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对于5G功率放大器的本地和远程数字预扭曲,具有安全的深度增强学习
Christian Spano1, Damiano Badini2, Lorenzo Cazzella1
1Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了基于深度强化学习的数字预扭曲 (DPD),以提高无线通信的效率. 一个新的安全增强学习算法确保了性能,同时保持5G系统的信号完整性.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 高数据速率和能源效率的需求将功率放大器 (PA) 推向非线性运行,导致信号扭曲.
- 现有的数字预扭曲 (DPD) 技术与复杂性,适应性和资源限制作斗争.
- PAs中的非线性扭曲会降低无线通信性能.
研究的目的:
- 引入基于深度强化学习的数字预扭曲 (DRL-DPD),以克服当前DPD系统的局限性.
- 减少计算负担,提高在动态环境中的适应性,并最大限度地减少资源消耗.
- 使用新的安全增强学习算法确保安全和监管合规.
主要方法:
- 开发了DRL-DPD,集成深度强化学习用于适应性DPD.
- 整合了谨慎可回收探索深度决定性政策梯度 (CRE-DDPG) 算法,以确保安全运行.
- 在本地和远程配置中通过广泛的模拟和硬件实验进行验证.
主要成果:
- 与CRE-DDPG相结合的DLR-DPD与现有的DPD方法相比,表现优越.
- 该系统有效地降低了计算复杂性和资源使用量.
- ACLR的测量保持在安全值以上,确保监管合规.
结论:
- 用CRE-DDPG增强的DRL-DPD为高效的无线通信提供了一个有希望的解决方案.
- 该方法远远超出了当前DPD的局限性,特别是在5G和未来的系统.
- 这项技术为更强大,更节省资源的无线网络铺平了道路.
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