Analyzing the Viterbi Algorithm

This summer I am taking CS5114 - Theory of Algorithms at Virginia Tech. Our first Project was to analyze a Dynamic Programming algorithm, and compare it to other approaches that solve the same problem. Since I am focusing on DSP/Communications, I decided to look at the Viterbi algorithm, a Dynamic Programming approach to decoding Convolutional Codes.

For the project I investigated why the Viterbi algorithm is so much more efficient than alternative approaches to decoding convolutional codes (Such as a brute force or recursive technique). The answer quickly becomes clear - as with most problems that can be solved with Dynamic Programming, standard approaches often re-compute the same values several times rather than just storing the values. In this case, the values that are being constanstly recomputed are the pth metrics for each transition in the Trellis diagram.

Of course the advantage of the Viterbi algorithm is that it finds a way to just compute each path metric once. This allows it to run in linear time, rather than exponential time like a recursive approach.

You can view my presentation below, or for a more complete treatment you can read the paper.

I've also posted all of the MATLAB code for my project on GitHub. The repository includes implementations of a Brute Force algorithm, a Recursive algorithm, and finally the Viterbi algorithm. There are also scripts to prove that they all work and to test the runtime.