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Fast Iterative ELAA-MIMO Detection Exploiting Static Channel Components

Jiuyu Liu, Yi Ma, Rahim Tafazolli

TL;DR

Two novel approaches to accelerate the convergence of current iterative MIMO detectors in ELAA channels by leveraging the static channel component, including the LoS paths and deterministic NLoS components that arise due to channel hardening.

Abstract

Extremely large aperture array (ELAA) is a promising multiple-input multiple-output (MIMO) technique for next generation mobile networks. In this paper, we propose two novel approaches to accelerate the convergence of current iterative MIMO detectors in ELAA channels. Our approaches exploit the static components of the ELAA channel, which include line of sight (LoS) paths and deterministic non-LoS (NLoS) components due to channel hardening effects. This paper proposes novel convergence acceleration techniques for fast iterative ELAA-MIMO detection by leveraging the static channel component, including the LoS paths and deterministic NLoS components that arise due to channel hardening. Specifically, these static channel components are utilized in two ways: as preconditioning matrices for general iterative algorithms, and as initialization for quasi-Newton (QN) methods. Simulation results show that the proposed approaches converge significantly faster compared to current iterative MIMO detectors, especially under strong LoS conditions with high Rician K-factor. Furthermore, QN methods with the proposed initialization matrix consistently achieve the best convergence performance while maintaining low complexity.

Fast Iterative ELAA-MIMO Detection Exploiting Static Channel Components

TL;DR

Two novel approaches to accelerate the convergence of current iterative MIMO detectors in ELAA channels by leveraging the static channel component, including the LoS paths and deterministic NLoS components that arise due to channel hardening.

Abstract

Extremely large aperture array (ELAA) is a promising multiple-input multiple-output (MIMO) technique for next generation mobile networks. In this paper, we propose two novel approaches to accelerate the convergence of current iterative MIMO detectors in ELAA channels. Our approaches exploit the static components of the ELAA channel, which include line of sight (LoS) paths and deterministic non-LoS (NLoS) components due to channel hardening effects. This paper proposes novel convergence acceleration techniques for fast iterative ELAA-MIMO detection by leveraging the static channel component, including the LoS paths and deterministic NLoS components that arise due to channel hardening. Specifically, these static channel components are utilized in two ways: as preconditioning matrices for general iterative algorithms, and as initialization for quasi-Newton (QN) methods. Simulation results show that the proposed approaches converge significantly faster compared to current iterative MIMO detectors, especially under strong LoS conditions with high Rician K-factor. Furthermore, QN methods with the proposed initialization matrix consistently achieve the best convergence performance while maintaining low complexity.
Paper Structure (21 sections, 1 theorem, 24 equations, 2 figures, 1 table)

This paper contains 21 sections, 1 theorem, 24 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model, Preliminaries and Problem Statement
  3. System Model
  4. Linear MIMO Detectors and Iterative Algorithms
  5. -Based Methods
  6. Methods
  7. Methods
  8. Problem Statement
  9. Fast Iterative Detection for ELAA-MIMO Systems Using Direct LoS Channel Component
  10. A Natural Way to Split $\mathbf{A}$
  11. The Proposed Preconditioning Approach
  12. Preconditioned RI (P-RI)
  13. Preconditioned GD Methods
  14. Preconditioned QN Methods
  15. The Proposed Initialization Approach
  16. ...and 6 more sections

Key Result

Theorem 1

Suppose that elements of $\mathbf{H}$ follow the Rician distribution given in (eqn02), and given $N$, as $M$ approaches infinity, the Gram channel matrix $\mathbf{A}$ converges to

Figures (2)

  • Figure 1: Convergence behavior of different iterative MIMO detectors for ELAA systems. There are $512$ service antennas and $32$ user antennas in the system.
  • Figure 2: Convergence comparison in strong LoS condition ($\kappa = 8$) with $256$ service antennas and $32$ user antennas in the ELAA-MIMO system.

Theorems & Definitions (2)

  • Theorem 1
  • Remark 1