Title
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 base-band 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.
Year
Venue
Field
2016
2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP)
WSDMA,Beamforming,Multi-user MIMO,GPU cluster,Computer science,Antenna array,Real-time computing,Computer engineering,Precoding,Scalability,Channel state information
DocType
ISSN
Citations 
Conference
2376-4066
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Kaipeng Li1386.03
Riski Skaran200.34
Yujun Chen3142.42
Joseph R. Cavallaro41175115.35
Tom Goldstein5174991.01
Christoph Studer6109785.83