## 8 Discussion
As a result, mobile networking researchers are also beginning to recognize the power and importance of deep learning, and are exploring its potential to solve various optimization and resource allocation problems. It is also worth noticing that the major part of the deep learning models proposed to be deployed in the next generation wireless networks is trained in a supervised way due to its supreme convergence speed and high accuracy compared to unsupervised and reinforcement learning approaches. Speaking of the deep learning architectures, the most of the models used are comprised of fully-connected layers which have few trainable parameters which allows them to learn faster compared to other structures. Fully-connected layers are often used even in the case of time-series data although using recurrent neural networks would probably allow for lower error and higher accuracy values [61]. In many AI/ML applications in the RAN domain, channel state matrices act as the input to the target models [28]. These matrices can often be interpreted as images with height and width being equal to the number of antennas and the number of OFDM subcarriers, respectively, whereas the real and imaginary part of the signal can be interpreted as the colour channels. Since convolutional layers are usually employed in image related problems, CNNs are frequently used in studies which focus on the analysis of channel state matrices. Although the attention mechanism provides state-of-the-art accuracy results in certain time-series processing tasks, the neural networks which rely on this technique, such as transformers, are rarely employed by mobile network researchers. Therefore, implementing and testing these deep learning architectures for solving various problems in the mobile network domain can be considered as one of the potential future research directions. In Section 4, we have provided an extensive review of machine learning driven frameworks found in recent scientific papers. As one can notice, AI/ML is often proposed to be deployed in mobile networks for various channel estimation related problems. The traditional way to learn the transmission channels is to have the receiver perform the measurements and send those back to the transmitter. Alternatively, an AI/ML model can be trained to achieve this task by using only a part of the channel state matrix which allows to reduce the feedback transmission overhead [28, 64]. Another category of AI/ML research focuses on substituting conventional algorithms used for mobile networking with machine learning ones aiming to reduce the processing time. The models that follow this approach are trained using the datasets in which the output labels are calculated using the corresponding conventional algorithm which might be computationally expensive to use in real time [68]. During the inference, the resulting AI/ML models trained allow for an accurate and time-efficient estimation of the optimal solution without occupying large amounts of computing resources. Finally, supreme predictive capabilities of state-of-the-art AI/ML models allow for efficient dynamic resource allocation in the mobile networking domain. For example, AI/ML can be employed to predict resource block usage in network slicing problems [86], estimate the probability of a certain content being requested in order to implement a proactive caching scheme [87], and discover energy-efficient power management strategies by analysing historical traffic data patterns [95]. It is however worth noticing that there are still not many studies in the 5G domain which employ the reinforcement learning approach for achieving the task given despite the recent progress in this field of study. As mentioned in Section 3, this can be partially explained by the fact that training an RL agent in a real world environment is often not feasible while implementing a simulation software may require some additional resources.