Spectrum sharing allows different protocols of the same standard (e.g.,
802.11 family) or different standards (e.g., LTE and DVB) to coexist in
overlapping frequency bands. As this paradigm continues to spread, wireless
systems must also evolve to identify active transmitters and unauthorized
waveforms in real time under intentional distortion of preambles, extremely low
signal-to-noise ratios and challenging channel conditions. We overcome
limitations of correlation-based preamble matching methods in such conditions
through the design of T-PRIME: a Transformer-based machine learning approach.
T-PRIME learns the structural design of transmitted frames through its
attention mechanism, looking at sequence patterns that go beyond the preamble
alone. The paper makes three contributions: First, it compares Transformer
models and demonstrates their superiority over traditional methods and
state-of-the-art neural networks. Second, it rigorously analyzes T-PRIME's
real-time feasibility on DeepWave's AIR-T platform. Third, it utilizes an
extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training,
which is released along with the code for community use. Results reveal nearly
perfect (i.e. $>98\%$) classification accuracy under simulated scenarios,
showing $100\%$ detection improvement over legacy methods in low SNR ranges,
$97\%$ classification accuracy for OTA single-protocol transmissions and up to
$75\%$ double-protocol classification accuracy in interference scenarios.

通过 T-PRIME：一种基于 Transformer 的机器学习方法，本文在具有故意失真、极低信噪比和具有挑战性信道条件下，通过设计 T-PRIME 的注意机制来学习发送帧的结构设计，从而克服了相关性匹配方法的局限性。文章通过比较 Transformer 模型与传统方法和最先进的神经网络的优越性、在 DeepWave 的 AIR-T 平台上对 T-PRIME 的实时可行性进行严格分析，以及利用开放 66 GB 的实时 OTA（Over-The-Air）WiFi 传输数据集进行训练，并发布了代码供社区使用，得出结果显示在模拟场景中几乎完美（即 > 98%）的分类准确率，显示在低信噪比范围内相比于传统方法有 100% 的检测改进，OTA 单协议传输的分类准确率为 97%，干扰场景下的双协议分类准确率高达 75%。