Energy-Efficient Optimization in Aerial IAB Networks for Emergency Communications

Unmanned aerial vehicles (UAVs) and tethered balloons are increasingly pivotal in enhancing wireless network coverage performance, particularly in scenarios that require rapid deployment and adaptable infrastructure. This article presents an optimization framework for maximizing energy efficiency in aerial integrated access and backhaul networks, specifically tailored for emergency communications scenarios. Utilizing a novel combination of deep reinforcement learning and convex optimization techniques, our framework is able to jointly optimize the trajectory design of UAVs and the resource allocation policy. Comprehensive simulation results demonstrate that our proposed algorithm achieves significant energy efficiency improvements compared to baseline approaches, highlighting its effectiveness for emergency communication networks.