论文标题
在线学习，用于密集的WLAN中的自适应探测和安排
Online Learning for Adaptive Probing and Scheduling in Dense WLANs
论文作者
论文摘要
现有的网络调度解决方案通常假设瞬时链接率在做出调度决定之前是完全知道的，或者考虑仅在将其用于数据传输后才能发现准确的链接质量的强盗设置。实际上，决策者可以在数据传输之前获得（相对准确的）通道信息，例如，通过在MMWave网络中进行波束形成。但是，频繁的波束形成会在密集部署的mmwave wlans中产生强大的开销。在本文中，我们考虑了通过关节链接探测和调度的吞吐量优化的重要问题。即使链接率分布是预先知道的（脱机设置），问题也很具有挑战性，这是因为必须平衡探测信息的信息收益和减少数据传输机会的成本。当探测决策是非自适应时，我们开发了具有保证性能的近似算法，并为更具挑战性的自适应设置提供了基于动态编程的解决方案。我们将解决方案进一步扩展到以未知的链接速率分布的方式扩展到在线设置，并开发基于上下文的算法并获得其遗憾。使用从现实世界MMWave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.
