Multi-Objective Resource Allocation for IRS-Aided SWIPT

The low efficiency of far-field wireless power transfer (WPT) limits the fundamental rate-energy (R-E) performance trade-off of the simultaneous wireless information and power transfer (SWIPT) system. To address this challenge, we propose in this letter a new SWIPT system aided by the emerging intelligent reflecting surface (IRS) technology. By leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts, IRS achieve a high passive beamforming gain, which is appealing for drastically enhancing the WPT efficiency and thereby the R-E trade-off of SWIPT systems. We consider an IRS being deployed to assist a multi-antenna access point (AP) to serve multiple information decoding receivers (IDRs) and energy harvesting receivers (EHRs). We aim to maximize the weighted sum-power received by EHRs via jointly optimizing the transmit precoders at the AP and reflect phase shifts at the IRS, subject to the individual signal-to-interference-plus-noise ratio (SINR) constraints for IDRs. Since this problem is non-convex, we propose efficient algorithms to obtain suboptimal solutions for it. In particular, we prove that it is sufficient to send information signals only at the AP to serve both IDRs and EHRs regardless of their channel realizations. Moreover, simulation results show significant performance gains achieved by our proposed designs over benchmark schemes.

Rate-splitting multiple access (RSMA) and the intelligent reflecting surface (IRS) are novel technologies to improve spectrum and energy efficiencies in multi-antenna networks. In this paper, we investigate a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted with an IRS and using RSMA. In particular, the IRS assists with data and energy transmission from the base station (BS) to power-splitting (PS) users under a non-linear energy harvesting (EH) model. Our objective is to minimize the transmission power subject to rate and EH requirements at the users. To solve the non-convex problem, we propose a bilevel programming problem in which a genetic algorithm is used to solve the outer optimization problem, and the inner optimization problem is addressed with the semidefinite relaxation (SDR) method. In addition, to study a practical scenario of imperfect channel state information (CSI), we consider a robust beamforming design based on imperfect cascade channels at the BS. Then, we propose a solution based on the S-procedure with the SDR technique. Simulation results show that our proposed RSMA-based scheme provides a significant reduction in transmission power compared to the space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) methods under perfect and imperfect CSI.

Intelligent Reflecting Surfaces (IRS) is a groundbreaking innovative technique to boost energy and spectral efficiency in upcoming wireless systems, enabling simultaneous wireless information and power transfer (SWIPT). This paper optimizes Energy Harvesting Efficiency (EHE) in a multi-input multi-output (MIMO) SWIPT system, where an access point (AP)/base station (BS) uses an IRS to serve multi energy receivers (ERs) and information receivers (IRs). We formulate an EHE optimization problem, designing active and passive beamforming while satisfying the AP/BS power constraints, IRS unit-modulus constraints and IR rate demands in a linear EH model. The Linear Matrix Inequalities (LMIs) are approximated for efficient solutions. And second-order cone programming (SOCP) is solved via convex optimization (CVX), while the penalty convex-concave procedure (CCP) guarantees convexity to non-convex problems. Next, an alternating optimization (AO)-reliant iterative algorithm derived sub-optimal solutions with slack variables. According to simulation results, for all ERs within a linear EH design/model, the proposed algorithm improves total EHE by 40% to 45% compared to randomize, No-IRS, and decode-and-forward (DF) relay strategies. Ultimately, highlighting IRS’s benefit in improving quality of service (QoS) by tweaking its elements and AP/BS antennas.

Recently, the demand for spectral and energy efficiency has significantly been increased along with new breakthroughs in programmable meta-material techniques. The integration of an intelligent reflecting surface (IRS) into simultaneous wireless information and power transfer (SWIPT) systems has attracted much attention from operators in advanced wireless communication networks (WCNs) such as fifth-generation (5G) and sixth-generation (6G) networks. In addition, an IRS-assisted SWIPT system faces many security risks that can easily be compromised by eavesdroppers. In this paper, we investigate the physical-layer secure and transmission optimization problem in an IRS-assisted SWIPT system where a power-splitting (PS) scheme is installed in the user equipment (UE). In particular, our purpose is to maximize the system secrecy rate by jointly finding optimal solutions for transmitter power, PS factor of UE, and phase shifts matrix of IRS under the required minimum harvested energy and maximum transmitter power. We propose the alternating optimization (AO)-based scheme to obtain optimal solutions. The proposed AO-based scheme can effectively solve both convex and non-convex problems; however, applying them in practice still poses some difficulties due to the complexity and long computation time. This is because many mathematical transformations are used and the optimal solution needs a number of iterations to achieve convergence. Therefore, we also propose 5 types of data and DNN structures to potentially achieve efficiency in computations by using a deep learning (DL)-based approach. The simulation results indicate that the proposed IRS scheme provides an improvement in terms of the average secrecy rate (ASR) by up to 38.91% when the number of reflecting elements is high (30 elements) compared to a scheme without an IRS. We also observe that the DL-based approach not only provides similar performance to the AO-based scheme but it also significantly reduces computation time.

The propagation loss of RF signals is a significant issue in simultaneous wireless information and power transfer (SWIPT) systems. Additionally, ensuring information security is crucial due to the broadcasting nature of wireless channels. To address these challenges, we exploit the potential of active intelligent reflecting surface (IRS) in a multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system. The active IRS provides better beamforming gain than the passive IRS, reducing the “double-fading” effect. Moreover, the noise introduced at the active IRS can be used as artificial noise (AN) to jam eavesdroppers. This paper formulates a secrecy sum-rate maximization problem related to precoding matrices, power splitting (PS) ratios, and the IRS matrix. Since the problem is highly non-convex, we propose a block coordinate descent (BCD)-based algorithm to find a sub-optimal solution. Moreover, we develop a heuristic algorithm based on the zero-forcing precoding scheme to reduce computational complexity. Simulation results show that the active IRS achieves a higher secrecy sum rate than the passive and non-IRS systems, especially when the transmit power is low or the direct link is blocked. Moreover, increasing the power budget at the active IRS can significantly improve the secrecy sum rate.

This paper optimizes the energy consumption of the downlink of a multiple-antenna base station (BS) transmitting to several single-antenna users. The BS utilizes simultaneous wireless information and power transfer (SWIPT) while receivers apply power-splitting (PS) with a nonlinear energy harvesting model leading to PS-SWIPT. We use an intelligent reflecting surface (IRS) and propose a joint design to optimize the active data and the BS's energy beamformers, IRS's passive beamformers, and the receivers' PS ratios under perfect and imperfect CSI availability. In particular, the total BS transmit power is minimized while guaranteeing a minimum rate and harvested energy for each receiver. We apply the block coordinate descent (BCD) method to optimize active and passive beamformers iteratively. We enforce the rank-one constraint and solve the corresponding optimization problem via successive convex approximation (SCA) for accurate semidefinite relaxations. Furthermore, we propose a worst-case robust design for the imperfect CSI case and reformulate this problem with infinitely many constraints. With the BCD method, the problem is iteratively solved via semidefinite programming (SDP) and a second sub-problem with a linear objective and quadratic matrix inequalities, which is also solved via SCA. Numerical results show significant improvements (e.g., 30% decrease in transmit power) than those of no-IRS and IRS with random phase shifts.

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system that integrates wireless sensing, communication, and powering. In this system, a multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with a multi-antenna information decoding (ID) receiver, wirelessly charges a multi-antenna energy harvesting (EH) receiver, and performs radar target sensing based on the echo signal concurrently. Under this setup, we aim to reveal the fundamental performance tradeoff limits among sensing, communication, and powering, in terms of the estimation Cramér-Rao bound (CRB), achievable communication rate, and harvested energy, respectively. In particular, we consider two different target models for radar sensing, namely the point and extended targets, for which we are interested in estimating the target angle and the complete target response matrix, respectively. For both models, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the minimum harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain partitionable optimal transmit covariance matrix solutions to the two formulated problems by applying advanced convex optimization techniques. The numerical results demonstrate the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark schemes based on time division and eigenmode transmission (EMT).

SummaryThe intelligent reflecting surface (IRS) has recently become the most promising technology to achieve maximum beam‐forming gain with a simultaneous wireless information and power transfer (SWIPT) system. Many existing studies perform a single IRS deployment or utilize space division multiple access (SDMA) and non‐orthogonal multiple access (NOMA) schemes. However, the existing schemes face high time complexity and block signal transmission for larger indoor communications. Hence, this article introduces a dual IRS‐aided multiuser SWIPT system by implementing rate‐splitting multiple access (RSMA) systems. To enhance the minimum achievable rate and ensure the minimum harvesting energy (HE), this study proposes a joint successive convex approximation with an integrated optimization algorithm (SCA‐IOA). The proposed algorithm jointly optimizes the reflecting beam forming and power splitting (PS) coefficient of all the users effectively. The simulation results demonstrate the effectiveness of the proposed techniques and produce an outstanding performance for deploying dual IRS and implementing RSMA compared to traditional SDMA and NOMA systems.

The intelligent transportation system (ITS) was supported by the sixth generation (6G) wireless networks, since it has great potential to realize intelligent transportation with the benefit for the society and economy. In order to overcome the practical problem of spectrum scarcity, ultra-low latency, large-scale connectivity in ITS, we propose a cooperative multiple-input single output non-orthogonal multiple access (MISO-NOMA) for ITS with intelligent reflecting surface (IRS) and simultaneous wireless information and power transfer (SWIPT). An user fairness optimization problem is formulated to maximize the fairness rate of the vehicles, subject to the quality of service requirements of the vehicles and the successive interference cancellation. The optimization problem involves the transmit beamformers design, the IRS reflection matrix design, and the power splitting ratio of the SWIPT, which lead to the problem is difficult to solve. For solving the challenging problem, an iterative successive convex approximation and semi-definite relaxation based algorithm is proposed. Explicitly, we firstly adopt the method of reconstructing epigraph for simplification due to the objective function is non-convex, and then the original problem is decomposed into two sub-problems that are easy to solve. Finally, Experimental results illustrate that the user fairness of the proposed cooperative MISO-NOMA for ITS with IRS and SWIPT is better than that of both the IRS-NOMA for ITS without SWIPT and the IRS-OMA for ITS.

In this paper, we investigate the simultaneous wireless information and power transfer (SWIPT) in a multiple-input multiple-output (MIMO) amplify-and-forward relay communication system, where the relay is an energy harvesting (EH) node and harvests the energy the signals transmitted from the source. The harvested energy is partially used to forward signals from the source to the destination, and the remaining energy is stored for other usages. The SWIPT in relay-assisted communication is interesting as long as the relay stores energy from the source and the destination receives successfully the data from the source. In this context, we propose to investigate the source and relay precoders that characterize the relationship between the achievable stored energy at the relay and the achievable source-to-destination rate, namely, the rate-stored energy (R-E) tradeoff region. First, we consider the ideal scheme, where there is the simultaneous operation of the EH and information decoding (ID) receivers at the relay. Then, we consider practical schemes, such as the power splitting and the time switching that separate the operation of EH and ID receivers over power domain or time domain, respectively. Moreover, we study the case of imperfect channel state information at the relay and the destination, and characterize its impact on the achievable R-E region. Through the simulation results, we show the effect of the position of the relay and the channel uncertainty on the achievable R-E regions of all the schemes when the used energy at the relay is constant or variable. We also show that, although it provides an outer bound on the achievable rate-energy region in one-hop MIMO systems, the ideal scheme provides only an upper bound on the maximum achievable end-to-end rate and not an outer bound on the R-E region.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS–UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

In this paper, we study a downlink unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) system with the aid of an intelligent reflecting surface (IRS). Considering the time switching (TS) scheme, the UAV is leveraged to charge a single battery limited smart device (SD) and for information transmission simultaneously. We aim to maximize the average harvested energy of the SD over a finite mission/communication period on the conditions of average data transmission requirement and other practical constraints, by jointly optimizing the UAV trajectory, TS factors, and the phase shifts of IRS. In existing literatures, the block coordinate descent and successive convex approximation (BCD-SCA) method is usually applied to obtain a local optimal solution, however, the solution is susceptible to the initial parameter settings. Therefore, in this paper, we obtain a more advantageous solution by using the Lagrange dual method. Numerical results show that under the same constraints, the proposed design scheme improves the average harvested energy compared with the BCD-SCA method, and complexity analysis indicates that our algorithm has lower computation complexity.

In this paper, we study resource allocation design for secure communication in intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) communication systems. To enhance physical layer security, artificial noise (AN) is transmitted from the base station (BS) to deliberately impair the channel of an eavesdropper. In particular, we jointly optimize the phase shift matrix at the IRS and the beamforming vectors and AN covariance matrix at the BS for maximization of the system sum secrecy rate. To handle the resulting non-convex optimization problem, we develop an efficient suboptimal algorithm based on alternating optimization, successive convex approximation, semidefinite relaxation, and manifold optimization. Our simulation results reveal that the proposed scheme substantially improves the system sum secrecy rate compared to two baseline schemes.