# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.11 Other Problems  
Some papers addressed problems which are not related to the ones previously listed. Thus, we will describe them separately. The work presented in [68] applied a deep learning model to a massive MIMO system to solve the pilot contamination problem [69]. The authors highlighted that conventional approaches of pilot assignment are based on heuristics that are difficult to deploy in a real system due to high complexity. The model was used to learn the relationship between the users' location and the near-optimal pilot assignment with low computational complexity, and consequently could be used in real MIMO scenarios. The self-interference problem was addressed in [70]. A digital cancellation scheme based on deep learning was proposed for full-duplex systems. The proposed model was able to discover the relationship between the signal sent through the channel and the self-interference signal received. The authors evaluated how the joint effects of non-linear distortion and linear multi-path channel impact the performance of digital cancellation using the deep learning model. The authors in [71] represented the characterization of radio frequency (RF) power amplifiers (PAs) using deep learning. While in previous works they have considered only linear aspects of PA, the authors included non-linear aspects of PA taking into account memory aspects of deep learning models in [71]. They defined the map between the digital base station stimulus and the response of PA as a non-linear function. However, the conventional methods to solve this function require a designer to extract the interest parameters for each input (base station stimulus) manually. As a result, a deep learning model was proposed to represent this function, extracting the parameters automatically from measured base station stimulus and giving as output the PA response. In [2], reinforcement learning was used to learn the optimal physical-layer control parameters of different scenarios. Authors proposed a self-driving radio, which learns the near-optimal control algorithm while taking int account the high-level design specifications provided by the network designer. A deep learning model was proposed to map the network specifications into physical-layer control instructions. This model was then used in the reinforcement learning algorithm to take decisions according to feedback from the environment. In [72], the spectrum auction problem was addressed using deep learning. The idea was to allocate spectrum among unlicensed users taking into account the interests of the channel for the auction, and the interference suffered during communication as well as economic capability. A deep learning model was proposed for spectrum auction, and it receives as input three factors: the interference, experience, and economic ability; and gives as output a number between zero and one that determines whether the channel will be allocated for a user or not. In [73], path scheduling in a multi path scenario was addressed using reinforcement learning. In these systems, the traffic is distributed across the different paths according to policies, packet traffic classes, and the performance of the available paths. Thus, reinforcement learning was used to learn from the network the best approach for scheduling packets across the different paths. The security aspect of cooperative NOMA systems was considered in [74]. In cooperative NOMA, the user with a better channel condition acts as a relay between the source and a user experiencing poor channel conditions (user receiver). The security may be compromised in the presence of an eavesdropper in the network. Therefore, a deep learning model was proposed to find the optimal power allocation factor of a receiver in a communication system has the presence of an eavesdropper node. The model input data are the channel realization while the output are the power allocation factor of the user with poor channel conditions. In [75], authors considered the propagation prediction using deep learning models.