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Performance Analysis of MIMO using Machine Learning in 5G Networks

Authors:
Christos Bouras
Ioannis Prokopiou
Apostolos Gkamas
Vasileios Kokkinos

Keywords: MIMO; Machine Learning; 5G; Deep Learning; Internet of Things; Big Data

Abstract:
Massive Multiple-Input Multiple-output (MIMO) is a high-potential radio antenna technology for mobile wireless networks, such as 5th Generation (5G). The use of hybrid analog and digital precoding to minimize the energy consumption as well as the hardware complexity of mixed signal components is an essential strategy. Machine Learning (ML) could be able to boost 5G technologies due to the rising difficulty of configuring cellular networks. More than ever, an ML computational framework focused on successfully processing the expected huge data generated normally by 5G networks with high subscriber cell density, is required. In the Ultra-Dense Networks (UDNs) of 5G and beyond high demanding networks paired with beamforming and massive MIMO technologies, ML struggles to define network traffic aspects distinctively, especially when they are projected to be much more dynamic and complicated. This paper presents a state-of-the-art analysis of the combined and multiple uses of ML along with MIMO technology in 5G Networks.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2022

Publication date: May 22, 2022

Published in: conference

ISSN: 2308-4219

ISBN: 978-1-61208-973-7

Location: Venice, Italy

Dates: from May 22, 2022 to May 26, 2022