Improved NSC decoding algorithm for polar codes based on multi-in-one neural network

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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Learning to Denoise and Decode: A Novel Residual Neural Network Decoder for Polar Codes

2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) ◽

10.1109/vtc2020-fall49728.2020.9348707 ◽

2020 ◽

Author(s):

Zhiwei Cao ◽

Hongfei Zhu ◽

Yuping Zhao ◽

Dou Li

Keyword(s):

Neural Network ◽

Polar Codes

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An Improved Multiple Bit-Flipping Successive Cancellation Decoding Algorithm for Polar Codes

2020 International Conference on Wireless Communications and Signal Processing (WCSP) ◽

10.1109/wcsp49889.2020.9299883 ◽

2020 ◽

Author(s):

Sha Sha ◽

Li Zhang ◽

Yejun He

Keyword(s):

Polar Codes ◽

Decoding Algorithm ◽

Successive Cancellation ◽

Successive Cancellation Decoding

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Optical Lensless-Camera Communications Aided by Neural Network

Applied Sciences ◽

10.3390/app9163238 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3238

Author(s):

Suhua Zhong ◽

Yuhong Zhu ◽

Xuefen Chi ◽

Hanyang Shi ◽

Hongliang Sun ◽

...

Keyword(s):

Neural Network ◽

Back Propagation ◽

Training Sequence ◽

Communication Model ◽

Decoding Algorithm ◽

Optical Components ◽

The Neural Network ◽

Blurred Image ◽

Varying Environments ◽

Image Decoding

Currently, the optical components of a camera embedded in the device constrain its overall thickness. Moreover, if the camera is strongly shaken, the lens and sensor may be misaligned, resulting in a defocusing effect. In this paper, we propose a novel lensless-camera communication model, which removes the lens of camera, therefore decreasing the overall thickness of the device without affecting communications. To decode the images captured by the lensless camera, a decoding algorithm aided by back propagation (BP) neural network was designed, which recognizes the blurred image patterns efficiently. To adapt to time-varying environments, an adaptive training sequence adjustment mechanism was designed. Simulation results show that the proposed image decoding algorithm presents a good bit-error-rate (BER) performance. The proposed system has robust movements and provides resilience to interference, benefiting from the neural network and the designed algorithm.

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A simplified decoding algorithm for multi-CRC polar codes

Journal of Systems Engineering and Electronics ◽

10.21629/jsee.2020.01.02 ◽

2020 ◽

pp. 12-18

Author(s):

Haifen YANG ◽

Keyword(s):

Polar Codes ◽

Decoding Algorithm

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A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes

Applied Sciences ◽

10.3390/app9050831 ◽

2019 ◽

Vol 9 (5) ◽

pp. 831

Author(s):

Yusheng Xing ◽

Guofang Tu

Keyword(s):

Error Correction ◽

Error Rate ◽

Complexity Analysis ◽

Signal To Noise Ratio ◽

Low Complexity ◽

High Signal ◽

Polar Codes ◽

Decoding Algorithm ◽

Low Snr ◽

Ordered Statistics

In this paper, we propose a low-complexity ordered statistics decoding (OSD) algorithm called threshold-based OSD (TH-OSD) that uses a threshold on the discrepancy of the candidate codewords to speed up the decoding of short polar codes. To determine the threshold, we use the probability distribution of the discrepancy value of the maximal likelihood codeword with a predefined parameter controlling the trade-off between the error correction performance and the decoding complexity. We also derive an upper-bound of the word error rate (WER) for the proposed algorithm. The complexity analysis shows that our algorithm is faster than the conventional successive cancellation (SC) decoding algorithm in mid-to-high signal-to-noise ratio (SNR) situations and much faster than the SC list (SCL) decoding algorithm. Our addition of a list approach to our proposed algorithm further narrows the error correction performance gap between our TH-OSD and OSD. Our simulation results show that, with appropriate thresholds, our proposed algorithm achieves performance close to OSD’s while testing significantly fewer codewords than OSD, especially with low SNR values. Even a small list is sufficient for TH-OSD to match OSD’s error rate in short-code scenarios. The algorithm can be easily extended to longer code lengths.

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Novel Low Complexity BP Decoding Algorithms for Polar Codes: Simplifying on Non-Linear Operations

Electronics ◽

10.3390/electronics11010093 ◽

2021 ◽

Vol 11 (1) ◽

pp. 93

Author(s):

Yuhuan Wang ◽

Jianguo Li ◽

Neng Ye ◽

Xiangyuan Bu

Keyword(s):

Computational Complexity ◽

Hardware Implementation ◽

Low Complexity ◽

Bp Algorithm ◽

Polar Codes ◽

Decoding Algorithm ◽

Hyperbolic Tangent ◽

Decoding Algorithms ◽

Tangent Function ◽

Non Linear

The parallel nature of the belief propagation (BP) decoding algorithm for polar codes opens up a real possibility of high throughput and low decoding latency during hardware implementation. To address the problem that the BP decoding algorithm introduces high-complexity non-linear operations in the iterative messages update process, this paper proposes to simplify these operations and develops two novel low complexity BP decoding algorithms, namely, exponential BP (Exp-BP) decoding algorithm and quantization function BP (QF-BP) decoding algorithm. The proposed algorithms simplify the compound hyperbolic tangent function by using probability distribution fitting techniques. Specifically, the Exp-BP algorithm simplifies two types of non-linear operations into single non-linear operation using the piece-wise exponential model function, which can approximate the hyperbolic tangent function in the updating formula. The QF-BP algorithm eliminates non-linear operations using the non-uniform quantization in the updating formula, which is effective in reducing computational complexity. According to the simulation results, the proposed algorithms can reduce the computational complexity up to 50% in each iteration with a loss of less than 0.1 dB compared with the BP decoding algorithm, which can facilitate the hardware implementation.

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