Energy Harvesting Constraints Research Articles

Power Allocation for SWIPT in Full-Duplex AF Relay Interference Channels Using Game Theory

A full-duplex, amplify-and-forward multiple relay system employing simultaneous wireless information and power transfer receivers operating in interference channels is studied. Since all links want to maximize their own achievable rate, a power allocation problem under transmit power and energy harvesting constraints is developed. Through alternative optimization, a distributed iterative water-filling algorithm based on game theory with provable convergence is devised. The existence and uniqueness of a Nash equilibrium point are both guaranteed and the convergence of the proposed algorithm is examined using a diagonally strict concavity condition. Numerical results show that the proposed algorithm is convergent and can achieve a higher sum rate than previous schemes.

Transceiver Design for AF MIMO Relay Systems With a Power Splitting Based Energy Harvesting Relay Node

In this article, a dual-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system is studied. With a splitting (PS) protocol, the relay node harvests the radio frequency (RF) energy in the signals sent by the source node and utilizes the harvested energy to forward signals from the source node to the destination node. We aim at maximizing the source-destination mutual information (MI) through a joint design of the source matrix, the relay matrix, and the PS ratios under the source node power constraint and the relay node harvested energy constraint. We consider a general sum energy constraint at the relay node with different PS ratios across relay antennas, which includes existing works based on uniform PS or per data stream energy constraint as special cases. Moreover, a practical nonlinear energy harvesting (EH) model is adopted, where the harvested energy is bounded as the incident RF signal power increases. We establish the structure of the source matrix and the relay matrix, which simplifies the complicated transceiver design problem with matrix variables to a power distribution problem with scalar variables. Three approaches are proposed to efficiently solve the optimal power distribution problem. In particular, the first proposed algorithm solves the original nonconvex power allocation problem using the sequential quadratic programming, while the other two algorithms convert the original problem to convex problems by exploiting a tight upper bound and a tight lower bound of the objective function, respectively. Numerical simulations demonstrate that when the EH circuit works in the linear region, the proposed algorithms have a larger system MI than existing PS and TS based MIMO AF relay systems. The peak harvest power constraint plays an important role in choosing the location of the relay node.

Joint Trajectory Design, Tx Power Allocation, and Rx Power Splitting for UAV-Enabled Multicasting SWIPT Systems

This article studies a joint resource allocation for an unmanned aerial vehicle (UAV) enabled simultaneous wireless information and power transfer (SWIPT) system in a multicasting scenario, where a UAV is dispatched to simultaneously send common information and wireless power to multiple users on the ground while the users decode information and harvest energy based on the power splitting (PS) receiver architecture. In this system, we aim to maximize the minimum achievable rate among all users under their harvested energy constraints by optimizing trajectory and transmit power of the UAV jointly with PS ratios of the users. To solve this challenging nonconvex problem, we first convert it into a more tractable form by exploiting the quadratic transform technique. Then, we propose an efficient iterative algorithm for the joint optimization. Numerical results show that the proposed scheme outperforms baseline schemes.

Transmitter-Side Wireless Information- and Power-Transfer in Massive MIMO Systems

Both time-switching (TS) and power splitting has been used at the receiver for wireless information and power transfer in the downlink of massive multiple-input-multiple-output systems. By contrast, this correspondence adopts the transmit-TS approach, where the energy and information are transferred over different fractions of a time slot. Our goal is to jointly optimize the transmit-TS factor and power allocation coefficients during energy and information transfer for maximizing the users' minimum throughput subject to transmit power and minimum harvested energy constraints. This nonconvex problem is solved by our path following algorithm. Our simulation results demonstrate the benefits of the proposed transmit-TS algorithm, which easily doubles the throughput compared to that of the existing techniques.

Security enhancement with joint transmit antenna selection and transmit beamforming under energy harvesting constraints

SummaryThis paper considers a multiuser multiple‐input single‐output (MISO) broadcasting system with simultaneous wireless information and power transfer (SWIPT), which consists of one information receiver (IR) and several energy harvesting receivers (ERs) which are capable of eavesdropping the legitimate signals. For reducing cost and hardware complexity, transmit antenna selection (TAS) is applied in the transmitter. We aim to maximize the achievable secrecy rate under the individual energy harvesting constraint at the ERs and the transmit power constraint at the transmitter by jointly optimizing TAS, transmit beamforming, and artificial noise (AN). The joint optimization problem is a non‐convex mixed integer programming problem. We apply variable replacements to decouple the variable couplings and relax and approach the binary constraint by the difference of two convex constraints. Afterwards, penalty method and constrained concave convex procedure (CCCP) are applied to transform the relaxed problem into a sequence of semi‐definiteness programming (SDP) problems. Simulation results shows that our proposed joint optimization scheme is superior over existing non‐joint optimization schemes. This paper studies the joint transmit antenna selection (TAS), transmit beamforming, and artificial noise (AN) optimization in a multiple‐input multiple‐output (MISO) wiretap system with simultaneous wireless information and power transfer (SWIPT). The joint optimization problem is nonconvex and we propose a penalty method based scheme to solve it. The simulation results show that our joint optimization scheme is superior to other non‐joint optimization schemes.

Lens Antenna Arrays Aided Co-Existing Radar and Communication Systems With Energy Harvesting

Coexisting radar and communication (CRC) systems on the same spectrum band have been recently recognized as a promising approach to solve the spectrum scarcity problem. Additionally, with the new requirements of achieving higher energy efficiency and restoring the consumed power, an initiative has been exploited to embed both information and energy within the same signal. Motivated by that, this paper presents a unified model to implement the concept of simultaneous wireless information and power transfer (SWIPT) in the coexistence environment between radar and communication systems. Specifically, we propose a novel energy harvesting based CRC model (CRCEH), in which the signals transmitted from both the communication and radar transmitters are precoded towards their conventional targets, communication receiver and radar targets respectively, as well as to an energy harvesting receiver. This system is capable of solving the spectrum scarcity problem as well as supporting low power devices with their energy requirements from the energy harvested at the energy harvesting receiver. The design problem is formulated as maximizing the radar detection probability subject to communication capacity and energy harvesting constraints. However, directly processing the data from the radar receiver requires high computational complexity and hardware costs. In order to circumvent this issue, this paper also proposes to deploy lens antenna array (LNA) to CRCEH system to sharply direct the signals to the energy harvesting receiver together with their respective destinations. Since LNA is able to focus the signal power on a subset of antennas, we may only deal with the data from the selective antenna subset associated with the maximal power. Numerical results show that the proposed method is able to achieve improved performance in terms of radar detection under communication capacity constraints compared to the conventional approaches besides the great benefit of harvesting the energy to feed some limited energy devices.

Distributed Beamforming Design for Nonregenerative Two-Way Relay Networks with Simultaneous Wireless Information and Power Transfer

This paper considers the distributed beamforming design for a simultaneous wireless information and power transfer (SWIPT) in two-way relay network, which consists of two sources, K relay nodes and one energy harvesting (EH) node. For such a network, assuming perfect channel state information (CSI) is available, and we study two different beamforming design schemes. As the first scheme, we design the beamformer through minimization of the average mean squared error (MSE) subject to the total transmit power constraint at the relays and the energy harvesting constraint at the EH receiver. Due to the intractable expression of the objective function, an upper bound of MSE is derived via the approximation of the signal-to-noise ratio (SNR). Based on the minimization of this upper bound, this problem can be turned into a convex feasibility semidefinite programming (SDP) and, therefore, can be efficiently solved using interior point method. To reduce the computational complexity, a suboptimal beamforming scheme is proposed in the second scheme, for which the optimization problem could be recast to the form of the Rayleigh–Ritz ratio and a closed-form solution is obtained. Numerical results are provided and analyzed to demonstrate the efficiency of our proposed beamforming schemes.

Hybrid Precoding for an Adaptive Interference Decoding SWIPT System With Full-Duplex IoT Devices

In this article, a simultaneous wireless information and power transfer (SWIPT) system with full-duplex (FD) Internet of Things (IoT) nodes is considered and investigated. We induce the adaptive interference decoding (AID) strategy as well as the switch and inverter structure with antenna selection (SIAS)-based hybrid precoding scheme to the SWIPT system. A joint optimization of hybrid precoder, decoding rule, and power splitting (PS) ratio problem is formulated to minimize the total transmission power, while satisfying the data rate and harvested energy constraints. As it is a nonconvex problem, we propose a suboptimal solution with three stages. In the first stage, a search algorithm which can reduce the complexity of exhaustive search is proposed to decide the sending mode of each node. In the second stage, we utilize the semidefinite relaxation (SDR) approach to find the optimal digital precoder and PS ratios. In the last stage, we propose an alternate minimization algorithm to obtain the hybrid precoding vectors. The simulation results show that our proposed suboptimal solutions can achieve a better performance in terms of power consumption and outage probability compared with those for treating interference as noise (IAN) systems and the digital precoding schemes. Both AID strategy and SIAS-based hybrid precoding are beneficial to the FD SWIPT system. Moreover, the self-interference causes little effect on the system performance as long as it can be eliminated up to 30 dB, which can be easily achieved by the antenna separation technique.

Conditional Capacity and Transmit Signal Design for SWIPT Systems With Multiple Nonlinear Energy Harvesting Receivers

In this paper, we study information-theoretic limits for simultaneous wireless information and power transfer (SWIPT) systems employing practical nonlinear radio frequency (RF) energy harvesting (EH) receivers (Rxs). In particular, we consider a SWIPT system with one transmitter that broadcasts a common signal to an information decoding (ID) Rx and multiple EH Rxs. Owing to the nonlinearity of the EH Rxs’ circuitry, the efficiency of wireless power transfer depends on the waveform of the transmitted signal. We aim to answer the following fundamental question: What is the optimal input distribution of the transmit signal waveform that maximizes the information transfer rate at the ID Rx conditioned on individual minimum required direct-current (DC) powers to be harvested at the EH Rxs? Specifically, we study the conditional capacity problem of a SWIPT system impaired by additive white Gaussian noise subject to average-power (AP) and peak-power (PP) constraints at the transmitter and nonlinear EH constraints at the EH Rxs. To this end, we develop a novel nonlinear EH model that captures the saturation of the harvested DC power by taking into account not only the forward current of the rectifying diode but also the reverse breakdown current. Then, we derive a novel semi-closed-form expression for the harvested DC power, which simplifies to closed form for low input RF powers. The derived analytical expressions are shown to closely match circuit simulation results. We solve the conditional capacity problem for real- and complex-valued signalling and prove that the optimal input distribution that maximizes the rate-energy (R-E) region is unique and discrete with a finite number of mass points. Furthermore, we show that, for the considered nonlinear EH model and a given AP constraint, the boundary of the R-E region saturates for high PP constraints due to the saturation of the harvested DC power for high input RF powers. In addition, we devise a suboptimal input distribution whose R-E tradeoff performance is close to optimal. All theoretical findings are verified by numerical evaluations.

Resource Allocation in NOMA-Enhanced Backscatter Communication Networks for Wireless Powered IoT

This letter considers a non-orthogonal-multiple-access (NOMA)-enhanced bistatic backscatter communication network where multiple backscatter devices (BDs) transmit to a backscatter receiver using a protocol integrating NOMA with dynamic time-division-multiple-access. We maximize the minimum throughput among all BDs by jointly optimizing the BDs’ backscatter time and power reflection coefficients, subject to the BDs’ harvested energy constraints and other constraints. By applying the block coordinated decent and successive convex optimization techniques, an efficient iterative algorithm is proposed to find a sub-optimal solution to the formulated non-convex problem. Numerical results show that the proposed scheme achieves significant throughput gain compared to the benchmark scheme.

Maximizing Quality Data-collection in Mobile-sink Based Energy-harvesting Sensor Networks

In energy-harvesting wireless sensor network (EHWSNs), sensor nodes (SNs) are rechargeable. But, the harvesting techniques used for recharging SNs’ are dynamic and may not provide evenly harvested energy to all sensors. Thus, maximizing quality data-collection (MQDC) is an interesting issue under energy harvesting constraints. In this article, we focus on this issue to solve. We consider a mobile-sink (MS) that moves on a constrained-path for collecting SNs’ data periodically. The SNs are distributed nearby the constrained-path. This scenario may exist in various real-world applications, such as traffic monitoring, environment monitoring and health monitoring of large buildings or bridges, etc. We transform the MQDC problem into a network-utility maximization problem. We then prove that the converted problem is an NP-hard. Thereafter, we develop a heuristic algorithm, referred to as Maximizing Quality Datacollection using Constrained-path Mobile-Sink (MQDCPMS), to solve it. We address the effect of change in the speed of MS on the quality data-collection. Finally, through extensive simulations, we find that the MQDCPMS algorithm maximizes the quality data-collection comparatively better than other baseline algorithms.

Simultaneous Wireless Information and Power Transfer Based on Generalized Triangular Decomposition

In this paper, a new approach, based on the generalized triangular decomposition (GTD), is proposed for simultaneous wireless information and power transfer (SWIPT) in the spatial domain for a point-to-point multiple-input multiple-output (MIMO) system. The proposed approach takes advantage of the GTD structure to allow the transmitter to use the strongest eigenchannel jointly for energy harvesting and information exchange while these transmissions can be separated at the receiver. The optimal structure of the GTD that maximizes the total information rate constrained by a given power allocation and a required amount of energy harvesting is derived. An algorithm is developed that minimizes the total transmitted power for given information rate and energy harvesting constraints with a limited total power at the transmitter. Both theoretical and simulation results show that our proposed GTD based SWIPT outperforms singular value decomposition (SVD) based SWIPT. This is due to the flexibility introduced by the GTD to increase the energy harvested via interstream interference.

Robust Simultaneous Wireless Information and Power Transfer in Beamspace Massive MIMO

We investigate the worst-case robust beamforming for simultaneous wireless information and power transfer in a multiuser beamspace massive multiple-input multiple-output (MIMO) system. The objective is to minimize the transmit power of the base station subject to the individual signal-to-interference-plus-noise ratio and the energy-harvesting constraints under imperfect channel state information. Instead of directly resorting to semi-definite relaxation, we convert the initial non-convex optimization to a power allocation problem, which greatly reduces the computational complexity. The beamforming vectors are proven to be scaled versions of the estimated channels. The optimal scaling factors are then derived in closed-form. The simulations demonstrate that the proposed robust beamforming method achieves the globally optimal point for the initial design when the channel estimation errors are small while leads to satisfactory performance when the channel estimation errors are large.

Proportional Fair Secrecy Beamforming for MISO Heterogeneous Cellular Networks With Wireless Information and Power Transfer

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to address the energy scarcity problem in heterogeneous cellular networks (HCNs). Since information security is a critical issue in the SWIPT HCNs, we consider the secrecy beamforming design for a multiple-input single-output HCN with one macrocell base station (BS) and multiple femtocell BSs. In the considered system, all BSs send confidential messages to information receivers and energy signals to energy receivers (ERs). The ERs have the potential to wiretap the confidential messages. Consequently, by taking fairness into account, we propose a sum logarithmic secrecy rates maximization beamforming design problem under the energy harvesting constraints for ERs. The formulated design problem is nontrivial to solve due to the nonconvexity in the objective and the constraints. To tackle the design problem, a semidefinite relaxation and successive convex approximation-based centralized beamforming design algorithm is proposed. Our analysis reveals that using single-stream beamforming to transmit confidential messages does not cause any loss of optimality. Moreover, an alternating direction method of multipliers-based distributed beamforming design is also proposed. Simulation results demonstrate the effectiveness of the proposed algorithms.

A Robust Transmission Scheduling Approach for Internet of Things Sensing Service with Energy Harvesting.

Maximizing the utility under energy constraint is critical in an Internet of Things (IoT) sensing service, in which each sensor harvests energy from the ambient environment and uses it for sensing and transmitting the measurements to an application server. Such a sensor is required to maximize its utility under the harvested energy constraint, i.e., perform sensing and transmission at the highest rate allowed by the harvested energy constraint. Most existing works assumed a sophisticated model for harvested energy, but neglected the fact that the harvested energy is random in reality. Considering the randomness of the harvested energy, we focus on the transmission scheduling issue and present a robust transmission scheduling optimization approach that is able to provide robustness against randomness. We firstly formulate the transmission scheduling optimization problem subject to energy constraints with random harvested energy. We then introduce a flexible model to profile the harvested energy so that the constraints with random harvested energy are transformed into linear constraints. Finally, the transmission scheduling optimization problem can be solved traditionally. The experimental results demonstrate that the proposed approach is capable of providing a good trade-off between service flexibility and robustness.

Robust Secrecy Beamforming and Power-Splitting Design for Multiuser MISO Downlink With SWIPT

In this paper, we investigate the physical layer security design in multiuser multiple-input single-output downlink with simultaneous wireless information and power transfer. Specifically, by considering that legitimate receivers use power splitting to extract information and harvest energy simultaneously, we investigate the robust weighted sum secrecy rate maximization design, subject to multiple signal-to-interference-and-noise ratio and harvested energy constraints. To solve the formulated highly nonconvex problem and avoid high-rank solution obtained by the common semidefinite relaxation method, we utilize the constrained concave convex procedure and second-order cone programming to formulate a tractable problem and propose an iterative algorithm to obtain the solution. In addition, we discuss the feasibility condition and computational complexity of our algorithm. Furthermore, our algorithm can be extended to other optimization designs. Simulation results show the superiority of our proposed design.

Sensing-Throughput Tradeoff for Superior Selective Reporting-Based Spectrum Sensing in Energy Harvesting HCRNs

In this paper, we investigate the performance of conventional cooperative sensing (CCS) and superior selective reporting (SSR)-based cooperative sensing in an energy harvesting (EH)-enabled heterogeneous cognitive radio network (HCRN). In particular, we derive expressions for the achievable throughput of both schemes and formulate nonlinear integer programming problems, in order to find the throughput-optimal set of spectrum sensors scheduled to sense a particular channel, given primary user (PU) interference and EH constraints. Furthermore, we present novel solutions for the underlying optimization problems based on the cross-entropy (CE) method, and compare the performance with exhaustive search and greedy algorithms. Finally, we discuss the tradeoff between the average achievable throughput of the SSR and CCS schemes, and highlight the regime where the SSR scheme outperforms the CCS scheme. Notably, we show that there is an inherent tradeoff between the channel available time and the detection accuracy. Our numerical results show that, as the number of spectrum sensors increases, the channel available time gains a higher priority in an HCRN, as opposed to detection accuracy.

Dynamic Time Switching for MIMO Wireless Information and Power Transfer

This paper studies simultaneous wireless information and power transfer (SWIPT) techniques for point-to-point multiple-input multiple-output channels, where a multi-antenna transmitter conveys information and energy at the same time to a multi-antenna receiver equipped with time switching (TS) circuits for an energy harvesting (EH) mode and an information decoding (ID) mode. Unlike conventional uniform TS (UTS) structure where all the receive antennas at the receiver employ a single TS circuit, in this paper, we propose a general dynamic TS (DTS) receiver architecture which has an individual TS circuit for each antenna. In the proposed DTS, the operation modes of the antennas can be dynamically changed to improve SWIPT performance. We aim to identify the achievable rate–energy (R–E) tradeoff of the DTS protocol for both linear and non-linear EH models by maximizing the information rate subject to the EH constraint. This results in joint optimization of the transmit covariance matrices and the time durations for the EH and the ID modes of the receive antennas, which is jointly non-convex in general. To tackle the non-convexity of the original problem, the successive convex approximation technique is adopted by addressing a series of approximated convex problems. As a result, efficient optimization algorithms are proposed for determining the boundary points of the achievable R–E region. We also provide a low-complexity algorithm which achieves near-optimal performance with much reduced complexity. Numerical results demonstrate that the proposed DTS presents significant performance gains over conventional UTS approaches.

Non-Myopic Energy Allocation for Target Tracking in Energy Harvesting UWSNs

For target tracking in underwater wireless sensor networks (UWSNs), how to improve energy utilization efficiency and target tracking accuracy with limited energy constraints is an important problem. Recent years, energy harvesting device has been developed and applied to UWSNs to guarantee energy supply, and reasonable and efficient energy allocation schemed are important. In this paper, energy allocation problem for target tracking in UWSNs is studied. The goal in this paper is to improve tracking accuracy under limited energy harvesting constraints. First, to maximize the overall accuracy in the whole process, the accumulated fisher information matrix is derived in a non-myopic way and used as performance metric. Second, based on the fact that energy consumption and tracking accuracy mainly depend on bit number of quantized measurement, an optimization problem is proposed to solve tradeoff between energy allocation and tracking accuracy under energy harvesting constraints. Third, to obtain optimal energy allocation for each time, the problem is formulated as a Markov decision process, which is solved by a dynamic programming algorithm in pseudo-polynomial time. The simulation results are presented to verify the effectiveness of our proposed scheme.

Optimal Resource Allocation in Full-Duplex Ambient Backscatter Communication Networks for Wireless-Powered IoT

This paper considers an ambient backscatter communication network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing signals to its legacy user (LU) and receives uplink signals backscattered from multiple backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP’s subcarrier power allocation, subject to the LU’s throughput constraint, the BDs’ harvested-energy constraints, and other practical constraints. For the case with a single BD, we obtain closed-form solutions and propose an efficient algorithm by using the Lagrange duality method. For the general case with multiple BDs, we propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. In addition, we study the throughput region which characterizes the Pareto-optimal throughput tradeoffs among all BDs. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.