ReinWiFi: A Reinforcement-Learning-Based Framework for the Application-Layer QoS Optimization of WiFi Networks

• URL: http://arxiv.org/abs/2405.03526v1
• Date: Mon, 6 May 2024 14:44:06 GMT
• Title: ReinWiFi: A Reinforcement-Learning-Based Framework for the Application-Layer QoS Optimization of WiFi Networks
• Authors: Qianren Li, Bojie Lv, Yuncong Hong, Rui Wang,
• Abstract summary: In this paper, a reinforcement-learning-based scheduling framework is proposed to optimize the application-layer quality-of-service of a wireless local area network (WLAN) suffering from unknown interference. A novel Q-network is trained to map from the historical scheduling parameters and observations to the current scheduling action.
• Score: 6.566362478263619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, a reinforcement-learning-based scheduling framework is proposed and implemented to optimize the application-layer quality-of-service (QoS) of a practical wireless local area network (WLAN) suffering from unknown interference. Particularly, application-layer tasks of file delivery and delay-sensitive communication, e.g., screen projection, in a WLAN with enhanced distributed channel access (EDCA) mechanism, are jointly scheduled by adjusting the contention window sizes and application-layer throughput limitation, such that their QoS, including the throughput of file delivery and the round trip time of the delay-sensitive communication, can be optimized. Due to the unknown interference and vendor-dependent implementation of the network interface card, the relation between the scheduling policy and the system QoS is unknown. Hence, a reinforcement learning method is proposed, in which a novel Q-network is trained to map from the historical scheduling parameters and QoS observations to the current scheduling action. It is demonstrated on a testbed that the proposed framework can achieve a significantly better QoS than the conventional EDCA mechanism.

Related papers

• Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment [8.853779271331508]
  One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data.
  arXiv  Detail & Related papers  (2024-07-19T12:40:07Z)
• Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approach [79.00655335405195]
  Network slicing is a key feature in 5G/NG cellular networks that creates customized slices for different service types with various quality-of-service (QoS) requirements. In Wi-Fi networks, there is limited prior work on slicing, and a potential solution is based on a multi-tenant architecture on a single access point (AP) that dedicates different channels to different slices. We show that state augmentation is crucial for generating slicing decisions that meet the ergodic requirements.
  arXiv  Detail & Related papers  (2024-05-09T13:13:34Z)
• Matching Game for Optimized Association in Quantum Communication Networks [65.16483325184237]
  This paper proposes a swap-stable request-QS association algorithm for quantum switches. It achieves a near-optimal (within 5%) performance in terms of the percentage of served requests. It is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.
  arXiv  Detail & Related papers  (2023-05-22T03:39:18Z)
• Adaptive Federated Pruning in Hierarchical Wireless Networks [69.6417645730093]
  Federated Learning (FL) is a privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. In this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.
  arXiv  Detail & Related papers  (2023-05-15T22:04:49Z)
• Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning [11.007816552466952]
  This paper focuses on the problem of scheduling inference queries on Deep Neural Networks in edge networks at short timescales. By means of simulations, we analyze several policies in the realistic network settings and workloads of a large ISP. We design ASET, a Reinforcement Learning based scheduling algorithm able to adapt its decisions according to the system conditions.
  arXiv  Detail & Related papers  (2023-01-31T13:23:34Z)
• Low Complexity Adaptive Machine Learning Approaches for End-to-End Latency Prediction [0.0]
  This work is the design of efficient, low-cost adaptive algorithms for estimation, monitoring and prediction. We focus on end-to-end latency prediction, for which we illustrate our approaches and results on data obtained from a public generator provided after the recent international challenge on GNN.
  arXiv  Detail & Related papers  (2023-01-31T10:29:11Z)
• An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things [19.277349546331557]
  Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. Non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization.
  arXiv  Detail & Related papers  (2022-07-17T16:52:51Z)
• QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning [2.3857747529378917]
  We propose a QA-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks. In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the satisfaction rate of users served by the network.
  arXiv  Detail & Related papers  (2021-07-14T09:18:39Z)
• Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling [60.48359567964899]
  We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
  arXiv  Detail & Related papers  (2021-05-01T10:18:34Z)
• Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System [54.588242387136376]
  We introduce KaiS, a learning-based scheduling framework for edge-cloud systems. First, we design a coordinated multi-agent actor-critic algorithm to cater to decentralized request dispatch. Second, for diverse system scales and structures, we use graph neural networks to embed system state information. Third, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration.
  arXiv  Detail & Related papers  (2021-01-17T03:45:25Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.