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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ayed M. Alrashdi & Houssem Sifaou, 2022. "Performance Analysis of Regularized Convex Relaxation for Complex-Valued Data Detection," Mathematics, MDPI, vol. 10(9), pages 1-11, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1585-:d:810507
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    Cited by:

    1. Rashid Ali & Hyung Seok Kim, 2022. "Applied Mathematics for 5th Generation (5G) and beyond Communication Systems," Mathematics, MDPI, vol. 10(16), pages 1-2, August.

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