Huang, Sijin;
Tang, Jie;
Zhou, Zihao;
Yang, Guangguang;
Davydov, Maksim V;
Wong, Kai Kit;
(2025)
A Q- Learning and Fuzzy Logic Based Routing Protocol for UAV Networks.
In:
2024 16th International Conference on Wireless Communications and Signal Processing (WCSP).
(pp. pp. 1090-1095).
IEEE: Hefei, China.
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Abstract
With the development of ad hoc network technol-ogy, unmanned aerial vehicle (UAV) swarm has demonstrated significant promise across civil and military domains. However, owing to unique attributions, such as high dynamic topology, 3-D mobility and low density, it's extremely challenging to establish a reliable and robust communication between flying nodes. In this paper, we proposed a Q-Learning and Fuzzy Logic based Routing Protocol (QFRP) for UAV networks, which adopted an efficient Q-value update mechanism based on HELLO and ACK. In this mechanism, we take neighbor set coherence and link lifetime into account. Since routing exploration has an important impact on routing performance, we proposed a fuzzy logic based mechanism for exploration and exploitation that considers Q-value, link quality and access delay to mitigate the blindness of random exploration. Simulation results demonstrate that QFRP can make efficient routing decisions within dynamic multi-hop UAV networks, and outperforms existing protocols regarding packet delivery ratio (PDR), end-to-end (E2E) delay, and routing overhead.
| Type: | Proceedings paper |
|---|---|
| Title: | A Q- Learning and Fuzzy Logic Based Routing Protocol for UAV Networks |
| Event: | 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP) |
| Dates: | 24 Oct 2024 - 26 Oct 2024 |
| ISBN-13: | 979-8-3503-9064-3 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/WCSP62071.2024.10826772 |
| Publisher version: | https://doi.org/10.1109/wcsp62071.2024.10826772 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | UAV swarm, Q-Learning, fuzzy logic, routing protocol, wireless communication, FANETs |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205326 |
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