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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

41 Scopus Citations
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  • Dongfang Xu
  • Vahid Jamali
  • Xianghao Yu
  • Derrick Wing Kwan Ng
  • Robert Schober


Original languageEnglish
Pages (from-to)1423-1441
Journal / PublicationIEEE Transactions on Communications
Issue number2
Online published5 Jan 2022
Publication statusPublished - Feb 2022
Externally publishedYes


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-of-service 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 physics-based 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.

Research Area(s)

  • Branch and bound approach, intelligent reflecting surface, optimal resource allocation, scalable optimization