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 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.