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

A low-complexity neural network based approach for channel estimation wasproposed recently, where assumptions on the channel model were incorporatedinto the design procedure of the estimator. Instead of using data from ameasurement campaign as done in previous work, we evaluate the performance ofthe convolutional neural network (CNN) based channel estimator by using areproducible mmWave environment of the DeepMIMO dataset. We further propose aneural network based predictor which is derived by starting from the linearminimum mean squared error (LMMSE) predictor. We start by deriving a weightedsum of LMMSE predictors which is motivated by the structure of the optimal MMSEpredictor. This predictor provides an initialization (weight matrices, biasesand activation function) to a feed-forward neural network based predictor. Witha properly learned neural network, we show that it is possible to easilyoutperform the LMMSE predictor based on the Jakes assumption of the underlyingDoppler spectrum in an reproducible indoor scenario of the DeepMIMO dataset.

Reproducible Evaluation of Neural Network Based Channel Estimators And Predictors Using A Generic Dataset