# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
###### Abstract  
Polar code has been adopted as the control channel coding scheme for the fifth generation (5G), and the performance of short polar codes is receiving intensive attention. The successive cancellation flipping (SC flipping) algorithm suffers a significant performance loss in short block lengths. To address this issue, we propose a double long short-term memory (DLSTM) neural network to locate the first error bit. To enhance the prediction accuracy of the DLSTM network, all frozen bits are clipped in the output layer. Then, Gaussian approximation is applied to measure the channel reliability and rank the flipping set to choose the least reliable position for multi-bit flipping. To be robust under different codewords, padding and masking strategies aid the network architecture to be compatible with multiple block lengths. Numerical results indicate that the error-correction performance of the proposed algorithm is competitive with that of the CA-SCL algorithm. It has better performance than the machine learning-based multi-bit flipping SC (ML-MSCF) decoder and the dynamic SC flipping (DSCF) decoder for short polar codes. polar codes; 5G; SC decoding; bit flipping; LSTM; robustness +
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Article  
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Since deep learning was adopted to locate the error bit more accurately than traditional methods [6; 7; 8; 9], many scholars have applied the neural network to decoders [10; 11; 12]. In [13; 14], a decoder based on the convolutional neural network (CNN) is proposed to lower the bit error rate (BER). [PERSON] et al. applied the long short-term memory (LSTM) network to the decoder [15]. The network can efficiently extract features from the decoding and improve the accuracy of error bit prediction, but the complexity increases with the list size. In order to obtain a better flipping strategy, both the LSTM and the reinforcement learning (RL) are applied to the decoder. In [16], the LSTM network predicts the first error bit and the RL identifies other error bits or undoes the previous error flipping. Similarly, a machine learning-based multi-bit flipping SC decoding scheme (ML-MSCF) was proposed by Author17 [17], which uses both the LSTM network and the Q-Learning algorithm to achieve multi-bit flipping. However, it needs a well-trained Q-table to select the flipping bit. The accuracy of the flipping bit greatly affects the performance of the SC flipping decoder. However, the current SC flipping works do not perform well with short codewords. The LSTM networks have achieved excellent performance in many fields [18; 19]. Thus, we propose a double long short-term memory (DLSTM) network to predict the error bits. The main contributions are as follows:  
* A DLSTM-based SC flipping decoder is proposed. In addition, all frozen bits are clipping in the output layer to enhance accuracy. We construct the flipping set of the first error bit according to the probability of the network output in descending order, and attempt one-bit flipping. * By exploring the reliability of bit channel, we propose a novel multi-bit flipping scheme for sequences that reach the maximum number of one-bit flipping and still fail the CRC detector. The candidate bits are sorted in ascending order in terms of the reliability, and the unreliable bits are selected in priority to multi-bit flipping. * In order to make the proposed algorithm robust, we design the DLSTM network architecture to be compatible with multiple block lengths. We adopt a padding strategy to maintain data integrity, so that the training is not limited by the block length. A masking method is taken to skip the timestep and eliminate the effect of padded invalid data. Simulation results show that the proposed decoding scheme has better error-correction performance than the ML-MSCF decoding and DSCF decoding for short block lengths. It can approach the performance of CA-SCL (\(L=8\)). The remainder of the paper is organized as follows. Section 2 introduces polar codes, SC flipping decoder, and LSTM network.