AI for wireless is already here, with applications in areas such as mobility management, sensing and localization, smart signaling and interference management. Recently, Qualcomm Technologies has prototyped the AI-enabled air interface and launched the Qualcomm 5G AI Suite. These developments are possible thanks to expertise in both wireless and machine learning from over a decade of foundational research in these complementing fields. Our approach brings together the modeling flexibility and computational efficiency of machine learning and the out-of-domain generalization and interpretability of wireless domain expertise. In this webinar, Qualcomm AI Research presents an overview of state-of-the-art research at the intersection of the two fields and offers a glimpse into the future of the wireless industry. Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc. Speakers: Arash Behboodi, Machine Learning Research Scientist (Senior Staff Engineer/Manager), Qualcomm AI Research Daniel Dijkman, Machine Learning Research Scientist (Principal Engineer), Qualcomm AI Research

1. Arash Behboodi, Daniel Dijkman
Qualcomm Technologies Netherlands B.V.
Qualcomm AI Research
May 25, 2022
@QCOMResearch
Bringing AI research to wireless
communication and sensing
Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc

2. 3
Arash
Behboodi
Sr. Staff Manager,
Engineering
Qualcomm AI Research
Our presenters
How ML and wireless
complement one another
How ML is improving
communications
How ML is enabling
RF sensing
3
1
2
4
Agenda
Daniel
Dijkman
Principal Engineer,
Qualcomm AI Research
5 Questions?
Future AI for wireless
research directions

3. 4
Wireless
Strengths
ML
Design with real world priors, fast
and flexible models
Accurate prediction in complex tasks
Accurate modeling of
generative process
Sensing and perception
Strengths
Wireless and ML have complementary strengths
Design driven by tractable mathematical
models
Interpretable solutions
Good generalization under different
deployment conditions
Simple model adaptation

4. 5
AI for wireless is here today
World’s only
modem-RF system
for all 5G bands from
0.6-41 GHz
World’s 1st
Modem-RF
5G AI Processor
0.6 GHz
6 GHz
24 GHz
41 GHz
10 Gigabit 5G
Snapdragon® X70 5G Modem-RF System
Snapdragon, Qualcomm mmWave Module and Qualcomm 5G AI Processor are products of Qualcomm Technologies, Inc. and/or its subsidiaries.
.

5. 6
Qualcomm 5G technology is licensed by Qualcomm Incorporated. Qualcomm
5G products and Qualcomm Cloud AI 100 Platform are products of Qualcomm
Technologies, Inc. and/or its subsidiaries.
MWCB 2022
Enabling AI/ML for air
interface evolution
Cross-node machine learning for channel
state feedback (CSF)
Using end-to-end over-the-air (OTA) testbed in San Diego
that operates in 3.5 GHz band over 100 MHz, utilizing
Qualcomm® Cloud AI 100 platform and Snapdragon®
Modem-RF system
Showing reduced communication overhead that leads to
improved throughput, latency, and capacity
Cross-node machine learning for beam
management
Using end-to-end over-the-air (OTA) testbed in San Diego
that operates in 28 GHz band capable of 800 MHz
bandwidth, utilizing Qualcomm® Cloud AI 100 platform and
Snapdragon® Modem-RF system
Bringing more efficient beam management to increase
usable capacity and extend device battery life

6. 7
Channel estimation Radio resource allocation
Power saving
Vehicular communications
Positioning
Security
Device non-linearity Contextual awareness
Environmental sensing
MIMO detection
Full duplex
TCP optimization Beam management
and optimization
Spectrum sensing
AI research areas
to enhance 5G

7. 8
Out-of-domain
generalization
Feasibility of
supervised learning
Adaptability of
ML models
Examples:
Unseen dopplers and channel conditions
Example:
Wireless fingerprinting for localization in dynamic environments
Examples:
Different antenna configurations and channel conditions
Challenges
in applying AI
to wireless

8. 9
How ML is improving
communications
How ML is enabling
RF sensing
Our fundamental AI research
is fueling wireless innovation
ML
research
Wireless
technology
Generative
modeling
Neural
augmentation
Self-supervised
learning
Unsupervised
learning
For channel
representation and
simulation
For channel
estimation receiver
algorithms
For active
positioning
For RF sensing and
passive positioning

9. 10
Machine learning is
enhancing wireless
communication
Machine learning design based on
wireless domain knowledge provides
superior gains
Channel modeling
Using generative modeling to provide a more accurate
channel representation and improve communications
Communication design
Using neural augmentation to enhance a
Kalman filter for improved communications

10. 11
The wireless channel is complex
and includes useful information
Reflections change the transmitted signal
and have multiple effects
Line of sight
Reflected by floor
Reflected by
human target
Reflected by wall
Transmitter
Receiver

11. 12
Neural models help address the challenges of classical channel models
Environment,
Antenna,
UE/gNB location,
Doppler,
Carrier frequency,
…
Channel
𝒉
Modeling physical propagation effects on wireless signals
Classical channel models
Data-driven neural channel models
Standard channel models:
3GPP TDL/CDL, WINNER, ray tracing
Neural channel models
Pro: Accurately match complex
field data distribution
Pro: Fast sampling for prototyping purposes
Pro: Works with simple traces
Con: Interpretability
Con: Cumbersome field measurements
Con: Hard-coded assumptions
Con: Limited scenarios, slow to prototype

12. 13
Channel impulse response and channel frequency response
include all paths between sender and receiver
Channel impulse response Channel frequency response
Fourier transform
Attenuation
Sum over all paths
Channel frequency
response for
subcarrier i
Frequency of
subcarrier i
Path delay
𝐶𝐼𝑅(𝑡) = (
!
"
𝐻#𝛿 𝑡 − 𝜏#
Attenuation
Impulse
response Path delay
𝐶𝐹𝑅𝑖 = (
!
"
𝐻# . 𝑒$%&'(!)"

13. 14
Data-driven neural channel models offer key benefits
Pro: it can model variable number
of antenna inputs and outputs
Pro: interpretable samples:
the channel between TX-𝑗 and RX-𝑖
(Unknown) Channel
𝒙[𝒎]
Transmitted signal Received signal
𝒚[𝒏]
Given I/O measurements
Learn parameters of channel model: 𝑦 = ℎ!(𝑥)
Channel ℎ!(𝑥)
𝒙 𝒚
Channel
𝒙 ∗ 𝒉

14. 15
Neural augmentation enhances classical channel models
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z ⇠ N(0, I)
<latexit sha1_base64="WNmCx7JwwE/UQQITqr2CY+kD70E=">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</latexit>
x ⇤ H
<latexit 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hAQPuigsjuVyu4ZTtzZLhm8DHq4OEymZZiBnATnAh7ybPRU9xh7Ql37lL5cWYhG9gg/8nsHvRWXiIk+rngb+vDJaocbrmjfJE7D2imM45SahnntKMn2OaVYYT6qk5Q9fa63vGctP9db3tuWlk3y2jmGzjnlEtkKe1bRlp/Vj4VRcjJTNapX5QH14484XfVzpaJ1y1e0bviK1u1e0brZK1q3ekXrRq9o3eYVrZu8onWLV7Ru8IrW7V3RurkrWrd2RevGrmjd1hWtm7qidUtXtG7oitbtXNG6mStat3JF60auaN3GFa2buKJ1C1c0beB++Hp9q/4frVYvbl5vbr3Z/O7yu/W//qX6T1hfrf127Xdrf1jbWvvz2l/XjtYu1q7XhmvR2t/W/r72jz/975uv3vzqzTe26k9/UrX59Zr38+Y3/wfAFhbL</latexit>
x <latexit sha1_base64="8QKN12NklYvijUd/F07kRcnO2go=">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</latexit>
y
Classical channel models
Generative model
Generative models can generate channel
impulse response from complex distributions
𝐇: channel impulse response
Random seed
Generator
𝐇: channel
impulse response
G

15. 16
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gH+/aL6G4i15l88zF9cvVxZ+3Zl9fSbpe++rf4a4rOF3y/8YeGrhbWFPy18t7C30F24XAgWsoW/Lvx94R9rYu1va/9c+5dp+uknVZ/fLTg/a//+H/wVUUM=</latexit>
x ⇤ H
GAN: generative adversarial networks
Generative MIMO channel model based on GAN learns the channel
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FErVjO/O2r0UVB9fcIvATUIh+hnST4fCggfdFDZnUll9wyn7myXDF4GfVwcprMyzEBOg3MBD3k2fmp6jD2hrn1KX64sRGN7hB/5vYP+ikvERB9XvA19+GSNww2XtG8Sp2HjFMZxSk3DvHGUZPucUqwwHzdJyp6+NFves5Zfmi3vbUvLJnnjHEPvnHKJbIU9q2jLz5vHwig5masG1a/ygObxR5yu5rlS0bnlKzo3fEXndq/o3OwVnVu9onOjV3Ru84rOTV7RucUrOjd4Ref2rujc3BWdW7uic2NXdG7ris5NXdG5pSs6N3RF53au6NzMFZ1buaJzI1d0buOKzk1c0bmFK9o2cD9+s77V/I9Wqxc3rze33mz+8O6H9T//qfpPWL9e+7e13699t7a19h9rf147WrtYu14brS3X/mvtv9f+54//9+Z3b168+dZW/eUvqjb/sub9vHn1/1MZHl4=</latexit>
hi,j(⌧) 2 CL
<latexit sha1_base64="I4JWqw7AzzrXhInhpG2C9Lys824=">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</latexit>
z ⇠ N(0, I)
Generator Discriminator
Transmit-receive
antenna index
Transmit-receive
antenna index channel
Spatial
correlation
Real/fake
The generator learns to generate the channel
Channel
output
The discriminator teaches the generator to learn channel distribution

16. 17
MIMO-GAN learns
the communication
channel precisely

17. 18
MIMO-GAN matches power and delay profile of ground truth channels
The method learns to find the channel function based on only input-output traces
MAE: mean absolute error
Orekondy et al., ICC 2022, https://arxiv.org/abs/2203.08588
TABLE 1: Power and delay statistics of MIMO-GAN and ground-truth (GT) channels
Total Power (dB) Average Delay (𝝁𝒔) RMS Delay Spread (𝝁𝒔)
TDL-A
MIMO-GAN 4.648 0.2643 0.2862
GT 4.628 0.2641 0.2897
MAE -18.69 3.57 x 10—3
3.57 x 10—3
TDL-B
MIMO-GAN 4.735 0.2276 0.2954
GT 4.688 0.2285 0.2987
MAE -14.99 3.37 x 10—3
3.37 x 10—3

18. 19
19
Communication channels are hard to accurately estimate
A more accurate channel estimate at all the time steps for
different dynamics enables more efficient communications
Regular pilot symbols are
transmitted to get periodic
noisy observations (𝑜") of
the ground truth channel
Complex
channel vector
Mobile device
trajectory
Communication
channel
Channel states
Noisy observations
(pilots)
𝑜!
ℎ!
𝑜"
𝑜#
𝑜$
𝑜%
ℎ%
ℎ"
ℎ#
ℎ$
𝑡! 𝑡" 𝑡# 𝑡$
𝑡%
Time 𝑡!
Time 𝑡"
Time 𝑡#
ℎ&

19. 20
Classical Kalman filters lose accuracy over different dynamics
Con: Optimal Kalman filter parameters
vary with Doppler values
Con: A single Kalman filter should not
be used for all the Doppler values
Pro: Kalman filter can work with arbitrary
SNR and pilot patterns
Pro: Kalman filter is interpretable
KF(𝜃)
Kalman state 𝑆$%" Estimated
channel $
ℎ$
Observation 𝑜$
Kalman tracks the channel
Time 𝑡$,
velocity 𝑣$
Time 𝑡%,
velocity 𝑣%
Time 𝑡&,
velocity 𝑣&
KF(𝜃#) KF(𝜃$) KF(𝜃%)

20. 21
Standalone ML solutions for channel tracking have limitations
Con: Cannot naturally deal with sporadically
available observations (pilots) as input
Con: Have non-interpretable hidden states
Con: Do not generalize to different
configurations (pilot patterns, SNR)
Pro: Learn complex dynamics
LSTM-based channel tracking

21. 22
Neural augmentation of Kalman filters offers the best of both worlds
Kalman filter parameters
RNN provides Kalman parameters at time 𝑡
Interpretability
Out-of-domain
generalization
Robustness
Expressive power
RNN Update

22. 23
Neural-augmented KF closely matches the ground truth channel

23. 24
Neural-augmented Kalman filter generalizes to unseen cases
Neural-augmented Kalman filter (NA-KF) outperforms LSTM and Kalman filter over unseen pilot ratio*
* Averaged error computed over high Dopplers
Kumar Pratik et al. https://arxiv.org/abs/2109.12561. Globecom 2021
When trained over the whole data, NA-KF performs
as good as or better than Kalman without knowledge
of the exact dynamics
LSTM breaks down on unseen pilot ratio
NA-KF generalizes across scenarios with
unseen Dopplers and pilot patterns
Seen pilot ratio / Doppler Unseen pilot ratio Seen pilot ratio / Doppler Unseen pilot ratio Unseen pilot ratio / Doppler
Kalman Filter
LSTM
Neural augmented Kalman
Channel
tracking
gain
(-NMSE
in
dB)
0
5
10
15
20
25
Channel
tracking
gain
(-NMSE
in
dB)
0
5
10
15
20
25 Kalman Filter
LSTM
Neural augmented Kalman

24. 25
Machine learning
is enabling RF
sensing
Detect gestures, movements, and
objects by monitoring signal reflection
patterns, enabling new use cases
Active positioning
A communications device along with nearby
access points are used for positioning
Passive positioning
Access points alone are used to track the
environment and determine positioning

25. 26
5G / Wi-Fi positioning is useful indoors
and assists GPS outdoors
Active positioning
with RF sensing has a
variety of use-cases
Indoor navigation Vehicular navigation
Asset tracking
AGV tracking

26. 27
TRP: Transmission/reception point; SRS: Sounding reference signal; PRS: Positioning reference signal
• Access points (TRPs)
have known locations
and are synchronized
• A reference signal
(SRS or PRS) is
exchanged between
phone and access points
• The location of the
phone is determined by
analyzing the Channel
Impulse Response (CIR)
5G can
provide (indoor)
positioning
services
X-Y-Z
location?
TRP#1
Blocker
R
e
f
l
e
c
t
o
r
TRP#2
TRP#3
CIR
CIR
CIR

27. 28
Testbed for indoor active positioning to prove technology

28. 29
Pro: no labels required
Con: not very accurate in non-line-of-sight conditions
Con: doesn’t use multipath information
Pro: very accurate, uses multipath
Con: requires dense labels
Con: robustness issues
Position
Time difference of arrival
(TDOA)
ML-assisted RF fingerprinting
(RFFP)
RFFP TDOA
Current precise positioning methods
have limitations in accuracy or feasibility
CIR #1
CIR #2

29. 30
Learn position and environment from multipath propagation
With enough unlabeled CSI samples, we can learn the geometry of the environment without labels
Reflector
Real Tx
Real Rx
access point
Virtual Rx
access point
Key idea 1
Multi-path components (e.g., from reflectors) can
help localize even with a single access point
Triangulation can use real and virtual access
points as reference.
Key idea 2
There is only one unique environment geometry
and access point location that can be compatible
with a collection of (unlabeled) CSI samples

30. 31
Learn position and environment from multipath propagation
Neural SLAM demonstrates an end-to-end trainable network to learn positions and environment
that best reconstructs the CSI samples
*Inputs and outputs may be CSI or related features such as ToF and/or AoA
𝑯𝒖
NN
p
𝑝% 𝑝& 𝑝'
𝒑𝟎
Propagation model
Learnable virtual
AP locations
Forward pass →
Backward pass ←
Predicted
UE location
Input
feature*
Learnable
encoder network
Maps CSI to location
(Fixed)
Decoder network
Incorporates physics of reflections
Reflector
$
𝑯𝒖
Reconstructed
input*
𝒑𝟎 𝑝%
p
𝑯𝒖

31. 32
32
Shreya Kadambi et al, ICC 2022, https://arxiv.org/abs/2203.08264
Neural RF SLAM achieves precise 3D positioning
Neural RF SLAM achieves ~43.4 cm accuracy for 90% of users using only
unlabeled CSI values from single anchor at 400 MHz bandwidth
3D ray tracing simulation
Neural RF
SLAM

32. 33
33
• RF signals can be employed
as bi-static radar
• Any change in the environment
also affects the wireless channel
• Specifically, human motion,
gestures, respiration
• The signal propagation is complex
• Self-supervised and weakly
supervised machine learning
enables robust analysis
of the signal
RF sensing
is powered by
machine learning

33. 34
RF sensing has a
variety of use cases
across industries
Home / enterprise / retail
automation and security
Consumer
electronics
Automotive
Healthcare
• Presence, positioning, tracking, activity classification
• Better privacy as compared with camera-based
• Works across walls
• Touchless control (Phone, TV, laptop)
• Proximity-based power save
• Baby presence alarm
• Presence-based setting
• Vitals, attention monitoring
• Contactless sleep monitoring, vitals, fall detection

34. 35
WiCluster enables non-line-of-sight
passive positioning
No precise labels required
To initialize the system, the user is
guided by an app to provide room-level
labels (kitchen, living room, …)
The access points record the
corresponding CSI packets
WiCluster: making
passive positioning
deployable at scale
• Three to four commercial IEEE 802.11
access points (AP), 5 GHz band
• Circular array with 4cm radius
• Bandwidth: 80 MHz
• Packet rate: 90 Hz

35. 36
36
Experiments in real environments to test feasibility of deployment
Environment #2
2D office,15m x 21m
Environment #1
2D office,14m x 20m
Environment #3
3D home

36. 37
Ilia Karmanov et al. WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels, GlobeCom 2021
End-to-end training
Triplet loss to exploit temporal prior
Clustering loss to exploit spatial prior
• Cluster labels are updated after every epoch
The crossed softmax ensures that the
CSI-to-3D mapping is bijective
Zone loss is used for embedding
the 3D location into the floor plan
• Floor plan can be 2D or 3D
• Only requires a few labels
Cluster
Project
Cluster
Softmax
loss
Self-supervised
Weakly-supervised
WiCluster is first to do weakly-
supervised passive positioning
Input: CSI ResNet
Latent labels
Latent space
3D labels
3D locations
Triplet loss
Zone loss
Floor plan
Zone labels

37. 38
WiCluster model performs well in a real-life scenario

38. 39
WiCluster works in strong non-line-of-sight
Conference room with concrete walls:
strong non-line-of-sight
Ground truth Inference
1.13m
Offices
2.08m
Conference room
Mean error
Mean error

39. 40
WiCluster works across multiple floors
96%accurate zone classification
+ approximate 3D location

40. 41
Machine learning
design for wireless
communication and
RF sensing
Unsupervised learning
• Learning distributions and manifolds
is an approach to obtain features in an
unsupervised way
• Examples: WiCluster, Neural RF SLAM
• Other perspectives: self-supervised learning,
transfer learning
Adaptive models
• Models should be able to adapt to different
channel conditions and setups
• Examples: Hypernetwork Kalman, MIMO GAN
Generalization
• Designing ML models based on inductive bias,
gained from domain knowledge, or neural
augmentation can help generalization
• Examples: Hypernetwork Kalman, MIMO-GAN
Interpretability
• Neural augmentation helps interpretability
of modules in an ML model
• Examples: Hypernetwork Kalman, MIMO-GAN

41. 42
Neural RF sensing and neural rendering offer synergistic capabilities
Analogous to the compelling capabilities of computer vision and computer graphics
Computer
vision
Computer
graphics
ML methods to recover
scenes/objects
Physical
world Image
ML methods
for rendering
Neural RF
SLAM/
sensing
Neural
rendering
Learning position and
environment from CSI
Environment
Channel State
Information (CSI)
Generate spatially consistent
CSI from the environment

42. 43
AI is enhancing wireless
communications with generative
modeling and neural augmentation
AI is enhancing RF sensing
through self-supervised and
unsupervised learning to better
understand the environment
Qualcomm AI Research is
conducting leading research in
applying AI for RF sensing and
improved communications

43. Nothing in these materials is an offer to sell any of the components
or devices referenced herein.
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companies. All Rights Reserved.
Qualcomm is a trademark or registered trademark of Qualcomm
Incorporated. Other products and brand names may be trademarks or
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Incorporated, Qualcomm Technologies, Inc., and/or other subsidiaries or
business units within the Qualcomm corporate structure, as applicable.
Qualcomm Incorporated includes our licensing business, QTL, and the vast
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