Locally optimal detector design in impulsive noise with unknown distribution

Luo, Zhiyong; Peng, Lü; Zhang, Gang

doi:10.1186/s13634-018-0560-x

Cited by 8 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…T and

σ

, to improve the detection performance of the GZMNL in

S α S

noise. To test the optimality of non‐linear function, the efficacy function is used by

{scriptE}_{α, γ} false(T, σ false) = \frac{{[\int_{- \infty}^{\infty} G_{T, σ} (x) f_{α, γ}^{'} (x) normald x]}^{2}}{\int_{- normal∞}^{normal∞} G_{T, σ}^{2} false(x false) f_{α, γ} false(x false) d x} .

The efficacy is closely related to the detection performances, as proved in [8, 9].…”

Section: Optimisation Design Of the Gzmnlmentioning

confidence: 99%

“…The efficacy is closely related to the detection performances, as proved in [8,9]. The optimal parameters should make the GZMNL achieve the maximum efficacy.…”

mentioning

confidence: 91%

“…This letter tends to design the GZMNL in the impulsive noise to be as nearly optimal as the LOD. By using the efficacy function [7,8], the GZMNL design is converted as an optimisation problem of maximising the efficacy function. This problem can be solved by the derivative-free algorithms.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optimal and efficient designs of Gaussian‐tailed non‐linearity in symmetric α ‐stable noise

et al. 2019

Self Cite

View full text Add to dashboard Cite

This Letter proposes two methods for the Gaussian-tailed zero-memory non-linearity (GZMNL) design in symmetric a-stable noise. The optimal GZMNL is designed by maximising the efficacy via a derivative-free method. The efficient GZMNL is designed by polynomial fitting with the derived coefficients. Simulated results demonstrate that the GZMNL designs are nearly as optimal as the locally optimal detector, with an advantage of closed-form formulas.

show abstract

“…T and

σ

, to improve the detection performance of the GZMNL in

S α S

noise. To test the optimality of non‐linear function, the efficacy function is used by

{scriptE}_{α, γ} false(T, σ false) = \frac{{[\int_{- \infty}^{\infty} G_{T, σ} (x) f_{α, γ}^{'} (x) normald x]}^{2}}{\int_{- normal∞}^{normal∞} G_{T, σ}^{2} false(x false) f_{α, γ} false(x false) d x} .

The efficacy is closely related to the detection performances, as proved in [8, 9].…”

Section: Optimisation Design Of the Gzmnlmentioning

confidence: 99%

“…The efficacy is closely related to the detection performances, as proved in [8,9]. The optimal parameters should make the GZMNL achieve the maximum efficacy.…”

mentioning

confidence: 91%

See 1 more Smart Citation

Optimal and efficient designs of Gaussian‐tailed non‐linearity in symmetric α ‐stable noise

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, the algebraic-tailed ZMNL (AZMNL) can approximate the LOD nonlinearity of the SαS distribution better than the blanker [26]. Our previous work has analyzed the ZMNL designs based on algebraic tail [33] and Gaussianization [34], both of which are only sub-optimal compared to the LOD. It is demonstrated that the match between tails and distributions is essential for nonlinearity in impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

Nonlinearity Design With Power-Law Tails for Correlation Detection in Impulsive Noise

Luo

Jonckheere

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Impulsive noise plays an important role in power line communication among other applications. To improve the communication performance, this paper proposes a novel design of nonlinear processing which improves the fundamental performance of signal detection in impulsive noise. Power-law tails are firstly introduced in nonlinearity design to provide adjustable decay factors for different distributions. Four modes of nonlinearity functions are developed and analyzed. By taking the exponent and the threshold as two arguments, we formulate the nonlinearity design as an optimization problem of maximizing the efficacy function, which is the fundamental measurement for detecting a deterministic signal in impulsive noise. Given that the efficacy function is differentiable, unimodal but without closed-form derivatives, we propose to solve the optimization problem by derivative-free methods, e.g. the Nelder-Mead simplex method. As concept demonstration, our method is used for three commonly-used distribution examples. Results show that our nonlinearity design can achieve almost the same efficacy and detection performance as the locally optimal detector, with the advantage of easy-to-apply closed form expressions.

show abstract

“…In recent two decades, there has been great interest in studying the symmetric α‐ stable

false(S α S false)

distribution, which has been tested to match the real impulsive noise [3–5]. The locally optimum detector (LOD) is of much interest since it is optimal in detecting weak signals from Neyman–Pearson lemma [6–10]. However, the LOD requires an explicit form for the noise probability density function (PDF), and therefore, cannot be implemented in the case of