The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications
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Abstract (arXiv)
Future wireless networks will integrate sensing, learning and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment, and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services, but also become more resilient to channel-dependent effects like blockage and better support adaptation in dynamic environments as networks reconfigure. In this paper, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization and machine learning techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray tracing measurements and mathematical models that mix the digital and physical worlds.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2024.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 3.
8 papers.
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
- 2024R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Modelscited by 5
- 2026Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrayscited by 1
- 2025PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communicationcited by 0
- 2025Secure mmWave Beamforming with Proactive-ISAC Defense Against Beam-Stealing Attackscited by 0
- 2025Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environmentscited by 0
- 2026Resolving Multi-Target Association in OFDM-based ISAC via Vision-aided Multi-Modal Learningcited by 0
- 2026CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC Systemcited by 0
- 2026CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System21 Jul 2026
- 2026Resolving Multi-Target Association in OFDM-based ISAC via Vision-aided Multi-Modal Learning20 Jun 2026
- 2026Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays21 Feb 2026
- 2025Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environments4 Sep 2025
- 2025Secure mmWave Beamforming with Proactive-ISAC Defense Against Beam-Stealing Attacks4 Aug 2025
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey3 Aug 2025
- 2025PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication27 Mar 2025
- 2024R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models16 Jul 2024