Max-min SINR low complexity transceiver design for single cell massive MIMO

Houssem Sifaou, Abla Kammoun, Luca Sanguinetti, Mérouane Debbah, Mohamed-Slim Alouini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This work focuses on large scale multi-user MIMO systems in which the base station (BS) outfitted with M antennas communicates with K single antenna user equipments (UEs). In particular, we aim at designing the linear precoder and receiver that maximizes the minimum signal-to-interference-plus-noise ratio (SINR) subject to a given power constraint. To gain insights into the structure of the optimal precoder and receiver as well as to reduce the computational complexity for their implementation, we analyze the asymptotic regime where M and K grow large with a given ratio and make use of random matrix theory (RMT) tools to compute accurate approximations. Although simpler, the implementation of the asymptotic precoder and receiver requires fast inversions of large matrices in every coherence period. To overcome this issue, we apply the truncated polynomial expansion (TPE) technique to the precoding and receiving vector of each UE and make use of RMT to determine the optimal weighting coefficients that asymptotically solve the max-min SINR problem. Numerical results are used to show that the proposed TPE-based precoder and receiver almost achieve the same performance as the optimal ones while requiring a lower complexity.
Original languageEnglish (US)
Title of host publication2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781509017492
DOIs
StatePublished - Aug 11 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: L. Sanguinetti and M. Debbah were supported by the ERC Starting Grant 305123 MORE. This research was also partially supported by the research project 5GIOTTO funded by the University of Pisa

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