Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks

TL;DR

This paper explores the use of deep reinforcement learning algorithms like DQN and PPO to optimize resource allocation in wireless networks, demonstrating improved efficiency over traditional methods.

Contribution

It introduces a DRL-based framework for dynamic resource management in wireless systems and compares different algorithms and parameters for optimal performance.

Findings

DRL algorithms outperform traditional resource allocation methods.

Algorithm choice and learning rate significantly affect system performance.

PPO and DQN show different strengths in resource optimization.

Abstract

This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods.