Non-parametric likelihood based channel estimator for Gaussian mixture noise

Bhatia, Vimal; Mulgrew, Bernard

doi:10.1016/j.sigpro.2007.04.006

Cited by 92 publications

(79 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In practice, the example described above can be encountered in detection of communications signals in the presence of co-channel interference, which can result in Gaussian mixture noise at the receiver [14].…”

Section: Numerical Results and Conclusionmentioning

confidence: 99%

Noise enhanced detection in the restricted Bayesian framework

Bayram

Gezici

Poor

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

show abstract

Section: Numerical Results and Conclusionmentioning

confidence: 99%

Noise enhanced detection in the restricted Bayesian framework

Bayram

Gezici

Poor

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

show abstract

“…It is also valid in the case of flat-fading channels assuming perfect channel estimation [1]. Note that the probability distribution of the noise component in (1) is not necessarily Gaussian since it is modeled to include the effects of interference and jamming as well [11].…”

Section: Optimal Channel Switchingmentioning

confidence: 99%

“…, K}). Since deterministic signaling is employed in each channel, the result given in (11) for the deterministic case should be applied for each channel. Then, the optimization problem in (9) becomes max {λ,…”

Section: Transmitting Exclusively Over a Single Channel Viamentioning

confidence: 99%

See 1 more Smart Citation

Optimal channel switching in the presence of stochastic signaling

Dulek

Varshney

Tutay

et al. 2013

2013 IEEE International Symposium on Information Theory

View full text Add to dashboard Cite

Abstract-Optimal channel switching and detector design is studied for M -ary communication systems in the presence of stochastic signaling, which facilitates randomization of signal values transmitted for each information symbol. Considering the presence of multiple additive noise channels (which can have nonGaussian distributions in general) between a transmitter and a receiver, the joint optimization of the channel switching (timesharing) strategy, stochastic signals, and detectors is performed in order to achieve the minimum average probability of error. It is proved that the optimal solution to this problem corresponds to either (i) switching between at most two channels with deterministic signaling over each channel, or (ii) time-sharing between at most two different signals over a single channel (i.e., stochastic signaling over a single channel). For both cases, the optimal solutions are shown to employ corresponding maximum a posteriori probability (MAP) detectors at the receiver. Numerical results are presented to investigate the proposed approach.

show abstract

“…For the non-Gaussian noise, we used a noise mixture generated by a linear combination of a pseudorandom bit sequence (PRBS) and a Gaussian noise. This is a typical model for ambient noise that consists of a mixture of an artificial electromagnetic signal from wireless communications and natural noise [14][15][16]. It is also used in image restoration [17,18].…”

Section: Introductionmentioning

confidence: 99%

Design and characterization of nonlinear functions for the transmission of a small signal with non-Gaussian noise

2013

View full text Add to dashboard Cite

We design nonlinear functions for the transmission of a small signal with non-Gaussian noise and perform experiments to characterize their responses. Using statistical design theory [A. Ichiki and Y. Tadokoro, Phys. Rev. E 88, 012124 (2013)], a static nonlinear function is estimated from the probability density function of the given noise in order to maximize the signal-to-noise ratio of the output. Using an electronic system that implements the optimized nonlinear function, we confirm the recovery of a small signal from a signal with non-Gaussian noise. In our experiment, the non-Gaussian noise is a mixture of Gaussian noises. A similar technique is also applied to the optimization of the threshold value of the function. We find that, for non-Gaussian noise, the response of the optimized nonlinear systems is better than that of the linear system.

show abstract

Non-parametric likelihood based channel estimator for Gaussian mixture noise

Cited by 92 publications

References 24 publications

Noise enhanced detection in the restricted Bayesian framework

Noise enhanced detection in the restricted Bayesian framework

Optimal channel switching in the presence of stochastic signaling

Design and characterization of nonlinear functions for the transmission of a small signal with non-Gaussian noise

Contact Info

Product

Resources

About