车联网V2I场景下基于GNN的SC-FDMA智能信道估计

廖勇; 尹子松; 田肖懿

doi:10.12263/DZXB.20220545

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车联网V2I场景下基于GNN的SC-FDMA智能信道估计

学术论文 | 更新时间：2025-12-11

- 车联网V2I场景下基于GNN的SC-FDMA智能信道估计
- Intelligent Channel Estimation of SC-FDMA Based on GNN for V2I Scenarios in Internet of Vehicles
- 电子学报 2024年52卷第3期页码：772-782
- 作者机构：
  
  重庆大学微电子与通信工程学院，重庆 400044
- 作者简介：
  
  [ "廖勇男， 1982年1月出生于四川省自贡市. 现为重庆大学副研究员、博士生导师. 主要研究方向为下一代无线通信、人工智能、量子计算及其在无线通信中的应用.中国电子学会会员编号：E190005972S.E-mail: liaoy@cqu.edu.cn" ]
  [ "尹子松男， 1996年8月出生于江西省吉安市. 现为重庆大学微电子与通信工程学院研究生. 主要研究方向为车联网通信下的信道估计算法. E-mail: yinzs@cqu.edu.cn" ]
  [ "田肖懿男，1998年1月出生于贵州省铜仁市.重庆大学硕士. 主要研究方向为人工智能算法及其在车联网通信下的应用. E-mail: tianxy@cqu.edu" ]
- 基金信息：
  
  国家自然科学基金(61501066);重庆市自然科学基金(cstc2019jcyj-msxmX0017)
- DOI：10.12263/DZXB.20220545
  中图分类号： TN929.5
- 收稿：2022-05-14，
  
  修回：2022-08-18，
  
  纸质出版：2024-03-25
- 稿件说明：
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廖勇,尹子松,田肖懿.车联网V2I场景下基于GNN的SC-FDMA智能信道估计[J].电子学报,2024,52(03):772-782.

LIAO Yong, YIN Zi-song, TIAN Xiao-yi.Intelligent Channel Estimation of SC-FDMA Based on GNN for V2I Scenarios in Internet of Vehicles[J].Acta Electronica Sinica, 2024, 52(03): 772-782.
廖勇,尹子松,田肖懿.车联网V2I场景下基于GNN的SC-FDMA智能信道估计[J].电子学报,2024,52(03):772-782. DOI：10.12263/DZXB.20220545

LIAO Yong, YIN Zi-song, TIAN Xiao-yi.Intelligent Channel Estimation of SC-FDMA Based on GNN for V2I Scenarios in Internet of Vehicles[J].Acta Electronica Sinica, 2024, 52(03): 772-782. DOI：10.12263/DZXB.20220545

摘要

随着车联网的迅猛发展，车对路基础设施（Vehicle to Infrastructure，V2I）通信对车联网的可靠性和时延提出了更高的要求，而信道估计是接收机高可靠低时延通信的重要保障.为解决传统信道插值算法不能有效拟合V2I信道快时变特性、自适应多普勒频移能力弱和传统神经网络可解释性不强的问题，本文提出基于图神经网络（Graph Neural Network，GNN）的单载波频分多址（Single Carrier-Frequency Division Multiple Access，SC-FDMA）智能信道估计算法.该算法将信道频率响应中的数据点作为图的节点、符号间时域相关性作为边，将图化后的数据送入GraphSAGE信道插值器（GraphSAGE Channel Interpolator，GCI）中，通过边更新、聚合操作、节点更新三大模块进行网络训练，同时采用多普勒频移矢量作为节点特征控制网络拟合不同多普勒条件的信道，使得网络具备可解释性.最后，系统仿真验证了在不同速度环境下算法的有效性和鲁棒性，较线性插值、样条插值以及全连接网络，本文所提GCI在低、中和高速移动环境下具有最优的误码率（Bit Error Rate，BER）和归一化均方误差（Normalized Mean Square Error，NMSE）性能，特别地，在200 km/h高速移动条件下GCI的优势更为明显.

Abstract

With the rapid development of the Internet of vehicles

vehicle to infrastructure (V2I) communication puts forward higher requirements for the reliability and delay of vehicle networking. Channel estimation is an important guarantee for high reliable and low-latency communication of receiver. To solve the problems that the traditional channel interpolation algorithm cannot effectively fit the fast time-varying characteristics of V2I channel

the ability of adaptive Doppler frequency shift is weak

and the interpretability of traditional neural network is not strong

this paper presents a single carrier-frequency division multiple access (SC-FDMA) intelligent channel estimation algorithm based on graph neural network (GNN). The proposed algorithm takes the data points in the channel frequency response as the nodes of the graph and the inter-symbol time domain correlation as the edges. The graphical data is fed into the GraphSAGE channel interpolator (GCI). The network training is carried out through the three modules of edge update

aggregation operation and node update. At the same time

the Doppler shift vector is used as the node feature control network to fit the channels with different Doppler conditions

making the network interpretable. Finally

the system simulation verifies the effectiveness and robustness of the algorithm in different speed environments. Compared with linear interpolation

spline interpolation and fully connected network

the proposed GCI has the best performance of bit error rate (BER) and normalized mean square error (NMSE) in low

medium and high-speed mobile environments

especially

the advantage of GCI is more obvious under the condition of 200 km/h high-speed movement.

关键词

Keywords

references

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