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April 18, 2026DronesOpen Access

Energy-Harvesting-Assisted UAV Swarm Anti-Jamming Communication Based on Multi-Agent Reinforcement Learning

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Authors

YLYongfang LiTZTianyu ZhaoZWZhijuan Wu

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Overview

Decentralized approach improves communication success in UAV swarms, indicating enhanced energy efficiency.

Key Points

  • Develop an anti-jamming communication framework for UAV swarms that balances transmission success with energy consumption.
  • Model the communication problem as a decentralized partially observable Markov decision process (Dec-POMDP)
  • Implement an independent advantage actor-critic (IA2C) algorithm for each cluster head to manage channel, power, and energy harvesting policies
  • Utilize a time-space-based extended Dec-POMDP to learn spatiotemporal correlations among neighboring nodes
  • Achieved a 17.26% improvement in average cumulative reward
  • Observed a 10.37% increase in average cumulative success rate
  • Successfully maintained a higher transmission success rate with lower energy consumption compared to benchmark schemes

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69e31f9e40886becb653ed1chttps://doi.org/10.3390/drones10040294
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