Performance Analysis of Regularized Convex Relaxation for Complex-Valued Data Detection

Ayed M. Alrashdi and Houssem Sifaou
Additional contact information
Ayed M. Alrashdi: Department of Electrical Engineering, College of Engineering, University of Ha’il, P.O. Box 2440, Ha’il 81441, Saudi Arabia
Houssem Sifaou: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK

Mathematics, 2022, vol. 10, issue 9, 1-11

Abstract: In this work, we study complex-valued data detection performance in massive multiple-input multiple-output (MIMO) systems. We focus on the problem of recovering an n -dimensional signal whose entries are drawn from an arbitrary constellation K ⊂ C from m noisy linear measurements, with an independent and identically distributed (i.i.d.) complex Gaussian channel. Since the optimal maximum likelihood (ML) detector is computationally prohibitive for large dimensions, many convex relaxation heuristic methods have been proposed to solve the detection problem. In this paper, we consider a regularized version of this convex relaxation that we call the regularized convex relaxation (RCR) detector and sharply derive asymptotic expressions for its mean square error and symbol error probability. Monte-Carlo simulations are provided to validate the derived analytical results.

Keywords: asymptotic analysis; massive MIMO; mean square error; probability of error; convex relaxation; regularization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/9/1585/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/9/1585/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:9:p:1585-:d:810507

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().