Cooperative Sensing in Cell-Free Massive MIMO ISAC Systems: Performance Optimization and Signal Processing

Emerging applications such as low-altitude economy and intelligent transportation hold the promise of significant economic and social benefits, requiring the support of technologies that integrate robust communication with precise sensing. Integrated sensing and communication (ISAC), as a technology enabled seamless connection between communication and sensing, is regarded a core enabling technology for these applications. However, the accuracy of single-node sensing in ISAC systems is limited, prompting the emergence of multi-node cooperative sensing. In multi-node cooperative sensing, the synchronization error limits the sensing accuracy, which can be mitigated by the architecture of cell-free massive multi-input multi-output (CF-mMIMO), whose fiber-optic interconnections ensure high synchronization accuracy. However, the multi-node cooperative sensing in CF-mMIMO ISAC systems faces the following challenges: 1) The joint optimization of placement and resource allocation of distributed access points (APs) to improve the sensing performance in multi-target detection scenario is difficult; 2) The fusion of the sensing information from distributed APs with multi-view discrepancies is difficult. To address these challenges, this paper proposes a joint placement and antenna resource optimization scheme for distributed APs to minimize the sensing Cramér-Rao bound for targets’ parameters within the area of interest. Then, a symbol-level fusion-based multi-dynamic target sensing (SL-MDTS) scheme is provided, effectively fusing sensing information from multiple APs. The simulation results validate the effectiveness of the joint optimization scheme and the superiority of the SL-MDTS scheme. Compared to state-of-the-art grid-based symbol-level sensing information fusion schemes, the proposed SL-MDTS scheme improves the accuracy of localization and velocity estimation by 41.8% and 38.7%, respectively.