# Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

## TL;DR

This paper surveys how machine learning and deep learning can enhance resource management in cellular and IoT networks, highlighting current solutions, challenges, and future research directions.

## Contribution

It provides a comprehensive review of ML and DL-based resource management techniques specifically tailored for various IoT and wireless network architectures.

## Key findings

- ML and DL improve resource allocation efficiency
- Existing techniques are tailored for HetNets, MIMO, D2D, and NOMA
- Future research directions include addressing open challenges

## Abstract

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.