Classification GMM based Binary Error Generation Model

The article extends Vector Quantization (VQ) binary error generation model by a soft boundary concept utilizing Gaussian Mixture Model (GMM). Although VQ based binary error model provides superior modeling for common stochastic characteristics of digital channels it depends on unknown distance, suffers from lack of data, hard boundary space division, etc. To alleviate these drawbacks GMM based approach was applied to binary error generation process. The model was evaluated by several statistical measures against real data acquired in a wireless sensor network. The experiments using GMM show lower averaged and minimal statistical distances observed across different modeling settings.