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相关概念视频

Maximum Power Transfer01:16

Maximum Power Transfer

223
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
223

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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
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深度增强网学习用于集成传感,通信和电力传输系统中的坚固束形.

Chenfei Xie1, Yue Xiu1, Songjie Yang1

  • 1National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
概括

本研究介绍了用于集成传感,通信和功率传输 (ISCPT) 系统的深度强化学习 (DRL) 框架. DRL优化了资源分配和光束成型,以提高效率,即使有不完美的通道状态信息.

关键词:
通信 通信 通信 通信 通信.深度强化学习的学习.不完美的通道状态信息信息.整合传感传感的整合多个用户的多用户.权力转移权力转移权力转移权力是什么强大的梁成型,强大的梁成型.

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科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 下一代无线网络需要集成的功能来实现可持续发展.
  • 综合传感,通信和电力传输 (ISCPT) 提供了一个同时传输信息,传感和能量传输的范式.
  • 优化ISCPT涉及复杂的,非凸的问题,具有多个约束,如服务质量 (QoS),传感精度和功率传输效率.

研究的目的:

  • 为优化ISCPT系统提出基于深度强化学习 (DRL) 的框架.
  • 解决ISCPT中非凸式优化和不完美的通道状态信息 (CSI) 的挑战.
  • 在动态环境中提高整体系统效率,可靠性和资源管理.

主要方法:

  • 在ISCPT中开发基于DRL的框架,用于适应性决策.
  • 使用DRL来管理复杂的环境状态,并动态调整传感,通信和能量收集参数.
  • 实施可靠,可学习的光束成形策略,以推断可实现的速率上限.

主要成果:

  • 基于DRL的ISCPT框架有效地管理系统变量,以提高性能.
  • 在资源分配,电力管理和信息传输方面观察到显著的改进.
  • 拟议的方法证明了稳定性和提高效率,特别是在具有不完美的CSI的动态环境中.

结论:

  • DRL提供了一种强大的方法来优化复杂的ISCPT系统.
  • 该框架通过适应动态条件和不完美的CSI来提高系统可靠性和效率.
  • 这项研究为更高效,更集成的无线通信系统铺平了道路.