Using the real wireless spectrum occupancy status in 2.4 and 5 GHz bands collected at a railway station as representative of a heavy wireless LAN (WLAN) traffic environment, this paper studies the modeling of durations of busy/idle (B/I) status and its predictability based on predictability theory. We first measure and model the channel status in the heavy traffic environment over almost all of the WLAN channels at 2.4 GHz and 5 GHz bands in a busy (rush hour) period and non-busy period. Then, using two selected channels at 2.4 GHz and 5 GHz bands, we analyze the upper bound (UB) and lower bound (LB) of predictability of the busy/idle durations based on predictability theory. The analysis shows that the LB predictability of durations can be easily increased by changing their probability distribution. Based on this property, we introduce the data categorization (DC) method. By categorizing the busy/idle durations into different streams, the proposed data categorization can improve the prediction performance of some streams with large LB predictability, even if it employs a simple low-complexity auto-regressive (AR) predictor.

中文翻译：

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高无线局域网流量环境下信道频谱状态的建模和可预测性分析


本文利用在火车站收集的 2.4 和 5 GHz 频段的真实无线频谱占用状态作为无线 LAN (WLAN) 流量大环境的代表，研究了忙/空闲 (B/I) 状态持续时间的建模及其基于可预测性理论的可预测性。我们首先对繁忙（高峰）时段和非繁忙时段的 2.4 GHz 和 5 GHz 频段上几乎所有 WLAN 信道的大流量环境中的信道状态进行测量和建模。然后，使用2.4 GHz和5 GHz频段的两个选定信道，基于可预测性理论分析了忙/空闲持续时间可预测性的上限（UB）和下限（LB）。分析表明，通过改变概率分布可以轻松提高 LB 持续时间的可预测性。基于这个性质，我们引入了数据分类（DC）方法。通过将繁忙/空闲持续时间分类到不同的流中，所提出的数据分类可以提高某些具有较大 LB 可预测性的流的预测性能，即使它采用简单的低复杂度自回归（AR）预测器也是如此。