# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 1 Introduction  
Nowadays, with the arrival of smart environments as part of the daily life of many people, it has been evident that it is of the utmost importance that its design needs to consider telecommunications networks. These networks allow the transmission of all the data between various environment components (Figure 1): the Internet of Things (IoT) devices, middleware, and applications. However, without a data network that has the intelligence to dynamically adapt to the conditions presented in complex environments, it will result in less-than-optimal communication and limit functions that need to be real-time. Because of this, there is particular interest in studying novel data networks. The arrival of these new generations of mobile networks, such as 5G, and the exponential growth of end-users who rapidly demand large data trans  
Figure 1: Connected environments through cognitive data networks. current networks to become more complex, with the need for faster and more intelligent learning mechanisms [1]. However, most current communications networks are limited by the layered protocol architecture, which causes individual elements to be unaware of the state of the network experienced by other factors. Consequently, the response to the conditions presented in the network has a limited and isolated scope, often resulting in sub-optimal performance [2]. Therefore, this forces us to rethink the design of next-generation networks to transform them into cognitive networks that satisfy these data communication needs. A cognitive network is an intelligent network that should be simple to manage, and its capabilities should be continuously developed and expanded with as little human interaction as feasible [3]. Recently, much effort has been made to improve network connectivity by developing reactive mechanisms to address different operational problems. However, these mechanisms work inefficiently when the network presents significant changes in its operation since they cannot collect data to continue learning and better adapt to these new changes, much less predict future changes. This leads to the use of deep learning (DL) to enable protocols to observe the network conditions and to use prior knowledge gained to efficiently respond to this complex and dynamic operation [4]. For this reason, part of this paper provides a concise discussion of the fundamental and predictive ability of DL methods and the many applications available for the next generation of cognitive networks. It is important to point out that DL is a type of machine learning (ML) algorithm, and ML is a subfield of artificial intelligence (AI) that allows the development of smart devices, products, and systems that mimic many human behaviors and capabilities. ML has become the primary tool for developing AI because ML provides algorithms that can learn from experience, which is powerful when generalizing (deducing new facts from old facts) because ML algorithms assume that the past predicts the future. However, the main difference between machine learning methods and conventional statistical learning methods is that most ML methods are nonparametric models. For this reason, nowadays, AI products being developed with ML have surpassed AI products developed with the symbolic AI (old fashion AI), which was the dominant AI paradigm before the ML paradigm's arrival. Human knowledge and behavior standards are explicitly included in computer programs using symbolic AI. The foundation of symbolic AI programs is the creation of explicit structures and behavior rules. Symbolic AI is the best option when the rules are explicit because the input can be simply received and translated into symbols. However, the breaking point of symbolic AI was its inability to learn from the past to predict the future. For this reason, after the 1980s, ML was adopted for AI because it gives computers the ability to learn without being explicitly programmed [5] and enables computers to act and make data-driven decisions to carry out a specific task.