Name
Abstract | ||
|---|---|---|
Present-day communication systems routinely use codes that approach the channel capacity when coupled with a computationally efficient decoder. However, the decoder is typically designed for the Gaussian noise channel, and is known to be sub-optimal for non-Gaussian noise distribution. Deep learning methods offer a new approach for designing decoders that can be trained and tailored for arbitrary channel statistics. We focus on Turbo codes, and propose (DEEPTURBO), a novel deep learning based architecture for Turbo decoding.The standard Turbo decoder (TURBO) iteratively applies the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm with an inter-leaver in the middle. A neural architecture for Turbo decoding, termed (NEURALBCJR), was proposed recently to create a module that imitates the BCJR algorithm using supervised learning, and to use the interleaver architecture along with this module, which is then fine-tuned using end-to-end training. However, knowledge of the BCJR algorithm is required to design such an architecture, which also constrains the resulting learnt decoder. Here we remedy this requirement and propose a fully end-to-end trained neural decoder - Deep Turbo Decoder (DEEPTURBO). With novel learnable decoder structure and training methodology, DEEPTURBO reveals superior performance under both AWGN and non-AWGN settings as compared to the other two decoders - TURBO and NEURALBCJR. |
Year | Venue | Field |
|---|---|---|
2019 | 2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019) | Turbo,BCJR algorithm,Computer science,Turbo code,Supervised learning,Artificial intelligence,Deep learning,Additive white Gaussian noise,Channel capacity,Gaussian noise,Computer engineering |
DocType | Volume | ISSN |
Journal | abs/1903.02295 | 2325-3789 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihan Jiang | 1 | 0 | 0.68 |
| Kim, Hyeji | 2 | 23 | 6.94 |
| Himanshu Asnani | 3 | 117 | 15.39 |
| Sreeram Kannan | 4 | 120 | 21.76 |
| Sewoong Oh | 5 | 843 | 60.50 |
| pramod viswanath | 6 | 2744 | 368.62 |