# Weighted Sum-Rate Maximization for Multi-IRS Aided Cooperative Transmission

arXiv: https://arxiv.org/abs/2002.04900 · HTML: https://mlsanity.com/paper/2002.04900/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network](https://mlsanity.com/paper/2201.02834/) (2022)
- [Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS](https://mlsanity.com/paper/2409.11270/) (2024)

## Abstract (arXiv)

This paper investigates multiple intelligent reflecting surfaces (IRSs) aided wireless network, where the IRSs are deployed to cooperatively assist communications between a multi-antenna base station (BS) and multiple single-antenna cell-edge users. We aim at maximizing the weighted sum rate of all the cell-edge users by jointly optimizing the BS's transmit beamforming and IRS's phase shifts. Especially, the beamforming is optimally solved by the Lagrangian method, and the phase shifts are obtained based on the Riemannian manifold conjugate gradient (RMCG) method. Numerical results show that a significant throughput is improved with aid of multiple IRSs.