Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/sym14061156 | SYMMETRY-BASEL |
Keywords | DocType | Volume |
fifth generation, channel coding, polar code, perturbation noise, neuro-evolution | Journal | 14 |
Issue | ISSN | Citations |
6 | 2073-8994 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingjun Kong | 1 | 0 | 0.34 |
| Haiyang Liu | 2 | 28 | 10.84 |
| Wentao Hou | 3 | 0 | 0.34 |
| Bin Dai | 4 | 4 | 4.48 |