1 / 15

WHERE

Presented by Suji Gunaratne PhD. WHERE. W ireless H ybrid E nhanced Mobile R adio E stimators. WHERE - Outline. Motivation - Why do we need WHERE ? Partners and their Role in WHERE Objectives of WHERE and WPs WHERE IT Contributions. Why do we need WHERE ?.

hina
Download Presentation

WHERE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Presented by Suji GunaratnePhD WHERE Wireless Hybrid Enhanced Mobile Radio Estimators

  2. WHERE - Outline • Motivation - Why do we need WHERE ? • Partners and their Role in WHERE • Objectives of WHERE and WPs • WHERE IT Contributions

  3. Why do we need WHERE ? Challenges for future networks covered by project objectives

  4. Main Objectives • The integration of communications and navigation. • Improvement of future wireless communications systems and integration of heterogeneous RAN infrastructures by location based procedures and protocols. • Estimation of MT position information based on terrestrial RANs to enable such location based RAN functions. • Exploitation of communication links to improve RAN based positioning through MT cooperation. • Provision of accurate MT position information to enable location based and context aware services.

  5. Hybrid/Cooperative Positioning Less equipped mobile Short range communication Mobile with GNSS Good knowledge of position • Surrounding mobiles know and provide their position information (e.g. by broadcasting or by answering a ‘ping’) • A less equipped mobile can receive this information via short range communication such as ZigBee or UWB • The mobile position is the intersection of several circles • Works well in dense populated areas. • Maybe such areas coincide with those, where pure GNSS positioning is difficult to achieve

  6. Project Overview • Topic: Cooperative mobile radio communications and localisation • EU Project Proposal: • Type: STREP • Duration: 30 Months • Volume: 529 PMs, 5.5 M€ (73% funding = 4.04 M€) • Goals: • Optimise ubiquitous and converged network and service infrastructures for communication and media • Adaptive and predictive communications exploiting location positioning information for future systems with multiple capabilities on PHY/MAC layer • Improvement of localisation for indoor and urban canyons

  7. Partnership: 12 (+2) partners, 7 (9) countries • Industrial partners • Mitsubishi Electric ITE (F) • SMEs • ACORDE (E) • SigINT (CY) • Siradel (F) • Universities • AAU (DK) • UniS (GB) • IETR (F) • IT (P) • UPM (E) • R&D Centres • CEA-LETI (F) • DLR (D) • Eurecom (F) • Outside Europe • University of Alberta (CDN) • City University of Hong Kong (CN)

  8. WP1: Scenarios and Framework Definition • Early Milestone: • Scenarios to be investigated in the algorithmic WPs and the demonstration WP – needs to be restricted: • Indoor vs. outdoor (urban canyon), Synchronised vs. non-synchronised, Static vs. Dynamic positioning, SISO vs. MISO vs. MIMO, Single vs. multi-cell, Single vs. cooperative system • Scenarios to synchronise different hardware platforms • Appropriate parameters derived from other IST projects and standardisation processes • Late Milestone: • Parameters may be redefined (e.g. communication systems that do not take positioning into account so far)

  9. WP2: Hybrid and Cooperative Positioning • Hybrid Data Fusion and Tracking • Cooperative Positioning WP3: Navigation-aided Cellular Communications • PHY Layer enhancements using localisation data • Location based cross-layer optimisation for PHY/MAC • Enhanced relaying and cooperative communication using positioning data • RAT selection policies and optimisation

  10. WP4: Channel Characteristion • Channel measurements • Creating a fingerprinting data base • Mobility model based on the channel measurements • Investigation of location-dependent channel parameters • Fingerprint-based localization WP5: Demonstration • Exploiting former platforms – get some enhancements to work with higher accuracy for localisation • 3GPP LTE devices • UWB devices • Zigbee devices • Wi-Fi devices

  11. WHERE IT Contributions I • Location assisted RAT selection for B3G Network optimisation • In this scenario it is assumed that the test mobile terminal is a multimode one and that the location of the mobile in both networks are available.

  12. WHERE IT Contributions II • The fact that the mobile can reach more than one network can be exploited towards providing more than one alternatives to questions like: • Detection: What RATs are currently available? • Selection: What RAT to choose; which is “best”?, one or multiple RATs in parallel? Criteria for RAT selection includes QoS, resource usage in terms of codes, power, channel conditions, etc...) • Reselection: Under what conditions reselection is necessary; which network to choose? How reselection is accomplished. Can we anticipate a reselection procedure?

  13. WHERE IT Contributions III • Task 2.2 Cooperative Positioning • Cooperative Positioning • To investigate algorithms/protocols for distributed processing to allow: • Node discovery; how do we identify suitable cooperative nodes. • Node selection. How to identify the “best nodes” to participate in cooperative dialogue; • technique required for selecting the most useful nodes that would provide the most accurate positional estimation • how to fuse the data between the selected cooperating nodes to enhance the positional estimation. • Node reselection. How to we use positional information to reselect new nodes in case of link failure.

  14. WHERE IT Contributions IV • Task 3.2: Location based cross-layer optimization for PHY/MAC • To investigate cross-layer optimization strategies for radio resource management protocols and algorithms that exploit positioning data, based on the underlying PHY layer enhancements from Task 3.1 • Management Responsibilities • WP3 Leader :Navigation-aided cellular communication systems • Task 3.2 Leader: Location based cross-layer optimization for PHY/MAC

  15. Wireless Hybrid Enhanced Mobile Radio Estimators Project Coordinator: Ronald Raulefs Institute of Communications and Navigation German Aerospace Center (DLR) Oberpfaffenhofen, Germany Email: {Armin.Dammann, Ronald.Raulefs}@DLR.de IT Coordinator: Jonathan Rodriguez Email: jonathan@av.it.pt Thank you

More Related