A Convex Optimization Assisted DDQL Algorithm for Computing Resource Allocation in Space-Aerial Integrated Network

This paper investigates space-aerial assisted mixed cloud-edge computing services for space-aerial integrated networks, where unmanned aerial vehicles (UAVs) provide edge computing services and one satellite (SAT) provides ubiquitous cloud computing services. To effectively and efficiently schedule such services under constraints on the available resources of computational capacity, energy, and communications of UAVs and the SAT, a problem for minimizing the total computing and offloading delay is formulated. A learning algorithm for handling the reformulated problem is proposed that alternatively performs convex optimization based computation capacity allocation (involving real variables) and double deep Q-learning (DDQL) based task assignment (involving binary variables) among all UAVs and the SAT. Extensive simulation results are presented to demonstrate that the efficacy of the proposed algorithm is significantly superior over some state-of-the-art reinforcement learning-based methods in terms of the algorithm running time and system scalability in the training stage and total computing and offloading delay in the testing stage.