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International Conference on Communication Systems & Networks (COMSNETS)

ATRNN: Using Seq2Seq Approach for Decoding Polar Codes

Aniket Dhok, Swapnil Bhole

Published Jan 1, 2020• 2 citations
Publisher

Abstract

Polar codes have been chosen by 3GPP as the official error-correcting codes in the control plane of 5G NR enhanced Mobile Broadband (eMBB) due to their low complexity decoding and near-DMC capacity achievement. Recently, deep learning methods have produced promising results in many fields, including linear and polar code decoding. Using end-to-end DL approach provides flexibility, thus empowering us to integrate powerful DL algorithms into channel decoders. In this paper, we propose a novel one-shot decoding framework for polar codes using Attention-based Recurrent Neural Network (ATRNN). Furthermore, we evaluate the decoding performance of ATRNN using bit error rate (BER) and compare it with the state-of-the-art techniques such as successive cancellation (SC).

DOI
10.1109/comsnets48256.2020.9027355

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Journal of Communications and Networks•Dec 1, 2024•2 citations
Polar codes have been chosen by 3GPP as the official error-correcting codes in the control plane of 5G NR enhanced Mobile Broadband (eMBB) due to their low complexity decoding and near-DMC capacity achievement. Recently, deep learning methods have produced promising results in many fields, including linear and polar code decoding. Using end-to-end DL approach provides flexibility, thus empowering us to integrate powerful DL algorithms into channel decoders. In this paper, we propose a novel one-shot decoding framework for polar codes using Attention-based Recurrent Neural Network (ATRNN). Furthermore, we evaluate the decoding performance of ATRNN using bit error rate (BER) and compare it with the state-of-the-art techniques such as successive cancellation (SC).
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.
Polar code is the first type of constructive coding with achievable capacity, which has become the encoding scheme for 5G mobile communication systems and is a cutting-edge direction in channel coding research. However, traditional decoding algorithms for Polar codes still need to be improved in terms of errorcorrection performance and decoding delay. For Polar code short codes, due to the phenomenon of incomplete Polar, the traditional SC decoding algorithm has a problem of poor decoding performance. Algorithms that utilize deep learning for direct or auxiliary operations often yield more accurate computational results with lower computational complexity. Therefore, considering that applying deep learning to the communication field is a mainstream trend in the 6G era, a (16,8) short Polar code decoder based on recurrent neural network (RNN) is proposed. At the same time, a suitable dataset construction method was proposed, which achieved better performance than SC decoding algorithm under low signal-to-noise ratio by adjusting network parameters and training under the appropriate dataset.
In recent years, the emerging Deep Learning (DL) technology has made progress in the field of decoding. Current polar code neural network decoder has faster convergence speed and better Bit Error Rate (BER) performance than Belief Propagation (BP) decoding. However, it still has the problem of high computational complexity. Therefore, in order to improve this problem, this paper adopts the idea of improving information update in the iterative process, and proposes a Recurrent Neural Network (RNN) Offset Min-Sum (OMS) BP decoding algorithm that improves Left information update (RNN-OMSBP-L) . The simulation results show that, compared with the Deep Neural Network (DNN) BP(DNN-BP) decoding algorithm, this algorithm replaces all multiplications with a cost of 6.25% addition. Compared with the current optimized RNN OMS and approximate BP(RNN-OMS-BP) decoding algorithm, the decoding algorithm in this paper uses improved information to reduce 25% of the addition operations with almost no loss in BER performance, while saving part of the storage space overhead. Under the same BER performance, it reduces the number of iterations by 37.5%.
In the 5G communication systems, a hybrid approach to support polar codes for control plane and LDPC codes for data plane has been identified as the channel coding solution for enhanced mobile broadband (eMBB) scenario. One of the major challenges to implement this approach is to design powerful decoders at the terminal side. Inspired from a useful machine learning based polar decoder, we proposed a deep learning based unified polar-LDPC by concatenating an indicator section. Through numerical experiments, we show that the proposed deep neural network (DNN) based decoding architecture can achieve the similar decoding performance compared with the traditional BP-based decoding algorithm. Meanwhile, the proposed unified approach shares the same network architecture and parameters with isolated approaches, which saves significant implementation resources consequently.
Several researchers are interested in polar codes for the sake of their capacity which nearly catches memoryless channels capacity. They have been used as part of the 5G technology. Polar decoders like belief propagation decoders showed unfavorable performance during limited iterations. Nowadays, Machine-learning (ML) techniques are used to enhance the efficiency of polar codes decoders. Recurrent neural network belief propagation (RNN-BP) decoders proved better efficiency in a lower number of cycles. In this paper, a performance scheduling learning technique is proposed. This technique achieves a coding gain of 0.2 dB for the RNN-BP decoder at BER of 2*10−4. A new approach is also introduced in the quantization step. In this approach, the quantization is done after the learning process is finished instead of after each iteration. This approach fixes the issue of model convergence. These combined approaches show up to 0.45 dB improvement at BER of 2*10−4.
With the continuous evolution of 5G communication technology to B5G and the next generation of communication technology, Deep Learning technology will also lead the automation and intelligent transformation of communication systems. Existing research has shown that the combination of deep learning and communication technology is expected to break some performance bottlenecks of traditional communication algorithms and solutions. This paper explores the application of deep learning (DL) in polar decoding algorithms, proposing a DL-aided-FSC (DL-FSC) polar code decoder algorithm. For the DL-FSC decoding algorithm, the conventional successive cancellation (SC) decoder is partitioned into multiple sub-blocks, which are replaced by R0 nodes, R1 nodes and sub-DL decoder. The log-likelihood ratio (LLR) and frozen bit pattern are input to the sub-DL decoder to predict decode codewords under any decoding code rate. Through simulation verification, under the PBCH channel of 5G communication, the DL-FSC decoder achieves similar block error rate (BLER) performance to the SC decoder, even improving by about 1%. In order to verify the performance optimization effect of the proposed algorithm at the hardware level, the DL-FSC deocder circuit design was completed. Through FPGA synthesis, the proposed decoder achieves a throughput of about 4571 Mbps, which is 1.71× improvement in decoding throughput at the expense of increased logic resources.