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Title: Feedforward Architectures for Decentralized Precoding in Massive MU-MIMO Systems
Massive multi-user multiple-input multiple-output (MU-MIMO) enables significant gains in spectral efficiency and link reliability compared to conventional, small-scale MIMO technology. In addition, linear precoding using zero forcing or Wiener filter (WF) precoding is sufficient to achieve excellent error rate performance in the massive MU-MIMO downlink. However, these methods typically require centralized processing at the base-station (BS), which causes (i) excessively high interconnect and chip input/output data rates, and (ii) high implementation complexity. We propose two feed-forward architectures and corresponding decentralized WF precoders that parallelize precoding across multiple computing fabrics, effectively mitigating the limitations of centralized approaches. To demonstrate the efficacy of our decentralized precoders, we provide implementation results on a multi-GPU system, which show that our solutions achieve throughputs in the Gbit/s regime while achieving (near-)optimal error-rate performance in the massive MU-MIMO downlink.  more » « less
Award ID(s):
1717218
NSF-PAR ID:
10119342
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2018 IEEE 52nd Asilomar Conference on Signals, Systems, and Computers
Page Range / eLocation ID:
1659 to 1665
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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