拼接频谱：基于宽带信号拼接的语义频谱分割
Stitching the Spectrum: Semantic Spectrum Segmentation with Wideband Signal Stitching
AI总结：
针对频谱感知中实时性和粒度不足的问题，提出基于语义频谱分割的新方法，结合非局部块提升性能，并利用信号拼接生成大规模数据集，实现高精度实时分类定位。
摘要：
频谱已成为一种极其稀缺和拥塞的资源。因此，频谱感知使得不同无线技术能够在共享频谱频段中共存。现有的大多数工作都需要频谱图来对信号进行分类。这最终意味着需要从I/Q样本中持续生成图像，从而为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级别，因为它们基于在像素上执行的目标检测，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，其中多个信号在I/Q级别上同时在时间和频率上被分类和定位。与最先进的计算机视觉算法相反，我们添加了非局部块来组合信号的空间特征，从而实现更好的性能。此外，我们提出了一种新颖的数据生成方法，其中有限的一组易于收集的真实世界无线信号被“拼接在一起”以生成大规模、宽带和多样化的数据集。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，在3天的时间内进行的实验结果表明，我们的方法在5种无线协议上以96.70%的平均交并比（IOU）对信号进行分类和定位，同时以2.6毫秒的延迟实时执行。此外，我们证明了基于非局部块的方法在分割最具挑战性的信号时，相对于最先进的U-Net算法，准确率提高了7%。我们将发布我们的17 GB数据集和代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
