# Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles

arXiv: https://arxiv.org/abs/2208.02157 · HTML: https://mlsanity.com/paper/2208.02157/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2022.

## Built on by (most cited first, 11 of 11)

- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [Joint Sensing and Semantic Communications with Multi-Task Deep Learning](https://mlsanity.com/paper/2311.05017/) (2023)
- [DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset](https://mlsanity.com/paper/2406.17908/) (2024)
- [Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access](https://mlsanity.com/paper/2312.13931/) (2023)
- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [A Multi-Modal Foundational Model for Wireless Communication and Sensing](https://mlsanity.com/paper/2602.04016/) (2026)
- [Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction](https://mlsanity.com/paper/2211.07569/) (2022)
- [Causality-Driven Reinforcement Learning for Joint Communication and Sensing](https://mlsanity.com/paper/2409.15329/) (2024)
- [Autoencoder for Position-Assisted Beam Prediction in mmWave ISAC Systems](https://mlsanity.com/paper/2511.18594/) (2025)

## Abstract (arXiv)

Integrating sensing and communication is a defining theme for future wireless systems. This is motivated by the promising performance gains, especially as they assist each other, and by the better utilization of the wireless and hardware resources. Realizing these gains in practice, however, is subject to several challenges where leveraging machine learning can provide a potential solution. This article focuses on ten key machine learning roles for joint sensing and communication, sensing-aided communication, and communication-aided sensing systems, explains why and how machine learning can be utilized, and highlights important directions for future research. The article also presents real-world results for some of these machine learning roles based on the large-scale real-world dataset DeepSense 6G, which could be adopted in investigating a wide range of integrated sensing and communication problems.