Design of Convolutional Codes for Varying Constraint Lengths
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917793205583872 |
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| author | Dhounde, Parag Bhute, Avinash |
| author_facet | Dhounde, Parag Bhute, Avinash |
| contents | This paper explores the design of convolutional codes for varying constraint lengths, focusing on their role in error correction in digital communication systems. Convolutional codes are essential in achieving reliable data transmission across noisy channels. The constraint length, which determines the memory of the encoder, plays a critical role in the performance of convolutional codes. This study investigates the effect of varying constraint lengths on coding performance, including code rate, complexity, and decoding accuracy. Simulation results and theoretical analysis illustrate the trade-offs between constraint length and decoding efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_01567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Design of Convolutional Codes for Varying Constraint Lengths Dhounde, Parag Bhute, Avinash Information Theory Signal Processing This paper explores the design of convolutional codes for varying constraint lengths, focusing on their role in error correction in digital communication systems. Convolutional codes are essential in achieving reliable data transmission across noisy channels. The constraint length, which determines the memory of the encoder, plays a critical role in the performance of convolutional codes. This study investigates the effect of varying constraint lengths on coding performance, including code rate, complexity, and decoding accuracy. Simulation results and theoretical analysis illustrate the trade-offs between constraint length and decoding efficiency. |
| title | Design of Convolutional Codes for Varying Constraint Lengths |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2410.01567 |