## 2 Previous Work  
In MCRN, resources such as frequency band, modulation type, distance, power, noise, and interference have an impact on the overall system. We try to balance or maximize the use of these resources according to the environment, the technology, and the user services needed. A modern machine learning (ML) technique can be useful for providing a mechanism to maximize the use of the available resources. In our case, the learning capability enables the MCRN to learn and adapt the frequency management according to the wireless environment in real-time [1]. Cognitive radio (CR) is based on intelligence [8], and the learning process is very important to obtain the cognitive part. CRs must learn from the environment and decide their actions. There has been a growing interest in applying ML algorithms to the cognitive radio network (CRN). ML has been proposed and discussed in topics such as complex computation and the error propagation of training algorithm, and the majority of machine learning researchers work on techniques such as linear regression, logistic regression and support vector machines to decide if there is PU presence. ML can be reduced to four learning strategies: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning [9]. Supervised ML applications such as support vector machines (SVM) result in better PU or PUE detection performance in an MCRN for an expected probability of false alarm when compared with traditional methods. We have to design the training set size, which is the input data in the learning process. Depending on this design, computation time and memory will be required to solve the decision problem. Once this learning process is done, the system can distinguish between signals, and only data points which lie at the margin of the hyperplane are qualified to be categorized as support vectors [2]. Individual spectrum sensing has the hidden primary user (PU) problem, but first we have to optimize the individual sensing process, because the cooperative scheme needs a common channel to transmit, and it requires more resources of the MCRN. It is expected to achieve a higher spectrum-sensing reliability when compared with individual spectrum sensing. All secondary users in the MCRN share their sensing information with the fusion center for a final decision with a hard or soft fusion algorithm and an OR/AND rule to make the final decision [10]. Machine learning techniques are also used for energy detection in cooperative spectrum sensing for CRN, and are even comparable with unsupervised techniques such as K-means [11]. According to [11], the supervised SVM classifier with the linear kernel performs well in terms of the detection probability. The computational complexity of the SVM-linear classifier is compensated by its higher detection capability and comparatively lower training and classification delay. The detection systems can integrate an artificial neuron network (ANN) into a cognitive engine to learn from environment measurements and analyze how the status of the network affect its performance on different channels. The ANN and the feed-backward ANN belong to supervised learning; the training dataset must be labeled by a human, which is not easy in a real RF environment. The environment adaptation of unsupervised learning algorithms is better than supervised learning algorithms for CRN [9]. Another application is CRN based on genetic algorithms, where these algorithms are used to control channel selection schemes (GA-CSS) for CRs by considering the random arrival of data rate and bit error rate (BER) in order to select the optimal control channel. Besides, an improved genetic algorithm-based spectrum assignment model helps to achieve higher system utility with more cognitive users under the same population size. Learning algorithm-based decision-making is another major task that has been widely investigated in with regard to CR applications such as pattern classification [12].