FastNet for Symbol Detection in Massive MIMO Systems

Bhattacharya, S and Hari, KVS (2023) FastNet for Symbol Detection in Massive MIMO Systems. In: 9th IEEE International Conference on Electronics, Computing and Communication Technologies, 14-16 July 2023, Bangalore, India.

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Abstract

In massive multiple-input-multiple-output (MIMO) systems, a major limiting factor for symbol detection is the amount of computational complexity required. Symbol detection in unquantized massive MIMO systems have been studied in the context of both traditional and machine learning methods. In this paper, we propose a hybrid framework that replaces some neural network layers with simple gradient descent layers to reduce complexity. Simulations showed that a judicious choice of the number of such layers can lead to significant reduction in the range of 35-50, in computational complexity, with marginal change in performance. © 2023 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Complex networks; Computational complexity; Deep learning; Gradient methods; Learning systems; MIMO systems; Multilayer neural networks; Signal detection, Deep learning; Deep unfolding; Gradient-descent; Multiple inputs; Multiple outputs; Multiple-Input Multiple- Output systems; Multiple-input-multiple-output; Symbols detection; Traditional learning; Unfoldings, Network layers
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	21 Dec 2023 04:28
Last Modified:	21 Dec 2023 04:28
URI:	https://eprints.iisc.ac.in/id/eprint/83541

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