使用UNet缓解干扰,通过延迟多普勒声响参考信号方法集成传感和通信车辆网络
1School of Information Science and Technology, Fudan University, Shanghai 200438, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究增强了自动驾驶的综合传感和通信 (ISAC),使用了新的延迟多普勒 (DD) 域方法. 深度学习UNet模型有效地减轻干扰,提高5G网络的传感精度和频谱效率.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 电信 电信服务 电信服务 电信服务
背景情况:
- 先进的通信系统,包括自动驾驶和集成传感和通信 (ISAC),需要高精度的环境感知和可靠的数据传输.
- 由于干扰和频谱效率限制,现有的4G和5G系统在优化ISAC性能方面面临挑战.
研究的目的:
- 提高现有4G和5G通信系统的综合传感和通信 (ISAC) 性能.
- 为了提高车辆网络的频谱效率和传感精度.
- 为了减轻无线信号的干扰,以提高系统性能.
主要方法:
- 在延迟多普勒 (DD) 域中使用二维偏移来利用响应参考信号 (SRS) 的资源.
- 提出了一种基于深度学习的干扰缓解解决方案,采用在Range-Doppler地图上运行的UNet架构.
- 进行模拟,以评估拟议的DD-domain方法和UNet模型的有效性.
主要成果:
- 拟议的DD-domain方法显著提高了环境传感的准确性.
- 联网架构有效地减轻干扰,提高整体系统性能.
- 这种方法表明,即使在密集的网络场景中,资源利用也得到了改进.
结论:
- 基于Delay-Doppler (DD) 域的方法为优化当前和未来通信系统中的ISAC功能提供了一个有希望的解决方案.
- 使用UNet架构的基于深度学习的干扰缓解对于提高ISAC性能是有效的.
- 该研究强调了提高车辆网络效率和传感精度的潜力.
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