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Journal of Physics Conference Series

Last Subcode Neural Network Assisted Polar Decoding

Hengyan Liu, Limen Zhang, Wenjun Yan, Yajie Li

Published Oct 1, 2022• 0 citations
Publisher

Abstract

A fast successive-cancellation decoding scheme based on neural network (NNFSCD) called last subcode neural network assisted decoding (LSNNACD) scheme for polar codes is proposed to improve decoding performance. First, a neural network node (NNN) is proposed and a neutral network model is trained to decode it. Then, the node recognizer is proposed to recognize a polar code with a NNN. Simulations with polar codes and binary phase shift keying (BPSK) shows good performance and low latency in the additive white Gaussian noise (AWGN) channel. Moreover, the proposed scheme has a wide application range.

DOI
10.1088/1742-6596/2356/1/012040

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A fast successive-cancellation decoding scheme based on neural network (NNFSCD) called last subcode neural network assisted decoding (LSNNACD) scheme for polar codes is proposed to improve decoding performance. First, a neural network node (NNN) is proposed and a neutral network model is trained to decode it. Then, the node recognizer is proposed to recognize a polar code with a NNN. Simulations with polar codes and binary phase shift keying (BPSK) shows good performance and low latency in the additive white Gaussian noise (AWGN) channel. Moreover, the proposed scheme has a wide application range.
The traditional fast successive-cancellation (SC) decoding algorithm can effectively reduce the decoding steps, but the decoding adopts a sub-optimal algorithm, so it cannot improve the bit error performance. In order to improve the bit error performance while maintaining low decoding steps, we introduce a neural network subcode that can achieve optimal decoding performance and combine it with the traditional fast SC decoding algorithm. While exploring how to combine neural network node (NNN) with R1, R0, single-parity checks (SPC), and Rep, we find that the decoding failed sometimes when the NNN was not the last subcode. To solve the problem, we propose two neural network-assisted decoding schemes: a key-bit-based subcode NN-assisted decoding (KSNNAD) scheme and a last subcode NN-assisted decoding (LSNNAD) scheme. The LSNNAD scheme recognizes the last subcode as an NNN, and the NNN with nearly optimal decoding performance gives rise to some performance improvements. To further improve performance, the KSNNAD scheme recognizes the subcode with a key bit as an NNN and changes the training data and label accordingly. Computer simulation results confirm that the two schemes can effectively reduce the decoding steps, and their bit error rates (BERs) are lower than those of the successive-cancellation decoder (SCD).
Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree.
Polar codes are considered as a competitive candidate for 5G ultra-reliable and low-latency communication (URLLC) channel codes, but its strict latency and reliability requirements pose signicant challenges to the existing decoding schemes, especially in terms of decoding latency. In this paper, we propose a neural network based successive cancellation (NN-SC) decoding algorithm. In the proposed algorithm, neural networks are used to replace the local decoders for rate-R constituent codes because of their one-shot decoding capacity, which greatly reduce the decoding latency and algorithmic complexity. The simulation results indicate that the NN-SC decoding algorithm can achieve comparable reliability performance against the successive cancellation (SC) decoding for a polar code of length 128 and rate 1/6, while reducing the decoding latency by 65.2% when compared with the simplified SC decoding. In addition, for two conventional decoding algorithms: SC and belief propagation (BP) decoding, the proposed algorithm can further reduce the decoding latency by 22.8% and 13.8%, respectively.
In 5G mobile communication standard, polar codes have been used as coding on control channels. Although the polar code has good performance, the performance of its short code is still facing huge challenges. Moreover, the size limitation of using the deep learning model as the decoder of the polar code has always been the focus of researchers seeking a breakthrough. In this paper, the convolutional neural network (CNN) model in deep learning is cascaded with a successive cancellation (SC) decoder to achieve the decoding of polar codes under additive correlated Gaussian noise (ACGN) channels, thereby improving the decoding performance of short polar codes under ACGN and alleviating the code length limitation of a single deep learning model as a decoder. The cascaded decoding structure is represented as a CNN-SC decoder, which can effectively estimate colored noise. The simulation shows that the CNN-SC decoder improves the performance of polar codes under ACGN channels significantly.
In order to reduce the latency of polar codes, several decoding methods have been proposed. However, most of them need a particular structure of nodes in the decoding tree. Thus, they ignore some unstructured nodes. To this end, we propose a modified version of the simplified successive cancellation decoder based on node error probability to complement the structured scheme. By a proper design, simulation results in additive white Gaussian noise channel show that the proposed method can efficiently reduce the decoding latency of the structured scheme while keeping the error performance almost unchanged.
In the context of decoding binary linear block codes by polar code decoding techniques, we propose in this paper a new optimization of the serial nature of decoding the polar codes equivalent to binary linear block codes. In addition to the special nodes proposed by the simplified successive-cancellation list technique, we propose a new special node allowing to estimate in parallel the bits of its sub-code. The simulation is done in an additive white gaussian noise channel (AWGN) channel for several linear block codes, namely bose–chaudhuri–hocquenghem codes (BCH) codes, quadratic-residue (QR) codes, and linear block codes recently designed in the literature. The performance of the proposed technique offers the same performance in terms of frame error rate (FER) as the ordered statistics decoding (OSD) algorithm, which achieves that of maximum likelihood decoder (MLD), but with high memory requirements and computational complexity.