一个卷积变压器剩余网络用于智能反射表面的通道估计,辅助MIMO系统
Qingying Wu1, Junqi Bao1, Hui Xu1
1Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了一种混合深度学习框架,用于在智能反射表面 (IRS) 辅助的MIMO系统中高效地估算通道. 拟议的ConvTrans-ResNet模型显著提高了未来无线通信的准确性和效率.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 智能反射表面 (IRS) 辅助的多输入多输出 (MIMO) 系统为无线网络提供了增强的频谱和能源效率.
- 精确的道估计至关重要,但由于IRS道的被动性和高维度,具有挑战性.
研究的目的:
- 提出一种轻量化混合框架,用于在IRS辅助的MIMO系统中高效的级联通道估计.
- 与现有方法相比,提高道估计的准确性和效率.
主要方法:
- 一个混合框架,将基于物理的双线交替最小平方 (BALS) 算法与深度神经网络 (ConvTrans-ResNet) 结合起来.
- ConvTrans-ResNet将卷积嵌入和变压器模块集成到剩余学习架构中.
- 进行了除研究,以优化网络架构,以减少复杂性和参数数量.
主要成果:
- 拟议的ConvTrans-ResNet方法在标准化平均平方误差 (NMSE) 中显著优于最先进的神经模型 (HA02,ReEsNet,InterpResNet).
- 在各种信号噪声比 (SNR) 级别和 IRS 元素大小中实现了卓越的估计准确性和效率.
- 展示了具有较低计算复杂性的紧网络配置.
结论:
- 混合BALS和ConvTrans-ResNet框架为IRS辅助的MIMO系统中的通道估计提供了实用和高效的解决方案.
- 优化的轻量级网络适合于现实世界的部署,推进未来的无线通信技术.
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