# Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey

arXiv: https://arxiv.org/abs/1901.07947 · HTML: https://mlsanity.com/paper/1901.07947/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 27 of 27)

- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
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- [Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification](https://mlsanity.com/paper/2101.10254/) (2021)
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- [Artificial Intelligence for Satellite Communication: A Review](https://mlsanity.com/paper/2101.10899/) (2021)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression](https://mlsanity.com/paper/2203.00517/) (2022)
- [Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN](https://mlsanity.com/paper/2105.01478/) (2021)
- [RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet](https://mlsanity.com/paper/2508.09140/) (2025)
- [MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio](https://mlsanity.com/paper/2204.04507/) (2022)
- [RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth](https://mlsanity.com/paper/2209.03142/) (2022)
- [Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth](https://mlsanity.com/paper/2210.02897/) (2022)
- [Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks](https://mlsanity.com/paper/2210.05874/) (2022)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.02425/) (2021)
- [The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting](https://mlsanity.com/paper/2105.03671/) (2021)
- [Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control](https://mlsanity.com/paper/2204.01950/) (2022)
- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Adversarial Classification of the Attacks on Smart Grids Using Game Theory and Deep Learning](https://mlsanity.com/paper/2106.03209/) (2021)
- [Digital Signal Processing Using Deep Neural Networks](https://mlsanity.com/paper/2109.10404/) (2021)
- [Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks](https://mlsanity.com/paper/2204.04390/) (2022)
- [Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.06800/) (2022)
- [Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation](https://mlsanity.com/paper/2302.02250/) (2023)
- [Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes](https://mlsanity.com/paper/2106.01114/) (2021)
- [Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples](https://mlsanity.com/paper/2201.08731/) (2022)

## Abstract (arXiv)

The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.