Cell-free massive multiple-input multiple-output (MIMO) and intelligent reflecting surface (IRS) are considered as the prospective multiple antenna technologies for beyond the fifth-generation (5G) networks. Cell-free MIMO systems powered by IRSs, combining both technologies, can further improve the performance of cell-free MIMO systems at low cost and energy consumption. Prior works focused on instantaneous performance metrics and relied on alternating optimization algorithms, which impose huge computational complexity and signaling overhead. To address these challenges, we propose a novel two-step algorithm that provides the long-term passive beamformers at the IRSs using statistical channel state information (S-CSI) and short-term active precoders and long-term power allocation at the access points (APs) to maximize the minimum achievable rate. Simulation results verify that the proposed scheme outperforms benchmark schemes and brings a significant performance gain to the cell-free MIMO systems powered by IRSs.
翻译:在第五代(5G)网络之外,无细胞的大规模多投入多输出(MIMO)和智能反射表面(IRS)被视为潜在的多种天线技术,由IRS供电的无细胞的MIMO系统,结合这两种技术,可以以低成本和低能消耗,进一步提高无细胞的MIMO系统的性能; 先前的工作侧重于即时性能衡量标准,并依赖于交替优化算法,这种算法造成了巨大的计算复杂性和信号性间接费用; 为了应对这些挑战,我们提议采用新型的两步算法,利用统计频道国家信息(S-CSI)和短期主动预言器以及接入点的长期电力分配,为无细胞的IMO系统提供长期被动信号,以最大限度地提高最低可实现的速率; 模拟结果核实拟议的计划比基准计划要好,并为IRS供电的无细胞的IMO系统带来显著的性能收益。