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

arXiv: https://arxiv.org/abs/1907.08965 · HTML: https://mlsanity.com/paper/1907.08965/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2019.

## Built on by (most cited first, 19 of 19)

- [NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging](https://mlsanity.com/paper/2310.04429/) (2023)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks](https://mlsanity.com/paper/2204.06760/) (2022)
- [Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions](https://mlsanity.com/paper/2407.04581/) (2024)
- [Self-play Learning Strategies for Resource Assignment in Open-RAN Networks](https://mlsanity.com/paper/2103.02649/) (2021)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2211.12201/) (2022)
- [Data Collection and Utilization Framework for Edge AI Applications](https://mlsanity.com/paper/2103.06518/) (2021)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning](https://mlsanity.com/paper/2505.06454/) (2025)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems](https://mlsanity.com/paper/2108.02551/) (2021)
- [Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets](https://mlsanity.com/paper/2202.13652/) (2022)
- [A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks](https://mlsanity.com/paper/2410.12320/) (2024)
- [Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review](https://mlsanity.com/paper/2202.02868/) (2022)
- [OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework](https://mlsanity.com/paper/2308.05757/) (2023)
- [Optimistic Learning for Communication Networks](https://mlsanity.com/paper/2504.03499/) (2025)
- [Distributed Dynamic Associative Memory via Online Convex Optimization](https://mlsanity.com/paper/2511.23347/) (2025)

## Abstract (arXiv)

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.