Geometric Tracking of Vehicular mmWave Channels to Enable Machine Learning of Onboard Sensors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU136228" target="_blank" >RIV/00216305:26220/18:PU136228 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8644440" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8644440</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/GLOCOMW.2018.8644440" target="_blank" >10.1109/GLOCOMW.2018.8644440</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Geometric Tracking of Vehicular mmWave Channels to Enable Machine Learning of Onboard Sensors

Popis výsledku v původním jazyce

Estimating time-selective millimeter wave wireless channels with directive antennas poses a challenging task. A feasible way of relaxing this channel estimation problem is to focus on the tracking of a few multipath components (MPCs). Aligning antenna beams to the tracked MPCs increases the channel coherence time by several orders of magnitude. We propose to track the MPCs geometrically. Our geometric trackers are based on algorithms known as Doppler-bearing tracking. We reformulate recent work on geometric-polar tracking into an efficient recursive version. If the relative position of the MPCs are known, other sensors on board of a vehicle, for example, lidar, radar, camera, will be capable of performing supervised learning based on their own observed data. Learning the relationship between sensor data and MPCs allows onboard sensors to participate in the channel tracking. Joint tracking from many onboard sensors possibly increases the reliability of the MPC tracking.

Název v anglickém jazyce

Geometric Tracking of Vehicular mmWave Channels to Enable Machine Learning of Onboard Sensors

Popis výsledku anglicky

Estimating time-selective millimeter wave wireless channels with directive antennas poses a challenging task. A feasible way of relaxing this channel estimation problem is to focus on the tracking of a few multipath components (MPCs). Aligning antenna beams to the tracked MPCs increases the channel coherence time by several orders of magnitude. We propose to track the MPCs geometrically. Our geometric trackers are based on algorithms known as Doppler-bearing tracking. We reformulate recent work on geometric-polar tracking into an efficient recursive version. If the relative position of the MPCs are known, other sensors on board of a vehicle, for example, lidar, radar, camera, will be capable of performing supervised learning based on their own observed data. Learning the relationship between sensor data and MPCs allows onboard sensors to participate in the channel tracking. Joint tracking from many onboard sensors possibly increases the reliability of the MPC tracking.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of IEEE Global Telecommunications Conference (GC Wkshps)

ISBN

978-1-5386-4920-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

neuveden

Místo konání akce

Abu Dhabi

Datum konání akce

9. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000462817000252