English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, the problem of channel estimation for LTE Downlink system inthe environment of high mobility presenting non-Gaussian impulse noiseinterfering with reference signals is faced. The estimation of the frequencyselective time varying multipath fading channel is performed by using a channelestimator based on a nonlinear complex Support Vector Machine Regression (SVR)which is applied to Long Term Evolution (LTE) downlink. The estimationalgorithm makes use of the pilot signals to estimate the total frequencyresponse of the highly selective fading multipath channel. Thus, the algorithmmaps trained data into a high dimensional feature space and uses the structuralrisk minimization principle to carry out the regression estimation for thefrequency response function of the fading channel. The obtained results showthe effectiveness of the proposed method which has better performance than theconventional Least Squares (LS) and Decision Feedback methods to track thevariations of the fading multipath channel.

Complex support vector machines regression for robust channel estimation in LTE downlink system