Intelligent Autonomous User Discovery and Link Maintenance for mmWave and TeraHertz Devices with Directional Antennas

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.

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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 and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 07 Apr 2021 09:28
Last Modified: 18 Jan 2023 22:54
DOI: 10.1109/tccn.2021.3071142
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