ve robustness. Besides improving the accuracy and robustness of spectrum sensing, another substantial sensing performance improvement comes from using ML to help the SU make efficient decisions regarding which channel to sense and when or how often to sense. The prediction ability can enable SUs to perform spectrum sensing in a more efficient manner. By enabling SUs to determine the channel selection for data transmission and predicting the period of channel idle status, sensing time can be significantly reduced. Therefore, the authors in [152] proposed an ML-based method that employed a Reinforce- ment Learning (RL) algorithm for channel selection and a Bayesian algorithm to determine the length of time for which sensing operation can be skipped. It was shown that the pro- posed method could effectively reduce the sensing operations while keeping interference with PUs at an acceptable level. This work also showed that by skipping unnecessary sens- ing, SUs can save more energy and achieve higher throughput by spending the saved sensing time for transmission. A Hidden Markov Model (HMM)-based Cooperative Spectrum Sensing (CSS) method was proposed in [91] to predict the status of the network environ- ment. First, the concept of an Interference Zone (IZ) was introduced to indicate the presence of PUs. Then, by combining the sensing results from SUs located in different IZs, the Fusion Center employed a fusion rule for modeling specific HMM. Moreover, the system adopted Baum-Welch (BW) algorithm to estimate the parameters of the HMM-based past spectrum sensing results. The estimated parameters were then passed to a forward algorithm to 10 Secure Spectrum Sharing with Machine Learning: An Overview predict the activity of PUs. Finally, SUs were classified into two categories according to the prediction results, i.e. Interfered by PU (IP) and Not Interfered by PU (NIP). SUs marked as IP do not need to perform spectrum sensing to avoid unnecessary energy consumption. 10.2.1.2 Spectrum Selection After the system receives