在大规模的MIMO系统中,使用蒙面令牌变压器进行联合通道估计和反
Mei Yin1, Mingming Zhao2, Lin Liu3
1ChangZhou Vocational Institute Of Mechatronic Technology, ChangZhou, China.
Scientific reports
|November 25, 2025
概括
本研究介绍了一种新的深度学习网络,用于在大型MIMO系统中进行联合通道估计和反. 该方法有效地利用频域相关性在通道状态信息中,以提高性能.
科学领域:
- 无线通信无线通信
- 深度学习应用程序
- 信号处理 信号处理
背景情况:
- 频道估计和反对于大规模的MIMO系统性能至关重要.
- 现有的深度学习方法往往忽视了道状态信息 (CSI) 中的内在相关性.
- 这种监督导致性能不足,特别是在具有挑战性的环境中.
研究的目的:
- 提出一个新的编码器-解码器网络,用于在大型MIMO系统中进行联合通道估计和反.
- 为了提高准确性,利用CSI内部的频域相关性.
- 为了提高运营效率和估计精度.
主要方法:
- 编码器-解码器网络架构用于通道压缩.
- 一个自我掩盖注意力编码机制捕获和重建相关性特征.
- 积极的掩盖策略可以提高运营效率.
- 一个多层感知无声模块在解码器中改进了通道估计.
主要成果:
- 与最先进的技术相比,拟议的方法在联合道估计和反任务中表现出卓越的性能.
- 该方法还在单个道估计和反任务中实现了竞争性表现.
- 实验结果验证了利用频率域相关性的有效性.
结论:
- 开发的编码解码器网络通过利用CSI相关性有效地解决了现有方法的局限性.
- 自封面注意力和无声化模块有助于提高准确性和效率.
- 这项工作为大规模MIMO系统中先进的通道估计和反提供了有希望的方向.
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