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联合任务卸载和资源分配,用于使用深度强化学习算法的智能反射面辅助集成传感和通信系统.

Liu Yang1, Yifei Wei1, Xiaojun Wang2

  • 1Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Egineering, Beijing University of Posts and Telecommunications, Xitucheng Road No. 10, Beijing 100876, China.

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|December 23, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个智能反射表面 (IRS) 辅助的综合传感和通信 (ISAC) 框架,以改进无线系统. 深度强化学习优化了资源配置,以增强传感,通信和计算卸载.

关键词:
深度强化学习的学习.集成传感和通信.智能反射表面是一个智能反射表面.资源分配的资源分配.

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科学领域:

  • 无线通信是一种无线通信.
  • 信号处理 信号处理
  • 人工智能的人工智能是人工智能.

背景情况:

  • 频谱稀缺和糟糕的无线环境阻碍了综合传感和通信系统 (ISAC).
  • 优化资源配置对于平衡传感,通信和计算卸载性能至关重要.

研究的目的:

  • 提出一个智能反射表面 (IRS) 辅助的ISAC框架.
  • 通过共同设计光束成型,相位转移和资源分配来优化系统性能.
  • 使用先进的AI技术解决非凸和NP硬优化问题.

主要方法:

  • 作为动态通道条件的马尔科夫决策过程的表述.
  • 开发了两个深度强化学习 (DRL) 方案:深度决定性政策梯度 (DDPG) 和双延迟的DDPG.
  • 包括优先重复经验,以加快DRL的融合.

主要成果:

  • 提出的基于DRL的方案有效优化了频谱和计算资源分配.
  • 实现了提高雷达传感质量和提高通信数据速率.
  • 提高能源效率和减少计算卸载中的延迟.

结论:

  • 美国国税局支持的ISAC框架与DRL显著优于基准方法.
  • 拟议的DRL方法为动态无线环境中的复杂优化提供了可行的解决方案.
  • 这项工作展示了人工智能在推进ISAC系统方面的潜力.