Task Offloading in NOMA-Based Fog Computing Networks: A Deep Q-Learning Approach

Abstract

Fog computing (FC) has the potential to enable computation-intensive applications for the next generation wireless networks. In parallel with the development of FC, nonorthogonal multiple access (NOMA) has been recognized as a promising solution to improve the spectrum efficiency. In this paper, a NOMA-based FC system is considered, where multiple task nodes perform task scheduling via NOMA to a helper node, the helper node with abundant computation resource is required to compute the computation task from the task nodes. We formulate a joint task scheduling, computational resource allocation, and power allocation problem with an objective to minimize the sum cost (i.e., delay and energy consumptions for all task nodes) realizing energy-delay tradeoff. It is challenging to obtain an optimal policy for such a combinatorial optimization problem. To this end, we propose an online learning-based optimization framework to tackle this problem. Simulation results show that the proposed scheme significantly reduces the sum cost compared to the baselines.