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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Broadband frequency-selective fading channels usually have the inherentsparse nature. By exploiting the sparsity, adaptive sparse channel estimation(ASCE) methods, e.g., reweighted L1-norm least mean square (RL1-LMS), couldbring a performance gain if additive noise satisfying Gaussian assumption. Inreal communication environments, however, channel estimation performance isoften deteriorated by unexpected non-Gaussian noises which include conventionalGaussian noises and impulsive interferences. To design stable communicationsystems, hence, it is urgent to develop advanced channel estimation methods toremove the impulsive interference and to exploit channel sparsitysimultaneously. In this paper, robust impulsive-mitigation adaptive sparsechannel estimation (IMAC) method is proposed for solving aforementionedtechnical issues. Specifically, first of all, the non-Gaussian noise model isdescribed by Gaussian mixture model (GMM). Secondly, cost function ofreweighted L1-norm penalized least absolute error standard (RL1-LAE) algorithmis constructed. Then, RL1-LAE algorithm is derived for realizing IMAC method.Finally, representative simulation results are provided to corroborate thestudies.

IMAC: Impulsive-mitigation adaptive sparse channel estimation based on Gaussian-mixture model