Improving Decodability of Polar Codes by Adding Noise
This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar deco...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/14/6/1156 |
| _version_ | 1797481965870383104 |
|---|---|
| author | Lingjun Kong Haiyang Liu Wentao Hou Bin Dai |
| author_facet | Lingjun Kong Haiyang Liu Wentao Hou Bin Dai |
| author_sort | Lingjun Kong |
| collection | DOAJ |
| description | This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar decoding fails. The new decoding algorithm converts uncorrectable received sequences into error-correcting regions of their decoding space for correct decoding by adding specific noises. To reduce the decoding complexity and delay, the PDNE decoding algorithm and sole neural-evolutionary (SNE) decoding algorithm for polar codes are further proposed, which provide a considerable tradeoff between the decoding performance and complexity by acquiring the neural-evolutionary noise in an offline manner. Numerical results suggest that our proposed decoding algorithms outperform the other conventional decoding algorithms. At high signal-to-noise ratio (SNR) region, the Online-PDNE decoding algorithm improves bit error rate (BER) performance by more than four orders of magnitude compared with the conventional simplified successive cancellation (SSC) decoding algorithm. Furthermore, in the mid-high SNR region, the average normalized complexity of the proposed algorithm is almost the same as that of the SSC decoding algorithm, while preserving the decoding performance gain. |
| first_indexed | 2024-03-09T22:21:51Z |
| format | Article |
| id | doaj.art-1f7ee6a899ac438f9a521e86bf181822 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T22:21:51Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-1f7ee6a899ac438f9a521e86bf1818222023-11-23T19:11:44ZengMDPI AGSymmetry2073-89942022-06-01146115610.3390/sym14061156Improving Decodability of Polar Codes by Adding NoiseLingjun Kong0Haiyang Liu1Wentao Hou2Bin Dai3Faculty of Network and Telecommunication Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaInstitute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, ChinaCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaThis paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar decoding fails. The new decoding algorithm converts uncorrectable received sequences into error-correcting regions of their decoding space for correct decoding by adding specific noises. To reduce the decoding complexity and delay, the PDNE decoding algorithm and sole neural-evolutionary (SNE) decoding algorithm for polar codes are further proposed, which provide a considerable tradeoff between the decoding performance and complexity by acquiring the neural-evolutionary noise in an offline manner. Numerical results suggest that our proposed decoding algorithms outperform the other conventional decoding algorithms. At high signal-to-noise ratio (SNR) region, the Online-PDNE decoding algorithm improves bit error rate (BER) performance by more than four orders of magnitude compared with the conventional simplified successive cancellation (SSC) decoding algorithm. Furthermore, in the mid-high SNR region, the average normalized complexity of the proposed algorithm is almost the same as that of the SSC decoding algorithm, while preserving the decoding performance gain.https://www.mdpi.com/2073-8994/14/6/1156fifth generationchannel codingpolar codeperturbation noiseneuro-evolution |
| spellingShingle | Lingjun Kong Haiyang Liu Wentao Hou Bin Dai Improving Decodability of Polar Codes by Adding Noise Symmetry fifth generation channel coding polar code perturbation noise neuro-evolution |
| title | Improving Decodability of Polar Codes by Adding Noise |
| title_full | Improving Decodability of Polar Codes by Adding Noise |
| title_fullStr | Improving Decodability of Polar Codes by Adding Noise |
| title_full_unstemmed | Improving Decodability of Polar Codes by Adding Noise |
| title_short | Improving Decodability of Polar Codes by Adding Noise |
| title_sort | improving decodability of polar codes by adding noise |
| topic | fifth generation channel coding polar code perturbation noise neuro-evolution |
| url | https://www.mdpi.com/2073-8994/14/6/1156 |
| work_keys_str_mv | AT lingjunkong improvingdecodabilityofpolarcodesbyaddingnoise AT haiyangliu improvingdecodabilityofpolarcodesbyaddingnoise AT wentaohou improvingdecodabilityofpolarcodesbyaddingnoise AT bindai improvingdecodabilityofpolarcodesbyaddingnoise |