Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources

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...

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Bibliographic Details
Published in:IEEE communications letters Vol. 25; no. 5; pp. 1463 - 1467
Main Authors: Chandak, Siddharth, Chiariotti, Federico, Popovski, Petar
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Combined source-channel coding
Complexity theory
Decoding
Encoding
Hidden Markov models
Internet of Things
Markov chains
Markov processes
Piercing
Receivers
Time correlation functions
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Summary: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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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2020.3044210