Realtime Scheduling and Power Allocation Using Deep Neural Networks

TL;DR

This paper introduces a deep neural network-based approach for real-time link scheduling and power control in dense 5G networks, significantly reducing computation time while maintaining near-optimal performance.

Contribution

It presents a novel deep learning framework combining DQN and DNN for fast, near-optimal scheduling and power allocation in multi-cell 5G networks.

Findings

Achieves over five orders of magnitude speed-up in computation.

Maintains less than nine percent performance loss.

Enables practical real-time interference management.

Abstract

With the increasing number of base stations (BSs) and network densification in 5G, interference management using link scheduling and power control are vital for better utilization of radio resources. However, the complexity of solving link scheduling and the power control problem grows exponentially with the number of BS. Due to high computation time, previous methods are useful for research purposes but impractical for real time usage. In this paper we propose to use deep neural networks (DNNs) to approximate optimal link scheduling and power control for the case with multiple small cells. A deep Q-network (DQN) estimates a suitable schedule, then a DNN allocates power for the corresponding schedule. Simulation results show that the proposed method achieves over five orders of magnitude speed-up with less than nine percent performance loss, making real time usage practical.