Decentralized signal detection via first-order approximate message passing for uplink massive MIMO systems

Abstract

Massive MIMO technology has been used in wireless communication systems, due to its high spectral efficiency. However, existing baseband processing methods for uplink massive MIMO systems mainly rely on centralized baseband processing architectures, which results in high data volume between base station antennas and baseband processing unit, and leads to high computational complexity in the single baseband processing unit. In response to these problems, some decentralized baseband processing (DBP) architectures have been presented recently. This paper investigates the performance of two kinds of DBP architectures, i.e., the Star architecture with a fusion unit and Daisy-Chain architecture without a fusion unit. For the Star architecture, a first-order approximate message passing based detection method is used in the decentralized-processing units (DUs) to reduce the computational complexity while keeping a comparable detection performance, compared to the expectation propagation based DBP detection method. Furthermore, for the Daisy-Chain architecture, a sequential detection scheme and a local fusion method is utilized to improve the detection performance, compared to the Star architecture. Simulation results show that the proposed methods exhibit desirable tradeoff between detection accuracy and computational complexity.