Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources
Abstract
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- January 2021
- DOI:
- 10.48550/arXiv.2101.07534
- arXiv:
- arXiv:2101.07534
- Bibcode:
- 2021arXiv210107534C
- Keywords:
-
- Computer Science - Networking and Internet Architecture;
- 94A05 (Primary);
- 94B35;
- 62M05 (Secondary);
- E.4;
- H.1.1
- E-Print:
- Preprint version of the paper published in IEEE Communications Letters