Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information
This addresses resource allocation challenges in wireless networks for improved data rates and fairness, but it is incremental as it applies existing DRL methods with enhancements to a specific domain.
The paper tackles the non-convex optimization problem of resource allocation in wireless OFDMA networks by using deep reinforcement learning with buffer state information, showing that their agents clearly outperform benchmark agents in scheduling performance.
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.