# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 5 Recommendations  
This section presents the most important recommendations that the authors of this paper obtained when conducting this review on applying DL models to the data networks domain. These recommendations become the future challenges that must be addressed in this matter. ML and DL are solving old problems in all areas of knowledge that remained unsolved, and now, thanks to new studies in this area and new computing technologies, outstanding results have been obtained. The use of ML and, in particular, DL in wireless data connectivity is an exceptional area of opportunity to build algorithms that can make more efficient decisions than traditional methods since the behavior of data networks is a challenging problem to solve automatically given the non-linearity and complexity of data. For example, DL models work very well to capture the behavior of patterns that change over time, such as RNNs that have a kind of artificial memory, as in the case of the LSTM in conjunction with the RL. Other DL models are instrumental in capturing spatial patterns and correlating the information with different designs, as is the case with CNN. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Work** & \begin{tabular}{c} **Deep Learning Technique to** \\ **Solve the Challenge** \\ \end{tabular} & **Issue to Solve** \\ \hline Wireless spectrum management & [4; 28; 29] & Convolutional Neural Network & \begin{tabular}{c} Improve and regulate the radio spectrum utilization; \\ \end{tabular} \\ \cline{3-4} \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & & \begin{tabular}{c} Convolutional Neural Network \\ \end{tabular} & \begin{tabular}{c} Smart learning of spectrum for the access control medium; \\ \end{tabular} \\ \cline{3-4}  & & \begin{tabular}{c} Spectrum interference image recognition. \\ \end{tabular} \\ \cline{2-4}  & [27; 30] & \begin{tabular}{c} Multilayer Perceptron and \\ Reinforcement Learning \\ \end{tabular} & Predict spectrum occupancy in wireless network. \\ \hline Efficiency on energy utilization & [33; 35] & \begin{tabular}{c} Reinforcement Learning \\ \end{tabular} & Prolong the network life-time. \\ \cline{3-4}  & [34] & \begin{tabular}{c} Multilayer Perceptron and \\ Convolutional Neural Network \\ \end{tabular} & \begin{tabular}{c} Allocate, dynamically, multiple carriers through a \\ single power amplifier. \\ \end{tabular} \\ \cline{3-4}  & [37] & \begin{tabular}{c} Residual Network and Recurrent \\ Neural Network \\ \end{tabular} & \begin{tabular}{c} Wireless network traffic prediction to improve energy \\ utilization in base stations. \\ \end{tabular} \\ \hline Enhanced data transmission & [39] & Deep Belief Network and Restricted Boltzmann Machine & Internet traffic prediction to guarantee QoS. \\ \cline{2-4}  & [41] & Multilayer Perceptron & Congestion control and wireless error detection. \\ \cline{2-4}  & [38; 40] & Deep Reinforcement Learning & Computational resources allocation for offloaded tasks. \\ \cline{2-4}  & [42] & \
\begin{tabular}{c} Multilayer Perceptron and \\ K-NearestNeighbor \\ \end{tabular} &
\begin{tabular}{c} Deal with the indoor location problem and determine \\ the final position of the mobile device. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Challenges of cognitive networks solved by deep learning applications. The preceding is ideal for data networks because their data have a non-linear behavior characterized by a high temporal and spatial correlation. Consequently, a mixture of various DL algorithms to capture these behavior patterns has to be done in a single framework to solve the optimization of the different responses needed in the operation of data networks. Another vital recommendation has to do with taking advantage of the use of DL to address problems in a multidimensional way. It automatically optimizes several parameters simultaneously with ease and without human error, unlike traditional methods that do not use DL or only one problem at a time.