基于隐马尔可夫模型的时间相关IoT信源编码
Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources
摘要：
随着物联网（IoT）设备用于监控目的变得无处不在，传感器通信的效率是现代互联网的一个主要问题。信道编码对于极短的分组效率较低，并且依赖于源压缩的传统技术需要广泛的信令或源动态的预先存在的知识。在这项工作中，我们提出了一个编码和解码方案，学习源动态在线使用隐马尔可夫模型（HMM），打孔短数据包代码优于现有的基于压缩的方法。我们的方法显示出显着的性能改进的源，在时间上高度相关，发送方没有额外的复杂性。
英文摘要：
As the use of Internet of Things (IoT) devices for monitoring purposes becomes ubiquitous, the efficiency of sensor communication is a major issue for the modern Internet. Channel coding is less efficient for extremely short packets, and traditional techniques that rely on source compression require extensive signaling or pre-existing knowledge of the source dynamics. In this work, we propose an encoding and decoding scheme that learns source dynamics online using a Hidden Markov Model (HMM), puncturing a short packet code to outperform existing compression-based approaches. Our approach shows significant performance improvements for sources that are highly correlated in time, with no additional complexity on the sender side.
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