## 2 Review
### Dynamic Spectrum Access: Signal Processing and ML/DL Approaches  
DSA was first considered two decades ago based on the measurements of actual spectrum usage performed by the Spectrum Policy Task Force of Federal Communications Commission (FCC) [20]. On the contrary of the majorities' belief of a well-crowded spectrum, the actual measurements indicated that many portions of the spectrum are idle at a given time and location, leading to spectral holes. This results from the fixed spectrum access policy. Under DSA, secondary users (i.e, unlicensed users) are allowed to utilize spectral holes of primary users (i.e., licensed users) without interfering with the operation of primary users through a _cognitive radio_[21; 22; 23; 24], which is an intelligent system built on a software-defined radio. However, there is an upper limit to the channel capacity that can be achieved with DSA [25]. Here, we briefly review _spectrum sensing_ techniques associated with DSA, and the reader is referred to [26; 27; 28] for other aspects of DSA, such as spectrum management and spectrum sharing policies. Several techniques have been proposed for both narrowband and wideband spectrum sensing. For narrowband sensing, the main non-ML techniques are energy detection [29; 30; 31; 32], matched filter-based detection [33; 34; 35], cyclostationary feature detection [36; 37], and covariance-based detection [38; 39; 40]. The energy detection and covariance-based detection approaches do not require prior knowledge of the primary user's signal; however, the former is not reliable at low signal-to-noise ratio (SNR) values. Matched filter-based detection provides optimal sensing and better detection at low SNR values, but this technique requires prior knowledge of the primary user's signal, which may not always be available. Cyclostationary feature detection is generally robust against noise uncertainly and works well at low SNR values; however, it suffer from a large sensing time. In wideband sensing, as a straightforward approach, a wideband signal may be divided into a set of narrowband signals via temporal DFT or a filter bank [41; 42; 43; 44] and sense sequentially or in parallel using narrowband sensing techniques. However, sequential sensing requires more time, whereas parallel sensing requires more resources. Because a wideband signal with spectral can be considered a sparse signal in the frequency domain, several compressive sensing-based techniques have been proposed for wideband sensing [45; 46; 47; 48]. These approaches require lower sampling rates and have lower power consumption in addition to the fast sensing. Thus, compressive sensing-based approaches eliminate most of the drawbacks of the sequential or parallel narrowband sensing of a wideband signal. In the last decade, a considerable number of ML and DL techniques have been proposed for spectrum sensing. ML algorithms have been mostly used to determine the channel occupancy patterns of a primary used and to classify the channel as either free or occupied. In [49; 50; 51; 52; 53], K-means clustering and Gaussian mixture models have been first used to determine the presence of primary users, and a classifier such as a support vector machine and K-nearest-neighbor is then employed to determine whether a channel is free or occupied. DL techniques such as convolutional neural networks and long short-term memory have been employed for spectrum sensing in [54; 55; 56; 57], which outperformed the conventional techniques. Recently, a transformer-based DL architecture was proposed in [58]. This DL approach outperforms the previously proposed approaches based on convolutional neural networks. Another DL approach employed for spectrum sensing in a cooperative way is deep reinforcement learning [59; 60; 61; 62]. These deep reinforcement learning approaches improve the robustness of the spectrum sensing system and allows it to make more accurate decisions in dynamic environments. The reader is referred to [63; 64; 65] for a comprehensive review of spectrum sensing techniques.