Robust Adaptive Fault-Tolerant Resource Optimization for Highly-Dynamic Integrated Air-Ground Broadcast Ad-Hoc Networks

Highly dynamic air–ground integrated broadcast ad-hoc networks are essential for wide-area wireless coverage. However, high node mobility, co-channel interference, and network faults hinder reliable delivery and efficient resource use. This paper presents a robust, adaptive, and fault-tolerant resource-optimization framework based on distributed chaotic neural networks. First, it introduces a space-air-ground broadcast-transmission model that accounts for node mobility, link failures, and interference. Then, it presents the design of a distributed local chaotic-neural-network controller that allocates spectrum, power, and link resources in real time, explicitly handling uncertainties and abrupt faults. Finally, simulations and experiments show substantial mitigation of performance loss under high mobility (relative velocities 180-210 km/h) and varying link distances (coverage radius 10-12 km). With real-time topology adaptation, broadcast-link stability improves by up to 60%. These results provide a basis for reliable deployment and efficient resource optimization of broadcast ad-hoc networks, supporting future low-altitude airborne networking applications.