Power Allocation in Multi-user Cellular Networks With Deep Q Learning Approach

Understand

The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades.

• Nowadays, the data-driven model-free machine learning-based approaches are rapidly developed in this field, and among them the deep reinforcement learning (DRL) is proved to be of great promising potential.
• Different from supervised learning, the DRL takes advantages of exploration and exploitation to maximize the objective function under certain constraints.
• In our paper, we propose a two-step training framework.