On the capacity of the discrete-time poisson channel
IEEE Transactions on Information Theory
On the capacity of free-space optical intensity channels
IEEE Transactions on Information Theory
On the capacity and energy efficiency of training-based transmissions over fading channels
IEEE Transactions on Information Theory
Channel capacity and non-uniform signalling for free-space optical intensity channels
IEEE Journal on Selected Areas in Communications - Special issue on optical wireless communications
Error rate analysis for peaky signaling over fading channels
IEEE Transactions on Communications
Capacity of optical intensity channels with peak and average power constraints
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
A channel model for inferring the optimal number of electrodes for future cochlear implants
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Capacity bounds for wireless optical intensity channels with Gaussian noise
IEEE Transactions on Information Theory
Problems of Information Transmission
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A conditionally Gaussian channel is a vector channel in which the channel output, given the channel input, has a Gaussian distribution with (well-behaved) input-dependent mean and covariance. We study the capacity-achieving probability measure for conditionally Gaussian channels subject to bounded-input constraints and average cost constraints. Many practical communication systems, including additive Gaussian noise channels, certain optical channels, fading channels, and interference channels fall within this framework. Subject to bounded-input constraint (and average cost constraints), we show that the channel capacity is achievable and we derive a necessary and sufficient condition for a probability measure to be capacity achieving. Under certain conditions, the capacity-achieving measure is proved to be discrete.