The document discusses device-level artificial intelligence (AI) for 5G networks and beyond. It describes how on-device AI can process and analyze data closer to its source, minimizing data transmission and protecting privacy while reducing latency. Examples of on-device AI applications include facial recognition and virtual assistants. The document also examines challenges of on-device AI like obtaining accurate data sets and balancing device autonomy with network impacts. It concludes that some level of device-level AI is inevitable as networks become more complex and intelligent devices are needed to help manage this complexity.

1. Device-level AI for 5G and Beyond
Yue Wang, Samsung Research UK
CW TEC 2018
27th September, 2018

2. From smart phones to smart everything
One
Network
2CW TEC 2018 - The inevitable automation of Next Generation Networks

3. On-device AI today
On-device AI:
• As opposed to ‘Cloud-AI’
• Dedicated processor for AI tasks performed on the device
Benefit:
• Data processed and analysed closer to the data source
• Minimised amount of data transmission
• Consumer data privacy protected
• Minimised latency
• Real time analytics
Applications:
• Facial/voice recognition
• Ad Targeting
• Virtual assistant
“AI and machine learning
increasingly will be embedded
into everyday things”
- Gartner’s 2017
"On-device AI will be a big
buzzword for new phones in 2018
So far, the strongest use cases are
in computational photography
and facial recognition” – IDC 2018
Intelligence for communications
3CW TEC 2018 - The inevitable automation of Next Generation Networks

4. Why?
• Network:
• More flexible, dynamic, and intelligent
• End devices:
• are connecting to an increasingly
complicated network
• 36.508, FDD frequency test
• over 50 tables (!!!) Rel. 14 2017
vs. ~30 tables Rel. 10 2012
• The intelligence on devices:
• Allow a simpler UE design
• Avoid unnecessary delays and signaling overhead
• Allow more flexibility of connecting
Frequency
bands
Below and
beyond 6GHz
bands
Carrier
aggregation
Access
technologies
Waveforms
Numerology:
Various subcarrier spacing
Variable carrier bandwidth
Variable SS block sweeping
4CW TEC 2018 - The inevitable automation of Next Generation Networks

5. AI in networks
Core Network/Cloud NFV
RCC
AI
AI
Orchestrator
AI
AI
AI
AI
AI
AI
Device-level AI:
• RF
• Power management
AI
Localised AI:
• RAN elasticity
End-to-end AI:
• Slice management
• Network service assurance
Device-level AI
Localised AI
End-to-end AI
Localised AI:
• Flexible functional split
VNFs
5CW TEC 2018 - The inevitable automation of Next Generation Networks

6. AI in networks
Device-level AI
Data is collected and stored
on device – better privacy,
reduce delay and no data
overhead
Effects to and from the
network
Localised AI
AI applied across network
domains, data needs to be
passed between
Data overhead
Localised decision may be
complimentary to end-to-end
AI
End-to-end AI
AI applied for the end to end
network, data/knowledge
gathered from the different
domains of the network
Data challenges
Deployment
Green field
Innovation
Network architecture,
policies, SLAs
Protocols and
signallings
6CW TEC 2018 - The inevitable automation of Next Generation Networks

7. An UE example – AI for cell selection
Increasingly complicated procedures in cell selection and reselection in LTE and 5G
• 35 parameters for system information
• 10 parameters for speed dependent selection
• 13 parameters for interworking
• The list is getting larger: CoMP, beam sweeping
• No adaptability to new technologies
Increased power consumption on the UE for cell selection
• Doubled power for LTE compared to 3G, RRC_IDLE -> RRC_CONNECTED
• 4 times higher for LTE than 3G, RRC_CONNECTED -> RRC_IDLE
Overhead
Delay
Power Consumption
7CW TEC 2018 - The inevitable automation of Next Generation Networks

8. AI for cell selection
UE
Actions (the
selected TRP)
cell selection
with AI
UE location,
speed,
measured
signal strengths
(RSRP/RSRQ)
Feedbacks from
the network
UE
8CW TEC 2018 - The inevitable automation of Next Generation Networks

9. Benefits
Current procedure Drawbacks With AI Benefits
Periodically
measured
measurement needed even without
reselection actually happening;
information may be outdated
Reselection is
triggered
Threshold based
measurements
Multiple factors affecting the threshold – not
optimal;
A massive list of parameters become
unbearable with changing environment,
and for different services
No thresholds, less
parameters and
configurations
Static configurations No forward compatibility – any new features
developed in the radio will need either new
parameters, or adding new configurations to
the parameters
Real-time, adapted to c
hanges of the context
(e.g., speed)
Less overhead
Faster selection
Reduced power consumption
9CW TEC 2018 - The inevitable automation of Next Generation Networks

10. Challenges
• Data
• Synthesized data vs real data
• Obtaining the accurate data set
• Learning
• The context to and from the network
• Isolated AI results in sub/local optimal or even negative impacts to the
network end to end
• not desired by the operators
• How much autonomy do you want to empower the devices?
10CW TEC 2018 - The inevitable automation of Next Generation Networks

11. Challenges
11
• Standard
• Support both ‘legacy’ and intelligent devices
• some devices may be smarter than others
• Long process in standardization
• leads to de-facto standard and fragmentation
• Production
• Device computational power, and impact on battery life
– not every device needs to compete to be the most intelligent
• The challenge in validation and deployment
– never know what is going to happen until it is put in the real network

12. Future Looking and Conclusion
On-device AI vs device-level AI
Different levels of intelligence
Network instructed device-level AI
Inevitable change in the industry
1
2
3
4
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13. “The measure of intelligence
is the ability to change.”
- Albert Einstein
13CW TEC 2018 - The inevitable automation of Next Generation Networks

14. Thank You
yue2.wang@samsung.com
@yuewuk