Asymptotics of Nonlinear LSE Precoders with Applications to Transmit Antenna Selection

Bereyhi A, Sedaghat MA, Müller R (2017)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2017

Publisher: IEEE

Conference Proceedings Title: Proceedings of IEEE International Symposium on Information Theory (ISIT)

Event location: Aachen DE

ISBN: 978-1-5090-4096-4

DOI: 10.1109/ISIT.2017.8006494

Open Access Link: https://arxiv.org/abs/1705.04294

Abstract

This paper studies the large-system performance of Least Square Error (LSE) precoders which minimize the input-output distortion over an arbitrary support subject to a general penalty function. The asymptotics are determined via the replica method in a general form which encloses the Replica Symmetric (RS) and Replica Symmetry Breaking (RSB) ansätze. As a result, the "marginal decoupling property" of LSE precoders for b-steps of RSB is derived. The generality of the studied setup enables us to address special cases in which the number of active transmit antennas are constrained. Our numerical investigations depict that the computationally efficient forms of LSE precoders based on "ℓ1-norm" minimization perform close to the cases with "zero-norm" penalty function which have a considerable improvements compared to the random antenna selection. For the case with BPSK signals and restricted number of active antennas, the results show that RS fails to predict the performance while the RSB ansatz is consistent with theoretical bounds.

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How to cite

APA:

Bereyhi, A., Sedaghat, M.A., & Müller, R. (2017). Asymptotics of Nonlinear LSE Precoders with Applications to Transmit Antenna Selection. In Proceedings of IEEE International Symposium on Information Theory (ISIT). Aachen, DE: IEEE.

MLA:

Bereyhi, Ali, Mohammad Ali Sedaghat, and Ralf Müller. "Asymptotics of Nonlinear LSE Precoders with Applications to Transmit Antenna Selection." Proceedings of the IEEE International Symposium on Information Theory (ISIT), Aachen IEEE, 2017.

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