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Register a new userOutage Analysis and Beamwidth Optimization for Positioning-Assisted Beamforming
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Conventional beamforming is based on channel estimation, which can be computationally intensive and inaccurate when the antenna array is large. In this work, we study the outage probability of positioning-assisted beamforming systems. Closed-form outage probability bounds are derived by considering positioning error, link distance and beamwidth. Based on the analytical result, we show that the beamwidth should be optimized with respect to the link distance and the transmit power, and such optimization significantly suppresses the outage probability.
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This paper considers an intelligent reflecting surface (IRS) assisted multi-input multi-output (MIMO) power splitting (PS) based simultaneous wireless information and power transfer (SWIPT) system with multiple PS receivers (PSRs). The objective is to maximize the achievable data rate of the system by jointly optimizing the PS ratios at the PSRs, the active transmit beamforming (ATB) at the access point (AP), and the passive reflective beamforming (PRB) at the IRS, while the constraints on maximum transmission power at the AP, the reflective phase shift of each element at the IRS, the individual minimum harvested energy requirement of each PSR, and the domain of PS ratio of each PSR are all satisfied. For this unsolved problem, however, since the optimization variables are intricately coupled and the constraints are conflicting, the formulated problem is non-convex, and cannot be addressed by employing exist approaches directly. To this end, we propose a joint optimization framework to solve this problem. Particularly, we reformulate it as an equivalent form by employing the Lagrangian dual transform and the fractional programming transform, and decompose the transformed problem into several sub-problems. Then, we propose an alternate optimization algorithm by capitalizing on the dual sub-gradient method, the successive convex approximation method, and the penalty-based majorization-minimization approach, to solve the sub-problems iteratively, and obtain the optimal solutions in nearly closed-forms. Numerical simulation results verify the effectiveness of the IRS in SWIPT system and indicate that the proposed algorithm offers a substantial performance gain.
In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for low-power Internet-of-Things (IoT) devices, where the IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmission energy minimization problem by a joint design of time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. Due to the high coupling of optimization variables, this problem is a non-convex optimization problem, which is difficult to solve directly. In order to solve the above-mentioned challenging problems, the alternating optimization (AO) is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is applied to solve the non-convex rank-one constraint in solving the IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the effectiveness of our proposed algorithm in reducing HAP transmission energy compared to other benchmarks.
The realization of practical intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the proper beamforming design exploiting accurate channel state information (CSI). However, channel estimation (CE) in IRS-MUC systems requires a significantly large training overhead due to the numerous reflection elements involved in IRS. In this paper, we adopt a deep learning approach to implicitly learn the historical channel features and directly predict the IRS phase shifts for the next time slot to maximize the average achievable sum-rate of an IRS-MUC system taking into account the user mobility. By doing this, only a low-dimension multiple-input single-output (MISO) CE is needed for transmit beamforming design, thus significantly reducing the CE overhead. To this end, a location-aware convolutional long short-term memory network (LA-CLNet) is first developed to facilitate predictive beamforming at IRS, where the convolutional and recurrent units are jointly adopted to exploit both the spatial and temporal features of channels simultaneously. Given the predictive IRS phase shift beamforming, an instantaneous CSI (ICSI)-aware fully-connected neural network (IA-FNN) is then proposed to optimize the transmit beamforming matrix at the access point. Simulation results demonstrate that the sum-rate performance achieved by the proposed method approaches that of the genie-aided scheme with the full perfect ICSI.
In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications.
Bistatic backscatter communication (BackCom) allows passive tags to transmit over extended ranges, but at the cost of having carrier emitters either transmitting at high powers or being deployed very close to tags. In this paper, we examine how the presence of an intelligent reflecting surface (IRS) could benefit the bistatic BackCom system. We study the transmit power minimization problem at the carrier emitter, where its transmit beamforming vector is jointly optimized with the IRS phase shifts, whilst guaranteeing a required BackCom performance. A unique feature in this system setup is the multiple IRS reflections experienced by signals traveling from the carrier emitter to the reader, which renders the optimization problem highly nonconvex. Therefore, we propose algorithms based on the minorization-maximization and alternating optimization techniques to obtain approximate solutions for the joint design. We also propose low-complexity algorithms based on successive optimization of individual phase shifts. Our results reveal considerable transmit power savings in both single-tag and multi-tag systems, even with moderate IRS sizes, which may be translated to significant range improvements using the original transmit power or reduce the reliance of tags on carrier emitters located at close range.
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