Intelligent Offloading and Resource Allocation in HAP-Assisted MEC Networks

An aerial platform such as high altitude platform (HAP) is emerging as a promising technology to enhance the capacity, coverage, and computation experience of user devices (UDs) in fifth (5G) and beyond (B5G) generation wireless networks, especially in underserved areas that have no coverage of ground base stations (GBSs). In particular, a HAP equipped with a computing server can provide computation and communication resources to resource-constrained UDs for computing their tasks with various delay requirements on demand. Thus, in this paper, we study the partial task offloading and communication resource allocation in HAP-assisted edge computing and formulated the problem to maximize the total number of accomplished tasks of UDs with satisfied delay requirements while minimizing their total energy consumption. To make a real-time decision while considering the network dynamics and heterogeneous task requirements, we transform the problem into an Markov decision process (MDP)-based problem and introduce an algorithm based on the deep deterministic policy gradient (DDPG), named DDPG-PORA. We carry out simulation experiments and the results show that DDPG-PORA reliably converges and provides higher performance than the comparison methods.