Online Learning for Adaptive Probing and Scheduling in Dense WLANs

• URL: http://arxiv.org/abs/2212.13585v1
• Date: Tue, 27 Dec 2022 19:12:17 GMT
• Title: Online Learning for Adaptive Probing and Scheduling in Dense WLANs
• Authors: Tianyi Xu, Ding Zhang and Zizhan Zheng
• Abstract summary: Existing solutions to network scheduling assume that the instantaneous link rates are completely known before a scheduling decision is made. We develop an approximation algorithm with guaranteed performance when the probing decision is non-adaptive. We extend our solutions to the online setting with unknown link rate distributions and develop a contextual-bandit based algorithm.
• Score: 4.585894579981477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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