  In this paper, we investigate resource allocation algorithm design for large
intelligent reflecting surface (IRS)-assisted simultaneous wireless information
and power transfer (SWIPT) systems. To this end, we adopt a physics-based IRS
model that, unlike the conventional IRS model, takes into account the impact of
the incident and reflection angles of the impinging electromagnetic wave on the
reflected signal. To facilitate efficient resource allocation design for large
IRSs, we employ a scalable optimization framework, where the IRS is partitioned
into several tiles and the phase shift elements of each tile are jointly
designed to realize different transmission modes. Then, the beamforming vectors
at the base station (BS) and the transmission mode selection of the tiles of
the IRS are jointly optimized for minimization of the BS transmit power taking
into account the quality-of-service requirements of both non-linear energy
harvesting receivers and information decoding receivers. For handling the
resulting non-convex optimization problem, we apply a penalty-based method,
successive convex approximation, and semidefinite relaxation to develop a
computationally efficient algorithm which asymptotically converges to a locally
optimal solution of the considered problem. Our simulation results show that
the proposed scheme enables considerable power savings compared to two baseline
schemes. Moreover, our results also illustrate that the advocated physics-based
model and scalable optimization framework for large IRSs allows us to strike a
balance between system performance and computational complexity, which is vital
for realizing large IRS-assisted communication systems.
