Abbasi, Muhammad Ali Babar, Fusco, Vincent, Yurduseven, Okan, Ansari, Rafay and Cotton, Simon (2021) Lens-loaded Cavity Antenna with Detector Diode as a Direction-of-Arrival Estimator. IEEE Antennas and Wireless Propagation Letters, 20 (11). pp. 2176-2180. ISSN 1536-1225
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Abstract
This letter proposes a novel direction-of-arrival (DoA) estimation approach that uses a lens-loaded, coded aperture antenna connected to a detector diode for millimeter-wave (mmWave) frequencies. The presented approach exploits three distinct but closely related properties of mmWave communication front-end hardware: First, the quasi-random radiation modes created by a lens-loaded cavity antenna; Second, the frequency-diverse impulse response of oversized metallic cavities; Third, the sensitivity of detector diodes at mmWave frequencies. Critically, we use a combination of the synthesis approach and experimental results to demonstrate that DoA estimation is possible with less complex hardware as compared to conventional methods, where an array of mmWave antennas is typically employed for DoA estimation. Bandwidth-to-accuracy tradeoff is thoroughly studied, which is an important commercial matrix. It is demonstrated that the lens-loaded cavity antenna with a detector diode circuit can be an apt choice for DoA estimation. Finally, it is shown that the proposed approach provides fast and accurate DoA estimation, thus inherently suitable for DoA estimation in future mmWave systems.
Item Type: | Article |
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Additional Information: | Funding information: This work was partially funded by the Engineering and Physical Sciences Research Council (EPSRC) under grant EP/P000673/1 and Leverhulme Trust under Research Leadership Award RL2019-019. |
Uncontrolled Keywords: | B5G, channel estimation, detector diode, direction-of-arrival estimation, frequency-diverse antenna, lens antenna, millimetre-wave |
Subjects: | G500 Information Systems G900 Others in Mathematical and Computing Sciences H600 Electronic and Electrical Engineering |
Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | Rachel Branson |
Date Deposited: | 27 Jul 2021 10:18 |
Last Modified: | 22 Dec 2021 16:18 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/46764 |
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