Optimization by Vector Space Methods
Optimization by Vector Space Methods
The noncoherent rician fading Channel-part I: structure of the capacity-achieving input
IEEE Transactions on Wireless Communications
The capacity of discrete-time memoryless Rayleigh-fading channels
IEEE Transactions on Information Theory
Capacity bounds for power- and band-limited optical intensity channels corrupted by Gaussian noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
On the discreteness of capacity-achieving distributions
IEEE Transactions on Information Theory
Capacity of noncoherent time-selective Rayleigh-fading channels
IEEE Transactions on Information Theory
On the asymptotic capacity of stationary Gaussian fading channels
IEEE Transactions on Information Theory
Capacity-achieving probability measure for conditionally Gaussian channels with bounded inputs
IEEE Transactions on Information Theory
Characterization and computation of optimal distributions for channel coding
IEEE Transactions on Information Theory
Capacity Results for Block-Stationary Gaussian Fading Channels With a Peak Power Constraint
IEEE Transactions on Information Theory
The capacity of average and peak-power-limited quadrature Gaussian channels
IEEE Transactions on Information Theory
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The capacity-achieving input distribution for many channels like the additive white Gaussian noise (AWGN) channel and the free-space optical intensity (FSOI) channel under the peak-power constraint is discrete with a finite number of mass points. The number of mass points is itself a variable, and figuring it out is a part of the optimization problem. We wish to understand the behavior of the optimal input distribution at the transition points where the number of mass points changes. To this end, we give a new set of necessary and sufficient conditions at the transition points, which offer new insights into the transition and make the computation of the optimal distribution easier. For the real AWGN channel case, we show that for the zero-mean unit-variance Gaussian noise, the peak amplitude A of 1.671 and 2.786 mark the points where the binary and ternary signaling, respectively, are no longer optimal. For the FSOI channel, we give transition points where binary gives way to ternary, and in some cases where ternary gives way to quaternary, in the presence of the peak-power constraint and with or without the average-power constraint.