CF-mMIMO-Based Computational Offloading for UAVs Swarm: System Design and Experimental Results

Swarm-based unmanned aerial vehicle (UAV) systems offer enhanced spatial coverage, collaborative intelligence, and mission scalability for various applications, including environmental monitoring and emergency response. However, their onboard computing capabilities are often constrained by stringent size, weight, and power limitations, posing challenges for real-time data processing and autonomous decision-making. This paper proposes a comprehensive communication and computation framework that integrates cloud-edge-end collaboration with cell-free massive multiple-input multiple-output (CF-mMIMO) technology to support scalable and efficient computation offloading in UAV swarm networks. A lightweight task migration mechanism is developed to dynamically allocate processing workloads between UAVs and edge/cloud servers, while a CF-mMIMO communication architecture is designed to ensure robust, low-latency connectivity under mobility and interference. Furthermore, we implement a hardware-in-the-loop experimental testbed with nine UAVs and validate the proposed framework through real-time object detection tasks. Results demonstrate over 30% reduction in onboard computation and significant improvements in communication reliability and latency, highlighting the framework’s potential for enabling intelligent, cooperative aerial systems.