exposed to double-Rayleigh fading. Finally, the performance of different system parameters is compared using theoretical derivations of BEP. The results provide a comprehensive analysis of the proposed system considering PS/DA-EH modes and L/NL-EH models, as well as deterministic/uniformly distributed placement of nodes. It is observed that PS-EH outperforms DA-EH assuming a placement of an equal number of antennas and distances. Moreover, optimal performance of PS/DA-EH is achieved by allocating more power and increasing the number of antennas for EH, respectively.

Wireless Information and Power Transfer Enabled Massive MIMO Multi-Way Relaying

This work considers a multi-way massive multi-input multi-output (mMIMO) relaying system wherein several wireless information and power transfer capable users, first harvest energy, and then exchange information via a relay. The mMIMO relaying system is practically modelled by considering spatially-correlated relay-user channels, and by using a non-linear energy harvesting (EH) model. A closed-form spectral efficiency expression is derived for this system, which is then used to jointly allocate the relay and users transmit powers, and the charging time to optimize the weighted sum energy efficiency (WSEE) metric. The spatially-correlated channel and the non-linear EH model not only couple the optimization variables, but also introduce nested scalar fractional optimization functions. This makes the WSEE metric non-convex. The proposed solution first uses the existing quadratic transformation (QT) to decouple the scalar fractional forms. It then applies novel results, which are developed by extending the QT framework, to decouple the optimization variables. The proposed WSEE optimization algorithm is shown to provide close-to-optimal WSEE. It is also shown that channel spatial correlation increases the amount of energy harvested by the users, but reduces their WSEE.

SLIPT-Enabled Multi-LED MU-MISO VLC Networks: Joint Beamforming and DC Bias Optimization

This paper studies a simultaneous lightwave information and power transfer (SLIPT)-enabled multi-user (MU) multiple input single output (MISO) visible light communication (VLC) network, where multiple user equipments (UEs) are simultaneously allowed to receive information by the photodiode (PD) and harvest energy by the solar panel, respectively. To improve the system spectral efficiency, an achievable sum rate maximization problem is formulated subject to the minimum energy harvesting (EH) requirement of each UE, the total transmit power constraint of the LEDs and the dimming control constraint of the LEDs, by jointly optimizing the beamforming vector and the direct current (DC) bias vector of the LEDs. To be general, the practical non-linear EH model and the information capacity model of the VLC channel are adopted. To solve the non-convex optimization problem, the objective function is equivalently transformed at first by using the epigraph reformulation, and then the transformed problem is relaxed with the semi-definite relaxation (SDR) method. Based on this, an iterative algorithm is designed to approximately obtain the maximal achievable sum rate based on the successive convex approximation (SCA). Simulation results indicate that there exists a non-trivial trade-off between the achievable sum rate and the harvested energy, and that DC bias vector dominates the amount of the harvested energy compared with the beamforming vector. Besides, for a given relatively high EH requirement, the achievable sum rate increases with the increment of the dimming level, especially for relatively small dimming level. Once the dimming level is larger than a specific value, with the increment of the dimming level, more transmit power will be allocated for illumination, resulting in the decrement of the achievable sum rate.

Physical Layer Security for Wireless-Powered Ambient Backscatter Cooperative Communication Networks

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where the BD with nonlinear energy harvesting model cooperates with decode-and-forward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secrecy energy efficiency (SEE). Specifically, the closed-form and asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmit power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks.

Power and Frequency Band Allocation Mechanisms for WPT System with Logarithmic-Based Nonlinear Energy Harvesting Model

Wireless power transmission (WPT) is expected to play a crucial role in supporting the perpetual operations of Internet of Things (IoT) devices, thereby contributing significantly to IoT services. However, the development of efficient power allocation algorithms has remained a longstanding challenge. This paper addresses the aforementioned challenge by proposing a novel strategy, called energy poverty-based device selection (EPDS), in conjunction with energy beamforming, where orthogonal frequency bands are allocated to energy harvesting IoT devices (EHIs). To solve two power allocation problems, a logarithmic-based nonlinear energy harvesting model (NEHM) is introduced. The first problem tackled is the total received power maximization (TRPM), which is initially presented and, then, solved optimally in closed-form by incorporating Karush–Kuhn–Tucker (KKT) conditions with the modified water-filling algorithm. The second problem formulated is the common received power maximization (CRPM), which takes into account energy fairness considerations. To assess the proposed algorithms and gain insights into the effects of mobility, the mobility of EHIs is modeled as a one-dimensional random walk. Extensive numerical results are provided to validate the advantages of the proposed algorithms. Both the TRPM and CRPM algorithms exhibit exceptional performance in terms of total and minimum received energy, respectively. Furthermore, in comparison to round-robin scheduling, the EPDS demonstrates superior performance in terms of minimum received energy. This paper highlights the impact of the proposed energy harvesting (EH) model, demonstrating 12.68% and 3.69% higher values than the linear model for the minimum and total received energy, respectively.

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.

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

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

Resource Allocation for a Secure SWIPT Network Based on a Quantitative Energy Harvesting Mechanism.

Simultaneous wireless information and power transfer (SWIPT) technology can effectively extend the lifecycle of energy-constrained networks. In order to improve the energy harvesting (EH) efficiency and network performance in secure SWIPT networks, this paper studies the resource allocation problem based on the quantitative EH mechanism in the secure SWIPT network. Based on a quantitative EH mechanism and nonlinear EH model, a quantified power-splitting (QPS) receiver architecture is designed. This architecture is applied in the multiuser multi-input single-output secure SWIPT network. With the goal of maximizing the network throughput, the optimization problem model is established under the conditions of meeting the legal user's signal-to-interference-plus-noise ratio (SINR), EH requirements, the total transmit power of the base station, and the security SINR threshold constraints. Due to the coupling of variables, the problem is a nonconvex optimization problem. To deal with the nonconvex optimization problem, a hierarchical optimization method is adopted. Firstly, an optimization algorithm based on the optimal received power of EH circuit is proposed, and a power mapping table is constructed through the optimization algorithm, from which the optimal power ratio to meet the user's EH requirements is obtained; then, the nonconvex problem is transformed into a convex problem by using variable substitution, semidefinite relaxation, dichotomous optimization, etc. The simulation results show that compared with the power splitting receiver architecture, the input power threshold range of the QPS receiver architecture is larger, which can avoid the EH circuit falling into the saturated working area and maintain high network throughput.

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

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

Design of a Reconfigurable Intelligent Surface- Assisted FM-DCSK-SWIPT Scheme With Non-Linear Energy Harvesting Model

In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted frequency-modulated (FM) differential chaos shift keying (DCSK) scheme with simultaneous wireless information and power transfer (SWIPT), called RIS-FM-DCSK-SWIPT scheme, for low-power, low-cost, and high-reliability wireless communication networks. In particular, the proposed scheme is developed under a non-linear energy-harvesting (EH) model which can accurately characterize the practical situation. The proposed RIS-FM-DCSK-SWIPT scheme has an appealing feature that it does not require channel state information, thus avoiding the complex channel estimation. We further derive the closed-form theoretical expressions for the energy shortage probability and bit error rate (BER) of the proposed scheme over the multipath Rayleigh fading channel. In addition, we investigate the influence of key parameters on the performance of the proposed transmission scheme in two different scenarios, i.e., RIS-assisted access point (RIS-AP) and dual-hop communication (RIS-DH). Finally, we carry out various Monte-Carlo experiments to verify the accuracy of the theoretical derivation, illustrate the performance advantage of the proposed scheme, and give some design insights for future study.

Coverage performance of nonlinear energy harvesting based wireless sensor networks for the healthcare in hospitals

Wireless sensors are important nowadays to collect real‐time data in hospital wards. Small sensors can be deployed either on/in patients' bodies or at fixed locations in wards. We propose an autonomous wireless system, where sensors can harvest energy from radio frequency signals, monitor and transmit data using the harvested energy. By properly modeling energy statuses and stochastic distributions of sensors, we analyze the coverage performance using Campbell's theorem, probability generating functional (PGFL), and Gil‐Pelaez inversion theorem. The nonlinear energy harvesting model and the general Nakagami‐m fading channels are considered in the analysis. Numerical results show that the coverage performance degrades when sensors are distributed more densely, the energy and data links are prolonged, or the transmission rate gets larger, while it improves when higher power is used for the energy transfer, or the energy and data links undergo lighter fading. Our proposed scheme can well facilitate the long‐time monitoring tasks in hospital wards, which can greatly alleviate the burdens of medical staff.

Simultaneous Wireless Information and Power Transfer in mmWave Networks Under User-Centric Base Station Clustering

User-centric base station (BS) deployment has been designed for the fifth-generation (5G) dense millimeter wave (mmWave) networks for alleviating the inter-cell interference and improving the cell-edge user experience. However, the system power consumption increases sharply with the network density. In this paper, we investigate a user-centric simultaneous wireless information and power transfer (SWIPT) mmWave system employing a time-switching protocol at users to allow both energy harvesting (EH) and data decoding. To enable user-centric BS cooperation, adaptive BS clustering model is used to determine the user’s serving cluster based on its channel condition. Considering both linear and non-linear EH models, we analyze the joint coverage, namely, the probability that the user harvests enough energy in a given time slot and receives the required data from its serving cluster. The random serving clusters and the correlation between the amount of harvested energy and received data rate make the joint coverage analysis more challenging. A tractable tight approximation of the joint coverage probability is thus derived for ultra-dense networks. A mathematical optimization model for the time switching coefficient is also developed to maximize the system joint rate and energy coverage performance. All mathematical expressions are validated by Monte-Carlo simulations. Our results show that the proposed analytical framework is accurate and efficient for the design and deployment of SWIPT-enabled user-centric mmWave networks.

Achieving Multi-Beam Gain in Intelligent Reflecting Surface Assisted Wireless Energy Transfer

Intelligent reflecting surface (IRS) is a promising technology to boost the efficiency of wireless energy transfer (WET) systems. However, for a multiuser WET system, simultaneous multi-beam energy transmission is generally required to achieve the maximum performance, which may not be implemented by using the IRS having only a single set of coefficients. As a result, it remains unknown how to exploit the IRS to approach such a performance upper bound. To answer this question, we aim to maximize the total harvested energy of a multiuser WET system subject to the user fairness constraints and the non-linear energy harvesting model. We first consider the static IRS beamforming scheme, which shows that the optimal IRS reflection matrix obtained by applying semidefinite relaxation is indeed of high rank in general as the number of energy receivers (ERs) increases, due to which the resulting rank-one solution by applying Gaussian Randomization may lead to significant loss. To achieve the multi-beam gain, we then propose a general time-division based novel framework by exploiting the IRS's dynamic passive beamforming. Moreover, it is able to achieve a good balance between the system performance and complexity by controlling the number of IRS shift patterns. Finally, we also propose a time-division multiple access (TDMA) based passive beamforming design for performance comparison. Simulation results demonstrate the necessity of multi-beam transmission and the superiority of the proposed dynamic IRS beamforming scheme over existing schemes.

Energy Efficiency Optimization for MISO-NOMA SWIPT System with Heterogeneous QoS Requirements

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

Wireless-powered backscatter communications for smart sustainable cities: A secrecy analysis

Due to its ability to enable self-sustainable communications, wirelessly powered backscatter communication (WP-BackCom) has been deemed a promising technology for supporting smart sustainable cities. We note that the existing works on analyzing the secrecy performance of WP-BackComs have largely ignored the spatial randomness of nodes that is used to model a large-scale network. To fill this gap, this paper evaluates the secrecy outage probability of the large-scale WP-BackComs based on a tractable framework of stochastic geometry (SG). In particular, the imperfect successive interference cancellation (SIC) at each gateway (GW) and eavesdropper (Eve), and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU) are considered. The energy outage probability and secrecy outage probability expressions are derived in the general case at first, then the closed-form expressions are obtained under the special scenario, i.e., the no-noise and the typical propagation loss scenario. Numerical simulations validate the analytical results and examine the impacts of various parameters on the secrecy performance. In particular, there exists an optimal reflection coefficient for secrecy outage probability minimization under certain conditions, and the optimal reflection coefficient increases with the increasing SIC coefficient at GW.

Optimization for IRS-Assisted MIMO-OFDM SWIPT System With Nonlinear EH Model

Simultaneous wireless information and power transfer (SWIPT) has emerged as an appealing solution to prolonging the lifetime of low-power Internet of Things (IoT) networks. Meanwhile, an intelligent reflecting surface (IRS) can reconstruct a favorable wireless propagation environment for IoT terminals to achieve high spectrum and energy efficiencies. To take full advantage of these two technologies, this article studies the optimization of an IRS-assisted multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system with nonlinear energy harvesting (EH) model. In particular, we aim to maximize the achievable data rate by jointly designing the transmit precoding matrices, the IRS matrix, as well as the power splitting (PS) ratio subject to the transmit power and harvested power constraints. Since the formulated problem is highly nonconvex, we develop an alternating optimization (AO)-based algorithm to find a high-quality suboptimal solution. Moreover, we further design a heuristic algorithm based on a two-stage optimization strategy to reduce the computational complexity. Simulation results verify that the proposed AO-based algorithm can significantly improve the achievable data rate compared to conventional benchmarks, and the proposed heuristic low-complexity algorithm can achieve comparable performance to the AO-based algorithm.