基于改进CPD的RIS辅助毫米波OFDM系统信道估计算法  

Improved CPD-based RIS-assisted Millimeter Wave OFDM System Channel Estimation Algorithm

作　　者：任进[1] 李一博 周培豫 李玉宇 REN Jin;LI Yibo;ZHOU Peiyu;LI Yuyu(School of Information Science and Technology,North China University of Technology,Beijing 100144,China;Litigation Service Center,Haidian District People s Court of Beijing,Beijing 100080,China)

机构地区：[1]北方工业大学信息学院,北京100144 [2]北京市海淀区人民法院诉讼服务中心,北京100080

出　　处：《无线电通信技术》2025年第1期29-35,共7页Radio Communications Technology

基　　金：2025年北京市大学生创新创业训练计划项目;2023年北京市高等教育学会面上课题(MS2023178)。

摘　　要：在无线通信领域,系统性能的优劣通常与无线信道的特性密切相关。精确掌握信道和信号参数对于提升信息传输的可靠性和效率至关重要,因此信道估计成为该领域的核心技术之一。由于无线信道的不可预测性以及信号在传输过程中涉及多个维度(如空间、时间、频率等),使得信道估计方法的设计变得异常复杂。近期研究表明,通过将这些多维度信号转换为张量并进行分析,可以显著降低信道估计的技术难度。探讨了信道估计算法的改进方法,特别是引入中心化处理作为数据预处理的一部分。中心化处理通过调整数据的均值来减少低频噪声,从而提高信噪比(Signal-to-Noise Ratio,SNR)。这不仅提升了信道估计的准确性,还降低了算法实现的复杂度,加快了模型的训练速度并提高了收敛效率。此外,还采用了因子分解的方法对张量进行分解,进一步降低了计算复杂度并提高了估计精度。仿真实验结果表明,改进后的算法不仅具有更高的准确度,而且在低SNR环境下表现出更优越的性能。In the field of wireless communication,the performance of a system is often closely related to the characteristics of wireless channel.Accurately grasping channel and signal parameters is crucial for improving the reliability and efficiency of information transmission,making channel estimation one of the core technologies in this field.However,due to the unpredictability of wireless channel and the involvement of multiple dimensions(such as space,time,frequency,etc.)during signal transmission,the design of channel estimation methods becomes extremely complex.Recent studies have shown that converting these multidimensional signals into tensors for analysis can significantly reduce the technical difficulty of channel estimation.This article mainly discusses the improvement methods of channel estimation algorithms,particularly introducing centralized processing as part of data pre-processing.Centralized processing improves the Signal-to-Noise Ratio(SNR)by adjusting the mean value of the data to reduce low-frequency noise.This not only enhances the accuracy of channel estimation but also simplifies the complexity of algorithm implementation,accelerating model training speed and improving convergence efficiency.Additionally,we employ factorization methods to decompose tensors,which further reduces computational complexity and improves estimation accuracy.Simulation results indicate that the improved algorithm not only has higher accuracy but also exhibits superior performance in low SNR environments.

关 键 词：智能反射面辅助 正交频分复用技术 信道估计 张量 因子分解 

分 类 号：TN929.5[电子电信—通信与信息系统]