Paper presented by Ioannis Mavromatis at VTC Fall, 24-27 September in Toronto, Canada. https://arxiv.org/abs/1705.08684

1. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
MmWave System for Future ITS: A MAC-layer
Approach for V2X Beam Steering
Ioannis Mavromatis, Andrea Tassi, Robert J. Piechocki, Andrew Nix
Department Of Electrical and Electronic Engineering
University of Bristol, UK
{ioan.mavromatis, a.tassi, r.j.piechocki, andy.nix}@bristol.ac.uk

2. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
Summary
• Connected and Autonomous Vehicle (CAV) networks.
• Legacy Millimetre Wave (mmWave) beamforming and CAVs.
• Performance investigation under a V2X scenario.
• System Model for V2X beamforming.
• Smart Motion-prediction Beam Alignment (SAMBA).
• Beamwidth Adaptation to maximise data rate
• Performance evaluation of SAMBA.
• Conclusions.

3. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
Connected and Autonomous Vehicles (CAVs)
• CAVs will act as key entities for Next-Generation Intelligent Transportation
Systems (ITSs).
• They will require gigabit-per-second data rates and tactile-like end-to-end
delays.
• Dedicated Short Range Communications (DSRC) cannot support these Quality-of-
Service (QoS) requirements.
• A potential enabler is Millimetre Waves (mmWaves) technology.
• However, the existing mmWaves cannot be utilized from CAVs.
• As they are right now…

4. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
MmWave Beamforming (BF) and CAVs
• IEEE 802.11ad MAC-layer introduced Virtual Antenna Sectors for the BF:
• Two-layer process with bidirectional frame exchange.
• Quasi-omnidirectional antenna pattern.
• Sector-level beam-sweeping.
• CAVs and Urban Environments  increased mobility and vehicle density.
• Increased mobility  Doppler Shifts.
• More frequent beam alignment (<30ms).
• Increased Latency.
• Increased Vehicle Density  More
Collisions during BF.
• Less trained antennas per Beacon Interval.
Initiator
Responder
BRPSLS
N number of SSW
frames, SIFS separated
MBIFS MBIFS
TX SSW
RX SSW
SSW-FB
SSW-ACK
SIFS
SIFS
BRP-RX
BRP-RX
SIFS
BRP-ACK
BRP-FB
RX BC
S number of BRP
frames, SIFS separated
MBIFS MBIFS
BRP-RX
BRP-RX
BRP-FB
BRP-FB
SIFS
BRP frames with
training fields
First-Layer Beamforming (“Virtual” Antenna Sectors) Second-Layer Beamforming (Beam Refinement)
SIFS
MBIFS
SSW-FB
SSW-ACK
Responder 1 Responder 2
Strongest Received
Frame

5. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
Perf. Investigation for Legacy BF within a CAV network
• Latency introduced from legacy
BF per Beacon Interval (BI).
• 16-sector antenna.
• Collision probability during
an A-BFT slot.
• Collisions lead to non-trained
vehicle beams  Idle
Stations.

6. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
System Model for V2X Beamforming
• Smart Motion-prediction Beam Alignment (SAMBA): Enhanced
beamforming for Vehicle-to-Everything (V2X) links.
• Heterogeneous system design: DSRC & mmWave technologies.
• Out-of-band information for
beam training.
• Motion Prediction for each
vehicle in the system.

7. EPSRC Centre for Doctoral Training in Communications
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Information Encapsulated in DSRC Beacons
• Estimated position affected with additive GPS error.
• Average Velocity per unit time.
• Motion Data acquired from Motion Sensors.
• Angular displacement around axis per unit time.
𝐸 𝑝𝑜𝑠 = 𝑅 𝑝𝑜𝑠 + 𝑒 𝑝𝑜𝑠 , 𝑒 𝑝𝑜𝑠~ log 𝑁(𝜇, 𝜎2)

8. EPSRC Centre for Doctoral Training in Communications
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Vehicle-to-Infrastracture (V2I) Beam Alignment
• Case 1: New Position Received.
• Beam aligned based on the estimated
position (given by the GPS).
• Case 2: Position Outdated
• Predict position based on information
encapsulated in DSRC beacons.
• Vehicle follows the surface of a sphere
(or a circle in 2D case).

9. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
Beamwidth Adaptation to Maximise Data Rate
• GPS error leads to outages.
• Beam covers a much wider area at its edge.
• Optimal solution: Narrower beams away from the
RSU and wider beamwidth for vehicles closer to the
antenna.
• GPS error solution: Centering the beam with
respect to the beam edges.
• Maximise Data Rate Di. RSU
Mean
position
error
range
Real
Position
Position
after error
Additive
Error
𝜃 = max
𝜃
D𝑖 𝑑𝑖𝑛, 𝜃𝑖
s. t. U ≥ 𝛾𝑖
𝜃𝑖 > 0 ∀𝑖 ∈ 1, … , 𝑁MCS Sensitivity Threshold
Distance

10. EPSRC Centre for Doctoral Training in Communications
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Performance Evaluation – The scenario
• Manhattan Grid with size 200m x 200m.
• RSUs placed at the top right corner of each block (~48m apart) to ensure
coverage.
• Real vehicle routes generated with
SUMO traffic generator.
• Seamless handover between
RSUs.

11. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
Performance Evaluation of SAMBA
• Average data rate for different
number of vehicles and
position errors.
• Beamwidth of 15o when the
optimisation was not considered.
• Packet Delivery Ratio is 1.
• Network Throughput (under
saturation conditions).
• Velocity was 14m/s.
• Position error equal to 3m.
Collisions are increased as vehicle
density increases (legacy BF)
SAMBA exploits better the
network resources
Saturation Effect due to
collisions and latency

12. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
• Average data rate as a
function of the beacon delivery
ratio.
• Position Error is equal to 1m.
Performance Evaluation of SAMBA
• Average data rate in contrast
with legacy BF for different
velocities.
• Worst case scenario for SAMBA
- 1 vehicle within the system.
For increased PDRs, still more that
1Gbps is achieved.
SAMBA compensates with increased velocity

13. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
Conclusions
• SAMBA, a novel heterogeneous beamforming strategy.
• Agile motion-prediction model capable of estimating the position of a vehicle
and its motion and beamforming accordingly.
• Smart beamwidth adaptation algorithm, compensating with the vehicle
movement and the beam shape.
• SAMBA manages to:
• Overcome beam misalignments introduced from the GPS error.
• Minimise BF overhead introduced from legacy BF process.
• SAMBA is a viable solution for mmWave beamforming training over
next-generation ITS networks.

14. EPSRC Centre for Doctoral Training in Communications
www.bristol.ac.uk/cdt-communications
MmWave System for Future ITS: A MAC-layer
Approach for V2X Beam Steering
Ioannis Mavromatis, Andrea Tassi, Robert J. Piechocki, Andrew Nix
Department Of Electrical and Electronic Engineering
University of Bristol, UK
{ioan.mavromatis, a.tassi, r.j.piechocki, andy.nix}@bristol.ac.uk
Thank you for your attention!
Questions?