Error Control Coding Based on Support Vector Machine

A novel approach of decoding convolutional codes using a multi-class support vector machine is presented in this paper. Support vector machine is a recently developed and well recognized algorithm for constructing maximum margin classifiers. Unlike traditional adaptive learning approaches such as a multi-layer neural network, it is able to converge to a global optimum solution, hence achieving a better performance. However, up to this date so far, no work has yet been done on applying support vector machine on error control coding. In this investigation, decoding is achieved by treating each codeword as a unique class. Hence the decoding procedure becomes a multi-class pattern classification problem. Simulation results show that the bit error rate performance of decoder based on such approach compare favorably with a conventional soft decision Viterbi Algorithm under a noisy channel with additive white Gaussian noise and achieve an extra 2 dB coding gain over the conventional method in a Rayleigh’s fading channel.