Generalized Feedback Decoding of Convolutional Codes

Abstract

The use of convolutional codes with feedback decoding is the most common error-correction technique in simple communication systems. A drawback of conventional feedback decoding is the limitation to a class of self-orthogonal codes which, in general, are non-optimum. Based on distance properties of the utilized code and test-error pattern analysis, we propose generalized feedback decoding that does not have the above mentioned limitation. This is minimum distance decoding and can be applied to any convolutional code while still maintaining its simplicity. Therefore, it has the advantage of being easily adopted in the existing systems. We can use complicated Viterbi or sequential decoder in large terminals and, with the same code, use the proposed decoder in small terminals; otherwise, both large and small terminals must utilize the same type decoder. Also, we may use the proposed decoding scheme to simplify and accelerate sequential type decoding. In addition, by means of the special recovery property of convolutional codes, advanced ARQ retransmission systems could be much improved; several practical applications are suggested and discussed in the last section of this paper.