5G Positioning for connected cars (mmW) 5G introduction - - PowerPoint PPT Presentation
5G Positioning for connected cars (mmW) 5G introduction Mathematical model of 5G-mmW positioning Mutiple aspects of the achievable error Estimation principle Summer school on 5G V2X communications June 2018 Giuseppe Destino, CTR,
Summer school on 5G V2X communications Giuseppe Destino, CTR, King’s College London
▪(mmW) 5G introduction ▪Mathematical model of 5G-mmW positioning ▪Mutiple aspects of the achievable error ▪Estimation principle
New carrier frequencies (sub-6 GHz and mmW)
Beam-based communication
New radio-access procedures
New communication models
Location-awareness
5G
6 GHz 100 GHz 0.8 GHz
0.8-2.8 GHz 4G
3.5-5 GHz 5G 24-28 GHz 5G 37-40 GHz 5G 64-71 GHz 5G
10Km 200m 10m 1m Frequency Range Com Wide range Medium range Very-short eMBB uRLLC mMTC
Representation with 4 physical dimensions Sparse due to high carrier frequency
Single BTS approach
Use channel sparsity
Use direction and distance infromation jointly
Use a geomteric model to exploit
1m 3 10 50 100 300 1km 3km 10km Availability Accuracy
remote rural sub- urban city in-door
A-GNSS
(GPS, GLONASS, …)
Satellite positioning
based methods
WiFi
BLE/UWB
Estimate location and rotation of the MS Location-based channel parameterisation
Inital access
Sweeping multiple directions allows channel discovery
No overhead (or reduced is specific pilots are needed)
Periodically to allow location tracking Benefit
Location-awareness prior communications
https://www.keysight.com/upload/cmc_upload/All/Understanding_the_5G_NR_Physical_Layer.pdf
Positioning using reference signals Positioning using beam sweeping
DL
UE collects and processes signals over multiple beams UL
gNB collects and processes signals over multiple beams
DATA Received OFDM signal at the m-th RF chain
Beam Training
No derivations … More information by increasing the subcarrier spacing More information by using edge-band subcarriers Bearing information depends on the sensitivity of the beampattern in angle domain AoA and AoD are coupled
FIM for channel parameters
Apply variable transformation
Compute the CRLB from the inverse of 𝐊η
Communications Workshops, 2017
DL mode
LoS link provides 3 types of information
AoA information: position-rotation dependent
AoD information: position dependent
Ranging: position dependent
NLoS link provides a combined infromation
AoA-Ranging information: position-scatter-rotation dependent
The FIM of Position-Rotation in rank 3 in 2D, therefore position-rotation is feasible with
1 LOS + N >= 0 NLOS
N >=3 NLOS
Distributed Center-localised Edge-localised
Scenario: AoA = 0 deg, AoD = 180 deg, d = 100m, 2 RF chains, 16 ULA
Scenario: AoA = 0 deg, AoD = 180 deg, d = 100m, 2 RF chains, 16 ULA, orthogonal beams 2 RF chains
Impact of array gain
Scenario: AoA = 0 deg, AoD = 180 deg, d = 100m, ULA, orthogonal beams
Due to the derivative of the beamforming
AoA and AoD information
Multiple beamforming to acquire information about AoA, AoD
LOS / LOS and >1 NLOS / >= 3 NLOS
Narrow beams for high SNR and high AoA/AoD resolution
’Spread’ pilots for high delay resolution
Exhaustive search Hierarchical search
Exhaustive search
Information is acquired when
main beam or sidebeam ”hit” the LOS ray
Resource consuming Beam-codebook dependent
Hierarchical search
Infromation is acquired at each
step of the search
High accuracy can be achieved
as beams point to the ”right” direction
Time efficient
efficient but more sensitive to noise
robust to noise, trade-off between rate and positioning accuracy
User rate Time sharing optimisation
Through Millimeter-Wave MIMO in 5G Systems," in IEEE Transactions on Wireless Communications, vol. 17, no. 3, pp. 1822- 1835, March 2018
Subcarrier 1 Subcarrier 2 Subcarrier N Angular domain
Signals over multiple
subcarriers share the same ”angular information”
Signals over multiple
MIMO channels share the same ”time information”
Step1: Sparse estimation with common support
Exploit common spatial-sparsity across carriers CS technique for common support model Estimate delay and channel gain per path
Step2: Refinement of the channel parameters
SAGE: per path refine the channel parameters
using a successive cancellation method
Step3: Non-linear mapping to location
Solve non-linear least square problem
θ1 θ2
MS BTS Sat1 Sat2
ψ2 ψ1
On going work !
1.
Positioning for Vehicular Networks," in IEEE Wireless Communications, vol. 24, no. 6, pp. 80-86, Dec. 2017.
2.
and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems," in IEEE Transactions on Wireless Communications, vol. 17, no. 3, pp. 1822-1835, March 2018
3.
mm-wave communication," 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, 2017, pp. 797-802.
4.
strategies from a rate-positioning trade-off perspective," 2017 European Conference on Networks and Communications (EuCNC), Oulu, 2017, pp. 1-5.
5.
Alignment on the Rate-Positioning Trade-Off”, 2018 IEEE Wireless Communications and Networking Conference (WCNC): Special Session Workshops