Intelligent reflecting surfaces (IRSs) have emerged as a promising economical solution to implement cell-free networks. However, the performance gains achieved by IRSs critically depend on smartly tuned passive beamforming based on the assumption that the accurate channel state information (CSI) knowledge is available, which is practically impossible. Thus, in this paper, we investigate the impact of the CSI uncertainty on IRS-assisted cell-free networks. We adopt a stochastic programming method to cope with the CSI uncertainty by maximizing the expectation of the sum-rate, which guarantees robust performance over the average. Accordingly, an average sum-rate maximization problem is formulated, which is non-convex and arduous to obtain its optimal solution due to the coupled variables and the expectation operation with respect to CSI uncertainties. As a compromising approach, we develop an efficient robust joint design algorithm with low-complexity. Particularly, the original problem is equivalently transformed into a tractable form, and then, the locally optimal solution can be obtained by employing the block coordinate descent method. We further prove that the CSI uncertainty impacts the design of the active transmitting beamforming of APs, but surprisingly does not directly impact the design of the passive reflecting beamforming of IRSs. It is worth noting that the investigated scenario is flexible and general, and thus the proposed algorithm can act as a general framework to solve various sum-rate maximization problems. Simulation results demonstrate that IRSs can achieve considerable data rate improvement for conventional cell-free networks, and confirm the resilience of the proposed algorithm against the CSI uncertainty.
翻译:智能反射表面(IRS)已成为实施无细胞网络的有希望的经济经济解决办法,然而,IRS的绩效增益主要取决于基于以下假设的智能调控被动波束成型,即准确的频道状态信息(CSI)知识具备,这实际上是不可能的。因此,我们在本文件中调查CSI不确定性对IRS协助的无细胞网络的影响。我们采取了一种随机的编程方法来应对CSI的不确定性,办法是尽可能扩大对无细胞网络的预期弹性,从而保证高于平均水平的强劲性能。因此,平均和率最大化问题主要取决于基于以下假设:由于各种变量并存的预期操作,它可以获得准确的频道状态(CSI)知识;因此,作为妥协办法,我们开发一种高效率的稳健的联合设计算法,其复杂性较低。 特别是,最初的问题可以等同于一种可缩放的形式,然后,通过使用块式协调下降法可以取得当地最佳的解决方案。我们进一步证明,CSI的不确定性对平均弹性递增率网络的设计产生了不易变的难度,由于CSI的变量和对CSI的预期结果进行直接反映。