Leveraging 5G RedCap and Spiking Neural Networks for Energy Efficiency in Edge Devices

This work presents an energy-efficient implementation of Unmanned Aerial Vehicle (UAV)-based systems over 5G networks with on-board accelerated processing capabilities and provides a preliminary evaluation of the integrated solution. The study is a two-fold comparative analysis focused on connectivity and edge processing for UAVs. Two discrete deployment scenarios are implemented, where standard 5G configuration with artificial neural network (ANN) processing is evaluated against 5G Reduced Capability (RedCap) connectivity, paired with Spiking Neural Networks (SNNs). Both proposed energy-efficient alternative solutions are designed to offer significant energy savings; this paper examines whether they are suitable candidates for energy-constrained environments, i.e., UAVs, and quantifies their impact on the overall energy consumption of the system. The integrated solution, with 5G RedCap/SNNs, achieves energy-use reductions approaching 60%, which translates to an approximate 35% increase in flight time. The experimental evaluations were performed in a real-world deployment using a 5G-equipped UAV with edge-processing capabilities based on NVIDIA’s Jetson Orin.