  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.
