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.
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