# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.9 Resource Allocation/Management  
As the numbers of users, services, and resources increase, the management and orchestration complexity of resources also increase. The efficient usage of resources can be translated into cost reduction and avoid over/under resource dimensioning. Fortunately, under such a very dynamic and complex network environment, recent achievements in machine learning that interact with surrounding environments can provide effective way to address these problems [57].  
Four papers addressed resource allocation in network slices using solutions based on deep learning [5; 57; 58; 59]. A network slice is a very important technology for 5G since it will allow a network operator to offer a diverse set of tailored and isolated services over a shared physical infrastructure.  
A deep learning-based solution was proposed in [58] to allocate slices in 5G networks. The authors proposed a metric called REVA that measures the amount of Physical Resource Blocks (PRBs) available to active bearers for each network slice, and a deep learning model was proposed to predict such metric.  
[PERSON] et al. proposed a framework that combined deep learning and reinforcement learning to resource scheduling and allocation [57]. The main goal was to minimize resource consumption at the same time guaranteeing the required performance isolation degree by a network slice. In [5], the authors proposed a framework for resource allocation in network slices and a deep learning model was used to predict the network status based on historical data. In [59], a model was proposed to predict the medium usage for network slices in 5G environments while meeting service level agreement (SLA) requirements.  
Three papers proposed deep learning-based solutions to optimize the energy consumption in 5G networks [60; 61; 62; 63]. The works proposed by [60; 61] focused on NOMA systems. A framework was proposed in [60] to optimize energy consumption. A deep learning model is part of the framework and was used to map the input parameters (channel coefficients, the user demands, user power, and the transmission deadline) into an optimal scheduling scheme. In [61], a similar strategy was used, where a deep learning model was used to find the approximated optimal joint resource allocation strategy to minimize energy consumption. In [62], a deep learning model was used in the MME for user association taking into account the behavior of access points in the offloading scheme. In [63], the authors proposed a deep learning model to allocate carriers in multi-carrier power amplifier (MCPA) dynamically, taking into account the energy efficiency. The main idea was to minimize the total power consumption while finding the optimal carrier to MPCA allocation. To solve this problem, two approaches were used: convex relaxation and deep learning. The deep learning model was used to approximate the power consumption function formulated in the optimization problem, since it is a non-convex and non-continuous function.  
In [64], the authors proposed a deep learning-based solution for downlink coordinated multi-point (CoMP) in 5G. The model receives physical layer measurements from the user equipment and \"formulates a modified CoMP trigger function to enhance the downlink capacity\" [64]. The output of the model is the decision to enable/disable the CoMP mechanism.  
In [65], the authors proposed a deep learning model for smart communication systems with high density D2D mmWave environments using beamforming. The model selects the best relay node taking into account multiple reliability metrics in order to maximize the average system throughput. The authors in [11] also proposed a deep learning-based solution to maximize the network throughput considering resource allocation in multi-cell networks. A deep learning model was proposed to predict the resource allocation solution (taking as input the channel quality indicator and user location) without intensive computations.