Equivalent channel for capacity analysis of differential detection over time-varying communication channels

This paper introduces the concept of equivalent channel for mutual information performance analysis of multiple symbol differential MPSK (M-phase shift keying) over time correlated,time-varying flat-fading communication channels. It is proven that differential detection scheme theoretically preserves the channel information capacity when the observation interval approaches infinity. A state space approach is used to model time correlation of time varying channel phase. It is shown that the differential decoding implicitly uses a sequence of innovations of the channel process time correlation and this sequence is essentially uncorrelated (i.i.d). It enables utilization of multiple-symbol differential detection, as a form of block by-block maximum likelihood sequence detection for capacity achieving mutual information performanc

Concept of Time Varying Binary Symmetric Model - Channel Uncertainty Modeling

In this paper, we develop the concept of Time Varying Bi" nary Symmetric Channel (TV-BSC) model, a basic model that shows a non-trivial influence on capacity due to the channel uncertainty and characterizes the important attributes of more general time varying mobile communications channels. That an influence of the channel uncertainty is captured serves to significantly differentiate the model from other two-state Markov models, especially from the Gilbert-Elliott model. We use several methods to calculate the information capacity of the TV-BSC. Furthermore, we find accurate approximations for the TV-BSC information capacity, providing better inside into the capacity analysis of general time varying communications channels. Finally, we present the generalization on the TV-BSC model and the concept of the communication channel with time varying memory to further confirm the uniqueness of the model

On Channel Uncertainty Modeling - An Information Theoretic Approach

This paper analyses time varying nature of the mobile communications channel which affects the channel information capacity. The channel uncertainty is a fundamental issue for the information theory of the time varying mobile communication channels and here we use an information theoretic approach to channel uncertainty modeling. Although we analyse the Final State Markov Channel (FSMC) models, our approach still does enable considerable conceptual insight to be gained. In the first step of our analysis, we define four axioms which qualitatively describe the non-trivial influence of the channel uncertainty to the time-varying channel information capacity. Then, we focus on the FSMC models which capture these axioms. We present the concept of Time Varying Binary Symmetric Channel (TV-BSC) to further support our analysis. The TV-BSC is the simplest FSMC model that shows a non-trivial influence on capacity due to the channel uncertainty. We provide the information theoretic analysis of the TV-BSC model including the information capacity calculation and the TV-BSC estimation analysis

Model-based Adaptive Algorithms for Time-Varying Communication Channels with Application to Adaptive Multiuser Detection

This paper shows through theory and simulation the superiority of model-based adaptive algorithms relative to observation-only-based adaptive algorithms, such as LMS and RLS, when applied to tracking time-varying channels. The model-based formulation reveals RLS as a degenerate algorithm which does not explicitly recognize the time-varying nature of the channel and consequently is ill-suited to tracking in non-stationary environments. Simulation results for MSE performance of the various adaptive algorithms applied to adaptive MMSE multiuser receiver corroborate the theoretical analysis

Optimal implicit channel estimation for finite state Markov communication channels

This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. A proper quantification of source redundancy information, implicitly used for channel estimation, is performed. This enables an optimal training rate to be determined as a tradeoff between input signal entropy rate reduction (source redundancy) and channel process entropy rate reduction (channel estimation). The maximal mutual information rate, assuming the optimal implicit training and the presence of channel noise, is shown to be strictly below the ergodic channel information capacity. It is also shown that this capacity penalty, caused by noisy time-varying channel process estimation, vanishes only if the channel process is known or memoryless (channel estimation cannot improve system performance)