Scalable Cell-Free Massive MIMO-Based Integrated Sensing and Communication

The advent of sixth-generation (6G) wireless communication networks heralds a new era of connectivity, with cellfree massive multiple-input multiple-output (CF-mMIMO) networks and integrated sensing and communication (ISAC) systems poised to play a major role. While promising numerous applications, the synergy between CF-mMIMO and ISAC technologies remains underexplored, particularly regarding scalability issues. This paper addresses this research gap by proposing a scalable CF-mMIMO-based ISAC framework integrating downlink (DL) payload data and sensing precoding schemes and fractional power allocation strategies. Building upon the non-scalable maximum a posteriori ratio test (MAPRT), our contributions also include the design of scalable partial MAPRT (P-MAPRT) and improved partial MAPRT (IP-MAPRT) target detectors, which consider both clutter and statistical radar cross-section information. Numerical results demonstrate that the proposed scalable detection schemes achieve competitive performance metrics compared to the non-scalable MAPRT, with notable trade-offs between detection probability and spectral efficiency per user. Our findings underscore the importance of designing reliable clutter-aware scalable target detection algorithms in CFmMIMO-based ISAC scenarios and provide insights into the process of selecting appropriate system parameters for balanced QoS metrics