Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information

TL;DR

This paper applies deep reinforcement learning to optimize resource allocation in wireless OFDMA networks, leveraging buffer state information and novel training techniques to outperform benchmark scheduling algorithms.

Contribution

It introduces a DRL-based scheduling method that incorporates buffer state info and mimicking learning, advancing wireless resource management.

Findings

DRL agents outperform benchmark schedulers in simulations.

Buffer state information improves scheduling efficiency.

Input feature compression and age capping enhance performance.

Abstract

As the number of user equipments (UEs) with various data rate and latency requirements increases in wireless networks, the resource allocation problem for orthogonal frequency-division multiple access (OFDMA) becomes challenging. In particular, varying requirements lead to a non-convex optimization problem when maximizing the systems data rate while preserving fairness between UEs. In this paper, we solve the non-convex optimization problem using deep reinforcement learning (DRL). We outline, train and evaluate a DRL agent, which performs the task of media access control scheduling for a downlink OFDMA scenario. To kickstart training of our agent, we introduce mimicking learning. For improvement of scheduling performance, full buffer state information at the base station (e.g. packet age, packet size) is taken into account. Techniques like input feature compression, packet shuffling and age capping further improve the performance of the agent. We train and evaluate our agents using Nokia's wireless suite and evaluate against different benchmark agents. We show that our agents clearly outperform the benchmark agents.