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：频谱已成为一种极度稀缺且拥挤的资源。因此，频谱感知使得不同无线技术能够在共享频段上共存。现有工作大多需要频谱图来对信号进行分类。这本质上意味着需要从I/Q样本连续生成图像，从而给实时操作带来不可接受的延迟。此外，基于频谱图的方法无法达到足够的粒度，因为它们依赖于对像素进行目标检测，并基于矩形边界框。为此，我们提出了一种全新的基于语义频谱分割的方法，能在I/Q层级同时对多个信号进行时间与频率的定位和分类。与最先进的计算机视觉算法不同，我们引入了非局部块来融合信号的空间特征，从而获得更优性能。此外，我们提出了一种新颖的数据生成方法，将有限且易于采集的真实无线信号“拼接”在一起，以生成大规模、宽带且多样化的数据集。在多个测试平台（包括Arena测试平台）上，使用多天线、多采样频率和多无线电设备历时3天获得的实验结果表明，我们的方法能以平均交并比（IOU）96.70%的准确率对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，相较于最先进的U-Net算法，基于非局部块的方法在处理最具挑战性的信号时准确率提升了7%。我们将发布17 GB的数据集和代码。