## 1 Introduction  
The rapidly growing mobile traffic and user base is fueling the demand for spectrum resources that are increasingly challenging to provision due to scarcity and access restrictions. One of the mitigation strategies consists of improving spectrum utilization efficiency by sharing the unlicensed operation of radio-access technologies (RATs). Recent examples include the use of the 5 GHz band, which Wi-Fi occupies (IEEE 802.11a) and an unlicensed version of Long-Term Evolution cellular standard (unlicensed-LTE) [1,2], as well as the unlocking of the 6 GHz band for Wi-Fi 6 (IEEE 802.11 ax) and 5G New Radio Unlicensed (NR-U) standards that will operate along with incumbent primary users [3].  
Realizing the benefits of the shared unlicensed operation to meet the increasingly stringent quality of service (QoS) application targets is contingent upon meeting challenging requirements, which include ensuring fair and harmonious coexistence between users, secure access in line with regulations, and maximal performance through optimized resource allocation and interference management. The challenge in meeting these requirements emanates from the complexity of the emerging access environments, which are influenced by a confluence of physical (channel effects), human (user access and mobility patterns), and technological (design and _modus operandi_ of RATs) factors.  
A potential solution is advancing RAT intelligence to equip radio assets with adaptive learning and decision-making capabilities that will enable a greater level of autonomous operation compared to centralized schemes [4]. Intelligent spectrum monitoring is arguably a core component of adaptive radio learning that enables RATs to collect measurements and make sophisticated real-time inferences about the spectral state that drive reasoning and intelligent decision-making. This has motivated several works that build on two enabling technologies: The first is the software-defined radio (SDR), allowing programmable radio frequency (RF) operation across a wide range of frequency bands and with diverse cost and form-factor options [5]. The second consists of Artificial Intelligence and Machine Learning (AIML) techniques, whose performance and sophistication have increased, especially in the area of Deep Learning (DL) [6]. DL allows learning hierarchical representations of discriminant features in a generalized and efficient manner compared with the intensive and rigid feature engineering by human experts [7]. The introduction of Machine Learning techniques in communication is detailed in [8]. Many researchers have investigated using DL for wireless communication [9; 10; 11; 12; 13], some of them proposing DL models for different applications than signal processing [14; 15; 16; 17; 18]. Detailed surveys about DL for mobile and wireless networking are provided in [19; 20].  
Applying DL to wireless communication is currently an active research area (as highlighted in [19; 21; 22] and the references therein); this is motivated by similarities with other domains of successful DL application, such as speech and object recognition [23], and by the ability to build size-appropriate training data sets as wireless networks inherently generate large data volumes that can be efficiently collected and ingested [24].