Khan, Zaheer, Lehtomaki, Janne J, Selis, Valerio
ORCID: 0000-0002-1856-4707, Ahmadi, Hamed and Marshall, Alan
ORCID: 0000-0002-8058-5242
(2021)
Intelligent Autonomous User Discovery and Link Maintenance for mmWave and TeraHertz Devices with Directional Antennas
IEEE Transactions on Cognitive Communications and Networking, 7 (4).
p. 1.
ISSN 2332-7731, 2372-2045
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main_accepted.pdf - Author Accepted Manuscript Download (4MB) | Preview |
Abstract
Use of smart directional antennas in handheld devices to generate a narrow beam in different directions for mmWave/TeraHertz communications present significant challenges. Devices using such antennas may have to scan several different directions in three-dimensional (3D) space to discover another user or an access point, a process that can result in problematic delays. Moreover, small movements of a user/device in the form of rotation and/or displacement may cause the discovered link to be lost. This paper proposes adaptive link discovery algorithms for devices in both infrastructure/ad hoc networks and evaluates their performance in terms of time-to-discovery. We show that one of the two proposed methods provides guaranteed discovery. We use an inertial measurement unit sensor to help intelligently rediscover a lost/degraded link. We propose sensor assisted link prediction methods for low-latency rediscovery in 3D space. We evaluate the effectiveness of our prediction-based rediscovery methods by testing them with real datasets representing various user/device 3D rotation patterns. We show that the smoothing based rediscovery can reach the prediction accuracy to 100% when two antenna sectors are searched, and it reduces the time-to-rediscovery by up to Sx (S ×l) as compared to the time-to-discovery, where S is the number of antenna sectors.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 5G, teraHertz, mmWave, directional antenna, IMU sensor, orientation, predictions, 6G |
| Divisions: | Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 07 Apr 2021 09:28 |
| Last Modified: | 01 Mar 2026 10:45 |
| DOI: | 10.1109/tccn.2021.3071142 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3118561 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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