Intelligent Offloading and Resource Allocation in Heterogeneous Aerial Access IoT Networks
Demeke Shumeye Lakew, Anh-Tien Tran, Nhu–Ngoc Dao, Sungrae Cho
Abstract
Aerial access networks, comprising a hierarchical model of high-altitude platforms (HAPs) and multiple unmanned aerial vehicles (UAVs), are considered a promising technology to enhance the service experience of Internet of Things Devices (IoTD), especially in underserved areas where terrestrial base stations (TBSs) do not exist. In such scenarios, optimally orchestrating the limited computation, communication, and energy resources in both HAPs and UAVs is crucial toward for an efficient aerial networking infrastructure. Thus, in this study, we investigate and formulate the joint IoTDs association, partial offloading, and communication resource allocations (JAPORAs) decisions problem in heterogeneous Aerial Access IoT (AAIoT) networks to maximize service satisfaction for IoTDs, while minimizing their total energy consumption. In particular, the formulated problem is transformed into a multiagent Markov decision process (MAMDP) to deal with its nonconvexity and environmental dynamicity. To solve the problem, we propose a multiagent policy-gradient-based deep actor–critic algorithm, named MADDPG-JAPORA, with centralized training and decentralized execution. Our extensive numerical experiments demonstrated that MADDPG-JAPORA reliably converges and provides superior performance compared with other state-of-the-art schemes.