In 2016, the IEEE task group ah (TGah) published a new standard IEEE 802.11ah, aimed at providing network connectivity among a large number of Internet of Things (IoT) devices. Restricted access window (RAW) is one of the fundamental MAC mechanisms of IEEE 802.11ah. It reduces the contention overhead in the dense wireless environment by dividing stations into different RAW groups. However, how to optimize the RAW parameters is still an open issue, especially in the run-time environment. In this paper, we propose a run-time RAW optimization scheme, namely RO-RAW, to improve the performance of RAW in the IEEE 802.11ah networks. RO-RAW adopts the Extended Kalman Filter method to estimate the channel status and adjusts the RAW parameters according to the number of competing stations in real-time. The evaluation via NS-3 simulations shows that, by tuning the RAW parameters appropriately, RO-RAW substantially improves throughput, latency, and packet loss performance compared with another RAW optimization scheme in different simulation scenarios. The results further show that, when the channel is relatively congested, RO-RAW improves the RAW performance more significantly.

中文翻译：

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RO-RAW：具有扩展卡尔曼滤波器的IEEE 802.11ah网络中的运行时受限访问窗口优化

2016年，IEEE任务组ah（TGah）发布了新标准IEEE 802.11ah，旨在在众多物联网（IoT）设备之间提供网络连接。受限访问窗口（RAW）是IEEE 802.11ah的基本MAC机制之一。通过将站点划分为不同的RAW组，可以减少密集无线环境中的竞争开销。但是，如何优化RAW参数仍然是一个未解决的问题，尤其是在运行时环境中。在本文中，我们提出了一种运行时RAW优化方案，即RO-RAW，以提高IEEE 802.11ah网络中RAW的性能。RO-RAW采用扩展卡尔曼滤波器方法估计信道状态，并根据竞争站点的数量实时调整RAW参数。通过NS-3模拟进行的评估表明，通过在不同的模拟情况下与其他RAW优化方案相比，通过适当地调整RAW参数，RO-RAW可以显着提高吞吐量，延迟和丢包性能。结果进一步表明，当通道相对拥塞时，RO-RAW可以显着提高RAW性能。