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.
@article{arxiv.2101.07534,
title = {Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources},
author = {Siddharth Chandak and Federico Chiariotti and Petar Popovski},
journal= {arXiv preprint arXiv:2101.07534},
year = {2021}
}
Comments
Preprint version of the paper published in IEEE Communications Letters