An Efficient Convex-Hull Relaxation Based Algorithm for Multi-User Discrete Passive Beamforming
<|reference_start|>An Efficient Convex-Hull Relaxation Based Algorithm for Multi-User Discrete Passive Beamforming: Intelligent reflecting surface (IRS) is an emerging technology to enhance spatial multiplexing in wireless networks. This letter considers the discrete passive beamforming design for IRS in order to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among multiple users in an IRS-assisted downlink network. The main design difficulty lies in the discrete phase-shift constraint. Differing from most existing works, this letter advocates a convex-hull relaxation of the discrete constraints which leads to a continuous reformulated problem equivalent to the original discrete problem. This letter further proposes an efficient alternating projection/proximal gradient descent and ascent algorithm for solving the reformulated problem. Simulation results show that the proposed algorithm outperforms the state-of-the-art methods significantly.<|reference_end|>
@article{lai2024an,
    title={An Efficient Convex-Hull Relaxation Based Algorithm for Multi-User
  Discrete Passive Beamforming},
    author={Wenhai Lai, Zheyu Wu, Yi Feng, Kaiming Shen, Ya-Feng Liu},
    journal={IEEE Signal Processing Letters 2024},
    year={2024},
    doi={10.1109/LSP.2024.3436669},
    archivePrefix={arXiv},
    eprint={2407.20914},
    primaryClass={cs.IT eess.SP math.IT}
}