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

In this paper, we propose first a mmWave channel tracking algorithm based onmultidimensional orthogonal matching pursuit algorithm (MOMP) using reducedsparsifying dictionaries, which exploits information from channel estimates inprevious frames. Then, we present an algorithm to obtain the vehicle's initiallocation for the current frame by solving a system of geometric equations thatleverage the estimated path parameters. Next, we design an attention networkthat analyzes the series of channel estimates, the vehicle's trajectory, andthe initial estimate of the position associated with the current frame, togenerate a refined, high accuracy position estimate. The proposed system isevaluated through numerical experiments using realistic mmWave channel seriesgenerated by ray-tracing. The experimental results show that our systemprovides a 2D position tracking error below 20 cm, significantly outperformingprevious work based on Bayesian filtering.

Sparse Recovery with Attention: A Hybrid Data/Model Driven Solution for High Accuracy Position and Channel Tracking at mmWave