| Paper ID | SPCOM-5.3 |
| Paper Title |
NEURAL LAYERED MIN-SUM DECODING FOR PROTOGRAPH LDPC CODES |
| Authors |
Dexin Zhang, Jincheng Dai, Kailin Tan, Kai Niu, Beijing University of Posts and Telecommunications, China; Mingzhe Chen, H. Vincent Poor, Princeton University, United States; Shuguang Cui, Chinese University of Hong Kong, China |
| Session | SPCOM-5: Detection and Decoding |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPC-MOD] Modulation, demodulation, encoding and decoding |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, layered min-sum (MS) iterative decoding is formulated as a customized neural network following the sequential scheduling of check node (CN) updates. By virtue of the lifting structure of protograph low-density parity-check (LDPC) codes, identical network parameters are shared among all derived edges originating from the same edge in the protograph, which makes the number of learnable parameters manageable. The proposed neural layered MS decoder can support arbitrary codelengths consequently. Moreover, an iteration-wise greedy training method is proposed to tune the parameters such that it avoids the vanishing gradient problem and accelerates the decoding convergence. |
