Feedforward Architectures for Decentralized Precoding in Massive MU-MIMO Systems
Open access
Date
2018Type
- Conference Paper
ETH Bibliography
no
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Abstract
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 feedforward 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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000455596Publication status
publishedExternal links
Editor
Book title
2018 52nd Asilomar Conference on Signals, Systems, and ComputersPages / Article No.
Publisher
IEEEEvent
Organisational unit
09695 - Studer, Christoph / Studer, Christoph
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ETH Bibliography
no
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