The document discusses advancements in millimeter wave (mmWave) technology for vehicle-to-everything (V2X) communications, highlighting significant research conducted at the University of Texas. It covers key applications such as fixed wireless, UAVs, and the challenges of beam alignment using machine learning methods. The research also explores using low-resolution ADCs for joint communication and radar applications, indicating potential for improved performance even with limited resources.

1. © 2018 Robert W. Heath Jr.© 2017, Robert W. Heath Jr.
Advances in millimeter wave
forV2X
Professor RobertW. Heath Jr.
Wireless Networking and Communications Group
Situation AwareVehicular Engineering Systems
Department of Electrical and Computer Engineering
The University ofTexas at Austin
Thanks to sponsors including the U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning
(D-STOP) Tier 1 University Transportation Center, the Texas Department of Transportation under Project 0-6877 entitled “Communications
and Radar- Supported Transportation Operations and Planning (CAR-STOP)”, National Instruments, Huawei, Toyota ITC, Honda, and Nokia.
http://www.profheath.org

2. © 2018 Robert W. Heath Jr.
Millimeter wave beamforming
2
1-bit
ADC
ADC
1-bit
ADC
ADC
Baseband
Combining
RF
Combining
RF
Chain
RF
Chain
Hierarchical beam
search used in IEEE
802.11ad/ay
Grid-of-beams the
defacto beam
configuration
method for 5G
Machine learning
for smart beam
alignment
Compressive
channel estimation
for NLOS and MIMO
beamforming
Current focus at UTTrend #2 More sophistication in methods for configuring
the beams for less overhead and/or multistream MIMO
Trend #1 Shift from directional beamforming
to more complicated MIMO architectures

3. © 2018 Robert W. Heath Jr.
Millimeter wave applications
3
Fixed wireless is the first
commercial 5G mmWave
use case, trials ongoing
UAVs are a future
application of
mmWave
MmWave forV2X likely to be the
killer app of mmWave in 5G, if
mobility challenges can be overcome
MmWave to handsets is
challenging and may not
happen quickly
Indoor mmWAve
WLAN is available, not
widely deployed

4. © 2018 Robert W. Heath Jr.
Toyota: Risk-aware online learning for position-aided beam alignment
4
Position
Position
Performance of
beam pattern
DB
Training request?
user’s position
Beam pair selection
Update learning param
Beam training
read
Read/write
Learning agent Environment
No
How to learn this
online while avoiding
severe misalignment?
Risk-aware selection balances the
learning speed (accurate database)
and large misalignment probability
Power loss versusTime
Multi-armed bandit
framework

5. © 2018 Robert W. Heath Jr.
Huawei: MmWaveV2X beam training with situational awareness
5
Blockage Predict beam
Collect vehicle locations
Reflection
0 5 10 15 20 25 30 35 40
Contents of the vehicle locations for the learning
0.4
0.45
0.5
0.55
0.6
0.65
Achievedalignmentprobability
1st lane cars
Add in 1st lane trucks
2nd lane trucks
2nd lane cars
Achieve 65% alignment
probability with 4x2 UPA antenna
Only receiver location
Not all surrounding vehicle locations are useful
Need hand-crafted features for learning
Situational awareness (vehicle locations, shapes) helps select beams

6. © 2018 Robert W. Heath Jr.
DSTOP: Out-of-band information for mmWave
6
Millimeter wave array
Sub-6 GHz array
Multi-antenna
multi-band
base-station
Multi-antenna
multi-band
mobile-terminal
A. Ali, N. Gonzalez-Prelcic, and R. W. Heath Jr., “Millimeter wave beam-selection using out-of-band spatial information,” IEEE Trans. Wireless Commun., vol. 17, no. 2, pp. 1038–1052, 2018.
With out-of-band information
Without
out-of-band
information

7. © 2018 Robert W. Heath Jr.
D-STOP: Low resolution ADCs/DACs as enabler for
joint mm-wave communication-radar
7
Source Vehicle
V1
Target Vehicle V2
Target Vehicle V3
1-bit
ADC
1-bit
ADC
Digital
Signal
Processing
M
RF
Chain
RF
Chain
RX Antenna Array
V2V Communication
V2I Communication
Radar Echoes
100
101
Target Distance (m)
10
-10
10-5
CRBd
(m2
)
ideal: Nrx
= 128
1-bit ADC: Nrx
= 128
ideal: Nrx
= 512
1-bit ADC: Nrx
= 512
(b)
10
0
10
1
Target Distance (m)
10-15
10
-10
10
-5
100
CRBφ
(rad2
)
ideal: N
rx
= 128
1-bit ADC: N
rx
= 128
ideal: N
rx
= 512
1-bit ADC: Nrx
= 512
(a)
50 100
Target Distance (m)
0
1
2
3
4
5
SpectralEfficiency(bits/s/Hz)
ideal: N
rx
= 128
1-bit ADC: Nrx
= 128
ideal: Nrx
= 512
1-bit ADC: N
rx
= 512
(c)
-1.5 -1 -0.5 0
log(MMSE
c
)
-4
-3
-2
-1
0
log(CRBd
)
-4
-3
-2
-1
0
log(CRBφ
)
ideal: d = 50 m
1-bit ADC: d = 50 m
ideal: d = 100 m
1-bit ADC: d = 100 m
Architecture with one-bit ADCs can perform closely
to the ideal case due to the low per-antenna SNR
High flexibility and robustness due to the mostly
digital implementation
P. Kumari, K. U. Mazher, A. Mezghani, and R.W. Heath Jr., “Low Resolution Sampling for Joint Millimeter-Wave MIMO Communication-Radar”, in IEEE SSP 2018
Studied quantization effects on the joint trade-off