Zarko B. Krusevac

On channel uncertainty modeling - an information theoretic approach

This work analyses the 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 enables 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 on 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.

The effect of memory order on the capacity of finite-state Markov and flat-fading channels

In this paper, we study the effect of memory order on the capacity of finite-state Markov channels (FSMC). We analytically compare the capacity of an originally high-order FSMC model with the capacity of its reduced memory order version. We show that the capacity difference is caused by two factors: 1) the channel entropy difference, and 2) the channel observability difference between the two models. While the first factor, alone, results in underestimation of the original FSMC capacity by the reduced-order FSMC model, due to the existence of the second factor, capacity overestimation can also occur. Explicit examples of FSMC models are provided, where the reduced-order FSMC model overestimates the capacity of the original high-order channel. To show the practical significance of the analysis, we model time-varying flat-fading (FF) channels with FSMC models. It is observed that the first-order FSMC models can provide both higher and lower estimates of the FF channel capacity, compared to higher order FSMC models

Information theoretic approach to finite state Markov modelling of time varying channels

The time varying binary symmetric channel (TVBSC) model is presented in this paper. The TVBSC is developed to be the simplest model which captures the most important non-trivial attributes of general time-varying channels. An information theoretic analysis verifies that this model holds the essential elements of the behavior of time varying channels. The influence of the channel uncertainty is captured serving to significantly differentiate the TVBSC model from other two-state Markov models, especially from the the Gilbert-Elliot model. This elementary form of time varying channel model has an information capacity that is calculated using several methods. Given this capacity, the most efficient way for the time varying communications channel response estimation can be considered. Finally, accurate approximations for the TVBSC information capacity are found, providing better inside into the capacity analysis of time varying communication channels.

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

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.

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)

On the tracking performance of LMS and RLS algorithms in an adaptive MMSE CDMA receiver

This paper provides some analytical and experimental results in comparing the non-stationary tracking characteristics of LMS and RLS in multi-user detection. It is shown that under slow fading conditions, the LMS algorithm outperforms the RLS algorithm. In both cases, only the Doppler shift of the user of interest, but not that of the interference, affects the tracking performances

Channel estimation and differential detection for MPSK signaling in time-varying flat-fading communication channels

This paper addresses the issue of capacity achieving reliable information transfer over time-varying flat-fading com- munication channels. We use M-state, M-ary symmetric finite- state Markov channels (FSMC) to model time variations of flat- fading channels. We analyze the conventional (separate) approach to channel parameter estimation and data detection by using decision-feedback and output-feedback FSMC estimators. Our analysis includes the metric function update analysis for the decision-feedback estimator and the mutual information rate penalty caused by the input signal entropy reduction, for the output-feedback estimation. Then, we consider the implementa- tion of differential detection instead of channel estimation. We show that differential detection transfers the memory of the channel process into a latent form, which does not interfere with the operation of standard ML coding for memoryless channels. Furthermore, we show that multiple-symbol differential detection practically achieves the channel information capacity with observation times only on the order of a few additional symbol intervals.

Channel uncertainty effect upon the information capacity of mobile communications channels

This paper analyzes the channel uncertainty which is a fundamental issue for the information theory of time varying mobile communication channels. We use an accumulated common experience to define six axioms related to the non trivial influence of the channel uncertainty to the information capacity of time-varying communication channels. Based on these axioms, we propose the concept of time varying binary symmetric channel (TV-BSC). We show that the TV-BSC is unique as the simplest model that exhibits a non-trivial influence on capacity due to the channel uncertainty. That an influence of the channel uncertainty is captured serves to significantly differentiate the model from other two-state Markov models, especially from the 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 insight into the capacity analysis of more general time varying communication channels.

Concept of time varying binary symmetric model-channel uncertainty modeling

In this paper, we develop the concept of time varying binary 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