Deep Reinforcement Learning-Based Intelligent Task Offloading and Dynamic Resource Allocation in 6G Smart City

Abstract

With the successful commercialization of 5G technology and the accelerated research process of 6G technology, smart cities are entering the 3.0 era. In 6G smart cities, Multi-access Edge Computing (MEC) can provide computing support for a large number of computation-intensive applications. However, the randomness of the wireless network environment and the mobility of nodes make designing the best offloading schemes is challenging. In this article, we investigate the dynamic offloading optimization problem of base station (BS) selection and computational resource allocation for mobile users (MUs). We first envision a MEC-enabled 6G Smart City Network architecture, then formulate the minimizing average system user cost problem as a Markov Decision Process (MDP), and propose a deep reinforcement learning-based offloading optimization and resource allocation algorithm (DOORA). Numerical results illustrate that DOORA scheme significantly outperforms the benchmarks and can remarkably improves the quality of experience (QoE) of MUs.