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基于深度学习的通道估计技术使用IEEE 802.11p协议,IEEE 802.11p的局限性和IEEE 802.11bd的未来方向:一篇评论

Saveeta Bai¹, Jeff Kilby¹, Krishnamachar Prasad¹

¹School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)

|March 14, 2026

概括

深度学习改善了IEEE 802.11p的车载通道估计,解决了动态环境中的局限性. 未来的研究重点是平衡智能运输系统的延迟,复杂性和准确性.

科学领域:

车辆通信网络车辆通信网络
无线通信系统无线通信系统
深度学习应用程序深度学习应用程序

背景情况:

IEEE 802.11p是车辆对车辆 (V2V) 通信的基础,但面临着有限的试点资源和固定结构的挑战.
由于这些限制,传统的通道估计技术在高流动性和动态的V2V环境中扎.
深度学习有可能提高车辆网络中复杂的通道动态的准确性和建模.

研究的目的:

对IEEE 802.11p.的基于深度学习的通道估计技术进行审查.
在V2V场景中批判性地检查经典和深度学习方法的局限性.
突出IEEE 802.11bd的进步,以改进通道估计.

主要方法:

在IEEE 802.11p中对深度学习进行道估计的综合文献综述.
在深度学习方法中分析计算复杂性与准确性权衡的分析.
检查IEEE 802.11bd中的增强功能,包括试点结构和调制方案.

主要成果:

深度学习方法在动态V2V环境中有望提高道估计准确度.
经典和深度学习方法在计算复杂性和准确性方面的权衡方面存在局限性.
IEEE 802.11bd提供了一个更强大的框架,增强了试点结构和适应性估计的调制.

结论:

基于深度学习的道估计对于可靠的V2V通信至关重要,尽管存在复杂性挑战.
在通道估计方面,IEEE 802.11bd 提供了比 IEEE 802.11p 的显著改进.
未来的研究应该专注于优化深度学习模型,以平衡智能运输系统的延迟,复杂性和准确性.

关键词:

在DSRC中,我们可以使用DSRC.在IEEE802.11bd中使用.在IEEE802.11p中使用.在V2V中,V2V是V2V.频道估计频道估计深度学习 802.11bd VANET 公司

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