加强可靠和节能无人机通信与RIS和深度强化学习.
Wasim Ahmad1, Umar Islam2, Abdulkadhem A Abdulkadhem3
1School of Arts and Creative Technology University of Greater Manchester, Bolton, United Kingdom.
PeerJ. Computer science
|September 24, 2025
概括
本研究引入了一种使用深度强化学习 (DRL) 和正方位相位转换 (QPSK) 来减轻无人机 (UAV) 系统中的电磁干扰 (EMI) 的新型框架,该系统具有可重新配置的智能表面 (RIS). 该系统提高了信号质量,能源效率和在具有挑战性的环境中覆盖范围.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 电磁学 电磁学 电磁学 电磁学
背景情况:
- 越来越多的无线通信需求需要提高可靠性,覆盖率和能源效率.
- 无人机 (UAV) 和可重新配置的智能表面 (RIS) 是增强无线系统的关键技术.
- 使用深度强化学习 (DRL) 将RIS与无人机集成的现有研究往往忽视了电磁干扰 (EMI) 的挑战.
研究的目的:
- 为RIS辅助无人机通信系统提出一个新的框架,解决化 (GaN) 功率放大器的EMI问题.
- 将DRL与方位相位移键 (QPSK) 调制集成,以实时优化无人机部署和RIS配置.
- 为了减轻EMI的影响,改善信号与干扰加噪声比 (SINR),并提高能源效率.
主要方法:
- 开发基于DRL的框架,用于动态优化UAV和RIS参数.
- 集成QPSK调制以管理在EMI条件下的信号传输.
- 实时调整系统配置以抵消EMI和优化性能指标.
主要成果:
- 在易受干扰的环境中,达到6.5dB的SINR改进.
- 与基线模型相比,能源效率提高了38%.
- 减少了70%以上的EMI影响,并扩大了35%的覆盖面积.
结论:
- 拟议的框架有效地减轻了RIS辅助无人机系统中的EMI,优于传统方法.
- 整合QPSK和DRL使通信质量和能源消耗的实时平衡成为可能.
- 该系统显示出在动态和具有挑战性的环境中部署的巨大潜力,如城市地区,灾害区和偏远地区.
相关概念视频
01:29Application of Linearization and Approximation
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01:24Absolute Motion Analysis- General Plane Motion
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01:23Reinforcement
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