Signaling-Efficient Access Point Selection in Distributed Sensing Cell-Free ISAC Systems

Integrated sensing and communication (ISAC) is a cornerstone of the 6G vision, particularly when integrated with cell-free (CF) architectures. However, conventional centralized processing designs face prohibitive fronthaul overhead due to the massive volume of data fusion required. To address this, this letter proposes a signaling-efficient access point selection framework for distributed sensing in CF-ISAC, validated under the 3GPP UMa channel model with environmental clutter. By deriving selection metrics from local detector states and transmitting only scalar statistics, the framework minimizes signaling while mitigating noise accumulation. Simulation results demonstrate that our strategy ensures scalable, accurate sensing, outperforming state-of-the-art geometric and communication-centric baselines by up to 3 dB in realistic, cluttered environments.