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

IET Microwaves, Antennas & Propagation

Mohsen Khalily¹  Mobayode O. Akinsolu²  Vincent F. Fusco³  Muhammad Ali Babar Abbasi³  Okan Yurduseven³  Simon L. Cotton³  Muhammad Ali Imran⁴  Bo Liu⁴ 

[1] 5GIC 6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK;Faculty of Arts, Science and Technology Wrexham Glyndŵr University Wrexham UK;Institute of Electronics, Communications and Information Technology (ECIT) Queen's University Belfast Belfast UK;James Watt School of Engineering University of Glasgow Glasgow UK;

关键词: antennas;    B5G mobile communication;    channel estimation;    diversity reception;    lens antennas;   

DOI  :  10.1049/mia2.12257

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown