物联网场景下基于信道估计的毫米波大规模多输入多输出(MIMO)系统的信道估计

IF 7.5 2区 计算机科学 Q1 TELECOMMUNICATIONS
Junhui Zhao , Ruixing Ren , Yao Wu , Qingmiao Zhang , Wei Xu , Dongming Wang , Lisheng Fan
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引用次数: 0

摘要

为了提高车联网(IoV)场景下毫米波(mmWave)大规模多输入多输出(MIMO)系统时变信道估计算法的准确性和效率,本文提出了一种深度学习(DL)算法——挤压激励注意残差网络(SEARNet),该算法集成了挤压激励注意(SEAttention)机制和残差模块。具体来说,SEARNet将信道信息视为一个图像矩阵,并在残差模块中嵌入一个SEAttention模块来构造SEAttention-残差块。通过数据驱动的方法,SEARNet可以利用SEAttention机制有效地从信道矩阵中提取关键信息,从而减少噪声干扰,准确高效地估计信道。仿真结果表明,在信噪比为-10 dB、信噪比为5 dB、信噪比为10 dB时,与传统信道估计算法和两种深度差分信道估计算法相比,提出的SEARNet信道估计算法的归一化均方误差(NMSE)最大降幅分别为97.66%、84.49%、78.18%和43.51%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SEAttention-residual based channel estimation for mmWave massive MIMO systems in IoV scenarios
To improve the accuracy and efficiency of time-varying channels estimation algorithms for millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) systems in Internet of Vehicles (IoV) scenarios, the paper proposes a deep learning (DL) algorithm, Squeeze-and-Excitation Attention Residual Network (SEARNet), which integrates Squeeze-and-Excitation Attention (SEAttention) mechanism and residual module. Specifically, SEARNet considers the channel information as an image matrix, and embeds a SEAttention module in residual module to construct the SEAttention-Residual block. Through a data-driven approach, SEARNet can effectively extract key information from the channel matrix using the SEAttention mechanism, thereby reducing noise interference and estimating the channel in an accurate and efficient manner. The simulation results show that compared to two traditional and two DL channel estimation algorithms, the proposed SEARNet can achieve a maximum reduction in normalized mean square error (NMSE) of 97.66% and 84.49% at SNR of -10 dB, 78.18% at SNR of 5 dB, and 43.51% at SNR of 10 dB, respectively.
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来源期刊
Digital Communications and Networks
Digital Communications and Networks Computer Science-Hardware and Architecture
CiteScore
12.80
自引率
5.10%
发文量
915
审稿时长
30 weeks
期刊介绍: Digital Communications and Networks is a prestigious journal that emphasizes on communication systems and networks. We publish only top-notch original articles and authoritative reviews, which undergo rigorous peer-review. We are proud to announce that all our articles are fully Open Access and can be accessed on ScienceDirect. Our journal is recognized and indexed by eminent databases such as the Science Citation Index Expanded (SCIE) and Scopus. In addition to regular articles, we may also consider exceptional conference papers that have been significantly expanded. Furthermore, we periodically release special issues that focus on specific aspects of the field. In conclusion, Digital Communications and Networks is a leading journal that guarantees exceptional quality and accessibility for researchers and scholars in the field of communication systems and networks.
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