Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
This addresses resource management in wireless communication systems, but it is incremental as it applies existing DRL methods to a known problem.
The paper tackled dynamic resource allocation in wireless networks by applying deep reinforcement learning algorithms like DQN and PPO, finding that DRL provides more efficient allocation than traditional methods, with performance influenced by algorithm choice and learning rates.
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.