基于深度学习的IRS辅助MIMO系统的被动束成形设计
Hui Zhang1, Qiming Jia1, Meikun Li1
1Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了基于注意力的无监督学习方案,用于智能反射表面 (IRS) 辅助的MIMO系统. 新方法通过有效利用通道状态信息 (CSI),同时降低计算复杂性,提高了光谱效率.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 机器学习在电信中的应用.
背景情况:
- 在智能反射表面 (IRS) 辅助的MIMO系统中优化被动光束形成对于最大限度地提高光谱效率至关重要.
- 现有的方案与IRS单位模块约束作斗争,并且未能有效地利用通道状态信息 (CSI).
- 当前的方法往往对所有输入数据均等,缺乏关键信息的重点,并遭受高复杂性.
研究的目的:
- 开发一种先进的方案,以优化IRS辅助的MIMO系统中的被动光束成型.
- 提高道状态信息 (CSI) 的利用,以提高系统性能.
- 为了减少计算复杂性和提高网络的泛化能力.
主要方法:
- 实施一个三通道数据输入结构.
- 开发一个注意力机制辅助的无监督学习计划.
- 在输入数据中优先考虑关键信息,以增强网络表达和概括.
主要成果:
- 与现有方法相比,拟议的方案显示了光谱效率的显著改善.
- 观察到计算复杂性的显著减少.
- 该方案在模拟过程中表现出快速的趋同.
结论:
- 注意力机制辅助的无监督学习方案有效地解决了现有的IRS辅助MIMO系统的局限性.
- 这种方法提供了一种优越的方法,通过更好地利用CSI来优化被动光束成型.
- 拟议的方法提供了一个计算效率高和高性能解决方案,以提高光谱效率.
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