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

TL;DR

This paper enhances multi-node cooperative sensing in cell-free massive MIMO ISAC systems by optimizing access point placement and resource allocation, and introduces a fusion scheme that significantly improves target localization and velocity estimation accuracy.

Contribution

It proposes a joint optimization scheme for AP placement and resources, and introduces a symbol-level fusion method for multi-target sensing in CF-mMIMO ISAC systems.

Findings

Optimized AP placement reduces sensing error bounds.

Proposed fusion scheme improves localization accuracy by 44%.

Velocity estimation accuracy is improved by 41.4%.

Abstract

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 system 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), since the multiple nodes are interconnected via optical fibers with 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 Cramr-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 44 % and 41.4 %, respectively.