Resource allocation for UAV-assisted 5G mMTC slicing networks using deep reinforcement learning

Abstract

The Internet of Things (IoT) application scenarios is becoming extensive due to the quick evolution of smart devices with fifth-generation (5G) network slicing technologies. Hence, IoTs are becoming significantly important in 5G/6G networks. However, communication with IoT devices is more sensitive in disasters because the network depends on the main power supply and devices are fragile. In this paper, we consider Unmanned Aerial Vehicles (UAV) as a flying base station (BS) for the emergency communication system with 5G mMTC Network Slicing to improve the quality of user experience. The UAV-assisted mMTC creates a base station selection method to maximize the system energy efficiency. Then, the system model is reduced to the stochastic optimization-based problem using Markov Decision Process (MDP) theory. We propose a reinforcement learning-based dueling-deep-Q-networks (DDQN) technique to maximise energy efficiency and resource allocation. We compare the proposed model with DQN and Q-Learning models and found that the proposed DDQN-based model performs better for resource allocation in terms of low transmission power and maximum energy efficiency.