Distributed Learning for MIMO Relay Networks

• Published in: IEEE journal of selected topics in signal processing Vol. 16; no. 3; pp. 343 - 357
• Main Authors: Wang, Rui, Jiang, Yi, Zhang, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array signal processing; Artificial neural network; Artificial neural networks; Beamforming; Data exchange; Data transmission; distributed learning; Frame design; instantaneous power constraints; Interference; interference suppression; Learning; Learning theory; Nodes; Power amplifiers; Radio frequency; relay network; Relay networks; Relay networks (telecommunication); Service introduction; Signal processing algorithms; Signal to noise ratio; Transceivers
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2022.3140953

This paper studies a multi-antenna multi-user and multi-relay network, where the radio frequency (RF) power amplifiers (PA) of the nodes are subject to instantaneous power constraints. To optimize the nonlinear transceivers of the distributed nodes, we introduce a novel perspective of relating a relay network to an artificial neural network (ANN). With this perspective, we propose a distributed learning-based relay beamforming (DLRB) scheme. Based on a set of pilot sequences, the DLRB scheme can optimize the transceivers to minimize the mean squared error (MSE) of the data stream in a distributed manner. It can effectively coordinate the distributed relay nodes to form a virtual array to suppress interferences, even assuming neither the channel state information (CSI) nor information exchange between the relay nodes or between the users. We also present a frame design to support the DRLB so that it can adapt well with time-varying channels. Extensive simulations verify the effectiveness of the proposed scheme.