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TranSIC-Net:一个端到端的变压器网络,用于OFDM符号解调,并对DroneID信号进行验证

Zhihong Wang¹, Zi-Xin Xu¹

¹College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China.

Sensors (Basel, Switzerland)

|October 29, 2025

概括

本研究介绍了TranSIC-Net,这是一个用于强大的正交频分裂多重复合 (OFDM) 信号调节的变压器神经网络. 它统一了通道估计和符号检测,在具有挑战性的无线环境中优于传统方法.

科学领域:

无线通信无线通信
信号处理信号处理
机器学习机器学习

背景情况:

在复杂的无线环境中,直角频率分割复合 (OFDM) 信号解调具有挑战性,特别是在信号噪声比 (SNR) 或载波频率偏移 (CFO) 低的情况下.
现有的方法通常需要明确的通道估计,在不利的条件下很难.
解码像DroneID这样的专有格式,由于非标准的框架结构,会带来独特的挑战.

研究的目的:

开发一个统一的神经网络架构,用于在OFDM系统中同时进行通道估计和符号检测.
为了解决从DJI无人机中解码DroneID信号的实际挑战.
创建一个灵活的模型,适用于除DroneID之外的各种OFDM系统.

主要方法:

一个基于变压器的端到端神经网络,TranSIC-Net,旨在整合通道估计和符号检测.
该模型隐式地从试点模式中学习道动态,并利用注意力机制来捕获子载体之间的相关性.
对合成OFDM波形和现实无人机 (UAV) 信号进行了评估.

主要成果:

与最小平方加零强迫 (LS+ZF) 和ProEsNet.Net等深度学习基线相比,TranSIC-Net表现优越.
该模型实现了更低的比特错误率 (BER) 和更高的估计准确性.
它在具有挑战性的无线条件下表现出显著的稳定性,以及对不同OFDM系统的概括性.

结论:

TranSIC-Net有效地统一了OFDM信号的通道估计和符号检测,提供了更好的性能和稳定性.
该模型的灵活性允许适应各种无线通信场景,包括涉及无人机 (UAV) 的无人机.
这种方法为实际的无线通信提供了一个强大的工具,特别是在复杂和低SNR环境中.

关键词:

无人机ID的无人机ID是什么?在OFDM中,OFDM是指OFDM.变压器变压器变压器频道估计频道估计深度学习是一种深度学习.符号决定符号决定

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