University of Oulu

P. Susarla, B. Gouda, Y. Deng, M. Juntti, O. Silvén and A. Tölli, "Learning-Based Beam Alignment for Uplink mmWave UAVs," in IEEE Transactions on Wireless Communications, vol. 22, no. 3, pp. 1779-1793, March 2023, doi: 10.1109/TWC.2022.3206714.

Learning-based beam alignment for uplink mmWave UAVs

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Author: Susarla, Praneeth1; Gouda, Bikshapathi1; Deng, Yansha2;
Organizations: 1Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2Department of Informatics, King’s College London, London, U.K.
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-03-24


Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 22
Issue: 3
Pages: 1779 - 1793
DOI: 10.1109/TWC.2022.3206714
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Academy of Finland (6Genesis Flagship) under Grant 346208; and in part by the Engineering and Physical Research Council (EPSRC), U.K., under Grant EP/W004348/1.
Academy of Finland Grant Number: 346208
Detailed Information: 346208 (Academy of Finland Funding decision)
Copyright information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see