# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 4 Integrating TinyML with Network Technologies
### 5G and TinyML
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Classes** & **Learning Models** & **SG Application Illustration** \\ \hline \multirow{7}{*}{Supervised learning} & Support Vector Machines (SVM) & Model for predicting path loss in urban contexts \\ \cline{2-3}  & Approaches for machine learning and statistical & In deployments of self-organized LTE dense small cells, dynamic \\  & Logistic regression & frequency and bandwidth allocation is used. \\ \cline{2-3}  & Neural-Network-based approximation & Channel learning is used to infer unobservable channel state \\  & & information (CSI) from an observable channel. \\ \cline{2-3}  & \multirow{2}{*}{Frameworks for Supervised Machine Learning} & Adjustment of the TDD Uplink-Downlink configuration in \\  & & XG-PON-LTE Systems to enhance network performance in the hybrid \\  & & optical-vireises network based on current traffic circumstances \\ \cline{2-3}  & Multi-Layer Perceptrons (MLPs) and Artificial Neural & In next-generation wireless networks, objective function modeling \\  & Networks (ANN) & and estimates for link budget and propagation loss are used \\ \hline \multirow{7}{*}{Reinforcement Learning} & Reinforcement Learning algorithm based on long short-term memory (RL-LSTM) cells. & Based on long-term WLAN activity in the channels and LTE-U traffic loads, proactive resource allocation in LTE-U networks, implemented as a non-cooperative game, enables SBS to determine which \\ \cline{2-3}  & & millisecond channel to utilize. \\ \cline{2-3}  & the modified Roth-Erev (MRE), Gradient follower (GF), and the modified Bush and Mosteller (MBM). & Allow Fento-Cells (FCs) to monitor the radio environment \\  & & autonomously and opportunistically and alter their settings in \\  & & HetNets to eliminate intra/inter-tier interference \\ \cline{2-3}  & Reinforcement Learning with Network assisted feedback. & Selection of Heterogeneous Radio Access Technologies (RATs) \\ \hline \multirow{7}{*}{Unsupervised Learning} & Clustering using Affinity Propagation. & Data-Driven Resource Management for Ultra-Dense Small Cells \\ \cline{2-3}  & Hierarchical Clustering. & Detection of anomalies, faults, and intrusions in mobile \\ \cline{2-3}  & ML Framework for Unsupervised Soft-Clustering. & In heterogeneous cellular networks, latency is reduced by grouping \\ \cline{2-3}  & ML Framework for Unsupervised Soft-Clustering. & fog nodes to automatically identify which low power node (LPN) gets \\ \cline{2-3}  & & converted to a high power node (HPN) \\ \cline{2-3}  & Expectation Maximization (EM), K-means clustering and & Relay node selection and cooperative spectrum sensing in \\ \cline{2-3}  & Gaussian Mixture Model (GMM). & vehicle networks \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Some Learning Methods and their Applications in 5G for the Three Types of Machine Learning. * In contrast to conventional ML methods, deep learning performance usually increases rapidly as the size of the training data increases. As a result, it can exploit the enormous amounts of mobile data created at high rates of speed. * Geometric mobile data learning: Geometric mobile data analysis has been transformed by specific deep learning architectures for modeling geometric mobile data. * Extraction of features: Through layers of varied depths, deep neural networks can automatically retrieve high-level information. This makes it possible to analyze heterogeneous and noisy mobile big data with lower cost human feature engineering. * Multitask learning: Using transfer learning, features gained from neural networks via hidden layers may be applied to other tasks. This minimizes the processing and memory needs for doing multitask learning in mobile devices.