A class of binary rate one-half convolutional codes that allows a modified stack decoder are introduced. The distribution of the number of computations per decoded frame is greatly improved. Simulations indicate that for a binary symmetric channel with transition probabilityp=2^{-5}the modified stack decoder requires less than one-fifth the stack size of the classical implementation.

A state-space approach to the syndrome decoding of binary ratek/nconvolutional 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.

The classical Viterbi decoder recursively finds the trellis path (code word) closest to the received data. Given the received data, the syndrome decoder first forms a syndrome, instead. A recursive algorithm like Viterbi's is used to determine the noise sequence of minimum Hamming weight that can be a possible cause of this syndrome. Given the estimate of the noise sequence, one derives an estimate of the original data sequence. While the bit error probability of the syndrome decoder is no different from that of the classical Viterbi decoder, the syndrome decoder can be implemented using a read only memory (ROM), thus obtaining a considerable saving in hardware.