Bidirectional Deep Learning Decoder for Polar Codes in Flat Fading Channels

One of the main issues facing in the future wireless communications is ultra-reliable and low-latency communication. Polar codes are well-suited for such applications, and recent advancements in deep learning have shown promising results in enhancing polar code decoding performance. We propose a rob...

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Vydáno v:	IEEE access Ročník 12; s. 149580 - 149592
Hlavní autoři:	Aziz, Md Abdul, Rahman, Md Habibur, Abrar Shakil Sejan, Mohammad, Tabassum, Rana, Hwang, Duck-Dong, Song, Hyoung-Kyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IEEE 2024
Témata:	Accuracy bi-directional long-short-term memory (Bi-LSTM) Binary phase shift keying convolutional neural network (CNN) Decoding deep learning (DL) deep neural network (DNN) Encoding Fading channels Iterative decoding Long short term memory PER Polar code Polar codes Reliability signal-to-noise ratio (SNR) successive cancellation (SC) Turbo codes wireless communication
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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