Machine learning‐assisted direction‐of‐arrival accuracy enhancement technique using oversized lens‐loaded cavity

Abbasi, M. A. B., Yurduseven, O., Akinsolu, M. O., Fusco, V. F., Liu, B. , Khalily, M., Cotton, S. L. and Imran, M. A. (2022) Machine learning‐assisted direction‐of‐arrival accuracy enhancement technique using oversized lens‐loaded cavity. IET Microwaves, Antennas and Propagation, 16(6), pp. 305-315. (doi: 10.1049/mia2.12257)

Abstract

This paper presents a framework for achieving machine learning (ML)‐assisted direction‐of‐arrival (DoA) accuracy enhancement using a millimetre‐wave (mmWave) dynamic aperture. The technique used for the enhanced DoA estimation accuracy leverages an over‐sized lens‐loaded cavity antenna connected to a single RF chain in the physical layer and a computational method in the computational layer of the framework. It is shown for the first time that by introducing a reconfigurable mode‐mixing mechanism inside the over‐sized lens‐loaded cavity hardware, a greater number of spatially orthogonal radiation modes can be achieved giving rise to many cavity states. If the best cavity state is determined and selected by means of design exploration using a contemporary ML‐assisted antenna optimisation method, the computational DoA estimation accuracy can be improved. The mode‐mixing mechanism in this work is a randomly oriented metallic scatterer located inside an over‐sized constant−ϵ r lens‐loaded cavity, connected to a stepper motor that is electronically controlled by inputs from the computational layer of the presented framework. Measurement results in terms of near‐field radiation mode scans are included in this study to verify and validate that the proposed ML‐assisted framework enhances the DoA estimation accuracy. Moreover, this investigation simultaneously provides a simplification in the physical layer implementation of mmWave radio hardware, and DoA accuracy enhancement, which in turn lends itself favourably to the adoption of the proposed framework for channel sounding in mmWave communication systems.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Liu, Professor Bo
• Authors: Abbasi, M. A. B., Yurduseven, O., Akinsolu, M. O., Fusco, V. F., Liu, B., Khalily, M., Cotton, S. L., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IET Microwaves, Antennas and Propagation
• Publisher: IET
• ISSN: 1751-8725
• ISSN (Online): 1751-8733
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in IET Microwaves, Antennas and Propagation 16(6):305-315
• Publisher Policy: Reproduced under a Creative Commons licence