Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS

Hu, Shaokang; Wei, Zhiqiang; Cai, Yuanxin; Ng, Derrick Wing Kwan; Yuan, Jinhong

doi:10.48550/arxiv.2005.11663

Cited by 5 publications

(20 citation statements)

References 12 publications

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“…Remark 1: We note that the physics-based model in ( 10) is a more general and more accurate model compared to the widely adopted conventional IRS model [24], [31], [32]. On the one hand, by setting T Assuming scatterer-based wireless environment, the channel vector of the direct link between the BS and IR k is given by [22] h…”

Section: ) Physics-based Modelmentioning

confidence: 99%

“…where W k P H N T and constraint C9 are imposed to guarantee that W k " w k w H k holds after optimization. We note that the binary constraint in C4 and rank-one constraint C9 are still obstacles to solving problem (31). Nevertheless, in the next subsection, we develop a BnB-based algorithm to optimally solve (31).…”

Section: A Problem Transformationmentioning

confidence: 99%

“…We note that the binary constraint in C4 and rank-one constraint C9 are still obstacles to solving problem (31). Nevertheless, in the next subsection, we develop a BnB-based algorithm to optimally solve (31).…”

Section: A Problem Transformationmentioning

confidence: 99%

“…BnB algorithms have been widely adopted to optimally solve communication resource allocation problems involving binary variables such as optimal user scheduling [35] and optimal subcarrier assignment [36]. Thanks to the series of transformations applied in Section IV-A, the reformulated equivalent problem in (31) is in the canonical form that allows the application of the BnB concept to develop an optimal algorithm. The basic principle of BnB-based algorithms is to exploit a tree traversal where the feasible set of the main problem is mapped to the root.…”

Section: B Optimal Resource Allocation Schemementioning

confidence: 99%

“…Following the above procedure, the gap between the upper bound and the lower bound gradually vanishes in each iteration and the BnBbased algorithm converges to the globally optimal solution of the considered optimization problem. In this subsection, we present the construction of the bounds, the partition rule, and the branching strategy for the problem in (31), and then develop the optimal resource allocation algorithm.…”

Section: B Optimal Resource Allocation Schemementioning

confidence: 99%

See 4 more Smart Citations

Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A Scalable Optimization Framework

Xü¹,

Jamali²,

Yu³

et al. 2021

Preprint

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In this paper, we study the optimal resource allocation algorithm design for large intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. To facilitate efficient system design for large IRSs, instead of jointly optimizing all the IRS elements, we partition the IRS into several tiles and employ a scalable optimization framework comprising an offline design stage and an online optimization stage. In the offline stage, the IRS elements of each tile are jointly designed to support a set of different phase shift configurations, referred to as transmission modes, while the best transmission mode is selected from the set for each tile in the online stage. Given a transmission mode set, we aim to minimize the total base station (BS) transmit power by jointly optimizing the beamforming and the transmission mode selection policy taking into account the quality-ofservice requirements of information decoding and non-linear energy harvesting receivers, respectively. Although the resource allocation algorithm design is formulated as a non-convex combinatorial optimization problem, we solve it optimally by applying the branch-and-bound (BnB) approach which entails a high computational complexity. To strike a balance between optimality and computational complexity, we also develop an efficient suboptimal algorithm capitalizing on the penalty method and successive convex approximation. Our simulation results show that the proposed designs enable considerable power savings compared to several baseline schemes. Moreover, our results reveal that by properly adjusting the numbers of tiles and transmission modes, the proposed scalable optimization framework indeed facilitates online design for large IRSs. Besides, our results confirm that the advocated physicsbased model and scalable optimization framework enable a flexible trade-off between performance and complexity, which is vital for realizing the performance gains promised by large IRS-assisted communication systems in practice.

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Section: ) Physics-based Modelmentioning

confidence: 99%

Section: A Problem Transformationmentioning

confidence: 99%

Section: A Problem Transformationmentioning

confidence: 99%

Section: B Optimal Resource Allocation Schemementioning

confidence: 99%

Section: B Optimal Resource Allocation Schemementioning

confidence: 99%

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Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A Scalable Optimization Framework

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Yu³

et al. 2021

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A Trellis-Based Passive Beamforming Design for an Intelligent Reflecting Surface-Aided MISO System

et al. 2022

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Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware

Nguyen

Munochiveyi

et al. 2022

Electronics

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In this article, we investigate the design of reconfigurable intelligent surface (RIS)-aided transmission as a smart method to reflect signals received from access points to users and, hence, improving users’ performance. To implement smart Internet of Things (IoT) networks, massive connectivity and low-cost deployment are essential in designing such systems. In particular, we consider two practical scenarios (dual-hop and single-hop transmissions). These scenarios highlight the potential of RIS in enhancing the system’s outage probability performance. Furthermore, to characterize channel conditions in practice, we pay particular attention to two-channel distributions that are non-central chi-square (NCCS) distributions that approximate the channel distribution of the RIS-aided wireless system and the squared KG distribution. In addition, the RIS-aided system may face imperfect hardware-related issues in practice. Therefore, we need to consider the degraded performance of practical RIS-aided systems by considering the detrimental impact of in-phase and quadrature-phase imbalance (IQI). To characterize the main system performance metric, we provide closed-form formulas of outage probability and ergodic capacity. We then evaluate system performance under the impacts of signal-to-noise ratio (SNR), the number of meta-surfaces, and channel parameters. All closed-form outage expressions are validated via Monte Carlo simulations. Simulation results indicate that the considered RIS scheme at dual-hop and single hop under the impact of IQI and RIS hardware impairment achieves significant improvements in terms of outage probability at high SNR and high meta-surface number N. Additionally, the simulation results demonstrate that the impact of IQI on the proposed system is limited. It is worth noting that, in terms of ergodic capacity, ergodic capacity faces an upper limit. Despite this limitation, the proposed system can still work well once some parameters are controlled well, such as the transmit SNR, levels of IQI, and the number of RIS components.

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Sum-Rate Maximization for Multiuser MISO Downlink Systems with Self-sustainable IRS

Cited by 5 publications

References 12 publications

Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A Scalable Optimization Framework

Optimal Resource Allocation Design for Large IRS-Assisted SWIPT Systems: A Scalable Optimization Framework

A Trellis-Based Passive Beamforming Design for an Intelligent Reflecting Surface-Aided MISO System

Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware

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