# AI-Enabled Compressive Spectrum Classification for Wideband Radios
## 1 Introduction  
Cognitive Radio (CR) is proposed as a promising solution to the spectrum scarcity and inefficient spectrum utilization problems, by enabling its capabilities of Opportunistic Spectrum Access (OSA) [1, 2, 3]. CR has received significant interest, specifically in TV white spaces (TVWS) [4, 5, 6, 7], 5G [8], and 6G networks, where the Secondary Users (SU) sense a wide range of radio spectrum and dynamically alter their transmission parameters to access the spectrum licensed to Primary User (PU). For example, 5G NR Release 16 proposes Dynamic Spectrum Access (DSA) which is a new approach that allows the co-existence of Long-Term Evolution (LTE) and 5G in the same frequency band [9]. Moreover, it is recommended that the communication system and radar will use the same spectral band [10]. Therefore, spectrum sensing plays a fundamental role in CR-based Dynamic Spectrum Access (DSA) techniques. Several sensing methods have been introduced in the previous decades, including the energy detector (ED) [11], the matching filter detector (MFD) [12], and the cyclostationary feature detector (CFD) [13]. Because of its ease of implementation and lack of need for signal knowledge, ED is included in IEEE 802.22. However, it fails in situations where low-power signals are needed to be detected. On the other hand, MFD is the best detector, butit requires extensive knowledge of the transmitted signal and, therefore, needs a dedicated receiver architecture, which makes it inadequate for use in many practical spectrum-sensing applications in the context of CR. On the other hand, CFD not only detects the signals in low-SNR conditions but also has the potential to differential the legitimate signals from interference [14; 15]. CFD exploits the built-in periodicity in man-made signals, such as carrier frequencies, and symbol rates, to differentiate between communications signals [16]. It computes the spectral correlation function (SCF) of signals that is, in fact, the Fourier Transform of cyclic auto-correlation function (CAF), and hence it produces different patterns for different communications signals under the consideration [17; 18]. Therefore, SCF can be used to differentiate between modulated legitimate signals and potentially malicious waveforms. In most cases, CR monitors the wideband spectrum to find the multiple idle sub-bands for data transmission, hence increasing the throughput of the system. However, the bandwidth of the scanned wideband signals is very large, from a few hundred MHz to several GHz; therefore, as a result of the Nyquist rate sampling bottleneck, the analog-to-digital (A/D) converters with significantly high resolution and computational power are needed which results in heavy memory overhead and power consumption. On the other hand, Compressed sensing (CS) [19] is a revolutionary signal processing technique that alleviates the requirements of Nyquist sampling rates on the condition that the signal under observation is scant in a proper domain. Hence, the fundamental need for CS in order to be used for spectrum sensing is signal sparsity, which is a practical assumption in the case of CR networks due to the fact of spectrum underutilization by primary system [1]. As a result of the PU's low spectral occupancy, the wideband signal is sparse in the frequency domain. To find the sparsest solution for wideband signal, CS requires non-linear optimization, which can be accomplished using greedy algorithms, for example, Matching Pursuit (MP) [20] or Orthogonal MP [21]. Another method is Basis Pursuit (BP) that uses Convex Programming to estimate the wideband. The SCF of the detected signals in the reconstructed wideband (WB) spectrum, must then be estimated by CFD. One method that has emerged is to first recover the WB at the Nyquist rate from sub-Nyquist samples, then find the SCF of each occupied sub-band signal and extract features. This method is described in [22]. The author used a modulated wideband converter (MWC) to recover Nyquist samples [23].