## 1 Introduction
### Related Work  
Existing intelligent spectrum works range from radio observatories and test-beds that monitor and provide high-level insights on a large geographical scale [25; 26; 27; 28], to systems for radio anomaly detection and device fingerprinting [29; 30; 31; 32; 33], and various aspects of waveform classification [9; 10; 34; 35; 36; 37; 38; 39].  
Several works are concerned with AIML-based emitter RAT ( also referred to as _protocol_, _wireless standard_, and _wireless technology_ classification) classification, which act as RAT-agnostic data-driven detectors that can be used for accurate access pattern detection and prediction of RATs operating in unlicensed shared bands. As the medium access schemes for unlicensed, shared RATs are mainly envisioned as variants of Listen-Before-Talk (LBT) schemes [40], where spectrum access is controlled by schemes based on sensing spectrum occupancy, RAT classification is the primary driver potentially for optimizing spectrum utilization and minimizing interference through intelligent and situation-aware dynamic spectrum access. It also can be utilized as a tool to support access policy enforcement for automated detection of violations that is more coverage efficient than manual human-resource based in-field analysis [41; 42].  
Emitter classification works include proposals based on feature engineering [43; 44; 45], as well as DL-based proposals using time-frequency (TF) representations that include spectrograms calculated using the Short-time Fourier Transform (STFT), as well as other custom TF representations [11; 46; 47; 48; 49; 50]. Compared to strictly time-based features, TF representations can lead to better performance in emitter classification [51] and allow visualizing rich multi-emitter scenarios as patterns recognizable by human domain experts [46]. Formulating emitter classification as an object recognition problem allows leveraging state-of-the-artDL algorithms mainly based on training supervised convolutional neural network (CNN) classifiers which achieved top performance in other application domains [52].  
However, there are limitations associated with CNN-based emitter classification. First, the visual patterns in TF representations are susceptible to corruption induced by the communication channel, which can severely degrade classification performance. Recent works showed that the visual features in spectrograms--the archetypal TF representations--could be indiscernible in low SNR conditions and significantly altered by frequency-selective fading [41; 47]. The degradation can be significantly more pronounced in harsh channel conditions encountered in typical indoor environments due to severe multipath and non-line-of-sight (NLOS) conditions and mobility-induced Doppler effects. Second, the performance gains of CNNs might come at a high computational cost. While an abundance of computing resources might be available at the training phase of CNNs, the resulting inference engines may be deployed settings such as network edge [53] that are constrained in terms of computational resources and energy consumption and favor tight coupling between the RF circuits (sensing component) [54; 55; 56]. Unless addressed, the high computation and energy cost of CNNs might be a significant limiting factor towards broader adoption.