Entropy and Channel Capacity under Optimum Power and Rate Adaptation over Generalized Fading Conditions

TL;DR

This paper investigates how nonlinear fading conditions affect channel capacity in communication systems, using information theory measures and deriving closed-form expressions validated by simulations.

Contribution

It introduces a novel analysis of nonlinear fading effects on channel capacity using entropy measures and provides closed-form expressions validated through simulations.

Findings

Fading nonlinearities have a larger impact on capacity than traditional fading parameters.

Closed-form expressions accurately predict capacity under nonlinear fading conditions.

Nonlinear effects significantly alter channel capacity compared to linear models.

Abstract

Accurate fading characterization and channel capacity determination are of paramount importance in both conventional and emerging communication systems. The present work addresses the nonlinearity of the propagation medium and its effects on the channel capacity. Such fading conditions are first characterized using information theoretic measures, namely, Shannon entropy, cross entropy and relative entropy. The corresponding effects on the channel capacity with and without power adaptation are then analyzed. Closed-form expressions are derived and validated through comparisons with respective results from computer simulations. It is shown that the effects of fading nonlinearities are significantly larger than those of fading parameters such as the scattered-wave power ratio, and the correlation coefficient between the in-phase and quadrature components in each cluster of multipath components.