이 제출물을 팔로우합니다
- 팔로우하는 게시물 피드에서 업데이트를 확인할 수 있습니다
- 정보 수신 기본 설정에 따라 이메일을 받을 수 있습니다
In this project, we seek to minimize the gap-to-capacity (given by Shannon’s theoretical limit) of a rate 1/3 code (also can be modified for 1/N). This is done via a convolutional encoder/decoder for varying memory elements as well for both soft and hard decoding scheme. We show that the gap-to-capacity can be minimized with respect to the suboptimal un-coded code word or a (3,1) repetition code. Although better schemes are available such as LDPC and turbo codes, we have chosen the convolutional code for its simplicity and generality. Our model of transmission is binary-input AWGN channel. Also, I attached my paper to demonstrate how the code can be easily modified for other rates and different varying element sizes. Provides detail overview of Convolution Coding scheme. If you decide to use this code, please cite the paper and this code in the proper manner.
For More Information: http://www.romeilsandhu.com
인용 양식
Romeil Sandhu (2026). Convolutional Encoder/Decoder of Rate 1/N Codes (https://kr.mathworks.com/matlabcentral/fileexchange/25859-convolutional-encoder-decoder-of-rate-1-n-codes), MATLAB Central File Exchange. 검색 날짜: .
