1. “Predictive Mobile Networks”
Francisco Martín Pignatelli
Group Head of Radio Product at Vodafone

2. Artificial Intelligence Today
Classification
Object Detection
Tabby, tabby cat (57,03%)
Tiger cat (14,6%)
Washbasin, handbasin (9,6%)
Egyptian cat (6,06%)
Toilet seat (1,66%)

3. Artificial Intelligence in 4G and 5G
A.I. is used in multiple industries: autonomous cars, weather prediction, voice, image, videos recognition…
Machine Learning, is a subset of A.I that allows to identify patterns through the statistical data analysis
Vodafone pioneers use of A.I. in Mobile Networks to evolve to a predictive network and automate complex tasks

4. Mobile Data growth has been folded in last 2 years
Vodafone Group
225 TB
564 TB (1Q, )

5. AI will be essential in Networks
Today 3-4 bands in use in 4G. In future, many more…
And every band will use different technologies: 4G, 5G, FDD/TDD, 2x2 MIMO (2 transmitters), 4x4 MIMO (4 transmitters) or even Massive MIMO (32 or 64 Tx).
Artificial Intelligence is necessary to solve the resources configuration, allocation and optimisation.
3.5 GHz (5G band)
2600 MHz TDD
2600 MHz
2100 MHz
1800 MHz
1400 MHz
MaMIMO
900 MHz
800 MHz
700 MHz (5G)

6. How will it be implemented?
Places where AI is located
What’s AI in RAN?: The application of Machine Learning algorithms to big data to learn and automate actions.
Machine Learning algorithms: Neural Network, Q-learning, Random Forest, etc
Process time:
eNB: milliseconds
Servers: seconds/minutes
Big data:
eNB: all radio info
Servers: counters, KPI, Traces (10 times less volume than eNB)
Operational servers
External Server
Centralised SON
Radio OSS
Core OSS
2017 Field Trial & Commercial
eNB Software
Core Network
NW elements
2017 PoC & Field Trial and commercial

7. What’s new? Problem areas: drop calls, interference, etc.
LEARN: With every radio call info, the network learns:
Problem areas: drop calls, interference, etc.
Areas not managed efficiently: handover triggers, carrier aggregation combination, etc.
PREDICT: The Network will estimate problems and inefficiencies when similar conditions to the learnt ones are happening.
FIX per USER and per CELL before the issues happen, making changes such as fix the misconfiguration, change handover decisions and parameters…
Drop predict

8. AI on the Radio from Evolution to Revolution
2017
SON Application
2018
eNB AI
2019
First 5G applications …
202x
Self management features

9. World 1st Predictive Load pilot
1 hour prediction
Problem: Traditional optimisation has 30-40mins delay due to counter cycle.
New respect to traditional C-SON: AI predicts the traffic load 1 hour in advance after analysing several months counters.
Trial Results: Load balanced before the load increases.
+26%
Accessibility
+5.6%
Voice Traffic
+5.7%
Data Traffic
+5.8%
Data Throughput
Centralised SON

10. ML applied to VoLTE Optimisation
New compared to traditional C- SON: ML algorithms (Dimension reduction, Q-learning and T-Test algorithms) analyse 400 counters and do a correlation of packet loss with Top 5 counters.
Trial Result:
Root cause analysis effort from months of One Engineer to 4 hours
Find the optimum parameter settings combination to reduce packet loss, improving VoLTE MOS quality.
Overall result
Top 10% worst cells
Gain 11%
Gain 33%
Gain 22%
Gain 81%
Commercial C-SON July’18
Cenralised SONt

11. ML applied to cell edge user speed
Problem: Cell edge scenarios are complex, too many parameters and many settings combinations.
New respect to traditional C-SON: ML Algorithm (Q-learning) reduce configuration complexity of 6 categories / parameters.
Trial Result: cell edge throughput improved.
Power
control
Schedule
GAP
Coverage
Resource
Handover
6 parameter categories
+ 30%
(0,86 to 1.1 Mbps)
Overall
+ 49%
(1,1 to 1.7 Mbps)
Top 10% gain cells
Centralised SON
C-SON feature by July’18

12. eNB
Radio 4G/5G
Real NW Traces from London and Barcelona (Huawei).
What’s new?
AI algorithms learn per cell and per user what are the best frequencies to allocate, using Radio-frequency fingerprinting, without the UE to measure.
Accuracy >80% when more than samples for Handover; 76 to 99% accuracy for best Carrier Aggregation selection.
PoC scope: Apply AI algorithms offline over real traces to estimate their performance gain and accuracy.
Live data analysis IFHO from 2100 to 800
July’17 September’ Q-18
Traces Collection
Evaluation of algo based on AI
Features implementation:
- IFHO, CA, Load Balancing, VoLTE and CSFB performance opt

13. First introduction of AI within eNB in 2H-2018
PoC result:
Thousands of millions of inter-frequency handover in half time (1 second to 0.5 seconds).
Adding automatically the right frequencies to do Carrier Aggregation, 10% higher average throughput.
Load balancing, redirecting the terminals to the best cell in a faster way.
A – 70 dBm B
A – 95 dBm
B – 102 dBm
C – 100 dBm C
Radio 4G/5G

14. Conclusions
AI (Machine Learning) algorithms can learn in the network automatically, predicting the traffic, and anticipating the problems per Cell and per Device.
Improvements in throughput, voice quality, OPEX troubleshooting, faster handover, more efficient carrier selection  and with precision achieved higher than 90%
Vodafone will integrate the AI within the networks during 2018.
These algorithms will be essential to manage, configure, and optimise the 4G and 5G network in the next years due to the traffic growth and therefore the deployment of new frequency bands.