Robust conjugate-gradient based LAS detector for massive MIMO systems. Issue 5 (4th May 2022)

• Record Type: Journal Article
• Authors: Solanki, Mitesh; Gupta, Shilpi
• Is Part Of: International journal of electronics. Volume 109:Issue 5(2022)
• Page Start: 794
• Page End: 810
• Publication Date: 2022-05-04
• Subjects: Massive MIMO -- conjugate gradient (CG) -- likelihood ascent search (LAS) -- unconstrained maximum-likelihood (ML) space; Electronics -- Periodicals; Electronic control -- Periodicals; 621.381
• DOI: 10.1080/00207217.2021.1941292
• Languages: English
• ISSNs: 0020-7217

Abstract

ABSTRACT: Low-complexity neighbourhood search algorithms for a massive multiple-input multiple-output (MIMO) wireless system has fascinated recent research attention. It performs iterative searches in a constrained maximum-likelihood (ML) space for the solution vector. However, they drive an inversion of large-dimensional matrices with an enormous amount of computations resulting in them becoming practically infeasible. It motivates for development of low-complexity matrix-inversion-free data detection algorithm that is proficient in achieving near-optimal performance in an unconstrained ML space. Using these concepts and the conjugate gradient (CG) approach, this article proposes the computationally efficient CG-based likelihood ascent search (CGLAS) detector. A CGLAS detection algorithm is proposed to achieve a fast update vector within unconstrained ML space in conjugate descent direction with few iterations. Simulation results demonstrate that this robust detection algorithm exerts more influence rather than other recent state-of-the-art detection algorithms that achieve much better performance for massive MIMO systems with superior running time efficiency.