# Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

arXiv: https://arxiv.org/abs/1809.08707 · HTML: https://mlsanity.com/paper/1809.08707/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2018.

## Built on by (most cited first, 30 of 30)

- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2105.04196/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Wireless Quantized Federated Learning: A Joint Computation and Communication Design](https://mlsanity.com/paper/2203.05878/) (2022)
- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Noise Learning Based Denoising Autoencoder](https://mlsanity.com/paper/2101.07937/) (2021)
- [A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments](https://mlsanity.com/paper/2110.12415/) (2021)
- [Active Learning for Network Traffic Classification: A Technical Study](https://mlsanity.com/paper/2106.06933/) (2021)
- [Deep Learning Based Uplink Multi-User SIMO Beamforming Design](https://mlsanity.com/paper/2309.16603/) (2023)
- [ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation](https://mlsanity.com/paper/2304.11396/) (2023)
- [Semi-Supervised RF Fingerprinting with Consistency-Based Regularization](https://mlsanity.com/paper/2304.14795/) (2023)
- [Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services](https://mlsanity.com/paper/2106.09261/) (2021)
- [Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications](https://mlsanity.com/paper/2303.16737/) (2023)
- [Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks](https://mlsanity.com/paper/2402.09710/) (2024)
- [Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design](https://mlsanity.com/paper/2406.08373/) (2024)
- [Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels](https://mlsanity.com/paper/2504.11667/) (2025)
- [Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks](https://mlsanity.com/paper/2202.12683/) (2022)
- [Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey](https://mlsanity.com/paper/2302.13876/) (2023)
- [Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach](https://mlsanity.com/paper/2308.07511/) (2023)
- [Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning](https://mlsanity.com/paper/2309.12534/) (2023)
- [Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach](https://mlsanity.com/paper/2401.10646/) (2024)
- [WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks](https://mlsanity.com/paper/2409.07964/) (2024)
- [Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing](https://mlsanity.com/paper/2411.01140/) (2024)
- [A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control](https://mlsanity.com/paper/2504.01752/) (2025)
- [Koopman-based Prediction of Connectivity for Flying Ad Hoc Networks](https://mlsanity.com/paper/2511.01286/) (2025)
- [SLA-Safe Energy Control for AI-Native NG-RAN Using Stability-Aware Constrained PPO](https://mlsanity.com/paper/2609.05861/) (2026)
- [RRM-GPT: A Framework and Vision for Radio Resource Management Foundation Models](https://mlsanity.com/paper/2610.04296/) (2026)

## Abstract (arXiv)

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.