  Decentralized baseband processing (DBP) architecture, which partitions the
base station antennas into multiple antenna clusters, has been recently
proposed to alleviate the excessively high interconnect bandwidth, chip
input/output data rates, and detection complexity for massive multi-user
multiple-input multiple-output (MU-MIMO) systems. In this paper, we develop a
novel decentralized Gaussian message passing (GMP) detection for the DBP
architecture. By projecting the discrete probability distribution into a
complex Gaussian function, the local means and variances iteratively calculated
in each antenna cluster are fused to generate the global symbol beliefs based
on the proposed message fusion rule in the centralized processing unit. We
present the framework and analysis of the convergence of the decentralized GMP
detection based on state evolution under the assumptions of large-system limit
and Gaussian sources. Analytical results corroborated by simulations
demonstrate that nonuniform antenna cluster partition scheme exhibits higher
convergence rate than the uniform counterpart. Simulation results illustrate
that the proposed decentralized GMP detection outperforms the recently proposed
decentralized algorithms.
