Dynamic Task Offloading for Air-Terrestrial Integrated Networks: A Learning Approach

With the popularization of artificial intelligence, 5G and other technologies, a number of emerging applications require both efficient communication and computing service, which poses enormous challenges to the computing ability and battery capacity of terminal equipment. Moreover, ground-based 5G system can not provide seamless service especially for hotspot and remote area. To tackle the above challenges, we minimize the weighting of delay and energy consumption by optimizing the task offloading decision and computing resource allocation, which, however, is a mixed integer nonlinear programming (MINLP) issue due to the strong coupling between optimization variables. Therefore, we decompose it into two subproblems and design a deep reinforcement learning-Based approach to address the first problem with offloading decision-making. For the second computing resource allocation subproblem,. a Greedy-based solution is proposed. The simulation results indicate that, in comparison to other benchmark approaches, the proposed method can achieve superior performance.