@inproceedings{25c4fde3bb7f4602856f614de39a1598,
title = "TinyTurbo: Efficient Turbo Decoders on Edge",
abstract = "In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO. TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TINYTURBO strongly generalizes across different rate, blocklengths, and trellises. We verify the reliability and efficiency of TINYTURBO via over-the-air experiments.",
author = "Hebbar, {S. Ashwin} and Mishra, {Rajesh K.} and Ankireddy, {Sravan Kumar} and Makkuva, {Ashok V.} and Hyeji Kim and Pramod Viswanath",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Information Theory, ISIT 2022 ; Conference date: 26-06-2022 Through 01-07-2022",
year = "2022",
doi = "10.1109/ISIT50566.2022.9834589",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2797--2802",
booktitle = "2022 IEEE International Symposium on Information Theory, ISIT 2022",
address = "United States",
}