# Study and Investigation on 5G Technology: A Systematic Review
## 4 5G Technologies
### Machine Learning Techniques for 5G  
Various machine learning (ML) techniques were applied in 5G networks and mobile communication. It provides a solution to multiple complex problems, which requires a lot of hand-tuning. ML techniques can be broadly classified as supervised, unsupervised, and reinforcement learning. Let's discuss each learning technique separately and where it impacts the 5G network. Supervised Learning, where user works with labeled data; some 5G network problems can be further categorized as classification and regression problems. Some regression problems such as scheduling nodes in 5G and energy availability can be predicted using Linear Regression (LR) algorithm. To accurately predict the bandwidth and frequency allocation Statistical Logistic Regression (SLR) is applied. Some supervised classifiers are applied to predict the network demand and allocate network resources based on the connectivity performance; it signifies the topology setup and bit rates. Support Vector Machine (SVM) and NN-based approximation algorithms are used for channel learning based on observable channel state information. Deep Neural Network (DNN) is also employed to extract solutions for predicting beamforming vectors at the BS's by taking mapping functions and uplink pilot signals into considerations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Data Rate** & **Security Requirement** & **Performance** \\ \hline [PERSON] et al. [65] & Good & Average & Good \\ [PERSON] et al. [14] & Average & - & Average \\ [PERSON] et al. [66] & Good & Average & Average \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of IoT-based approaches in 5G technology. In unsupervised Learning, where the user works with unlabeled data, various clustering techniques are applied to enhance network performance and connectivity without interruptions. K-means clustering reduces the data travel by storing data centers content into clusters. It optimizes the handover estimation based on mobility pattern and selection of relay nodes in the V2V network. Hierarchical clustering reduces network failure by detecting the intrusion in the mobile wireless network; unsupervised soft clustering helps in reducing latency by clustering fog nodes. The nonparametric Bayesian unsupervised learning technique reduces traffic in the network by actively serving the user's requests and demands. Other unsupervised learning techniques such as Adversarial Auto Encoders (AAE) and Affinity Propagation Clustering techniques detect irregular behavior in the wireless spectrum and manage resources for ultradense small cells, respectively. In case of an uncertain environment in the 5G wireless network, reinforcement learning (RL) techniques are employed to solve some problems. Actor-critic reinforcement learning is used for user scheduling and resource allocation in the network. Markov decision process (MDP) and Partially Observable MDP (POMDP) is used for Quality of Experience (QoE)-based handover decision-making for Hetnets. Controls packet call admission in HetNets and channel access process for secondary users in a Cognitive Radio Network (CRN). Deep RL is applied to decide the communication channel and mobility and speeds up the secondary user's learning rate using an antijamming strategy. Deep RL is employed in various 5G network application parameters such as resource allocation and security [67]. Table 9 shows the state-of-the-art ML-based solution for 5G network. Highlights of machine learning techniques for 5G are as follows:  
* Machine learning (ML) is a part of artificial intelligence. It processes and analyses the data that automates a systematic model that finds patterns and carries out decisions with minimum human interference. As shown in the Figure 6.
* In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.