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In this paper, we study the optimal resource allocation algorithm design forlarge intelligent reflecting surface (IRS)-assisted simultaneous wirelessinformation and power transfer (SWIPT) systems. To facilitate efficient systemdesign for large IRSs, instead of jointly optimizing all the IRS elements, wepartition the IRS into several tiles and employ a scalable optimizationframework comprising an offline design stage and an online optimization stage.In the offline stage, the IRS elements of each tile are jointly designed tosupport a set of different phase shift configurations, referred to astransmission modes, while the best transmission mode is selected from the setfor each tile in the online stage. Given a transmission mode set, we aim tominimize the total base station (BS) transmit power by jointly optimizing thebeamforming and the transmission mode selection policy taking into account thequality-of-service requirements of information decoding and non-linear energyharvesting receivers, respectively. Although the resource allocation algorithmdesign is formulated as a non-convex combinatorial optimization problem, wesolve it optimally by applying the branch-and-bound (BnB) approach whichentails a high computational complexity. To strike a balance between optimalityand computational complexity, we also develop an efficient suboptimal algorithmcapitalizing on the penalty method and successive convex approximation. Oursimulation results show that the proposed designs enable considerable powersavings compared to several baseline schemes. Moreover, our results reveal thatby properly adjusting the numbers of tiles and transmission modes, the proposedscalable optimization framework indeed facilitates online design for largeIRSs.

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