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Open Access

ARTICLE

Predictive-Q Learning Based Interference-and-Mobility Aware Dual-Path Routing for UAV Swarm Networks with Mobile Edge Computing

Zhihao Dong1,2, Huakui Sun1,2,*, Yueyue Tao2, Daosen Zhai2
1 School of Airspace Science and Engineering, Shandong University, Weihai, China
2 School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
* Corresponding Author: Huakui Sun. Email: email
(This article belongs to the Special Issue: Deep Reinforcement Learning for Space-Air-Ground Integrated Edge Computing: Architectures, Algorithms, and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.084301

Received 20 April 2026; Accepted 03 June 2026; Published online 29 June 2026

Abstract

High-mobility Unmanned Aerial Vehicle (UAV) swarm networks suffer from fast-varying connectivity and interference, and therefore routing decisions must jointly account for link instability and topology changes. By leveraging mobile edge computing (MEC) capabilities, each UAV can perform online routing decisions locally without relying on centralized controllers. This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility, where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs, predicts link lifetime from relative motion, and anticipates forward connectivity and neighbor redundancy. To further enhance reliability under harsh interference, we extend Predictive-Q with a lightweight dual-path forwarding mechanism. Specifically, it conditionally splits a backup routing when the primary next hop becomes unreliable, and terminates the backup early when continued forwarding is unlikely to be beneficial, thereby controlling overhead. Simulation results demonstrate that, compared with existing methods, the proposed Predictive-Q-Dual improves packet delivery ratio by more than 15% over GPSR under strong interference while maintaining low delay and energy consumption across varying interference intensity, node density, and mobility speed.

Keywords

UAV swarm networks; routing; reinforcement learning; dual-path forwarding; mobile edge computing

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