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arXiv 2102.05149cs.NI
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共存WiFi网络中比例公平资源分配的分布式学习

Distributed Learning for Proportional-Fair Resource Allocation in Coexisting WiFi Networks

Piotr Gawłowicz, Jean Walrand, Adam Wolisz

摘要:

在本文中,我们重新审视了广为人知的性能异常,导致严重的网络效用下降时,节点使用不同的调制和编码方案的WiFi网络。比例公平分配被证明可以缓解这种异常,并为站点提供良好的吞吐量。它可以通过基于优化问题的显式解选择竞争窗口值来实现,或者如最近提出的,通过遵循使用集中式梯度下降算法的基于学习的方法来实现。在本文中,我们利用我们最近的异步分布式优化的理论工作,并提出了一个简单的算法,允许WiFi节点独立调整其竞争窗口,以实现比例公平。我们比较的吞吐量和空中时间分配,该算法实现了标准的WiFi二进制指数回退,并显示改进。

英文摘要:

In this paper, we revisit the widely known performance anomaly that results in severe network utility degradation in WiFi networks when nodes use diverse modulation and coding schemes. The proportional-fair allocation was shown to mitigate this anomaly and provide a good throughput to the stations. It can be achieved through the selection of contention window values based on the explicit solution of an optimization problem or, as proposed recently, by following a learning-based approach that uses a centralized gradient descent algorithm. In this paper, we leverage our recent theoretical work on asynchronous distributed optimization and propose a simple algorithm that allows WiFi nodes to independently tune their contention window to achieve proportional fairness. We compare the throughputs and air-time allocation that this algorithm achieves to those of the standard WiFi binary exponential back-off and show the improvements.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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