# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 4 Experimental Validation
### Experimental Results  
For comparison, we evaluate the system performance under two configurations: (i) all object detection is executed locally on UAVs without offloading; and (ii) computation tasks are dynamically offloaded to the cloud using the proposed framework. Figure 5 shows the CPU and GPU usage of the cloud and edge servers, Figure 6 presents the CPU  
Figure 4: Ground command and control platform for UAV flight testing.  
usage on different UAVs, and Figure 7 illustrates their GPU usage. The decrease in GPU utilization results from transferring the YOLOv5 inference to the cloud. Meanwhile, the slight increase in CPU usage is attributed to communication overhead, data formatting, and control signaling required for offloading and feedback coordination.  
According to the measurements, the cloud server's CPU utilization increased from 18.8% and 12.5% to 33.3% and 26.7%, respectively, while its GPU usage rose from 3% to 27%. The edge server's task-related CPU usage slightly increased from 1.18% to 1.24%. For the UAVs:  
* Drone A: CPU increased from 12.6% to 14.5%, GPU dropped from 91.2% to 0.8%;
* Drone B: CPU increased from 11.7% to 13.1%, GPU dropped from 90.8% to 0.6%;
* Drone C: CPU increased from 13.3% to 15.8%, GPU dropped from 88.9% to 0.7%;
* Drone D: CPU increased from 12.2% to 13.5%, GPU dropped from 92.8% to 1.1%.  
Figure 5: Comparison of CPU and GPU utilization on the cloud and edge servers before and after computation offloading.  
Figure 6: CPU utilization comparison of different UAVs before and after computation offloading.  
The detection accuracy was quantified using IoU consistency in bounding box outputs. These flight experiments validate the effectiveness of the proposed cloud-edge-end computational orchestration framework and the distributed formation control mechanism. The results demonstrate that after task migration, the target detection algorithm continues to operate correctly. The significant increase in cloud GPU usage and the corresponding reduction in onboard GPU load, without compromising detection accuracy or display latency, confirm the efficiency and reliability of the proposed system.  
To analyze latency savings, we measured both communication and computation delays, as shown in Figure 8.  
* Communication latency: After each UAV terminal completed network access, we used iperf to generate uplink traffic and standard ping to measure round-trip time (RTT). With a 100 Mbps uplink rate, the average RTT was 115 ms; increasing the rate to 150 Mbps led to a slightly higher RTT of 157 ms due to increased queuing and resource contention.
* Computation latency: Using the YOLOv5 model on the UAV's onboard module resulted in an average inference delay of 44.5 ms, due to constrained processing. After offloading the task to the cloud/edge platform, the inference delay dropped to 6.7 ms, achieving a notable latency reduction while maintaining detection accuracy.  
These results confirm that the proposed offloading framework improves system responsiveness by significantly reducing both computation and transmission delay components.  
Figure 8: \(Cont\).  
Figure 7: GPU utilization comparison of different UAVs before and after computation offloading.