On the Capacity of a Class of Signal-Dependent Noise Channels

Abstract

In some applications, the variance of additive measurement noise depends on the signal that we aim to measure. For instance, additive Gaussian signal-dependent noise (AGSDN) channel models are used in molecular and optical communication. Herein we provide lower and upper bounds on the capacity of additive signal-dependent noise (ASDN) channels. The idea of the first lower bound is the extension of the majorization inequality, and for the second one, it uses some calculations based on the fact that h (Y ) > h (Y |Z). Both of them are valid for all additive signal-dependent noise (ASDN) channels defined in the paper. The upper bound is based on a previous idea of the authors ("symmetric relative entropy") and is used for the additive Gaussian signal-dependent noise (AGSDN) channels. These bounds indicate that in ASDN channels (unlike the classical AWGN channels), the capacity does not necessarily become larger by making the variance function of the noise smaller. We also provide sufficient conditions under which the capacity becomes infinity. This is complemented by a number of conditions that imply capacity is finite and a unique capacity achieving measure exists (in the sense of the output measure).

Noise Analysis and Modulation Optimization for Nonlinear Visible Light Communication System With Signal-Dependent Noise

“…In works [8] and [9], the capacity bounds under peakpower and average-power constraints were derived for VLC channel models with SDN. Unlike the classical additive white Gaussian noise channels, the capacity in the SDN channels does not necessarily increase by reducing the noise variance [10]. Under certain technical conditions, the capacity-achieved distribution is proven to be a discrete input distribution with a finite number of mass points [11].…”