Integrated sensing and communication (ISAC) is a key enabling technology for sixth-generation (6G) mobile communication systems, achieving seamless integration of communication and sensing functions. Cooperative sensing, where the transmitter and receiver are not co-located, serves as a key enabler for ISAC, significantly enhancing the sensing performance while reducing the implementation complexity of the receiver. However, practical deployments of cooperative sensing still face numerous challenges, such as synchronization and interference. This paper presents a set of advanced beam management methods specifically designed for the cooperative sensing system, offering a comprehensive framework to address these challenges. Specifically, we first analyze the strong/weak path effect (SWPE), a critical phenomenon caused by diverse target reflectivities and propagation paths, which severely degrades both synchronization accuracy and target detection. To counteract this, we propose an adaptive path power allocation method compatible with both all-digital and hybrid beamforming architectures. This method intelligently allocates power across different paths to mitigate the SWPE, thereby ensuring reliable synchronization via the direct path while enhancing the detectability of weak targets. As a result, the proposed method improves the target detection probability by over 30%. Furthermore, an adaptive interference suppression method is designed to reduce interference while maintaining sensing/communication quality, which obtains the SINR gain of around 5 dB, compared to the traditional full nulling method. Experimental results validate the effectiveness of robust synchronization and our proposed power allocation. This study lays a solid foundation for beamforming optimization in cooperative sensing systems, facilitating high-accuracy sensing and communication in complex environments.

The 6th generation mobile communication system (6G) will provide everything as a service (XaaS), where X includes communication, sensing, computing, artificial intelligence (AI), big data and security and more. Novel features such as sensing as a Service (SaaS) will contribute to further realising Internet of Everything (IoE). Integrated Sensing and Communication (ISAC) is identified as one of the six usage scenarios for 6G by the International Telecommunication Union Radiocommunication Sector (ITU-R), and the corresponding studies on the detailed technical performance requirements and evaluation methodologies have begun in 2024. Although ISAC has become a popular topic, there are no systematic performance requirements metrics and corresponding evaluation methodologies defined for SaaS in a mobile communication system, while conventional key performance indicators (KPI) for radar systems have been borrowed currently. Therefore, to fill this gap, this paper proposes SensCAP, a systematic CAPability performance metric composed of Sensing Capacity, Accuracy and Probability. The sensing capacity reflects the comprehensive sensing performance, which can be expressed as the number of targets that can be detected per unit area within unit time, given Sensing Quality of Service (QoS) requirements consisting of sensing accuracy and probability. Furthermore, the performance evaluation of the SensCAP is conducted through system simulation using proposed evaluation methodologies, and the KPI values are suggested as the guidelines for further study in ITU-R.

The combination of communication and sensing is envisioned as a novel feature in the forthcoming sixth-generation (6G) wireless communication. The conventional approach to the joint sensing and communication (JSAC) system is utilizing one base station (BS) as both a sensing transmitter and a sensing receiver, which is known as monostatic sensing. However, the resulting self-interference issue requires additional hardware promotion to achieve full-duplexing. To overcome this issue, in this paper, we focus on cooperative sensing where the transmitter and receivers are non-co-located, which includes the bistatic and multistatic sensing. Specifically, the system model of cooperative sensing based on mobile networks is established. To demonstrate the feasibility of cooperative sensing, the bistatic radar cross section (RCS) is provided. As for the sensing method, a refined orthogonal matching pursuit (R-OMP) method is proposed to estimate the channel parameters and data fusion is also provided to derive the objects’ positions and velocities. Considering the non-negligible interference in the cooperative JSAC networks, we also discuss interference management in this paper. Simulation results show that the proposed cooperative sensing system improves the position and velocity estimation accuracy by over 20% when compared with monostatic sensing. The preliminary experiment results also verify the feasibility of the proposed system.