High-Efficiency Device Localization in 5G Ultra-Dense Networks: Prospects and Enabling Technologies

Transcription

1 High-Efficiency Device Localization in 5G Ultra-Dense Networks: Prospects and Enabling Technologies Aki Hakkarainen*, Janis Werner*, Mário Costa, Kari Leppänen and Mikko Valkama* *Tampere University of Technology, Finland Huawei Technologies Oy (Finland) Co. Ltd, Finland R&D Center

2 Scope and Outline: Scope: 5G user node localization, tracking and location prediction Outline: Exploiting location information Properties of 5G ultra-dense networks: Technical enablers Accurate measurements Fusion of measurements: Collaborative localization Exploiting location-awareness: New location-based services

3 Properties of 5G Ultra- Dense Networks

4 Ultra-Dense 5G Networks Access nodes (ANs) are deployed with a very high density à user nodes (UNs) can see/ hear multiple ANs Housing block: width = 120m* Access nodes: density ~ 60m** Streets: width = 12m* 50 m * Values from Madrid model [1] ** Similar to density in [2] [1] METIS, Deliverable D6.1: Simulation guidelines, Oct, 2013 [2] Hoydis et al, Making smart use of excess antennas: massive MIMO, small cells, and TDD, Bell Labs Technical Journal, vol. 18, no. 2, Sep, 2013

5 Ultra-Dense 5G Networks High density à High probability for line of sight (LoS) conditions 42 m 60 m [m] Figure: LoS probability in METIS stochastic channel model [1]. [1] METIS, Deliverable D1.4: METIS channel models, Feb, 2015

6 5G Small Cell Radio Frames Provide Frequent UL Beacons Proposal [1] (frame duration µs): Proposal [2] (frame duration 500 µs): Proposal [3] (frame duration 250 µs): Use UL beacons/reference symbols for network-centric localization [1] P. Kela et al., A novel radio frame structure for 5G dense outdoor radio access networks, Proc. IEEE VTC Spring, 2015 [2] T. Levanen et al., Radio interface evolution towards 5G and enhanced local area communications, IEEE Access, vol. 2, Sep [3] P. Mogensen et al., Centimeter-wave concept for 5G ultra-dense small cells, Proc. IEEE VTC Spring, 2014

7 Properties of 5G Networks à Accurate Measurements Ultra-dense network: High probability for line of sight Radio frames: Frequent uplink pilot signals for uplink channel estimation Bandwidths: Bandwidths are expected to be in the order of 100 MHz and beyond Time of arrival (ToA) Time difference of arrival (TDoA) Direction of arrival (DoA) Antenna types: ANs are likely to have antenna arrays or other directional antennas Received signal strength (RSS) 7

8 Example: Direction of Arrival Estimation in a 5G AN Figure: Cramer Rao bound (CRB) on direction of arrival (DoA) estimation with a circular array and 100 samples. Based on the results in [1]. [1] Stoica et al, The stochastic CRB for array processing: a textbook derivation, IEEE Signal Process. Lett. vol. 8, no. 5, May 2001

9 Fusion of the Measurements

10 Measurements from ANs are fused into a location estimate 10

11 5G Ultra-Dense Networks à High Localization Accuracy Fusion of various accurate measurements across multiple measurement/observation points à extremely high localization accuracy is expected (even in sub-meter range [1-2]) Compare to the accuracies of the existing localization systems OTDoA* in LTE: a few tens of meters [3] GNSS: around 5 meters [4] WLAN fingerprinting: 3-4 meters [5] *OTDoA = Observed time difference of arrival [1] 5G Forum, 5G white paper: New wave towards future societies in the 2020s, Mar Available: [2] NGMN Alliance, 5G White Paper, Feb Available: 5G White Paper V1 0.pdf [3] J. Medbo, I. Siomina, A. Kangas, and J. Furuskog, Propagation channel impact on LTE positioning accuracy: A study based on real measurements of observed time difference of arrival, in Proc. IEEE PIMRC, Sep [4] D. Dardari, P. Closas, and P. M. Djuric, Indoor tracking: Theory, methods, and technologies, accepted for publication in IEEE Trans. Vehicular Tech., [5] H. Liu et al., Push the limit of WiFi based localization for smartphones, in Proc. 18th Annu. Int. Conf. Mobile Computing and Networking (MobiCom), 2012

12 Example: Fusion of DoA Estimates from 4 ANs Figure: Example geometry. Figure: Localization CRB on the localization RMSE for example geometry. 4 ANs, each equipped with 10 antennas. RMSE p CRB x +CRB y = D ' p 2 12

13 Tracking and Location Prediction Accurate, fast and always on UN localization enables UN tracking Tracking, combined with predictive estimation algorithms, such as extended Kalman filters, enables UN location prediction Estimated velocity Predicted trajectory Current location Estimated trajectory 13

14 Time for a short video clip Video available in:

15 Prospects of 5G Localization

16 Prospects of 5G Localization Localization can be carried out on the network side using UN/uplink reference signals à always on network-centric localization without heavy load on UN batteries Tracking and prediction of UN positions à environment learning New markets for network operators: self driving cars, VTX, robots, intelligent traffic systems, Advanced radio network functionalities: e.g. proactive radio resource management and mobility management 16

17 Prospects #1 Radio Environment Maps location estimates + measured radio parameters REM processing Radio environment maps can be used, e.g., for proactive radio resource management 17

18 Prospects #2 Prediction and Cognitive Localization Human mobility depends highly on historical behavior and is predictable up to 88 % [1] à mobility database Mobility database (inc. e.g. most common routes, traffic patterns) Location information (inc. current location, tracking) Machine learning type of algorithms UN localization & movement prediction with a higher accuracy and for a longer period of time [1] X. Lu, et. Al. Approaching the limit of predictability in human mobility, Nature: Scientific Reports, vol. 3, no. 2923, Oct

19 Prospects #3 Predictive Radio Resource Management Assume that we are tracking a car and can predict it s movement Then it is possible to allocate radio resources proactively Point A à AN 1 Point B à AN 2 19

20 Prospects #4 Content Prefetching Video streaming in a car with a predicted route ANs 3 and 4 are congested because of a match in a stadium à this would result in a poor quality of service (QoS) However, the user can be prioritized while still under ANs 1 and 2 with free resources à proactive content delivery to a buffer à good user experience 20

21 Prospects #5 Routing in the Backhaul Network observes a truck which would block the visibility from AN 1 to the user in a car à proactive re-routing of the user data to AN 2 à seamless service and better user experience 21

22 Conclusions Properties of 5G ultra-dense networks enable network-centric UN localization with a very high accuracy frequently updated UN tracking UN location prediction This location-awareness can be utilized in the UN, ANs or by third parties to provide new location-based services such as Radio environment maps Cognitive localization and prediction Proactive radio resource management Content prefetching Routing in the backhaul 22

23 Thank You! Aki Hakkarainen*, Janis Werner*, Mário Costa, Kari Leppänen and Mikko Valkama* *Tampere University of Technology, Finland Huawei Technologies Oy (Finland) Co. Ltd, Finland R&D Center