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Cell-Free Massive Multiple-input Multiple-output (mMIMO) consists of manyaccess points (APs) in a coverage area that jointly serve the users. Thesesystems can significantly reduce the interference among the users compared toconventional MIMO networks and so enable higher data rates and a largercoverage area. However, Cell-Free mMIMO systems face multiple practicalchallenges such as the high complexity and power consumption of the APs' analogfront-ends. Motivated by prior works, we address these issues by considering alow complexity hybrid beamforming framework at the APs in which each AP has alimited number of RF-chains to reduce power consumption, and the analogcombiner is designed only using the large-scale statistics of the channel toreduce the system's complexity. We provide closed-form expressions for thesignal to interference and noise ratio (SINR) of both uplink and downlink datatransmission with accurate random matrix approximations. Also, based on theexisting literature, we provide a power optimization algorithm that maximizesthe 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 theimpact of power optimization. We observe that the derived approximationsaccurately follow the exact simulations and that in uplink scenario while usingMMSE combiner, power optimization does not improve the performance much.

Cell-Free Massive MIMO with Low-Complexity Hybrid Beamforming