用复数神经网络提升弱信号频谱感知精度与训练效率
I Can't Believe It's Not Real: CV-MuSeNet: Complex-Valued Multi-Signal Segmentation
- 采用复数域网络结构，显式建模信号相位与幅度特性
- 在低信噪比下准确率高达98.98%-99.90%，较实数网络提升9.2个百分点
- 仅需2个训练轮次即达实数网络效果，训练时间减少92.2%
射频频谱日益拥挤，高效利用面临挑战。认知无线电系统依赖神经网络实现动态频谱接入，但传统实数神经网络（RVNNs）在低信噪比（SNR）环境下表现不佳，因其无法有效捕捉信号的相位与幅度特征。本文提出复数多信号分割网络CMuSeNet，用于宽带频谱感知。研究表明，简单将现有实数网络转为复数形式无效。基于复数神经网络（CVNNs）与残差结构，CMuSeNet引入复数傅里叶谱焦点损失（CFL）和复平面交并比（CIoU）相似性度量，显著提升训练性能。在合成数据、室内实测及真实世界数据集上的评估表明，其平均准确率达98.98%–99.90%，相较实数对应模型最高提升9.2个百分点，持续优于现有最佳方法。尤为突出的是，CMuSeNet仅用2个训练轮次即可达到实数网络的效果，而后者需27轮；训练时间相比当前最优方案减少92.2%。结果证明复数架构在弱信号检测与训练效率方面具有显著优势。数据集可访问：https://dx.doi.org/10.21227/hcc1-6p22
原文摘要 · Abstract (English)
The increasing congestion of the radio frequency spectrum presents challenges for efficient spectrum utilization. Cognitive radio systems enable dynamic spectrum access with the aid of recent innovations in neural networks. However, traditional real-valued neural networks (RVNNs) face difficulties in low signal-to-noise ratio (SNR) environments, as they were not specifically developed to capture essential wireless signal properties such as phase and amplitude. This work presents CMuSeNet, a complex-valued multi-signal segmentation network for wideband spectrum sensing, to address these limitations. Extensive hyperparameter analysis shows that a naive conversion of existing RVNNs into their complex-valued counterparts is ineffective. Built on complex-valued neural networks (CVNNs) with a residual architecture, CMuSeNet introduces a complexvalued Fourier spectrum focal loss (CFL) and a complex plane intersection over union (CIoU) similarity metric to enhance training performance. Extensive evaluations on synthetic, indoor overthe-air, and real-world datasets show that CMuSeNet achieves an average accuracy of 98.98%-99.90%, improving by up to 9.2 percentage points over its real-valued counterpart and consistently outperforms state of the art. Strikingly, CMuSeNet achieves the accuracy level of its RVNN counterpart in just two epochs, compared to the 27 epochs required for RVNN, while reducing training time by up to a 92.2% over the state of the art. The results highlight the effectiveness of complex-valued architectures in improving weak signal detection and training efficiency for spectrum sensing in challenging low-SNR environments. The dataset is available at: https://dx.doi.org/10.21227/hcc1-6p22
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