在OFDM系统中用于联合室内定位和盲道估计的多任务学习
Maria Camila Molina1, Iness Ahriz1, Lounis Zerioul1
1Conservatoire National des Arts et Métiers, CEDRIC, 292 rue Saint Martin, 75141 Paris, France.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了用于无线系统的新型神经网络,同时估计通道和定位设备. 这种综合方法提高了效率,并减少了WiFi和5G网络中的模型开销.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 机器学习用于通信.
背景情况:
- 精确的设备定位和通道估计对于无线系统性能至关重要.
- 现有的方法通常将本地化和通道估计视为单独的任务,增加复杂性.
- 频道状态信息 (CSI) 包含丰富的空间和频道特征.
研究的目的:
- 开发一个统一的框架,同时进行本地化和道估计.
- 为了利用道特征和空间信息之间的固有关系.
- 提高无线通信系统的效率和可靠性.
主要方法:
- 提出了一个多任务神经网络架构.
- 该网络从多个基站执行盲通道估计和用户终端定位.
- 在单一模型中,相同的通道状态信息 (CSI) 数据用于两个任务.
- 评估是在室内环境中使用不同的天线配置的WiFi和5G直角频率分割多重复合 (OFDM) 系统进行的.
主要成果:
- 拟议的方法实现了与现有方法相比较的通道估计准确性.
- 同时定位的50百分点误差为1.62米 (3点的道) 和0.89米 (10点的道).
- 综合框架减少了模型的开销,并有效地利用了空间环境.
结论:
- 这种新的多任务学习框架成功地集成了本地化和道估计.
- 这种方法为提高现实世界无线应用的效率提供了巨大的潜力.
- 该系统与新兴的综合传感和通信 (ISAC) 系统的目标保持一致.
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