The paper investigates resource allocation in an IRS-assisted multiuser multiple-input single-output (MISO) communication system, where the goal is to minimize the total transmit power at the base station (BS) while ensuring the minimum signal-to-interference-plus-noise ratio (SINR) requirements of each user. The authors consider practical discrete phase shifters at the IRS and address the cases of both perfect and imperfect channel state information (CSI).
For the case of perfect CSI, the authors first reformulate the original non-convex mixed integer nonlinear programming (MINLP) problem into a more tractable form. They then develop a globally optimal algorithm based on the generalized Benders decomposition (GBD) method and a low-complexity suboptimal algorithm based on successive convex approximation (SCA).
For the case of imperfect CSI, the authors introduce a robust SINR constraint and reformulate the problem as a robust MINLP problem. They then extend the proposed GBD-based and SCA-based methods to obtain the globally optimal and a locally optimal solution, respectively.
The numerical results confirm the optimality of the proposed GBD-based algorithms and the effectiveness of the proposed SCA-based algorithms in achieving a favorable balance between performance and complexity. Compared to the state-of-the-art alternating optimization (AO)-based solution, the proposed schemes achieve significant performance gains, especially for moderate-to-large numbers of IRS elements.
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by Yifei Wu,Don... at arxiv.org 04-30-2024
https://arxiv.org/pdf/2310.04063.pdfDeeper Inquiries