Bayesian Estimation of Class A Noise Parameters with Hidden Channel States

Jiang, Yiyi; Hu, Xiulin; Xu, Kai; Zhai, Qi

doi:10.1109/isplc.2007.371088

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By observing (10) it is clear that h SL (x, n; α) is continuous with respect to α ∈ R + and h SL (x, n; α)h (1,α) SL (x, n; α) =…”

Section: Appendix B Sle Derivationmentioning

confidence: 99%

“…The MSNR thresholds for SL and BN are obtained by Matlab R numerical solutions of (36) and (35), respectively. The series with infinite terms, which are induced in all the analytical results by the Class-A pdf in (2), have been approximated by considering only the first M = 50 terms (although, M ∈ [10,20] would be enough in most of the cases).…”

Section: Computer Simulationsmentioning

confidence: 99%

“…This fact is also confirmed by the shape of the MSE obtained by simulations in Figs. [5][6][7][8][9][10][11], which show that any classical numerical solution of J (1,α) BN (α) = 0 will easily converge to the optimal threshold, as well as the iterative algorithm A2 that solves the fixed-point equation in (70).…”

Section: Appendix B Sle Derivationmentioning

confidence: 99%

“…Middleton's noise models were widely investigated to identify the interference behavior [1]- [5], to estimate their canonical parameters [6]- [10], and to detect finite alphabets in digital communications [11]- [16], to the best of the author knowledge results for the optimum Bayesian estimator (OBE) of Gaussian sources in Class-A impulsive noise are still lacking or, at least, not widely acknowledged.…”

mentioning

confidence: 99%

“…In particular, low values of A identify rare and highly-peaked impulsive sources, while conversely high values of A makes the impulsive noise more similar to an AWGN, by a CLT argument. The interested readers are July 30, 2018 DRAFT redirected to [2], [4], [5] and references therein for further details on the Class-A model, and to [6]- [10] for the estimation of the canonical parameters A, T , and σ 2 N . This paper assumes that the canonical parameters have been perfectly estimated by the observing system.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Bayesian Estimation of a Gaussian Source in Middleton's Class-A Impulsive Noise

Banelli

2013

IEEE Signal Process. Lett.

View full text Add to dashboard Cite

The paper focuses on minimum mean square error (MMSE) Bayesian estimation for a Gaussian source impaired by additive Middleton's Class-A impulsive noise. In addition to the optimal Bayesian estimator, the paper considers also the soft-limiter and the blanker, which are two popular suboptimal estimators characterized by very low complexity. The MMSE-optimum thresholds for such suboptimal estimators are obtained by practical iterative algorithms with fast convergence. The paper derives also the optimal thresholds according to a maximum-SNR (MSNR) criterion, and establishes connections with the MMSE criterion. Furthermore, closed form analytical expressions are derived for the MSE and the SNR of all the suboptimal estimators, which perfectly match simulation results. Noteworthy, these results can be applied to characterize the receiving performance of any multicarrier system impaired by a Gaussianmixture noise, such as asymmetric digital subscriber lines (ADSL) and power-line communications (PLC).

show abstract

“…By observing (10) it is clear that h SL (x, n; α) is continuous with respect to α ∈ R + and h SL (x, n; α)h (1,α) SL (x, n; α) =…”

Section: Appendix B Sle Derivationmentioning

confidence: 99%