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IEEE International Conference on Big Data and Artificial Intelligence (BDAI)

Machine Learning for Polar Codes in Small IoT Devices

Xian Liu, Changcheng Huang

Published Jul 5, 2024• 1 citation
Publisher

Abstract

Error-correction in the communication module of IoT systems is a mission-critical task. The Polar code is one of the modern 5G wireless technologies. Most conventional decoding methods are iterative. It is interesting to investigate the feasibility of AI methodology, such as machine learning, for small Polar codes. In this paper, we design a small artificial neural network for this purpose. It is shown that this neural network can successfully decode four small Polar codes: both non-systematic and systematic, with codeword length 8 and 16, respectively.

DOI
10.1109/bdai62182.2024.10692447

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Error-correction in the communication module of IoT systems is a mission-critical task. The Polar code is one of the modern 5G wireless technologies. Most conventional decoding methods are iterative. It is interesting to investigate the feasibility of AI methodology, such as machine learning, for small Polar codes. In this paper, we design a small artificial neural network for this purpose. It is shown that this neural network can successfully decode four small Polar codes: both non-systematic and systematic, with codeword length 8 and 16, respectively.
In this paper, we proposed a block neural network (BlockNN) algorithm for polar code. We equally divide the 2nbit polar code into many small sub-blocks according to the encoding rules of polar code, then put these sub-blocks into the neural network of the same structure for processing. This decoding algorithm is non-iterative and inherently enables a high level of parallelization, while showing a competitive BER(bit error arte) performance. On the aspect of hardware implementation, this decoding structure of the neural network can be multiplexed and the computational complexity does not increase with the code length, only related to the size of the block.
Deep learning recently shows strong competitiveness to improve polar code decoding. However, suffering from prohibitive training and computation complexity, the conventional deep neural network (DNN) is only possible for very short code length. In this paper, the main problems of deep learning in decoding are well solved. We first present the multiple scaled belief propagation (BP) algorithm, aiming at obtaining faster convergence and better performance. Based on this, deep neural network decoder (NND) with low complexity and latency, is proposed for any code length. The training only requires a small set of zero codewords. Besides, its computation complexity is close to the original BP. Experiment results show that the proposed (64,32) NND with 5 iterations achieves even lower bit error rate (BER) than the 30-iteration conventional BP and (512, 256) NND also outperforms conventional BP decoder with same iterations. The hardware architecture of basic computation block is given and folding technique is also considered, saving about 50% hardware cost.
Polar code is selected for the 5-th generation of wireless communication standard. Meanwhile, with the demand of low latency in 5G and great success in deep learning area, neural network decoder (NND) becomes a promising candidate for future decoder. However, NND is currently only feasible for very short code lengths. In this paper, we proposed a belief propagation-neural network (BP-NN) algorithm for polar codes based on partitioning. The core of our algorithm is replacing subblock of belief propagation (BP) decoder with NNDs, and using BP decoding framework to connect them. In addition, we also introduce a weight function to complete the message propagation process. Simulation results show the proposed algorithm reduces the decoding delay compared to the traditional BP algorithm and have a well bit error rate (BER) performance. At the same time, it also suitable for long polar codes.
With the development of communication technology, it is urgent to design channel decoding algorithms to maximize the high throughput and minimize the latency. As the capacity achieving polar codes are adopted by 5-th generation wireless system (5G), it is required to design efficient the decoding algorithms for polar codes with high performance and low complexity. Deep neural network (DNN)-based algorithms which have shown superiority in other fields, can obtain better performance using multi-scale belief propagation (BP) algorithm, whereas the decoding complexity needs to be decreased. We propose a residual neural network based belief propagation (ResNet- BP) algorithm to solve the inherent drawbacks in the existing methods, by reducing the number of variables in the network and reducing the complexity in terms of multiplication. Simulation results demonstrate that the proposed method can achieve the approximating performance of recurrent neural network-based belief propagation (RNN-BP) with a lower number of iterations.
The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block. These blocks are then connected via the remaining conventional belief propagation decoding stage(s). The resulting decoding algorithm is non-iterative and inherently enables a highlevel of parallelization, while showing a competitive bit error rate (BER) performance. We examine the degradation through partitioning and compare the resulting decoder to state-of-the art polar decoders such as successive cancellation list and belief propagation decoding.
Polar code is the first capacity-achieving channel coding scheme which has been selected for the next generation of wireless communication standard(5G). However, the traditional decoding schemes such as CA-SCL and BP algorithms cannot take into account both BER and latency performance. Inspired by the deep learning technology, we propose a scheme that combine convolutional neural network (CNN) with traditional Belief Propagation (BP) decoding. By exploiting noise correlation for channel decoding under colored noise environment, the proposed scheme can obtain a more accurate estimation of channel noise. We simulate the performance of the proposed scheme under BPSK and 4QAM modulation. For QAM modulation, a scheme that extend the conventional real-valued CNN technique to the complex-valued one is given. The results demonstrate that bit error rates (BER) performance can be improved after removing the estimated noise. Compare to the traditional Polar decoding schemes, the proposed scheme has better trade-off between BER and latency performance under colored noise environment.