Soft decoding of variable-length codes

Abstract

We present the results of two methods for soft decoding of variable-length codes. We first show that maximum likelihood (ML) sequential decoding and maximum a posteriori (MAP) sequence estimation gives significant decoding improvements over hard decisions alone, then we show that further improvements can be gained by additional transmission of the symbol length. Finally, we show that it is possible to make use of the inherent meaning of the codewords without additional transmission of side information which results in a further gain.