Soft Decision Decoding of a Fixed-rate Entropy-coded Trellis Quantizer over a Noisy Channel

This report presents some new techniques to improve the performance of a Fixedrate Entropy-coded Trellis Quantizer (FE-TCQ) in transmission over a noisy channel. In this respect, we rst present the optimal decoder for a Fixed-rate Entropy-coded Vector Quantizer (FEVQ). A trellis structure is used to model the set of possible codewords in the FEVQ and the Viterbi algorithm is subsequently applied to select the most likely path through this trellis. In order to add quantization packing gain to the FEVQ, we take advantage of a Trellis Coded Quantization (TCQ) scheme. To prevent the error propagation, it is necessary to use a block structure obtained through a truncation of the corresponding trellis. To perform this task in an e cient manner, we apply the idea of tail-biting to the trellis structure of the underlying TCQ. It is shown that the use of a tail-biting trellis signi cantly reduces the required block length with respect to some other possible alternatives known for trellis truncation. This results in a smaller delay and also mitigates the e ect of the error propagation in signaling over a noisy channel. Finally, we present methods and numerical results for the combination of the proposed FEVQ soft decoder and a tail-biting TCQ. These results show that by an appropriate design of the underlying components, one can obtain a substantial improvement in the overall performance of such a xed-rate entropy-coded scheme. keywords Fixed-rate Entropy-coded Vector Quantization, Combined Source and Channel Coding Error Propagation