基于深度强化学习的功率分配,以尽量减少信息时代和能源消耗在多输入多输出和非直角多接入物联网系统中的多输入多输出和非直角多接入
Qiong Wu1,2, Zheng Zhang1,2, Hongbiao Zhu1,2
1School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
深度强化学习优化了多输入多输出和非对等多访问 (MIMO-NOMA) 物联网 (IoT) 系统中的功率分配. 这种方法大大减少了信息时代和实时应用的能源消耗.
科学领域:
- 无线通信系统无线通信系统
- 物联网 (IoT) 的互联网.
- 信号处理 信号处理
背景情况:
- 多输入多输出和非对角多访问 (MIMO-NOMA) 系统提高了实时物联网应用的容量和效率.
- 信息时代 (AoI) 对实时物联网数据的及时性至关重要.
- 基站 (BS) 控制采样和电力分配,影响 AoI 和能源使用.
研究的目的:
- 在MIMO-NOMA物联网系统中优化样本采集命令和功率分配.
- 尽量减少信息时代 (AoI) 和能源消耗.
- 为了利用深度强化学习 (DRL) 来实现最佳的功率分配策略.
主要方法:
- 提出了使用深度强化学习 (DRL) 的最佳功率分配策略.
- 与其他算法对比,模拟了拟议的基于DRL的功率分配.
- 在BS中利用连续干扰取消 (SIC) 来进行信号解码.
主要成果:
- 基于DRL的最佳功率分配实现了较低的AoI和能源消耗.
- 与遗传算法 (GA) 和随机算法相比,表现出显著的性能改善.
- 与GA相比,实现了6.44%的奖励降低,与随机算法相比,降低了11.78%.
结论:
- 在MIMO-NOMA物联网系统中,优化功率分配对于最大限度地减少AOI和能源消耗至关重要.
- DRL提供了一种有效的方法来实现最佳的功率分配.
- 拟议的DRL方法比现有的实时物联网应用方法提供了更高的性能.
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