Information Theory and Reliable Communication
Information Theory and Reliable Communication
Semidefinite relaxation based multiuser detection for M-ary PSK multiuser systems
IEEE Transactions on Signal Processing - Part I
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
IEEE Transactions on Information Theory
Capacity-achieving probability measure for conditionally Gaussian channels with bounded inputs
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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Putting bounding constraints on the input of a channel leads in many cases to a discrete capacity-achieving distribution with a finite support. Given a finite number of signaling points, we determine reduced subsets and the corresponding optimal probability measures to simplify the receiver design. The objective for the subset selection is to keep the channel quality high by maximizing mutual information and cutoff rate. Two approaches are introduced to obtain a capacity-achieving probability measure for the reduced subset. The first one is based on a preceded signaling point selection while the second one chooses the signaling points and corresponding probabilities simultaneously. Numerical results for both approaches show that using only a small number of signaling points achieves a very high mutual information compared to channels utilizing the full set of signaling points.