# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 3 UAV Formation Control Technologies  
UAV formation control techniques primarily include the leader-follower approach, virtual structure method, consensus-based control, artificial potential field method, and behavior-based control. Among these, the leader-follower approach is currently the most widely adopted and mature method. It features a relatively simple control structure and is easy to implement. However, this method is highly dependent on the leader UAV, and any malfunction of the leader may result in the failure of the entire formation. To address this limitation, a virtual leader can be introduced as the overall formation reference. The position and velocity of this virtual leader are estimated and shared among UAVs in real time. This design enhances the fault tolerance of the formation and reduces its reliance on a single physical leader. The virtual structure method conceptualizes the UAV formation as a rigid geometric configuration. During coordinated flight, each UAV maintains a fixed relative position within this virtual structure. When the formation changes its shape or trajectory, individual UAVs simply track their assigned virtual coordinates, enabling the formation to adaptively follow pre-defined inspection paths. This method ensures geometric consistency but poses challenges in large-scale implementations due to the rigidity of the structural assumptions. The consensus-based method enables each UAV to update its state by exchanging information with neighboring UAVs, ultimately achieving global agreement on variables such as position, velocity, or heading. This approach is well-suited for distributed networks and enhances the formation's responsiveness to dynamic environments. It also improves system robustness, as the loss or failure of a single UAV does not compromise the overall stability. However, consensus algorithms are typically complex and impose stringent requirements on communication channel capacity and latency. Prolonged communication delays or packet losses may prevent the formation from maintaining consistent states across all UAVs. The artificial potential field method, widely used in trajectory planning, models the influence of obstacles and targets as virtual attractive and repulsive forces. While the goal exerts an attractive force on UAVs, obstacles exert repulsive forces to avoid collisions. The resultant force governs UAV motion and navigation. Although this method is simple andoffers good real-time performance, it is limited in handling UAV kinematic constraints, and thus is rarely used in precise formation control tasks. The behavior-based control method categorizes UAV responses into four fundamental behaviors: collision avoidance, obstacle avoidance, target acquisition, and formation maintenance. Each UAV determines its control actions based on weighted behavior responses to sensory inputs. Inspired by biological swarm intelligence, this method offers high flexibility and fault tolerance. However, it lacks the ability to maintain strict formation geometry and poses difficulties in mathematical stability analysis. To ensure safe operation in large-scale UAV swarms, collision avoidance between UAVs and dynamic network reconfiguration must be considered. Among the existing formation control strategies, the leader-follower method suffers from poor anti-interference performance and single-point failure risk; the virtual structure method lacks flexibility for large-scale swarm task assignment; and the behavior-based method cannot guarantee global formation stability. Considering these limitations, we adopt a flocking-based distributed formation control strategy. The flocking algorithm combines the advantages of consensus theory and artificial potential fields, and classifies UAVs, obstacles, and target points as \(\alpha\)-agents, \(\beta\)-agents, and \(\gamma\)-agents, respectively.