A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications

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Date
2018-08-24
Department
Dept Signal Process and Acoust
Department of Communications and Networking
Carleton University
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Mcode
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Language
en
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2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018, Volume 2018-June, IEEE International Workshop on Signal Processing Advances in Wireless Communications
Abstract
S-The 5G and beyond wireless networks target novel use-cases with stringent requirements on physical layer parameters such as latency, which may necessitate design decisions with trade-offs. Choosing a channel coding scheme which gives the best error performance may not be the most appropriate choice in mission-critical machine-type communications which require very low latency. In such contexts, convolutional codes may be preferable due to their faster decoding and simplicity. Motivated by this observation, we introduce a convolutionally encoded OSTBC system with SNR-adaptive constellations for low-latency and low-complexity use-cases. Numerical results obtained for the versatile Nakagami-m channel demonstrate that the investigated scheme results in considerable gains in terms of error performance and decoding latency.1.
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Keywords
5G network, constellation shaping, convolutional coding, low-latency communications
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DOI
10.1109/SPAWC.2018.8445954
Citation
Cagri Ilter , M , Wichman , R , Hamalainen , J & Yanikomeroglu , H 2018 , A Convolutionally Encoded OSTBC System with SNR-Adaptive Constellations for Low-Latency and Low-Complexity Communications . in 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 . vol. 2018-June , 8445954 , IEEE International Workshop on Signal Processing Advances in Wireless Communications , IEEE , United States , IEEE International Workshop on Signal Processing Advances in Wireless Communications , Kalamata , Greece , 25/06/2018 . https://doi.org/10.1109/SPAWC.2018.8445954
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https://urn.fi/URN:NBN:fi:aalto-201812106376
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[article-cris] Sähkötekniikan korkeakoulu / ELEC