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Low-Complexity Sum-Capacity Maximization for Intelligent Reflecting Surface-Aided MIMO Systems
IEEE Wireless Communications Letters ( IF 6.3 ) Pub Date : 2022-04-15 , DOI: 10.1109/lwc.2022.3167731
Ahmad Sirojuddin, Dony Darmawan Putra, Wan-Jen Huang

Reducing computational complexity is crucial in optimizing the phase shifts of Intelligent Reflecting Surface (IRS) systems since IRS-assisted communication systems are generally deployed with a large number of reflecting elements (REs). This letter proposes a low-complexity algorithm, designated as Dimension-wise Sinusoidal Maximization (DSM), to obtain the optimal IRS phase shifts that maximizes the sum capacity of a MIMO network. The algorithm exploits the fact that the objective function for the optimization problem is sinusoidal w.r.t. the phase shift of each RE. The numerical results show that DSM achieves near maximal sum-rate and faster convergence speed than two other benchmark methods.

中文翻译:

智能反射表面辅助 MIMO 系统的低复杂度和容量最大化

降低计算复杂性对于优化智能反射面 (IRS) 系统的相移至关重要,因为 IRS 辅助通信系统通常部署有大量反射元件 (RE)。这封信提出了一种低复杂度算法,称为维度正弦最大化 (DSM),以获得最大化 MIMO 网络总容量的最佳 IRS 相移。该算法利用了这样一个事实,即优化问题的目标函数是正弦的,与每个 RE 的相移有关。数值结果表明,与其他两种基准方法相比,DSM 实现了接近最大的和速率和更快的收敛速度。
更新日期:2022-04-15
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