Device-level AI for 5G and Beyond

Yue Wang, Samsung Research UK

CW TEC 2018

27th September, 2018

From smart phones to smart everything

One Network

2CW TEC 2018 - The inevitable automation of Next Generation Networks

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

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

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

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

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

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

Benefits

Current procedure Drawbacks With AI Benefits

Periodicallymeasured

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 changes of the context (e.g., speed)

Less overhead

Faster selection

Reduced power consumption

9CW TEC 2018 - The inevitable automation of Next Generation Networks

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

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

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

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12CW TEC 2018 - The inevitable automation of Next Generation Networks

“The measure of intelligence

is the ability to change.”

- Albert Einstein

13CW TEC 2018 - The inevitable automation of Next Generation Networks

Thank Youyue2.wang@samsung.com

@yuewuk