Linear precoding based on polynomial expansion: reducing complexity in massive MIMO.

Citation data:

EURASIP journal on wireless communications and networking, ISSN: 1687-1472, Vol: 2016, Issue: 1, Page: 63

Publication Year:
2016
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Citations 19
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Repository URL:
http://hdl.handle.net/10754/601296
PMID:
27429610
DOI:
10.1186/s13638-016-0546-z
PMCID:
PMC4922392
Author(s):
Mueller, Axel; Kammoun, Abla; Björnson, Emil; Debbah, Mérouane
Publisher(s):
Springer Nature; Springer Science + Business Media
Tags:
Computer Science; Massive MIMO; Linear precoding; Multiuser systems; Polynomial expansion; Random matrix theory
article description
Massive multiple-input multiple-output (MIMO) techniques have the potential to bring tremendous improvements in spectral efficiency to future communication systems. Counterintuitively, the practical issues of having uncertain channel knowledge, high propagation losses, and implementing optimal non-linear precoding are solved more or less automatically by enlarging system dimensions. However, the computational precoding complexity grows with the system dimensions. For example, the close-to-optimal and relatively "antenna-efficient" regularized zero-forcing (RZF) precoding is very complicated to implement in practice, since it requires fast inversions of large matrices in every coherence period. Motivated by the high performance of RZF, we propose to replace the matrix inversion and multiplication by a truncated polynomial expansion (TPE), thereby obtaining the new TPE precoding scheme which is more suitable for real-time hardware implementation and significantly reduces the delay to the first transmitted symbol. The degree of the matrix polynomial can be adapted to the available hardware resources and enables smooth transition between simple maximum ratio transmission and more advanced RZF. By deriving new random matrix results, we obtain a deterministic expression for the asymptotic signal-to-interference-and-noise ratio (SINR) achieved by TPE precoding in massive MIMO systems. Furthermore, we provide a closed-form expression for the polynomial coefficients that maximizes this SINR. To maintain a fixed per-user rate loss as compared to RZF, the polynomial degree does not need to scale with the system, but it should be increased with the quality of the channel knowledge and the signal-to-noise ratio.