Recurrent neural network based turbo decoding algorithms for different code rates
Application of deep learning to error control coding is gaining special attention and neural network architectures on decoding are approached to compare with conventional ones. Turbo codes conventionally use BCJR algorithm for decoding. In this paper, performances of neural Turbo decoder and deep le...
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| Format: | Article |
| Language: | English |
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Springer
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820303323 |
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| author | Shridhar B. Devamane Rajeshwari L. Itagi |
| author_facet | Shridhar B. Devamane Rajeshwari L. Itagi |
| author_sort | Shridhar B. Devamane |
| collection | DOAJ |
| description | Application of deep learning to error control coding is gaining special attention and neural network architectures on decoding are approached to compare with conventional ones. Turbo codes conventionally use BCJR algorithm for decoding. In this paper, performances of neural Turbo decoder and deep learning-based Turbo decoder are examined. A category of sequential codes are utilized to construct the RSC (Recursive Systematic Convolutional) codes as basic elements for Turbo encoder. Sequential codes suit the requirement of memory element present in convolution codes, which act as components for Turbo encoder. Turbo decoders are constructed by two means; as neural Turbo decoder and deep learning Turbo decoder. Both structures are based on recurrent neural network (RNN) architectures. RNN architectures are preferred due to the presence of memory as a feature. BER performance of both is compared with that of a convolutional Viterbi decoder in awgn channel. Both the structures are studied for different input data-lengths and code rates. |
| format | Article |
| id | doaj-art-e827307f738947eb8eaf73727a01bac6 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-e827307f738947eb8eaf73727a01bac62025-08-20T03:48:35ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-06-013462666267910.1016/j.jksuci.2020.03.012Recurrent neural network based turbo decoding algorithms for different code ratesShridhar B. Devamane0Rajeshwari L. Itagi1Department of Electronics & Communication Engineering, APS College of Engineering, Bengaluru-560082, Karnataka, India; Corresponding author.Department of Electronics & Communication Engineering, KLE Institute of Technology, Hubballi-580030, Karnataka, IndiaApplication of deep learning to error control coding is gaining special attention and neural network architectures on decoding are approached to compare with conventional ones. Turbo codes conventionally use BCJR algorithm for decoding. In this paper, performances of neural Turbo decoder and deep learning-based Turbo decoder are examined. A category of sequential codes are utilized to construct the RSC (Recursive Systematic Convolutional) codes as basic elements for Turbo encoder. Sequential codes suit the requirement of memory element present in convolution codes, which act as components for Turbo encoder. Turbo decoders are constructed by two means; as neural Turbo decoder and deep learning Turbo decoder. Both structures are based on recurrent neural network (RNN) architectures. RNN architectures are preferred due to the presence of memory as a feature. BER performance of both is compared with that of a convolutional Viterbi decoder in awgn channel. Both the structures are studied for different input data-lengths and code rates.http://www.sciencedirect.com/science/article/pii/S1319157820303323Block error rateSignal to noise ratioAdditive white Gaussian noiseRecurrent neural networkDeep learningTurbo code |
| spellingShingle | Shridhar B. Devamane Rajeshwari L. Itagi Recurrent neural network based turbo decoding algorithms for different code rates Journal of King Saud University: Computer and Information Sciences Block error rate Signal to noise ratio Additive white Gaussian noise Recurrent neural network Deep learning Turbo code |
| title | Recurrent neural network based turbo decoding algorithms for different code rates |
| title_full | Recurrent neural network based turbo decoding algorithms for different code rates |
| title_fullStr | Recurrent neural network based turbo decoding algorithms for different code rates |
| title_full_unstemmed | Recurrent neural network based turbo decoding algorithms for different code rates |
| title_short | Recurrent neural network based turbo decoding algorithms for different code rates |
| title_sort | recurrent neural network based turbo decoding algorithms for different code rates |
| topic | Block error rate Signal to noise ratio Additive white Gaussian noise Recurrent neural network Deep learning Turbo code |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820303323 |
| work_keys_str_mv | AT shridharbdevamane recurrentneuralnetworkbasedturbodecodingalgorithmsfordifferentcoderates AT rajeshwarilitagi recurrentneuralnetworkbasedturbodecodingalgorithmsfordifferentcoderates |