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

This paper presents a data-driven localization framework with high precisionin time-varying complex multipath environments, such as dense urban areas andindoors, where GPS and model-based localization techniques come short. Weconsider the angle-delay profile (ADP), a linear transformation of channelstate information (CSI), in massive MIMO systems and show that ADPs preserveusers' motion when stacked temporally. We discuss that given a staticenvironment, future frames of ADP time-series are predictable employing a videoframe prediction algorithm. We express that a deep convolutional neural network(DCNN) can be employed to learn the background static scattering environment.To detect foreground changes in the environment, corresponding to path blockageor addition, we introduce an algorithm taking advantage of the trained DCNN.Furthermore, we present DyLoc, a data-driven framework to recover distortedADPs due to foreground changes and to obtain precise location estimations. Weevaluate the performance of DyLoc in several dynamic scenarios employingDeepMIMO dataset to generate geo-tagged CSI datasets for indoor and outdoorenvironments. We show that previous DCNN-based techniques fail to perform withdesirable accuracy in dynamic environments, while DyLoc pursues localizationprecisely. Moreover, simulations show that as the environment gets richer interms of the number of multipath, DyLoc gets more robust to foreground changes.

DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks