论文标题
智能反射表面辅助的MIMO系统的低复杂性总容量最大化
Low-Complexity Sum-Capacity Maximization for Intelligent Reflecting Surface-Aided MIMO Systems
论文作者
论文摘要
降低计算复杂性对于优化智能反射表面（IRS）系统的相移至关重要，因为IRS辅助通信系统通常通过大量反射元素（RES）部署。这封信提出了一种低复杂性算法，指定为尺寸的正弦最大化（DSM），以获得最佳的IRS相位移位，从而最大程度地提高了MIMO网络的总和。该算法利用了优化问题的目标函数是正弦曲线W.R.T.每个RE的相移。数值结果表明，与其他两种基准方法相比，DSM达到了接近最大的总和速率和更快的收敛速度。
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 maximize 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 a near-maximal sum rate and faster convergence speed than two other benchmark methods.
