Basic binary state-space model for time-varying communication channels: uncertainty and information capacity

Abstract

This paper looks at capacity achieving detection strategies for information transfer over time-varying channels. The time-varying binary symmetric channel (TV-BSC) is identified as the basic binary state-space model. Separation of entropies principles and the TV-BSC model-based state-space approach are used to determine the performance bounds for coherent and non-coherent detection over time-varying communication channels. The mutual information rate over the TV-BSC, assuming channel estimation in the presence of channel noise, is shown to be below the channel information capacity because of lack of perfect channel knowledge. Furthermore, it is shown that TV-BSC model-based differential detection has a fundamental advantage over the channel estimation based detection since it theoretically preserves the TV-BSC information capacity when the observation interval approaches infinity. Simulation analysis corroborates the theoretical results, showing that multiple-symbol differential detection practically achieves the TV-BSC capacity in just a few symbol observation times.