Energy Saving in Cell-Free Massive MIMO ISAC for Ultra-Reliable Target-Aware Actuation

Emerging 6G sensing-based applications rely on ultra-reliable target-aware actuation, where timely and accurate sensing information triggers critical actions. Achieving this requires tightly integrated sensing and communication (ISAC) under stringent reliability and latency constraints. This paper investigates ISAC in a downlink cell-free massive multiple-input multiple-output (CF-mMIMO) system supporting multi-static sensing and ultra-reliable low-latency communications (URLLC). We propose a joint power and blocklength allocation algorithm to minimize total energy consumption, accounting for both radio-site and cloud-side processing energy for communication and sensing, while meeting communication and sensing requirements. The non-convex optimization problem is solved using a combination of feasible point pursuit-successive convex approximation (FPP-SCA), concave-convex programming (CCP), and fractional programming techniques. We consider two types of target detectors: clutter-aware and clutter-unaware, each with distinct complexity and performance trade-offs. A computational complexity analysis based on giga-operations per second (GOPS) is conducted to quantify the processing requirements of communication and sensing tasks. We also introduce the refreshing rate for sensing information and derive a closed-form expression that accounts for both observation and processing delays. Simulation results show that the proposed algorithm achieves up to 34% energy reduction compared to schemes using the maximum allowable blocklength and enhances detection capability while consuming less total energy. Clutter-aware detectors, despite higher complexity, require fewer antennas and sensing receive APs, yielding up to 40% energy savings.