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Decentralized baseband processing (DBP) architecture, which partitions thebase station antennas into multiple antenna clusters, has been recentlyproposed to alleviate the excessively high interconnect bandwidth, chipinput/output data rates, and detection complexity for massive multi-usermultiple-input multiple-output (MU-MIMO) systems. In this paper, we develop anovel decentralized Gaussian message passing (GMP) detection for the DBParchitecture. By projecting the discrete probability distribution into acomplex Gaussian function, the local means and variances iteratively calculatedin each antenna cluster are fused to generate the global symbol beliefs basedon the proposed message fusion rule in the centralized processing unit. Wepresent the framework and analysis of the convergence of the decentralized GMPdetection based on state evolution under the assumptions of large-system limitand Gaussian sources. Analytical results corroborated by simulationsdemonstrate that nonuniform antenna cluster partition scheme exhibits higherconvergence rate than the uniform counterpart. Simulation results illustratethat the proposed decentralized GMP detection outperforms the recently proposeddecentralized algorithms.

Decentralized Baseband Processing with Gaussian Message Passing Detection for Uplink Massive MU-MIMO Systems