On Divergence-Power Inequalities

Expressions for (EPI Shannon type) Divergence-Power Inequalities (DPI) in two cases (time-discrete and band-limited time-continuous) of stationary random processes are given. The new expressions connect the divergence rate of the sum of independent processes, the individual divergence rate of each process, and their power spectral densities. All divergences are between a process and a Gaussian process with same second order statistics, and are assumed to be finite. A new proof of the Shannon entropy-power inequality EPI, based on the relationship between divergence and causal minimum mean-square error (CMMSE) in Gaussian channels with large signal-to-noise ratio, is also shown.Comment: Submitted to IEEE Transactions on Information Theor

Performance Analysis of Linear and Non-Linear Equalizer in Rician Channel

AbstractIn this paper, equalization algorithms applying soft–decision feedback, designed for quaternary phase–shift keying (QPSK) and 8PSK (phase–shift keying) transmission are introduced. The method employed is a minimum mean– squared error (MMSE) in which each iteration is done in order to refine the data estimates. The rule for generating soft decisions is adapted continuously to the current state of the algorithm. We show that standard Decision Feedback Equalization (DFE-Non linear Equaliser) methods are clearly outperformed the minimum mean–squared error (MMSE linear Equaliser). We use the MATLAB to show that the MMSE-DFE provide better performance with the increasing value of SNR in scattering environment