Distributed Optimization of Uplink Cell-Free Massive MIMO Networks

This paper studies distributed optimization of an uplink cell-free Massive MIMO (CF-mMIMO) network. By observing some interesting analogies between the CF network and an artificial neural network (ANN), we propose to relate the uplink CF network to a so-called quasi-neural network. Borrowing the idea of the back-propagation (BP) algorithm, we propose a novel scheme to optimize the central processing unit (CPU) and the access points (APs) of the network. The proposed scheme can achieve multi-AP cooperation using only the pilot sequences, but without the channel state information (CSI). To reduce the required throughput of the fronthaul, we let each AP beamform the received vector signals into scalar ones before passing them to the CPU. The effectiveness of the proposed scheme is verified by the simulations.