A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks

Understand

Optimal resource allocation is a fundamental challenge for dense and heterogeneous wireless networks with massive wireless connections.

• Because of the non-convex nature of the optimization problem, it is computationally demanding to obtain the optimal resource allocation.
• Recently, deep reinforcement learning (DRL) has emerged as a promising technique in solving non-convex optimization problems.
• Unlike deep learning (DL), DRL does not require any optimal/ near-optimal training dataset which is either unavailable or computationally expensive in generating synthetic data.