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.

翻译：由于为监测目的使用物联网(IoT)装置已变得无处不在,传感器通信的效率是现代因特网的一个主要问题;对极短的包件来说,频道编码效率较低,依赖源压缩的传统技术需要广泛的信号或对源动态的先前知识;在这项工作中,我们提议采用编码和解码办法,利用隐藏的Markov模型(HMM)在网上学习源动态,将短的包码穿透现有压缩法则。我们的方法显示,对于与源高度相关、在发送方没有额外复杂性的源码,在时间上有很大的性能改进。