Xi Haolei, Guo Wei, Yang Yanqing, Yuan Rong, Ma Hui
Xinjiang University, School of Computer Science and Technology, Urumqi, 830046, China.
Ministry of Emergency Management Big Data Center, Beijing, 100013, China.
Sci Rep. 2024 Oct 6;14(1):23261. doi: 10.1038/s41598-024-74341-4.
Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm ( ) of 10% while still ensuring the probability of detection ( ) of over 90%.
频谱感知（SS）技术对于认知无线电（CR）网络有效识别和利用空闲频谱资源至关重要。由于信道中噪声特性的影响，提供准确的感知结果具有挑战性。为了提高非高斯噪声下频谱感知的性能并克服现有大多基于单一特征的方法的局限性，我们提出了一种新颖的时频交叉融合网络（TFCFN）。具体而言，我们利用门控循环单元（GRU）在时域中捕获原始信号的长期依赖性，同时，我们对原始信号进行快速傅里叶变换（FFT）以获得频域信息，随后使用卷积神经网络（CNN）提取频域中的局部空间特征。最终，这些时域和频域特征通过交叉注意力机制进行动态融合，以构建更全面、更稳健的特征用于信号分类。我们使用广义高斯分布（GGD）作为噪声模型并重建RadioML2016.10a数据集，以探索各种噪声条件下的性能。实验结果表明：与基线方法相比，TFCFN在高斯和非高斯噪声环境中均表现出更好的检测能力且保持较低的复杂度。值得注意的是，当GGD的形状参数设置为0.5且接收信号的信噪比（SNR）为-16dB时，它可以保持10%的误报概率（ ），同时仍确保超过90%的检测概率（ ）。
