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Application of Deep Transfer Learning for Optimal Wireless Beam Selection in a Distributed RAN

Authors:
Chitwan Arora
Abheek Saha

Keywords: Transfer Learning; Deep Learning; Beam prediction; Distributed/Cloud RAN

Abstract:
This paper continues previous explorations in the area of deep learning applications in the field of cellular wireless networks, specifically the problem of identifying optimal beams in a highly directional urban environment, using topographical data. In our previous work, we have studied the problem and demonstrated how deep-learning can be used on static topographical data for prediction of optimal beams. In this paper, we show a potential architecture for realization of the same for a network of nodes in a given area, taking into account challenges of computational complexity, response time and the inherent architecture of the next generation RAN. This is achieved by using deep transfer learning as a way of translating between a global feature space inherent to the coverage area and local variations thereof, specific to the location of each radio-unit.

Pages: 1 to 4

Copyright: Copyright (c) IARIA, 2021

Publication date: May 30, 2021

Published in: conference

ISSN: 2308-4030

ISBN: 978-1-61208-860-0

Location: Valencia, Spain

Dates: from May 30, 2021 to June 3, 2021