Decentralized beamforming for massive MU-MIMO on a GPU cluster

Abstract

In the massive multi-user multiple-input multiple-output (MU-MIMO) downlink, traditional centralized beamforming (or precoding), such as zero-forcing (ZF), entails excessive complexity for the computing hardware, and generates raw baseband data rates that cannot be supported with current interconnect technology and chip I/O interfaces. In this paper, we present a novel decentralized beamforming approach that partitions the base-station (BS) antenna array into separate clusters, each associated with independent computing hardware. We develop a decentralized beamforming algorithm that requires only local channel state information and minimum exchange of consensus information among the clusters. We demonstrate the efficacy and scalability of decentralized ZF beamforming for systems with hundreds of BS antennas using a reference implementation on a GPU cluster.