Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey
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Abstract (arXiv)
Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing under cellular topology, and sensing-assisted communications. We conclude the paper by listing key open research problems for the aforementioned topics and sharing some lessons that we have learned.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1; 2025: 3; 2026: 2.
9 papers.
- 2021Roadmap for Edge AI: A Dagstuhl Perspectivecited by 4
- 2024Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Datacited by 4
- 2023Deep Learning-based Estimation for Multitarget Radar Detectioncited by 3
- 2023ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communicationcited by 3
- 2025WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanismcited by 2
- 2026QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networkscited by 1
- 2025Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarmscited by 0
- 2025PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communicationcited by 0
- 2026Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networkscited by 0
- 2026Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks4 Aug 2026
- 2026QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks1 Jul 2026
- 2025WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism4 Dec 2025
- 2025PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication27 Mar 2025
- 2025Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms11 Jan 2025
- 2024Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data10 May 2024
- 2023ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication14 Jul 2023
- 2023Deep Learning-based Estimation for Multitarget Radar Detection5 May 2023
- 2021Roadmap for Edge AI: A Dagstuhl Perspective27 Nov 2021