  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.
