Robust vector quantization for wireless channels

This study focuses on two issues: parametric modeling of the channel and in dex assignment of codevectors, to design a vector quantizer that achieves h igh robustness against channel errors. We first formulate the design of a r obust zero-redundancy vector quantizer as a combinatorial optimization prob lem leading to a genetic search for a minimum-distortion index assignment. Performance is further enhanced by the use of the Fritchman channel model t hat more closely characterizes the statistical dependencies between error s equences. This study also presents an index assignment algorithm based on t he Fritchman model with parameter values estimated using a real-coded genet ic algorithm. Simulation results indicate that the global explorative prope rties of genetic algorithms make them very effective in estimating Fritchma n model parameters, and use of this model can match index assignment to exp ected channel conditions.