Joint energy and reliability optimization with dual-channel switching in ground-air collaborative networks

Connected and Autonomous Vehicle (CAV) networks are rapidly advancing as a critical technology for enabling intelligent transportation systems. One promising approach to support such vehicular networks is the use of Unmanned Aerial Vehicles (UAVs) to enhance communication and computational capabilities through air-ground collaboration. However, existing UAV-assisted vehicular communication methods often face challenges, particularly in urban environments with frequent obstructions that degrade communication quality and reliability. Additionally, ensuring energy efficiency while maintaining high communication reliability remains an unsolved issue. In this paper, we investigate a UAV-assisted ground-air collaborative network for a platoon of vehicles navigating urban areas with frequent obstacles. Our objective is to maximize communication reliability while minimizing system energy consumption, specifically utilizing a dual-channel switching mechanism to adapt between Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) communication channels. We propose a joint optimization model that integrates UAV trajectory planning with communication and computation resource allocation for platooning vehicles. To solve the non-convex optimization problem, we propose a novel optimization algorithm that integrates the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method with Sequential Quadratic Programming (SQP). This combined approach iteratively determines effective search directions, ensuring convergence towards a locally optimal solution. Furthermore, we theoretically analyze the convergence of the proposed joint optimization model. Extensive simulations are carried out to demonstrate the effectiveness of the proposed optimization model and algorithm, showing significant improvements in communication reliability and energy efficiency compared to conventional single-channel approaches.