Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks

TL;DR

This paper introduces an actor-critic learning-based scheduler for reconfigurable wireless networks that effectively manages resource allocation to meet diverse QoS requirements, outperforming traditional methods in delay and transmission success.

Contribution

It presents a novel AI-driven scheduler using actor-critic learning tailored for dynamic reconfigurable wireless networks with mobility considerations.

Findings

Outperforms traditional schedulers in delay reduction

Achieves higher successful transmission rates

Handles diverse QoS levels effectively

Abstract

The flexibility offered by reconfigurable wireless networks, provide new opportunities for various applications such as online AR/VR gaming, high-quality video streaming and autonomous vehicles, that desire high-bandwidth, reliable and low-latency communications. These applications come with very stringent Quality of Service (QoS) requirements and increase the burden over mobile networks. Currently, there is a huge spectrum scarcity due to the massive data explosion and this problem can be solved by helps of Reconfigurable Wireless Networks (RWNs) where nodes have reconfiguration and perception capabilities. Therefore, a necessity of AI-assisted algorithms for resource block allocation is observed. To tackle this challenge, in this paper, we propose an actor-critic learning-based scheduler for allocating resource blocks in a RWN. Various traffic types with different QoS levels are assigned to our agents to provide more realistic results. We also include mobility in our simulations to increase the dynamicity of networks. The proposed model is compared with another actor-critic model and with other traditional schedulers; proportional fair (PF) and Channel and QoS Aware (CQA) techniques. The proposed models are evaluated by considering the delay experienced by user equipment (UEs), successful transmissions and head-of-the-line delays. The results show that the proposed model noticeably outperforms other techniques in different aspects.