Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks
Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks
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DOI：
10.1109/tnse.2021.3070476
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发表时间：
2022-01-01
影响因子：
6.6
通讯作者：
Wilson, Rodney
中科院分区：
文献类型：
--
作者：
Mollahasani, Shahram;Erol-Kantarci, Melike;Wilson, Rodney
可重新配置的无线网络提供的灵活性，为需要高带宽、可靠和低延迟通信的各种应用提供了新的机会，例如在线 AR/VR 游戏、高质量视频流和自动驾驶汽车。这些应用程序具有非常严格的服务质量 (QoS) 要求，并增加了移动网络的负担。目前，由于海量数据爆炸，出现了巨大的频谱短缺，这个问题可以通过可重构无线网络（RWN）来解决，其中节点具有重新配置和感知能力。因此，观察到人工智能辅助资源块分配算法的必要性。为了应对这一挑战，在本文中，我们提出了一种基于 actor-critic 学习的调度器，用于在 RWN 中分配资源块。具有不同 QoS 级别的各种流量类型被分配给我们的代理，以提供更真实的结果。我们还在模拟中加入了移动性，以增加网络的动态性。将所提出的模型与另一个演员-评论家模型以及其他传统调度器进行比较；比例公平 (PF) 以及通道和 QoS 感知 (CQA) 技术。通过考虑用户设备 (UE) 经历的延迟、成功传输和队头延迟来评估所提出的模型。结果表明，所提出的模型在不同方面明显优于其他技术。
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.