标题： 基于CSI不确定性的IRS辅助通信中联合功率分配与反射元件激活以最大化能量效率 显示英文标题

标题： Joint Power Allocation and Reflecting-Element Activation for Energy Efficiency Maximization in IRS-Aided Communications Under CSI Uncertainty

摘要： 我们研究了在智能反射表面（IRS）辅助的通信系统中，联合功率分配和反射元件（RE）激活以最大化能量效率（EE），同时考虑信道状态信息（CSI）的不完善性。 鲁棒优化问题是一个混合整数问题，即优化变量是连续的（发射功率）和离散的（RE的二进制状态）。 为了求解这个具有挑战性的问题，我们开发了两种算法。 第一种是一种交替优化（AO）方法，该方法基于Lambert W函数和动态规划（DP）算法，以较低的复杂度获得次优解。 第二种是一种分支定界（B&B）方法，它将AO作为其子程序，并且形式上保证能够达到全局最优解。 这两种算法在实现时都不需要任何外部优化求解器。 此外，数值结果表明，所提出的算法优于基准方案，AO在大多数情况下能够达到接近最优的性能，而B&B在平均情况下具有较低的计算复杂度。 显示英文摘要

摘要： We study the joint power allocation and reflecting element (RE) activation to maximize the energy efficiency (EE) in communication systems assisted by an intelligent reflecting surface (IRS), taking into account imperfections in channel state information (CSI). The robust optimization problem is mixed integer, i.e., the optimization variables are continuous (transmit power) and discrete (binary states of REs). In order to solve this challenging problem we develop two algorithms. The first one is an alternating optimization (AO) method that attains a suboptimal solution with low complexity, based on the Lambert W function and a dynamic programming (DP) algorithm. The second one is a branch-and-bound (B&B) method that uses AO as its subroutine and is formally guaranteed to achieve a globally optimal solution. Both algorithms do not require any external optimization solver for their implementation. Furthermore, numerical results show that the proposed algorithms outperform the baseline schemes, AO achieves near-optimal performance in most cases, and B&B has low computational complexity on average.