# When 5G Meets Deep Learning: A Systematic Review
## 1 Introduction  
According to Cisco, the global Internet traffic will reach around 30 GB per capita by 2021, where more than 63% of this traffic is generated by wireless and mobile devices [1]. The new generation of mobile communication system (5G) will deal with a massive number of connected devices at base stations, a massive growth in the traffic volume, and a large range of applications with different features and requirements. The heterogeneity of devices and applications makes infrastructure management even more complex. For example, IoT devices require low-power connectivity,trains moving at 300 KM/h need a high-speed mobile connection, users at their home need fiber-like broadband connectivity [2] whereas Industry 4.0 devices require ultra reliable low delay services. Several underlying technologies have been put forward in order to support the above. Examples of these include multiple-input multiple-output (MIMO), antenna beamforming [3], virtualized network functions (VNFs) [4], and the use of tailored and well provisioned network slices [5]. Some data based solutions can be used to manage 5G infrastructures. For instance, analysis of dynamic mobile traffic can be used to predict the user location, which benefits handover mechanisms [6]. Another example is the evaluation of historical physical channel data to predict the channel state information, which is a complex problem to address analytically [7]. Another example is the network slices allocation according to the user requirements, considering network status and the resources available [2]. All these examples are based on data analysis. Some examples are based on historical data analysis, used to predict some behavior, and others are based on the current state of the environment, used to help during decision making process. These type of problems can be addressed through machine learning techniques. However, the conventional machine learning approaches are limited to process natural data in their raw form [8]. For many decades, constructing a machine learning system or a pattern-recognition system required a considerable expert domain knowledge and careful engineering to design a feature extractor. After this step, the raw data could be converted into a suitable representation to be used as input to the learning system [9]. In order to avoid the effort for creating a feature extractor or suffering possible mistakes in the development process, techniques that automatically discover representations from the raw data were developed. Over recent years, deep learning has outperformed conventional machine learning techniques in several domains such as computer vision, natural language processing, and genomics [10]. According to [9], deep learning methods _\"are representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transforms the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level\"_. Therefore, several complex functions can be learned automatically through sufficient and successive transformations from raw data. Similarly to many application domains, deep learning models can be used to address problems of infrastructure management in 5G networks, such as radio and compute resource allocation, channel state prediction, handover prediction, and so on. This paper presents a systematic review of the literature in order to identify how deep learning has been used to solve problems in 5G environments. In [11], [PERSON] et al. presented some works that applied deep learning and reinforcement learning to address the problem of resource allocation in wireless networks. Many problems and limitations related to resource allocation, such as throughput maximization, interference minimization, and energy efficiency were examined.