Deep Learning Method of Polar Codes under Colored Noise

Xiong, Danqiu; Tian, Bangsen

doi:10.1109/iccchina.2019.8855887

Cited by 3 publications

(1 citation statement)

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“…Based on the above algorithm ideas, this paper implements a polar code noise optimization algorithm based on a CNN for channel decoding under correlated noise. We use the powerful prediction model of CNN to extract and train the relevant characteristics of noise [24,25], estimate and whiten channel noise more accurately, calculate a more reliable log-likelihood ratio (LLR), and reduce the impact of noise on decoding performance. Compared with the literature mentioned above [20], the main contribution of this paper is to apply the concatenated algorithm of CNN and BP to the decoding of polar codes.…”

Section: Introductionmentioning

confidence: 99%

Channel Noise Optimization of Polar Codes Decoding Based on a Convolutional Neural Network

Yan

Lou

Wang

2021

Wireless Communications and Mobile Computing

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Polar code has the characteristics of simple coding and high reliability, and it has been used as the control channel coding scheme of 5G wireless communication. However, its decoding algorithm always encounters problems of large decoding delay and high iteration complexity when dealing with channel noise. To address the above challenges, this paper proposes a channel noise optimized decoding scheme based on a convolutional neural network (CNN). Firstly, a CNN is adopted to extract and train the colored channel noise to get more accurate estimation noise, and then, the belief propagation (BP) decoding algorithm is used to decode the polar codes based on the output of the CNN. To analyze and verify the performance of the proposed channel noise optimized decoding scheme, we simulate the decoding of polar codes with different correlation coefficients, different loss function parameters, and different code lengths. The experimental results show that the CNN-BP concatenated decoding can better suppress the colored channel noise and significantly improve the decoding gain compared with the traditional BP decoding algorithm.

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Section: Introductionmentioning

confidence: 99%