Decentralized Data Detection for Massive MU-MIMO on a Xeon Phi Cluster

Abstract

Conventional centralized data detection algorithms for massive multi-user multiple-input multiple-output (MU-MIMO) systems, such as minimum mean square error (MMSE) equalization, result in excessively high raw baseband data rates and computing complexity at the centralized processing unit. Hence, practical base-station (BS) designs for massive MU-MIMO that rely on state-of-the-art hardware processors and I/O interconnect standards must find new means to avoid these bottlenecks. In this paper, we propose a novel decentralized data detection method, which partitions the BS antenna array into separate clusters. Each cluster is associated with independent computing hardware to perform decentralized data detection, which requires only local channel state information and receive data, and a minimum amount of information exchange between clusters. To demonstrate the benefits of our approach, we map our algorithm to a Xeon Phi cluster, which shows that BS designs with hundreds or thousands of BS antennas can be supported.