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Decentralized Data Detection for Massive MU-MIMO on a Xeon Phi Cluster
(2016)2016 50th Asilomar Conference on Signals, Systems and ComputersConventional 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 ...Conference Paper -
Decentralized beamforming for massive MU-MIMO on a GPU cluster
(2016)2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)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 ...Conference Paper