一个变压器-LSTM混合探测器用于OFDM-IM信号检测
Leijun Wang1, Zian Tong2, Kuan Wang1
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了FullTrans-IM,这是一个用于直角频率分割多重复合与索引调制 (OFDM-IM) 系统的深度学习探测器. 它大大提高了比特错误率 (BER) 性能,而不是雷利色变频道中的现有方法.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 与索引调制 (OFDM-IM) 的正角频率分割复杂化提出了独特的信号检测挑战.
- 传统的检测方法经常与OFDM-IM系统的复杂性作斗争.
研究的目的:
- 为OFDM-IM系统开发一种基于深度学习的新型探测器.
- 为了在具有挑战性的通道条件下提高信号检测准确性和稳定性.
主要方法:
- 提出了一个深度学习探测器,FullTrans-IM,集成变压器和长短期存储器 (LSTM) 网络.
- 检测问题被重新定义为序列预测任务,利用变压器解码器的能力.
- 该模型在雷利衰变通道下进行了评估.
主要成果:
- 该FullTrans-IM检测器展示了优越的比特错误率 (BER) 性能.
- 它的性能优于传统的零强力 (ZF) 探测器.
- 与现有的基于深度学习的探测器相比,它也表现出更好的性能.
结论:
- 拟议的FullTrans-IM探测器在OFDM-IM信号检测方面取得了重大进展.
- 利用序列建模用于信号检测,可以有效地提高性能.
- 深度学习技术,特别是变压器-LSTM集成,对未来的无线通信系统非常有希望.
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