Minimum mean-squared error echo cancellation and equalization for digital subscriber line transmission. II. A simulation study

1990 ◽  
Vol 38 (1) ◽  
pp. 39-45 ◽  
Author(s):  
D.W. Lin
Keyword(s):  
Simulation Study ◽  
Mean Squared Error ◽  
Echo Cancellation ◽  
Digital Subscriber Line ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Subscriber Line

scholarly journals Attack-resilient minimum mean-squared error estimation

2014 ◽  
Author(s):  
James Weimer ◽  
Nicola Bezzo ◽  
Miroslav Pajic ◽  
Oleg Sokolsky ◽  
Insup Lee
Keyword(s):  
Error Estimation ◽  
Mean Squared Error ◽  
Minimum Mean Squared Error ◽  
Squared Error

scholarly journals Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data

Econometrics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 40
Author(s):  
Erhard Reschenhofer ◽  
Manveer K. Mangat
Keyword(s):  
Simulation Study ◽  
High Frequency ◽  
Mean Squared Error ◽  
Estimation Methods ◽  
High Frequency Data ◽  
Frequency Data ◽  
Periodic Patterns ◽  
Typical Sample ◽  
Squared Error ◽  
Proper Interpretation

For typical sample sizes occurring in economic and financial applications, the squared bias of estimators for the memory parameter is small relative to the variance. Smoothing is therefore a suitable way to improve the performance in terms of the mean squared error. However, in an analysis of financial high-frequency data, where the estimates are obtained separately for each day and then combined by averaging, the variance decreases with the sample size but the bias remains fixed. This paper proposes a method of smoothing that does not entail an increase in the bias. This method is based on the simultaneous examination of different partitions of the data. An extensive simulation study is carried out to compare it with conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance and its smoothed competitors with respect to the bias. Using the results of the simulation study for the proper interpretation of the empirical results obtained from a financial high-frequency dataset, we conclude that significant long-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings against daily and weekly periodic patterns is established.

Asymmetric digital subscriber line echo cancellation

2000 ◽  
Vol 36 (3) ◽  
pp. 274 ◽  
Author(s):  
Oh-Sang Kwon ◽  
Gea-Ok Cho ◽  
Ki-Won Lee
Keyword(s):  
Echo Cancellation ◽  
Digital Subscriber Line ◽  
Asymmetric Digital Subscriber Line ◽  
Subscriber Line

scholarly journals Target and Equalizer Design for High-Density Bit-Patterned Media Recording

1970 ◽  
Vol 6 (2) ◽  
pp. 128-135
Author(s):  
Santi Koonkarnkhai ◽  
Phongsak Keeratiwintakorn ◽  
Piya Kovintavewat
Keyword(s):  
Partial Response ◽  
Mean Squared Error ◽  
Bit Patterned Media ◽  
Patterned Media ◽  
Media Noise ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Cross Track ◽  
Partial Response Maximum Likelihood ◽  
Better Than

In bit-patterned media recording (BPMR) channels, the inter-track interference (ITI) is extremely severe at ultra high areal densities, which significantly degrades the system performance. The partial-response maximum-likelihood (PRML) technique that uses an one-dimensional (1D) partial response target might not be able to cope with this severe ITI, especially in the presence of media noise and track mis-registration (TMR). This paper describes the target and equalizer design for highdensity BPMR channels. Specifically, we proposes a two-dimensional (2D) cross-track asymmetric target, based on a minimum mean-squared error (MMSE) approach, to combat media noise and TMR. Results indicate that the proposed 2D target performs better than the previously proposed 2D targets, especially when media noise and TMR is severe.

scholarly journals New robust iterative minimum mean squared error-based interference alignment algorithm

2014 ◽  
Vol 2014 (2) ◽  
pp. 49-50 ◽  
Author(s):  
Sara Teodoro ◽  
Adão Silva ◽  
Rui Dinis ◽  
Daniel Castanheira ◽  
Atílio Gameiro
Keyword(s):  
Mean Squared Error ◽  
Interference Alignment ◽  
Alignment Algorithm ◽  
Minimum Mean Squared Error ◽  
Squared Error
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