Training-based channel estimation for massive MIMO systems using LAS algorithm Online publication date: Mon, 20-Jun-2022
by Mitesh Solanki; Shilpi Gupta
International Journal of Ultra Wideband Communications and Systems (IJUWBCS), Vol. 5, No. 2, 2022
Abstract: Likelihood ascent search (LAS) recently emerged as a promising low-complexity near-optimal detection algorithm for massive multiple-input multiple-output (MIMO) systems. LAS detection algorithm design with perfect channel state information (CSI) consideration is an area of research attention. In the real-time scenario, channel estimation has been challenging in communication systems. This article proposes a lattice reduction-based LAS (LR-LAS) detection for massive MIMO systems under imperfect CSI. Our proposed LR-LAS take the medium-to-strong channel estimation error of antennas into account when computing the maximum likelihood (ML) cost function. Our approach presents a novel iterative detection and training-based channel estimation framework. Simulation results show that the proposed LR-LAS detector significantly improves bit error rate (BER) performance compared to their conventional LAS counterpart. Hence, our proposed detector can be used as an efficient candidate under imperfect CSI in the system.
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