Spectrum has become an extremely scarce and congested resource. As a consequence, spectrum sensing enables the coexistence of different wireless technologies in shared spectrum bands. Most existing work requires spectrograms to classify signals. Ultimately, this implies that images need to be continuously created from I/Q samples, thus creating unacceptable latency for real-time operations. In addition, spectrogram-based approaches do not achieve sufficient granularity level as they are based on object detection performed on pixels and are based on rectangular bounding boxes. For this reason, we propose a completely novel approach based on semantic spectrum segmentation, where multiple signals are simultaneously classified and localized in both time and frequency at the I/Q level. Conversely from the state-of-the-art computer vision algorithm, we add non-local blocks to combine the spatial features of signals, and thus achieve better performance. In addition, we propose a novel data generation approach where a limited set of easy-to-collect real-world wireless signals are ``stitched together'' to generate large-scale, wideband, and diverse datasets. Experimental results obtained on multiple testbeds (including the Arena testbed) using multiple antennas, multiple sampling frequencies, and multiple radios over the course of 3 days show that our approach classifies and localizes signals with a mean intersection over union (IOU) of 96.70% across 5 wireless protocols while performing in real-time with a latency of 2.6 ms. Moreover, we demonstrate that our approach based on non-local blocks achieves 7% more accuracy when segmenting the most challenging signals with respect to the state-of-the-art U-Net algorithm. We will release our 17 GB dataset and code.

翻译：频谱已成为极度稀缺且拥挤的资源。因此，频谱感知技术能够实现不同无线技术在共享频段内的共存。现有工作大多依赖频谱图对信号进行分类，这本质上意味着需要持续从I/Q样本中生成图像，从而在实时操作中产生不可接受的延迟。此外，基于频谱图的方法因依赖于对像素进行目标检测且使用矩形边界框，无法达到足够的粒度水平。为此，我们提出一种基于语义频谱分割的全新方法——在I/Q层级上同时对多个信号进行时频域分类与定位。与现有最先进的计算机视觉算法不同，我们通过添加非局部块来融合信号的空间特征，从而获得更优性能。同时，我们提出一种新型数据生成方法：将易于采集的有限真实无线信号"拼接"起来，构建大规模、宽带且多样化的数据集。在3天内使用多天线、多采样频率及多台无线电设备，基于多个测试平台（包括Arena测试平台）的实验结果表明：该方法能以96.70%的平均交并比（IOU）对5种无线协议进行实时信号分类与定位，延迟仅为2.6毫秒。此外，我们证明在分割最具挑战性信号时，基于非局部块的方法相比现有最先进的U-Net算法准确率提升7%。我们将开源17GB的数据集与代码。