Machine learning‐assisted direction‐of‐arrival accuracy enhancement technique using oversized lens‐loaded cavity
Abbasi, M.A.B, Yurduseven, O., Akinsolu, Mobayode O., Fusco, V. F., Liu, Bo, 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 & Propagation, 16 (6). pp. 305-315. ISSN 1751-8733
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GURO_577_Machine learning‐assisted direction‐of‐arrival accuracy enhancement.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
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−ϵr 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: | Article |
|---|---|
| Keywords: | antennas, B5G mobile communication, channel estimation, diversity reception, and lens antennas |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 03 Jul 2023 13:23 |
| Last Modified: | 03 Jul 2023 13:23 |
| URI: | https://glyndwr.repository.guildhe.ac.uk/id/eprint/18032 |
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