Performance Analysis and Blocklength Optimization in Cell-Free Multi-Static ISAC Systems with URLLC

Integrated Sensing and Communication (ISAC) is a key technology for 6G applications, such as autonomous driving, which demands ultra-reliable and low-latency communication (URLLC). This paper focuses on blocklength optimization to enhance the ISAC performance in cell-free (CF) multi-static networks while meeting stringent URLLC requirements. We derive closed-form expressions for ISAC metrics, including the transmission delay, the decoding error probability (DEP), and the Cramér-Rao Bound (CRB) for sensing accuracy. Based on theoretical derivation, the trade-off between communication and sensing performance is explored through Pareto boundaries, highlighting the critical role of blocklength. To optimize ISAC performance, a Double Deep Q-Network (DDQN)-based finite blocklength optimization (FBLO) algorithm is proposed. The algorithm dynamically adjusts the blocklength to achieve the balanced performance of delay, reliability, and sensing accuracy. Simulation results validate its effectiveness to balance the ISAC performance under URLLC scenarios.