具有多种通道条件的NOMA波形的RNN-Bi-LSTM频谱传感算法
Arun Kumar1, Aziz Nanthaamornphong2, Mehedi Masud3
1Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Rangpo, India.
Scientific reports
|August 23, 2025
概括
一种基于循环神经网络的双向长期记忆 (RNN-Bi-LSTM) 模型显著改善了非正角多重访问 (NOMA) 系统的频谱传感. 这种先进的模型提高了检测准确度,并减少了Rician和Rayleigh色通道的光谱泄漏.
科学领域:
- 无线通信
- 信号处理
- 机器学习
背景情况:
- 非直角多重接入 (NOMA) 对于5G/6G系统的频谱效率至关重要.
- NOMA性能对通道条件敏感,需要强大的频谱传感.
- 现有的频谱传感方法难以应对不同的频道损害.
研究的目的:
- 提出并评估一种基于循环神经网络的双向长期短期记忆 (RNN-Bi-LSTM) 模型.
- 为了提高 NOMA 系统在 Rician 和 Rayleigh 色通道下的光谱性能.
- 在先进的无线网络中提高频谱传感精度和效率.
主要方法:
- 开发一个用于频谱传感的RNN-Bi-LSTM模型.
- 使用关键性能指标进行评估:检测概率 (PD),错误报警概率 (PFA),比特错误率 (BER) 和功率光谱密度 (PSD).
- 与RNN,LSTM,CSD,匹配过器 (MF) 和能量检测 (ED) 等传统方法进行比较分析.
主要成果:
- RNN-Bi-LSTM在Rician和Rayleigh通道中分别达到100%的PD和-2.5dB的SNR,其性能优于其他方法.
- 与雷利相比,在里斯条件下PSD抑制得到了23. 36%的改善,这表明光谱泄漏减少了.
- 实现了更高的BER性能 (在8.8dB和5.8dBSNR下为10−5) 和更准确的PSD估计.
结论:
- 该RNN-Bi-LSTM模型为NOMA频谱在不同的通道条件下提供了卓越的适应性和稳定性.
- 拟议的模型显著提高了下一代无线系统的频谱效率和利用率.
- RNN-Bi-LSTM为先进的无线通信频谱管理提供了高效可靠的解决方案.
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