Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance

Kaur, J. , Bhatti, S., Tan, K. , Popoola, O. R. , Imran, M. A. , Ghannam, R. , Abbasi, Q. and Abbas, H. T. (2024) Contextual beamforming: Exploiting location and AI for enhanced wireless telecommunication performance. APL Machine Learning, 2(1), 016113. (doi: 10.1063/5.0176422)

Abstract

Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

• Item Type: Articles
• Additional Information: This work was funded by a PhD studentship from EIT Digital EU and the University of Glasgow.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Popoola, Dr Olaoluwa and Ghannam, Professor Rami and Tan, Kang and Bhatti, Mr Satyam and Abbas, Dr Hasan and Abbasi, Professor Qammer and Kaur, Jaspreet and Imran, Professor Muhammad
• Authors: Kaur, J., Bhatti, S., Tan, K., Popoola, O. R., Imran, M. A., Ghannam, R., Abbasi, Q., and Abbas, H. T.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: APL Machine Learning
• Publisher: AIP Publishing
• ISSN: 2770-9019
• Published Online: 26 February 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in APL Machine Learning 2(1):016113
• Publisher Policy: Reproduced under a Creative Commons licence