Unified low-complexity decision feedback equalizer with adjustable double radius constraint

Cheng, Haozhong; Wu, An-Yeu

doi:10.1016/j.dsp.2016.01.012

Cited by 1 publication

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our analysis, we use the following assumptions: whenever L c is sufficiently large and the learning rate μ is sufficiently small [44]. Based on (20), Assumptions 1-3, and the Price's Theorem [25] for E[e * a,m csgn(e m )], it can be straightforwardly shown that [25,39] E |e a,m | 2 | g(e m ) = csgn(e m ) = ζ SA E |e a,m | 2 | g(e m ) = e m = ζ LMS E |e a,m | 2 | g(e m ) = e 3 m = ζ LMF .…”

Section: Mse Analysis Of the First Order Methodsmentioning

confidence: 99%

“…Thus, some preprocessing steps can reduce the impulsive noise effect and improve the performance. Also, in [20], for localization of multiple acoustic sources in an impulsive-noise environment, the authors employ a data-adaptive zero-memory nonlinear preprocessor to process each sample of the contaminated data to obtain less-noisy data. Specifically, if the sample's value is less than a threshold, they assume it is not affected by the impulsive noise, hence, its value does not change during the process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis

Kari

Marivani

Khan

et al. 2017

Digital Signal Processing

View full text Add to dashboard Cite

We introduce a novel family of adaptive robust channel estimators for highly challenging underwater acoustic (UWA) channels. Since the underwater environment is highly non-stationary and subjected to impulsive noise, we use adaptive filtering techniques based on minimization of a logarithmic cost function, which results in a better trade-off between the convergence rate and the steady state performance of the algorithm. To improve the convergence performance of the conventional first and second order linear estimation methods while mitigating the stability issues related to impulsive noise, we intrinsically combine different norms of the error in the cost function using a logarithmic term. Hence, we achieve a comparable convergence rate to the faster algorithms, while significantly enhancing the stability against impulsive noise in such an adverse communication medium. Furthermore, we provide a thorough analysis for the tracking and steady-state performances of our proposed methods in the presence of impulsive noise. In our analysis, we not only consider the impulsive noise, but also take into account the frequency and phase offsets commonly experienced in real life experiments. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.

show abstract

Section: Mse Analysis Of the First Order Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%