The ability to reliably predict the future quality of a wireless channel, as seen by the media access control layer, is a key enabler to improve performance of future industrial networks that do not rely on wires. Knowing in advance how much channel behavior may change can speed up procedures for adaptively selecting the best channel, making the network more deterministic, reliable, and less energy-hungry, possibly improving device roaming capabilities at the same time. To this aim, popular approaches based on moving averages and regression were compared, using multiple key performance indicators, on data captured from a real Wi-Fi setup. Moreover, a simple technique based on a linear combination of outcomes from different techniques was presented and analyzed, to further reduce the prediction error, and some considerations about lower bounds on achievable errors have been reported. We found that the best model is the exponential moving average, which managed to predict the frame delivery ratio with a 2.10\% average error and, at the same time, has lower computational complexity and memory consumption than the other models we analyzed.

翻译：可靠预测媒体访问控制层所观测的无线信道未来质量的能力，是提升不依赖有线连接的未来工业网络性能的关键推动因素。提前了解信道行为可能变化的程度，可以加速自适应选择最佳信道的流程，使网络更具确定性、可靠性，并降低能耗，同时可能改善设备的漫游能力。为此，基于移动平均和回归的主流方法被用于从真实Wi-Fi设置中捕获的数据上，借助多个关键性能指标进行比较。此外，本文提出并分析了一种基于不同技术结果的线性组合的简单方法，以进一步降低预测误差，并给出了关于可达误差下界的一些思考。我们发现，最佳模型是指数移动平均，它能以2.10%的平均误差预测帧传输成功率，同时，与我们分析的其他模型相比，具有更低的计算复杂度和内存消耗。