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

Muhammad Ali Babar Abbasi*, Okan Yurduseven, Mobayode O. Akinsolu, Vincent F. Fusco, Bo Liu, Mohsen Khalily, Simon L. Cotton, Muhammad Ali Imran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
93 Downloads (Pure)

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.


Original languageEnglish
Pages (from-to)305-315
Number of pages11
JournalIET Microwaves, Antennas and Propagation
Volume16
Issue number6
Early online date25 Apr 2022
DOIs
Publication statusPublished - May 2022

Keywords

  • ORIGINAL RESEARCH
  • SELECTED PAPERS FROM THE 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION
  • antennas
  • B5G mobile communication
  • channel estimation
  • diversity reception
  • lens antennas

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