Fading Channel Coding Based on Entropy and Compressive Sensing

Abstract

In this paper, channel code length is investigated under Rayleigh and Rician fading assumptions along with additive noise consideration. Fading distributions means and variances are known. Rayleigh and Rician fading along with Central Limit Theorem are used in entropy calculations. Compressive Sensing reduced number of samples for distributions reconstruction are also derived. Finally, the inverse problem of identifying the corresponding distributions from the derived channel code lengths and Compressive Sensing based number of samples is addressed with promising results for distribution channel knowledge and estimation.