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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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 149580 - 149592
Main Authors: Aziz, Md Abdul, Rahman, Md Habibur, Abrar Shakil Sejan, Mohammad, Tabassum, Rana, Hwang, Duck-Dong, Song, Hyoung-Kyu
Format: Journal Article
Language:English
Published: IEEE 2024
Subjects:
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
Online Access:Get full text
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Summary: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 robust decoder based on a bidirectional long short-term memory (Bi-LSTM) network, which processes sequences in both forward and backward directions simultaneously. This approach leverages the strengths of bidirectional recurrent neural networks to improve the decoding of polar-coded short packets. Our study focuses on packet transmission over frequency-flat quasi-static Rayleigh fading channels, using a simple codebook originally designed for additive white Gaussian noise channels. We evaluate the packet error rate for various signal-to-noise ratio levels using different modulation schemes. The simulation results demonstrate that the proposed Bi-LSTM-based decoder closely approaches the theoretical outage performance and achieves significant coding gains in fading channels. Furthermore, the proposed decoder outperforms convolutional neural network and deep neural network-based decoders, validating its superiority in decoding polar codes for short packet transmission in challenging wireless environments.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3476471