Cell-Free Massive Multiple-input Multiple-output (mMIMO) consists of many access points (APs) in a coverage area that jointly serve the users. These systems can significantly reduce the interference among the users compared to conventional MIMO networks and so enable higher data rates and a larger coverage area. However, Cell-Free mMIMO systems face multiple practical challenges such as the high complexity and power consumption of the APs' analog front-ends. Motivated by prior works, we address these issues by considering a low complexity hybrid beamforming framework at the APs in which each AP has a limited number of RF-chains to reduce power consumption, and the analog combiner is designed only using the large-scale statistics of the channel to reduce the system's complexity. We provide closed-form expressions for the signal to interference and noise ratio (SINR) of both uplink and downlink data transmission with accurate random matrix approximations. Also, based on the existing literature, we provide a power optimization algorithm that maximizes the minimum SINR of the users for uplink scenario. Through several simulations, we investigate the accuracy of the derived random matrix approximations, trade-off between the 95% outage data rate and the number of RF-chains, and the impact of power optimization. We observe that the derived approximations accurately follow the exact simulations and that in uplink scenario while using MMSE combiner, power optimization does not improve the performance much.
翻译:在一个共同服务用户的覆盖地区,这些系统可以大大减少用户与传统的MIMO网络相比的干扰,从而能够提高数据率和更大的覆盖面积。然而,无细胞MIMIMIMO系统面临着多种实际挑战,例如APs模拟前端的模拟数据传输的高度复杂和能量消耗。受先前著作的驱动,我们通过考虑在APs中每个AP都拥有有限的RF链以减少电力消耗的低复杂混合波束框架来解决这些问题。这些系统可以大大减少用户与传统的MIMO网络相比的干扰,从而能够降低数据率和更大的覆盖面积。然而,无细胞MIMIMIMO系统面临着多种实际挑战,如APs模拟前端的模拟数据传输的高度复杂和能量消耗。此外,根据现有文献,我们提供了一种权力优化算法,使每个APs都有有限的RF链链条减少电力消耗量,而模拟组合组合的模拟组合器仅利用频道的大规模统计数据来降低系统的复杂性。我们用最精确的精确的模型来测量了RFSMM的精确度,我们用最精确的推算法和最精确的精确的精确度,同时测量了RFMRM的精确度。