# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
###### Abstract  
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 processing is limited by stringent size, weight, and power constraints, posing challenges for real-time computation and autonomous control. This paper presents an integrated communication and computation framework that combines cloud-edge-end collaboration with cell-free massive multiple-input multiple-output (CF-mMIMO) to enable scalable and efficient task offloading in UAV swarms. Furthermore, we implement a prototype system 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, highlighting the framework's potential for enabling intelligent, cooperative aerial systems. works in this domain primarily focus on optimizing task allocation, modeling wireless communication channels, and designing adaptive scheduling strategies [10; 11; 12]. Approaches such as joint trajectory planning and dynamic computation partitioning have been proposed to improve overall offloading efficiency. Meanwhile, the adoption of high-capacity communication technologies, such as millimeter-wave (mmWave) and device-to-device (D2D) links, has further enhanced the feasibility of real-time task migration. However, UAV swarms operating in dynamic and dense environments still suffer from co-channel interference, intermittent connectivity, and channel fading--factors that severely degrade the quality-of-service (QoS) of computation offloading systems. In recent years, federated learning (FL) has attracted growing interest as a privacy-preserving distributed learning paradigm that enables collaborative model training across multiple UAVs or edge devices without directly sharing raw data. Integrating FL into multi-agent systems can significantly enhance the swarm's adaptability and generalization by allowing decentralized knowledge sharing and continual learning [13; 14; 15; 16]. Recent work has further explored latency-sensitive FL for aerial edge environments [17; 18; 19]. For example, the authors in [19] propose an intelligent orchestration framework for federated aerial computing that considers multi-task dependencies, heterogeneous resource demand, and the drones' limited flight time. These ideas highlight the potential of integrating real-time FL or decentralized intelligence into UAV swarm architectures for future extension. Against this backdrop, cell-free massive multiple-input multiple-output (CF-mMIMO) has been proposed as a promising wireless architecture to enhance spectral efficiency, coverage uniformity, and user-centric service provisioning [20; 21; 22; 23]. Unlike conventional cellular networks, CF-mMIMO eliminates cell boundaries by deploying a large number of distributed access points (APs), all jointly coordinated by a central processing unit (CPU). CF-mMIMO is a user-centric wireless paradigm in which a large number of distributed APs jointly serve users via coherent transmission, coordinated by a CPU. Unlike cellular systems, CF-mMIMO eliminates inter-cell interference and offers consistent signal quality by avoiding hard cell boundaries. This architecture is particularly advantageous for UAV swarm networks characterized by high mobility and sparse terrestrial infrastructure, which has recently been explored for UAV communications, offering potential advantages in mitigating inter-UAV interference, enhancing link robustness, and supporting highly mobile devices in a scalable fashion. Moreover, when combined with edge/cloud computing, CF-mMIMO can facilitate ultra-reliable and low-latency task offloading in complex aerial environments [24; 25; 26; 27; 28].