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IEEE Vehicular Technology Conference

A Machine Learning Based Multi-flips Successive Cancellation Decoding Scheme of Polar Codes

He Bi, Shaohua Wu, Yajing Deng, Hao Yin, Jian Jiao, Qinyu Zhang

Published May 1, 2020• 13 citations
Publisher

Abstract

The flip-successive cancellation (SCF) decoding algorithm is a decoding scheme to improve the performance of the SC decoding algorithm under short code length by flipping erroneous bits in initial SC decoding. The degraded performance of the SCF decoding algorithm is usually caused by the wrong locating of the first erroneous bit or additional erroneous bits. To address this issue, we propose a machine learning based multi-flips SC decoding scheme (ML-MSCF), which can improve the performance of the SCF decoding algorithm with multiple flips based on the long short-term memory (LSTM) network and reinforcement learning (RL). Specifically, we use a LSTM network to locate the first erroneous bit when initial SC decoding fails, then the outputs of the LSTM network are used as the action space of RL to identify additional erroneous bits in the followed procedure. Simulation results show that the proposed scheme can achieve performance improvement of 0.2-0.3dB over the stateof-art SCF decoding algorithm on both the bit error ratio (BER) and the frame error rate (FER) with less decoding latency.

DOI
10.1109/vtc2020-spring48590.2020.9128875

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Although original successive cancellation flip (SCF) decoding of the polar codes can identify the first error position and flip the error, decoding still fails when other errors occur after the corrected error bit. Therefore, the improvement of the block error rate (BLER) performance is limited. In this paper, we present an improved algorithm that can correct multiple error bits, named as improved multiple bit-flipping successive cancellation decoding algorithm. First, multiple sets are constructed by calculating the log likelihood ratio (LLR) of each information bit. Then, a bit is selected and corrected from the first set when original decoding fails; two bits are selected and flipped from the second set when all bits in the first set cannot be successfully decoded; when all bits combinations in the second set are flipped but the output vector still does not match the input vector, three bits are selected and corrected from the third set. Simulation results show that the BLER performance of the improved algorithm is better than that of the successive cancellation list decoding (SCL) decoding algorithm with a list size of L = 8 for N = 256. When N = 1024 and SNR = 3 dB, the BLER performance of improved algorithm is slightly better than that of SCL decoding algorithm with a list size of L = 4.

International Conference on Wireless Communications and Signal Processing (WCSP)•Oct 21, 2020•1 citation
The flip-successive cancellation (SCF) decoding algorithm is a decoding scheme to improve the performance of the SC decoding algorithm under short code length by flipping erroneous bits in initial SC decoding. The degraded performance of the SCF decoding algorithm is usually caused by the wrong locating of the first erroneous bit or additional erroneous bits. To address this issue, we propose a machine learning based multi-flips SC decoding scheme (ML-MSCF), which can improve the performance of the SCF decoding algorithm with multiple flips based on the long short-term memory (LSTM) network and reinforcement learning (RL). Specifically, we use a LSTM network to locate the first erroneous bit when initial SC decoding fails, then the outputs of the LSTM network are used as the action space of RL to identify additional erroneous bits in the followed procedure. Simulation results show that the proposed scheme can achieve performance improvement of 0.2-0.3dB over the stateof-art SCF decoding algorithm on both the bit error ratio (BER) and the frame error rate (FER) with less decoding latency.
The key to successive cancellation (SC) flip decoding of polar codes is to accurately identify the first error bit. The optimal flipping strategy is considered difficult due to lack of an analytical solution. Alternatively, we propose a deep learning aided SC flip algorithm. Specifically, before each SC decoding attempt, a long short-term memory (LSTM) network is exploited to either (i) locate the first error bit, or (ii) undo a previous "wrong" flip. In each SC attempt, the sequence of log likelihood ratios (LLRs) derived in the previous SC attempt is exploited to decide which action to take. Accordingly, a two-stage training method of the LSTM network is proposed, i.e., learn to locate first error bits in the first stage, and then to undo "wrong" flips in the second stage. Simulation results show that the proposed approach identifies error bits more accurately and achieves better block error rate performance than the state-of-the-art SC flip algorithms.
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 are the first error-correcting code proven to achieve channel capacity based on infinite code length. The Successive Cancellation List Flip (SCLF) decoding algorithm was proposed by flipping an erroneous bit during the next decoding attempt. To identify the erroneous bits, the Log-Likelihood Ratio (LLR) is used to indicate the reliability of each decision bit. To improve the accuracy of the erroneous bit prediction, we propose deep-learning-aided (DL-aided) SCLF decoding algorithms. We first offer a stacked LSTM network that contains new features to train our models, which are able to improve the accuracy of the prediction of positions of erroneous bits. Then we separately train the stacked LSTM models to predict the position of both the first and second erroneous bits and whether to continue flipping. As a result, the DL-aided SCLF decoding algorithms based on the proposed stacked LSTM flip-1 model, stacked LSTM flip-2 model, and the stacked LSTM continue-flipping check (CFC) model are able to provide a better performance at a lower number of average decoding attempts when compared to other state-of-the-art decoding algorithms.
A traditional successive cancellation (SC) decoding algorithm produces error propagation in the decoding process. In order to improve the SC decoding performance, it is important to solve the error propagation. In this paper, we propose a new algorithm combining reinforcement learning and SC flip (SCF) decoding of polar codes, which is called a Q-learning-assisted SCF (QLSCF) decoding algorithm. The proposed QLSCF decoding algorithm uses reinforcement learning technology to select candidate bits for the SC flipping decoding. We establish a reinforcement learning model for selecting candidate bits, and the agent selects candidate bits to decode the information sequence. In our scheme, the decoding delay caused by the metric ordering can be removed during the decoding process. Simulation results demonstrate that the decoding delay of the proposed algorithm is reduced compared with the SCF decoding algorithm, based on critical set without loss of performance.
Polar codes have been selected as the channel coding scheme for the Enhanced Mobile Broadband (eMBB) control channel in 5G, which requires short length codes. One of the main challenges in utilizing polar codes is the design of low-complexity decoders with adequate error-correcting performance. The Successive Cancellation (SC) decoder was introduced as the first algorithm enabling polar codes to achieve the channel capacity for long codelengths. However, for shorter codelengths, this decoder does not provide satisfactory error-correction performance. To address this limitation, the Successive Cancellation List (SCL) decoder has been proposed, which performs decoding by maintaining a list of candidate paths and selecting the best one based on a path metric. In this paper, we propose improving the error-correction performance of the SCL decoder using concatenated Cyclic Redundancy Check (CRC) codes and Long Short-Term Memory (LSTM) network. Simulation results demonstrate that the proposed method outperforms the Belief Propagation (BP) decoder in terms of Bit Error Rate (BER) curves. Furthermore, the accuracy of the LSTM model in this study improves from 73 percent in previous research to 93.77 percent.
Although original successive cancellation flip (SCF) decoding of the polar codes can identify the first error position and flip the error, decoding still fails when other errors occur after the corrected error bit. Therefore, the improvement of the block error rate (BLER) performance is limited. In this paper, we present an improved algorithm that can correct multiple error bits, named as improved multiple bit-flipping successive cancellation decoding algorithm. First, multiple sets are constructed by calculating the log likelihood ratio (LLR) of each information bit. Then, a bit is selected and corrected from the first set when original decoding fails; two bits are selected and flipped from the second set when all bits in the first set cannot be successfully decoded; when all bits combinations in the second set are flipped but the output vector still does not match the input vector, three bits are selected and corrected from the third set. Simulation results show that the BLER performance of the improved algorithm is better than that of the successive cancellation list decoding (SCL) decoding algorithm with a list size of L = 8 for N = 256. When N = 1024 and SNR = 3 dB, the BLER performance of improved algorithm is slightly better than that of SCL decoding algorithm with a list size of L = 4.