RLS-Based Detection for Massive Spatial Modulation MIMO

Bereyhi A, Asaad S, Gäde B, Müller R (2019)

Publication Type: Conference contribution

Publication year: 2019

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2019-July

Pages Range: 1167-1171

Conference Proceedings Title: IEEE International Symposium on Information Theory - Proceedings

Event location: Paris FR

ISBN: 9781538692912

DOI: 10.1109/ISIT.2019.8849366

Abstract

Most detection algorithms in spatial modulation (SM) are formulated as linear regression via the regularized least-squares (RLS) method. In this method, the transmit signal is estimated by minimizing the residual sum of squares penalized with some regularization. This paper studies the asymptotic performance of a generic RLS-based detection algorithm employed for recovery of SM signals. We derive analytically the asymptotic average mean squared error and the error rate for the class of bi-unitarily invariant channel matrices.The analytic results are employed to study the performance of SM detection via the box-LASSO. The analysis demonstrates that the performance characterization for i.i.d. Gaussian channel matrices is valid for matrices with non-Gaussian entries, as well. This justifies the partially approved conjecture given in [1]. The derivations further extend the former studies to scenarios with non-i.i.d. channel matrices. Numerical investigations validate the analysis, even for practical system dimensions.

Authors with CRIS profile

Ali Bereyhi Lehrstuhl für Digitale Übertragung Saba Asaad Lehrstuhl für Digitale Übertragung Bernhard Gäde Lehrstuhl für Digitale Übertragung Ralf Müller Professur für Informationsübertragung

How to cite

APA:

Bereyhi, A., Asaad, S., Gäde, B., & Müller, R. (2019). RLS-Based Detection for Massive Spatial Modulation MIMO. In IEEE International Symposium on Information Theory - Proceedings (pp. 1167-1171). Paris, FR: Institute of Electrical and Electronics Engineers Inc..

MLA:

Bereyhi, Ali, et al. "RLS-Based Detection for Massive Spatial Modulation MIMO." Proceedings of the 2019 IEEE International Symposium on Information Theory, ISIT 2019, Paris Institute of Electrical and Electronics Engineers Inc., 2019. 1167-1171.

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