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• W2940989281 abstract "Abstract Gradient of the information measures can be considered as the building block of the fundamental relations between information and estimation theories established in the literature. This paper introduces the gradients of two fundamental information measures, i.e., differential entropy and mutual information in some general cases. Our approach is based on the utilizing a general functional model to describe the stochastic systems. This approach enables us to give a simple new proof for De Bruijn’s identity. We also derive a general expression for the gradient of the mutual information in systems with both additive and multiplicative noises. Systems with additive noise, as the most common model for the communication channels, is considered as well, where in the Gaussian case some of the available results in the literature are recovered. Moreover, the aforementioned general functional model allows us to study the sensitivity of the input–output mutual information to the system parameters in some other practical scenarios. Specifically, Orthogonal Frequency Division Multiplexing (OFDM) systems with High Power Amplifier (HPA) are considered and an information theoretic analysis for the behavior of these systems is given." @default.
• W2940989281 created "2019-05-03" @default.
• W2940989281 creator A5012541823 @default.
• W2940989281 creator A5054633685 @default.
• W2940989281 date "2019-09-01" @default.
• W2940989281 modified "2023-09-23" @default.
• W2940989281 title "Gradients of the fundamental information measures: Theory and applications" @default.
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• W2940989281 doi "https://doi.org/10.1016/j.sigpro.2019.04.019" @default.
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