Search for: All records

This research project aims to develop a resource management framework for efficient allocation of 5G network resources to IoT (Internet of Things) devices. As 5G technology is increasingly integrated with IoT applications, the diverse demands and use-cases of IoT devices necessitate dynamic resource management. The focus of this study is to develop an IoT device environment utilizing reinforcement learning (RL) for resource adjustment. The environment observes IoT device parameters including the current BER (bit-error-rate), allocated bandwidth, and current signal power levels. Actions that can be taken by the RL agent on the environment include adjustments to the bandwidth and the signal power level of an IoT device. One implementation of the environment is currently tested with PPO (Proximal Policy Optimization), and DDPG (Deep Deterministic Policy Gradient) RL algorithms using a continuous action space. Initial results show that PPO models train at a faster rate, while DDPG models explore a wider range of states, leading to better model predictions. Another version is tested with PPO and DQN (Deep Q-Networks) using a discrete action space. DQN demonstrates slightly better results than the PPO, possibly due to its value-based approach and that it is better suited for discrete action spaces.