An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Multiuser Detection
Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection
IEEE Transactions on Signal Processing
Fundamental limits on time delay estimation in dispersed spectrum cognitive radio systems
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
Noise enhanced hypothesis-testing in the restricted Bayesian framework
IEEE Transactions on Signal Processing
Optimal stochastic signaling for power-constrained binary communications systems
IEEE Transactions on Wireless Communications
Theory of the Stochastic Resonance Effect in Signal Detection—Part II: Variable Detectors
IEEE Transactions on Signal Processing - Part II
Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors
IEEE Transactions on Signal Processing - Part I
On UWB Impulse Radio Receivers Derived by Modeling MAI as a Gaussian Mixture Process
IEEE Transactions on Wireless Communications
Convexity properties in binary detection problems
IEEE Transactions on Information Theory
On random rotations diversity and minimum MSE decoding of lattices
IEEE Transactions on Information Theory
Unified error probability analysis for generalized selection combining in Nakagami fading channels
IEEE Journal on Selected Areas in Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Stochastic signaling in the presence of channel state information uncertainty
Digital Signal Processing
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An M-ary communication system is considered in which the transmitter and the receiver are connected via multiple additive (possibly non-Gaussian) noise channels, any one of which can be utilized for the transmission of a given symbol. Contrary to deterministic signaling (i.e., employing a fixed constellation), a stochastic signaling approach is adopted by treating the signal values transmitted for each information symbol over each channel as random variables. In particular, the joint optimization of the channel switching (i.e., time sharing among different channels) strategy, stochastic signals, and decision rules at the receiver is performed in order to minimize the average probability of error under an average transmit power constraint. It is proved that the solution to this problem involves either one of the following: (i) deterministic signaling over a single channel, (ii) randomizing (time sharing) between two different signal constellations over a single channel, or (iii) switching (time sharing) between two channels with deterministic signaling over each channel. For all cases, the optimal strategies are shown to employ corresponding maximum a posteriori probability (MAP) decision rules at the receiver. In addition, sufficient conditions are derived in order to specify whether the proposed strategy can or cannot improve the error performance over the conventional approach, in which a single channel is employed with deterministic signaling at the average power limit. Finally, numerical examples are presented to illustrate the theoretical results.