Downlink Wireless Energy Transfer Research Articles

IRS-Assisted Wireless Powered IoT Network With Multiple Resource Blocks

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS’s transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.

Wireless-Powered Intelligent Radio Environment With Nonlinear Energy Harvesting

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

RIS Assisted Wireless Powered IoT Networks With Phase Shift Error and Transceiver Hardware Impairment

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

Joint Dynamic Passive Beamforming and Resource Allocation for IRS-Aided Full-Duplex WPCN

This paper studies intelligent reflecting surface (IRS)-aided full-duplex (FD) 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 meanwhile receives information signals in the uplink (UL) with the help of IRS. Particularly, we propose three types of IRS beamforming configurations to strike a balance between the system performance and signaling overhead as well as implementation complexity. We first propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully dynamic IRS beamforming</i> , where the IRS phase-shift vectors vary with each time slot for both DL wireless energy transfer (WET) and UL wireless information transmission (WIT). To further reduce signaling overhead and implementation complexity, we then study two special cases, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">partially dynamic IRS beamforming</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static IRS beamforming</i> . For the former case, two different phase-shift vectors can be exploited for the DL WET and the UL WIT, respectively, whereas for the latter case, the same phase-shift vector needs to be applied for both DL and UL transmissions. We aim to maximize the system throughput by jointly optimizing the time allocation, HAP transmit power, and IRS phase shifts for the above three cases. Two efficient algorithms based on alternating optimization and penalty-based algorithms are respectively proposed for both perfect self-interference cancellation (SIC) case and imperfect SIC case by applying successive convex approximation and difference-of-convex optimization techniques. Simulation results demonstrate the benefits of IRS for enhancing the performance of FD-WPCN, especially with fully dynamic IRS beamforming, and also show that the IRS-aided FD-WPCN is able to achieve significantly performance gain compared to its counterpart with half-duplex when the self-interference (SI) is properly suppressed.

Resource Allocation for Intelligent Reflecting Surface Assisted Wireless Powered IoT Systems With Power Splitting

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

Sum‐throughput maximization for overlay cognitive wireless powered network with energy harvesting capability

AbstractIn this article, a novel time division multiple access (TDMA)‐based approach for optimal resource allocation in an overlay cognitive wireless powered communication network is proposed. In this network, power in the secondary network (SN) is provided by the primary network (PN) in addition to its own wireless network. Using the TDMA and a harvest‐then‐transmit protocol, the time and power allocation for downlink wireless energy transfer (WET) and uplink wireless information transmission (WIT) are jointly optimized to maximize the sum‐throughput in SN. Quality of service in PN and power and time in SN, as the main constrains are considered for sum‐throughput maximization in this network. This is a non‐convex problem which gets converted to a convex problem using active interference‐temperature control technique. Moreover, a new closed‐form expression which results in sub‐optimal power and time‐allocation (SO‐PT‐A) is addressed. The performance of proposed algorithm is compared with the equal time allocation (ETA) and fixed TDMA‐allocation (FTDMA‐A) algorithms which impose some special constraints for optimal power and time allocation. The simulation results depict that the SO‐PT‐A algorithm outperforms the ETA and FTDMA‐A algorithms. Eventually, the performance of the SO‐PT‐A algorithm is evaluated with other well‐known algorithms considering different values of the average and maximum transmit power of the hybrid‐access point (H‐AP), the number of secondary users (SUs) and path loss exponent.

Time Allocation and Optimization in Time-Reversal Wireless Powered Communication Networks

This paper studies optimal resource allocation in the wireless powered communication networks (WPCN) combined with time reversal (TR) in which one hybrid access point (H-AP) broadcasts constant wireless energy to a set of distributed users in the downlink (DL) and receives information from the users via space division multiple access (SDMA) in the uplink (UL). Inevitable interferences will occur when users transmit information in the UL simultaneously and the special space-time focusing of TR is used to suppress the interferences. An efficient protocol is proposed to support wireless energy transfer (WET) and TR in the DL and wireless information transmission in the UL for the proposed TR-WPCN. We optimize the time allocations to the H-AP for DL WET, DL TR, and UL WIT to maximize the sum throughput. Due to the nonconvexity of the studied optimization problem, we optimize variables successively, where the nonconvex optimization problem is transformed into the convex optimization problem. The approximate convex optimization problem can then be solved iteratively combined with the dichotomy method. Simulation results show that the proposed scheme can effectively suppress interferences and improve system performance.

Joint Optimization of Mobile Charging and Data Gathering for Wireless Rechargeable Sensor Networks

Recent advances in radio frequency (RF) power transfer provide a promising technology to power sensor nodes. Adoption of mobile chargers to replenish the nodes’ energy has recently attracted a lot of attention and the mobility assisted energy replenishment provides predictable and sustained power service. In this paper, we study the joint optimization of mobile charging and data gathering in sensor networks. A wireless multi-functional vehicle (WMV) is employed and periodically moves along specified trajectories, charge the sensors and gather the sensed data via one-hop communication. The objective of this paper is to maximize the uplink throughput by optimally allocating the time for the downlink wireless energy transfer by the WMV and the uplink transmissions of different sensors. We consider two scenarios where the WMV moves in a straight line and around a circle. By time discretization, the optimization problem is formulated as a 0-1 programming problem. We obtain the upper and lower bounds of the problem by converting the original 0-1 programming problem into a linear programming problem and then obtain the optimal solution by using branch and bound algorithm. We further prove that the network throughput is independent of the WMV’s velocity under certain conditions. Performance of our proposed algorithm is evaluated through extensive simulations. The results validate the correctness of our proposed theorems and demonstrate that our algorithm outperforms two baseline algorithms in achieved throughput under different settings.

Throughput of Wireless-Powered Based Multiuser System with Large-Scale Distributed Antennas

This paper investigates energy harvesting based multiuser system with large-scale distributed antennas, where a large number of remote antenna units (RAUs) are evenly separated across multiple circles. An efficient wireless energy and information transmission protocol is proposed. To save the signaling and the radio frequency chains overheads, the RAU with the shortest distance towards a user equipment (UE) is employed for the downlink wireless energy transfer (WET). In the uplink phase, we analyze the probability of wireless information transmission (WIT) of UEs. Then, linear zero-forcing detection and minimum-mean-square error are used to separate the data information among all the UEs that satisfy the requirement of WIT. The asymptotic throughput for an arbitrary UE is derived in closed-form. The time fraction used for the WET is optimized through maximizing the asymptotic throughput. Numerical and simulation results are given to verify the theoretical analysis, and bring to light the time fraction planning and the RAUs deployment for the system.

Utility-Based Resource Allocation in Wireless-Powered Communication Networks

This paper investigates the joint optimization of time allocation and transmit power in wireless-powered communication networks (WPCNs), where the downlink wireless energy transfer happens in the first phase, and then uplink wireless information transfer takes place in the second phase. Considering the rate fairness among users, we formulate the resource allocation in WPCNs as a network utility maximization (NUM) problem that aims to maximize the sum of utilities over all users subject to the energy causality constraint, the quality of service constraint, and the physical constraint. We further prove that the NUM problems with different channel access schemes (the time-division multiple access scheme, the concurrent transmission scheme, and the nonorthogonal multiple access scheme) can be equivalently transformed to convex problems by deliberated math transformations. Simulations demonstrate the efficiency of this paper.

Efficient data collection in wireless powered communication networks with node throughput demands

In the wireless powered communication networks (WPCNs) where nodes are powered by the energy harvested from radio-frequency (RF) transmissions, efficiently scheduling the downlink wireless energy transfer (WET) time and the uplink wireless information transmission (WIT) time is critical to achieve good throughput performance. In this paper, the following type of star-topology WPCNs are considered: each node has its own desired throughput but the throughput demands of all nodes cannot be satisfied due to the nodes’ very low energy harvesting rates. For such WPCNs, it is meaningful to minimize the sum throughput-gap. Unfortunately, this requirement cannot be satisfied by the existing data collecting schemes like the sum throughput maximization (STM) scheme. We study the weighted sum throughput-gap minimization (W-STGM) by jointly optimizing the time allocations for the WET and the WITs. Specifically, we first formulate the W-STGM problem as a non-linear optimization problem and prove that it covers the STM problem studied before as a special case, where all nodes have the same throughput weight and the nodes’ throughput demands are too high. Second, after proving it is non-convex, we decompose it into two sub-problems: the master problem, which determines the optimal WET time, and the slave problem, which determines the optimal time allocations to WITs for a given WET time. Considering that the slave problem is convex, we develop a dual decomposition method to solve it. Meanwhile, we design a golden section search algorithm to solve the master problem. Simulation results show that, compared to the STM, the W-STGM can satisfy in an adequate manner the throughput demands of the nodes by avoiding node throughput over-provisioning, which wastes system resource, and also increasing the throughput of nodes with large throughput weights by up to several tens of percentage points.

Energy beamforming for full-duplex wireless-powered communication networks

In this paper, we consider a full-duplex (FD) wireless-powered communication network (WPCN), where one FD hybrid access point (HAP) equipped with multiple antennas simultaneously transmits energy to and receives information from multiple users. Firstly, we propose a space division wireless energy allocation scheme and calculate the harvested energy in downlink wireless energy transfer (WET) for each user. Secondly, we derive an approximate closed-form expression of user’s achievable ergodic rate in uplink wireless information transfer (WIT). Thirdly, the energy allocation for different users is optimized under the max–min user fairness constraint, and a closed-form solution is obtained. Numerical results show that the simulation and the approximation of achievable rates are well matched, and energy beamforming can effectively suppress self-interference (SI), and improve rates as well as fairness among users. Moreover, FD-WPCNs are shown to outperform half-duplex (HD) WPCNs in rates with same number of antennas.

Joint Downlink and Uplink Energy Minimization in WET-Enabled Networks

Wireless energy transfer (WET) has received considerable attention for green communications. Unbalanced energy distribution and performance outages bring difficulties to the application of WET. This paper considers a network consisting of a WET-enabled access point (AP) and several energy-harvesting and source-powered user equipments (UEs). We investigate time-frequency resource allocation of downlink (DL) and uplink (UL) wireless information transfer (WIT) and DL WET. To improve energy efficiency and resource utilization, WET and WIT are orthogonally assigned at a low-frequency narrowband and a high-frequency wideband, respectively. A practical energy harvesting model is adopted, where power conversion efficiency depends on the received power instead of being a constant. To meet UEs’ different energy demands with the highest conversion efficiency, we develop beam switching, where the AP employs beamforming to charge UEs by jointly controlling power and time based on energy distributions and channel conditions. We formulate the overall energy minimization as a non-convex stochastic optimization problem, which is decomposed by fixing DL–UL time allocation ratio, transformed through Markov decision processes, and finally solved via linear programming. Simulation results verify the effectiveness of our proposed scheme on energy conservation and reveal the tradeoff of time allocation between DL and UL.

Online Power and Time Allocation in MIMO Uplink Transmissions Powered by RF Wireless Energy Transfer

Wireless energy transfer (WET) has been a promising technology to tackle the lifetime bottlenecks of energy-limited wireless devices in recent years. In this paper, we study a WET-enabled multiple-input multiple-output system including a base station (BS) and a user equipment (UE), which has a finite battery capacity. We consider slotted transmissions, where each slot includes two phases, namely, a downlink (DL) WET phase and an uplink (UL) wireless information transmission (WIT) phase. In the WET phase (a fraction $\tau$ of a slot), the BS transfers energy and the UE stores the received energy in the battery. In the WIT phase (a fraction $1-\tau$ of a slot), the UE transmits information to the BS by using the energy in the battery. Considering the power sensitivity $\alpha$ of the radio frequency to DC conversion circuits, the BS transfers energy only if the UE received power is larger than $\alpha$ , and the DL WET is formulated as a Bernoulli process. Based on the formulation, we propose an online power and time allocation algorithm to maximize the average data rate of UL WIT. We also extend the proposed algorithm to multiple user systems. The numerical results show that the proposed algorithm outperforms the existing schemes in terms of average data rate, energy efficiency, and outage probability.

Wireless Energy Transfer Beamforming With Löwner-John Ellipsoidal Approximation

The physical layer beamforming design of downlink wireless energy transfer (WET) is investigated. To minimize the power of WET with respect to the terminals’ quality of service requirements, the physical beamforming design is modeled as a non-convex quadratically constrained quadratic program (QCQP) problem. A two-step method to solve the QCQP is proposed. First, the QCQP is tightened into an ellipsoidal approximation problem with the aid of a Lowner-John ellipsoid, by solving a semidefinite program (SDP). Second, due to the approximation problem’s ellipsoidal nature, it is solved as another SDP. The proposed method is compared using numerical results to the conventional semidefinite relaxation (SDR) approach. It is demonstrated that for equivalent computing time cost, the proposed method usefully improves the energy efficiency of the downlink WET compared with SDR.

Convexity of Fairness-Aware Resource Allocation in Wireless Powered Communication Networks

This letter focuses on fairness-aware power and time allocation in wireless powered communication networks under the “harvest-then-transmit” protocol, where downlink wireless energy transfer is implemented at first and then uplink wireless information transfer takes place in a spectrum-sharing fashion. We aim to achieve the rate fairness of all users under three fairness criteria named max–min, proportional, and harmonic fairness. Although these problems are intractable in their original nonconvex formulations, all of them can be equivalently transformed to convex programming ones. As a result, the global optimal solution can be efficiently obtained via mature convex solvers. Simulation results verify the effectiveness of the proposed approach in terms of minimum-rate, geometric mean rate, and mean delay.

Asymptotically Optimal Power Allocation for Massive MIMO Wireless Powered Communications

In this letter, we consider the downlink wireless energy transfer as the sole source of power to support uplink data transmission in a multiuser massive multiple-input multiple-output (MIMO) system. The asymptotically optimal closed-form downlink power allocation strategy is derived to maximize the uplink sum rate using matched filter-based beamforming. Moreover, it is interesting to find that the obtained power allocation for uplink sum rate maximization simultaneously maximizes the minimum user rate under the asymptotic massive MIMO scenario.

Cognitive Wireless Powered Network: Spectrum Sharing Models and Throughput Maximization

The recent advance in radio-frequency (RF) wireless energy transfer (WET) has motivated the study of wireless powered communication network (WPCN) in which distributed wireless devices are powered via dedicated WET by the hybrid access-point (H-AP) in the downlink (DL) for uplink (UL) wireless information transmission (WIT). In this paper, by utilizing the cognitive radio (CR) technique, we study a new type of CR enabled secondary WPCN, called cognitive WPCN, under spectrum sharing with the primary wireless communication system. In particular, we consider a cognitive WPCN, consisting of one single H-AP with constant power supply and distributed wireless powered users, shares the same spectrum for its DL WET and UL WIT with an existing primary communication link, where the WPCN’s WET/WIT and the primary link’s WIT may interfere with each other. Under this new setup, we propose two coexisting models for spectrum sharing of the two systems, namely underlay - and overlay -based cognitive WPCNs, depending on different types of knowledge on the primary user transmission available for the cognitive WPCN. For each model, we maximize the sum-throughput of the cognitive WPCN by optimizing its transmission under different constraints applied to protect the primary user transmission. Analysis and simulation results are provided to compare the sum-throughput of the cognitive WPCN versus the achievable rate of the primary user under two proposed coexisting models. It is shown that the overlay based cognitive WPCN outperforms the underlay based counterpart, thanks to its fully co-operative WET/WIT design with the primary WIT, while also requiring higher complexity for implementation.

On the Performance of Wireless-Energy-Transfer-Enabled Massive MIMO Systems With Superimposed Pilot-Aided Channel Estimation

This paper concerns with a wireless-energy-transfer (WET)-enabled massive multiple-input-multiple-output (MIMO) system with superimposed pilot (SP)-aided channel estimation. Unlike the conventional WET-enabled frame transmission schemes, with the aid of SP, both the uplink (UL) channel estimation and wireless information transmission (WIT) that powered by the downlink (DL) WET can be operated simultaneously, and thus provide the potential for improving the UL achievable throughput. The impact that the SP has on the performance of such a WET-enabled massive MIMO system is mathematically characterized, and the optimal solution, including the SP power-allocation and the ratio of time-allocation between the duration of UL WIT and DL WET, is derived with regard to maximize the UL achievable throughput. Numerical results demonstrate the proposed SP-aided WET technique yields a superior performance than the conventional pilot-only-based schemes.

Optimal Resource Allocation in Full-Duplex Wireless-Powered Communication Network

This paper studies optimal resource allocation in the wireless-powered communication network (WPCN), where one hybrid access point (H-AP) operating in full duplex (FD) broadcasts wireless energy to a set of distributed users in the downlink (DL) and, at the same time, receives independent information from the users via time-division multiple access in the uplink (UL). We design an efficient protocol to support simultaneous wireless energy transfer (WET) in the DL and wireless information transmission (WIT) in the UL for the proposed FD-WPCN. We jointly optimize the time allocations to the H-AP for DL WET and different users for UL WIT and the transmit power allocations over time at the H-AP to maximize the users' weighted sum rate of UL information transmission with harvested energy. We consider both the cases with perfect and imperfect self-interference cancellation (SIC) at the H-AP, for which we obtain optimal and suboptimal time and power allocation solutions, respectively. Furthermore, we consider the half-duplex (HD) WPCN as a baseline scheme and derive its optimal resource allocation solution. Simulation results show that the FD-WPCN outperforms the HD-WPCN when effective SIC can be implemented and more stringent peak power constraint is applied at the H-AP.