Syndrome decoding of binary-rate<tex>k/n</tex>convolutional codes

Abstract

A state-space approach to the syndrome decoding of binary rate k/n convolutional codes is described. State-space symmetries of a certain class of codes can be exploited to obtain a reduction in the exponent of growth of the decoder hardware. Aside from these savings it is felt that the state-space formalism developed has some unique intrinsic value.