The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications. In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity.

翻译：预测无线信道在帧投递率方面的行为具有重要价值，例如可在运行时优化无线网络运行参数，或主动应对信道质量恶化，以满足工业应用通常对可靠性和端到端延迟的严苛要求。本研究深入探讨了基于指数移动平均（EMA）的预测模型，该模型被证明性能优于其他简单统计方法，且其预测精度与人工神经网络相当，但计算需求大幅降低。关于本文的创新点与动机，我们提出一种称为EMA线性组合（ELC）的新模型，并对其进行理论阐述、实验验证。基于真实Wi-Fi设备采集的数据库测试表明，ELC在所有实验条件下均能显著提升EMA的预测精度，唯一代价是计算复杂度略有增加。