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Robust conjugate-gradient based LAS detector for massive MIMO systems

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.