Abstract
Network-assisted full-duplex enable simultaneous uplink (UL) and downlink (DL) communications by allocating remote antenna units (RAUs) to perform either UL reception of DL transmission at each time slot, which improve the spectral efficiency (SE) of cell-free massive multiple-input multiple-output (MIMO) systems. However, previous studies mainly focus on analyzing the system performance under given duplex mode of RAUs. In this letter, we propose to dynamically optimize the duplex mode of RAUs to further improve the SE. An algorithm based on parallel successive convex approximation is proposed to solve the non-convex duplex mode optimization problem. To reduce algorithm complexity and achieve higher SE, a reinforcement learning algorithm based on enhanced Q-learning is also proposed. Simulation results show that the proposed algorithms improve the SE of cell-free massive MIMO systems greatly with lower complexity.
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