A study published in Engineering delves into cooperative integrated sensing and communication (ISAC), a key paradigm for sixth-generation (6G) mobile information networks, and presents a comprehensive analysis of its core concepts, key technologies, performance evaluation frameworks and real-world field trial results. Conducted by researchers from China Mobile Research Institute and ZGC Institute of Ubiquitous-X Innovation and Applications, the research outlines how cooperative ISAC leverages large-scale deployed mobile networks to realize ubiquitous sensing, marking a critical step toward transforming connected things into connected intelligence.

The research traces the evolutionary path of cooperative ISAC, noting the natural convergence of sensing and communication technologies—from monostatic radar to distributed MIMO radar, and from MIMO to cell-free MIMO in communications, ultimately leading to the emergence of cooperative ISAC in 2023 as a solution to the limitations of 5G-A’s monostatic, single-modal and single-band ISAC systems. The study defines four core features of cooperative ISAC, summarized as NICE: network-enabled, integration, cooperation and everything. Network-enabled leverages existing cellular network infrastructure for expanded sensing coverage and low-latency data transmission; integration combines communication and sensing functions on shared hardware and software, with information fusion at signal, symbol and data levels; cooperation spans multi-node, multi-band and multi-function dimensions, enabling mutual enhancement of sensing and communication; everything refers to the participation of diverse devices—base stations, user equipment and sensors—as sensing transmitters and receivers, supporting multi-source multi-modal sensing.

The paper elaborates on key technologies underpinning cooperative ISAC, including adaptive antenna array and beam design tailored to different sensing areas, node selection and deployment strategies based on path SINR metrics for optimal cluster construction, high-precision timing and frequency synchronization via air-interface calibration methods, and three-tier data fusion with symbol-level fusion identified as a balanced option for accuracy and computational efficiency. It also proposes a ring-shaped networking scheme to mitigate intra and inter-site interference, a critical challenge in large-scale cooperative sensing.

A novel performance evaluation framework is established, with sensing capacity—defined as the number of QoS-compliant targets per square kilometer—introduced as a system-level metric complementing traditional radar and communication KPIs. System-level simulations for UAV detection scenarios show a suggested detection probability KPI of 95% at a false alarm probability of 10⁻⁶, with cooperative sensing achieving 25 dB higher energy accumulation than non-cooperative schemes. Field trials across high-frequency millimeter-wave, bistatic, multistatic and networked ISAC setups validate the technology’s feasibility: high-frequency prototypes achieve decimeter-level ranging accuracy and 683.78 Mbps communication rates; bistatic systems deliver 700 m coverage with 6.41 m horizontal accuracy at 95% confidence; multistatic setups achieve sub-20 m positioning accuracy; and a 13-site networked trial in urban areas attains 14 m positioning accuracy for UAVs at 100–300 m altitudes, with 3% false alarm and 3.2% missed detection rates.

The research concludes by outlining future research directions, including AI-driven signal processing, semantic sensing, agent-based cooperative systems, high-precision multi-node synchronization, multi-band and multi-modal sensing fusion, and 3D environmental reconstruction. These directions aim to further enhance the performance, scalability and intelligence of cooperative ISAC, laying the groundwork for its standardization and commercialization in 6G networks.

The paper “Cooperative Sensing for 6G ISAC: Concept, Key Technologies, Performance Evaluation, and Field Trial,” is authored by Guangyi Liu, Lincong Han, Rongyan Xi, Liang Ma, Zixiang Han, Yahui Xue, Hanting Zhao, Jing Jin, Qixing Wang, Fei Xu. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.08.033. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.