Online Learning for Adaptive Probing and Scheduling in Dense WLANs
Online Learning for Adaptive Probing and Scheduling in Dense WLANs
复制标题
DOI：
10.1109/infocom53939.2023.10228988
复制
发表时间：
2022-12
期刊：
影响因子：
--
通讯作者：
Tianyi Xu;Ding Zhang;Zizhan Zheng
中科院分区：
文献类型：
--
作者：
Tianyi Xu;Ding Zhang;Zizhan Zheng
现有的网络调度解决方案通常假设在做出调度决策之前完全知道瞬时链路速率，或者考虑Bandit设置，其中仅在将其用于数据传输之后才发现准确的链路质量。在实践中，决策者可以获得（相对准确的）信道信息，例如，通过毫米波网络中的波束成形，就在数据传输之前。然而，频繁的波束成形在密集部署的毫米波WLAN中引起了巨大的开销。在本文中，我们考虑的重要问题的吞吐量优化与联合链路探测和调度。即使当链路速率分布是预先已知的（离线设置）时，由于平衡来自探测的信息增益和减少数据传输机会的成本的必要性，该问题也是具有挑战性的。我们开发了一个近似算法，保证性能的探测决策时，是非自适应和动态规划为基础的解决方案更具挑战性的自适应设置。我们进一步扩展我们的解决方案的在线设置未知的链路速率分布，并开发了一个上下文的强盗算法，并推导出其遗憾界。使用从现实世界的毫米波部署收集的数据跟踪的数值结果证明了我们的解决方案的效率。
Existing solutions to network scheduling typically assume that the instantaneous link rates are completely known before a scheduling decision is made or consider a bandit setting where the accurate link quality is discovered only after it has been used for data transmission. In practice, the decision maker can obtain (relatively accurate) channel information, e.g., through beamforming in mmWave networks, right before data transmission. However, frequent beamforming incurs a formidable overhead in densely deployed mmWave WLANs. In this paper, we consider the important problem of throughput optimization with joint link probing and scheduling. The problem is challenging even when the link rate distributions are pre-known (the offline setting) due to the necessity of balancing the information gains from probing and the cost of reducing the data transmission opportunity. We develop an approximation algorithm with guaranteed performance when the probing decision is non-adaptive and a dynamic programming-based solution for the more challenging adaptive setting. We further extend our solutions to the online setting with unknown link rate distributions and develop a contextual-bandit based algorithm and derive its regret bound. Numerical results using data traces collected from real-world mmWave deployments demonstrate the efficiency of our solutions.