  Recently, the decentralized baseband processing (DBP) paradigm and relevant
detection methods have been proposed to enable extremely large-scale massive
multiple-input multiple-output technology. Under the DBP architecture, base
station antennas are divided into several independent clusters, each connected
to a local computing fabric. However, current detection methods tailored to DBP
only consider ideal white Gaussian noise scenarios, while in practice, the
noise is often colored due to interference from neighboring cells. Moreover, in
the DBP architecture, linear minimum mean-square error (LMMSE) detection
methods rely on the estimation of the noise covariance matrix through averaging
distributedly stored noise samples. This presents a significant challenge for
decentralized LMMSE-based equalizer design. To address this issue, this paper
proposes decentralized LMMSE equalization methods under colored noise scenarios
for both star and daisy chain DBP architectures. Specifically, we first propose
two decentralized equalizers for the star DBP architecture based on
dimensionality reduction techniques. Then, we derive an optimal decentralized
equalizer using the block coordinate descent (BCD) method for the daisy chain
DBP architecture with a bandwidth reduction enhancement scheme based on
decentralized low-rank decomposition. Finally, simulation results demonstrate
that our proposed methods can achieve excellent detection performance while
requiring much less communication bandwidth.
