面向C-V2I的基于边缘计算的智能信道估计
Intelligent Channel Estimation Based on Edge Computing for C-V2I
查看参考文献22篇
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文摘
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车联网对于超高可靠与低时延通信(Ultra-Reliable and Low Latency Communications,URLLC)具有严格的要求,特别对于车到基础设施(Vehicle to Infrastructure,V2I)场景,URLLC对传输管理交通状况至关重要. 3GPP Cellular- V2X(C-V2X)作为现在支撑车联网URLLC主流的无线技术,仍存在技术挑战.为进一步提升通信性能,本文在V2I场景下,基于车载终端、路侧单元(Road Side Unit,RSU)与边缘计算车联网服务器(Internet of Vehicles Server, IoV Server)的交互,设计了一种基于C-V2I规范的智能信道估计框架.在IoV Server中,本文提出了一种基于深度学习的信道估计算法,该算法利用一维卷积神经网络(One Dimensional Convolution Neural Network,1D CNN)完成频域插值和条件循环单元(Conditional Recurrent Unit,CRU)进行时域状态预测,通过引入额外的速度编码矢量和多径编码矢量跟踪环境的变化,对不同移动环境下的信道数据进行精确训练.最后通过系统仿真与分析表明,所提算法能够通过信道参数编码追踪不同高速移动环境下的信道变化,实现对信道数据的精确训练.与车联网代表性信道估计算法相比,所提算法提升了信道估计精度,降低了误码率和增强了鲁棒性. |
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其他语种文摘
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Internet of vehicles has strict requirements in Ultra-Reliable and Low Latency Communications (URLLC). Especially in vehicle to infrastructure (V2I) scenario,URLLC is crucial to correctly transport and manage traffic conditions. 3GPP Cellular-V2X (C-V2X),as the current mainstream wireless technology supporting the URLLC, still has technical challenges. In order to further improve the communication performance, this paper designs an intelligent channel estimation framework based on C-V2I specification based on the interaction between vehicle terminal, road side unit (RSU) and edge computing Internet of Vehicles server (IoV Server) in V2I communication scenario. In IoV Server, this paper proposes a channel estimation algorithm based on deep learning,which uses one-dimensional convolutional neural network (1D CNN) to complete frequency-domain interpolation and conditional recurrent unit (CRU) to predict the time-domain state. By introducing additional velocity coding vector and multipath coding vector, the channel data in different mobile environments are accurately trained. Finally, system simulation and analysis show that the proposed algorithm can track the channel changes in different high-speed mobile environments through channel parameter coding, and realize the accurate training of channel data. Compared with the representative channel estimation algorithms in the IoV, the proposed algorithm improves the channel estimation accuracy, reduces the bit error rate and enhances the robustness. |
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来源
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电子学报
,2021,49(5):833-842 【核心库】
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DOI
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10.12263/DZXB.20200953
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关键词
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车联网
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边缘计算
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V2I
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C-V2X
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信道估计
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深度学习
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地址
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重庆大学微电子与通信工程学院, 重庆, 400044
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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电子技术、通信技术 |
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基金
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国家自然科学基金
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重庆市自然科学基金
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文献收藏号
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CSCD:6982165
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