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

5G mmWave technology can turn multipath into a friend, as multipathcomponents become highly resolvable in the time and angle domains. Multipathsignals have not only been used in the literature to position the userequipment (UE) but also to create a map of the surrounding environment. Yet,many multipath-based methods in the literature share a common assumption, whichentails that multipath signals are caused by single-bounce reflections only,which is not usually the case. There are very few methods in the literaturethat accurately filters out higher-order reflections, which renders theexploitation of multipath signals challenging. This paper proposes an ensemblelearning-based model for classifying signal paths based on their order ofreflection using 5G channel parameters. The model is trained on a large datasetof 3.6 million observations obtained from a quasi-real ray-tracing based 5Gsimulator that utilizes 3D maps of real-world downtown environments. Thetrained model had a testing accuracy of 99.5%. A single-bounce reflection-basedpositioning method was used to validate the positioning error. The trainedmodel enabled the positioning solution to maintain sub-30cm level accuracy 97%of the time.

A Step Closer Towards 5G mmWave-based Multipath Positioning in Dense Urban Environments