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The Journal of Supercomputing

Erschienen in:

16.04.2018

AI-enabled turbo-coded OFDM system for improved BER performance

verfasst von: Priti Subramanium, Rajeshree D. Raut

Erschienen in: The Journal of Supercomputing | Ausgabe 6/2020

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Abstract

The BER performance of any wireless communication system is the primary factor of consideration for the network under evaluation. Turbo-coded OFDM systems have always outperformed other communication standards in terms of delay of communication, overall system efficiency and flexibility. But the BER performance of turbo-coded OFDM systems has always been a challenge for network designers, due to the fact that turbo encoders vary largely in terms of their primary specifications like code length and code rate among other network parameters. In this paper, we propose an artificial intelligence (AI) layer for turbo-coded OFDM systems, which improves the BER performance of the system, and also reduces the delay of communication by intelligent selection of network specifications. Our AI is trained for minimizing the BER of the communication network, but it can be used to target one or many parameters as per the requirements of the network designer.

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Titel: AI-enabled turbo-coded OFDM system for improved BER performance
verfasst von: Priti Subramanium
Rajeshree D. Raut
Publikationsdatum: 16.04.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 6/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-2370-1

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