Presentation delivered during 9th Seminar on Media and Digital Economy (21-22 March 2019, Florence)

1. Artificial and Augmented Intelligence applications
in the telecommunications industry
Network efficiency, user experience and new services
Stephen Schmidt
VP Telecom Policy & Chief Regulatory Legal Counsel
TELUS Communications (Canada)
Stanford Levin
Emeritus Professor, Southern Illinois University Edwardsville
Roberto Balmer
Adjunct Professor, University of Lugano
Florence School of Regulation
Annual scientific conference on Communications and Media
Parallel Session B: Telecoms, AI and Quantum Computers
20 March 2019, 11.30h, Sala Capitolo
Disclaimer: The opinions expressed in this paper are those of the authors and do not represent the opinions of TELUS Communications Company.
Slide 1

2. Transformational potential of AI over time is exponentially growing
1943:
McCulloch & Pitts lay
the theoretical
foundations for
artificial neural
networks.
1950:
Turing Test
1997:
Deep Blue defeats
Gary Kasparov
Since 2010
- Higher performance and lower
latency in telecommunications
networks
- Further strong increase of
computing power
- Big data and sensors
- Plug and play cloud
technologies
- Example: In 2018 Alibaba
language processing AI
outscores top humans at a
Stanford University reading
and comprehension test,
scoring 82.44 against 82.304
on a set of 100,000 questions.
Slide 2

3. • This paper analyzes the AI applications already in place, and those expected to be in place, in
telecommunications with the objective to reduce roll-out and operating costs, to improve performance or to
introduce new services.
• A more sophisticated understanding of the type of transformations AI will bring to core processes in
telecommunications and other network industries is also a precondition for a more strategic analysis of the
economic impact of AI or of the future of sector-specific regulation.
• The paper also explores AI developments in other network industries, such as electricity, gas, water and
transport to both confirm the developments in telecommunications and to help predict future AI
applications.
• Methodology: Unlike other papers exploring the potential of AI solutions, the results of this literature review
of AI applications are validated by in-depth interviews with executives in several large European and North
American telecommunications and other network companies.
Research question
Slide 3
• Disclaimer: AI algorithms tend to be very complex. This paper will not analyze in detail how the proposed
algorithms in the literature work. For many of them there were written multiple dedicated papers in the field
of Mathematics and Computer Science. The same is true for AI applications identified by operators.

4. 1) Expert Systems (ES) which use a knowledge base representing facts and if-then rules but lack the ability to
learn autonomously from external data. An inference engine then applies the rules to the known facts to
deduce new facts. Typical applications include simple classifications. ES usually support human decision-
making in cases with low variance. Some experts argue, however, that ES are not "true" AI as they lack the
ability to learn autonomously from external data.
2) Machine Learning (ML) on the other hand is able to learn by itself with new observations and to create rules
on its own (e. g., for classifications such as spam or not). As such, more transformational potential is
attributed to ML. Neural networks and genetic algorithms are the two main machine learning techniques.
This paper focuses on ML.
Finally, distributed artificial intelligence (DAI) attempts to solve problems in a distributed manner. A DAI system
consists of a society of agents, each agent in charge of a subpart of the problem. Different levels of
communication, cooperation, and control among the agents might be necessary in order to achieve a coherent
global solution.
Additional definition:
3) Augmented intelligence: Stronger focus on complementing human intelligence rather than substituting for it
when compared to artificial intelligence.
What is AI ? AI consists of …
Slide 4

5. The mobile telecommunications network –
today and tomorrow
Backbone
Situation: 5G requires a much higher cell density, and therefore more repeaters
Challenges (examples):
• How to operate the network and assign resources?
• How to maintain and repair the network?
• How to design the network?
• Where to place repeaters?
Slide 5

6. Manhole
Copper (up to 750m)
Copper (up to 200m)
Street
Cabinet
Optical fibre
Copper
Fiber to the
Curb (FTTC)
Fiber to the
Street (FTTS)
Fiber to the
Building (FTTB)
Fiber to the
Home (FTTH)
Local
Exchange
Copper „Last Mile“, depending on geographic area, around 1.5km
Building Entry
Point (BEP)
E.g. VDSL
Fibre Backbone
network
The fixed telecommunications network -
today and tomorrow
Situation: Fiber is rolled out ever closer to the customer (FTTX). Fully fibre based networks offer much higher speeds (at best P2P or PON FTTH
networks). .
Challenges (examples):
• How to operate the network and assign resources?
• How to maintain and repair the network?
• How to design the network (position of active equipment / repeaters)?
Slide 6

7. 1. Network roll-out: Architecture and implementation
• Access network: Some authors propose AI algorithms to minimize the number
of optical regenerators (in P2P) and nodes (in PON) to minimize costs and
maximize quality. Other authors apply similar techniques also to wireless (Fi-Wi)
and to intra-datacenter networks (e.g. genetic algorithms and neural networks).
E.g. Martinelli et al. (2014)
• Backbone: Some authors use AI techniques to optimize the resource allocation
in the backbone and for its reconfiguration. E.g. Morales et. al. (2017)
• Roll-out implementation: Some telcos seem to use robots for the roll-out of
optical networks (at least in the access network). Swisscom for instance stated
publicly that such robots can reduce roll-out costs by up to 50%. It is possible
that AI could in the future improve the effectiveness of such robots.
Where can AI help?
Applications identified in Literature review
Slide 7

8. Where can AI help?
Applications identified in Literature review
2. Network operation
• Optimization of technical parameters of transmission: AI can be used to optimize parameters of optical
transmission in telecoms networks. This may include, for instance, laser amplitude and phase noise (e.g.
Zibar et al., 2015). Other AI techniques support measurement of quality of transmission (Mata, 2017)
• Failure detection and predictive maintenance: It is crucial to
be able to swiftly identify network problems and to predict
maintenance needs. Some authors propose AI techniques
for fault diagnosis and maintenance prediction in optical
access networks (e.g. probabilistic modeling and machine
learning). E.g. Zhang et al. (2016).
• Routing (“smart grid”): Some authors propose AI methods
to choose the optimal network paths for connections (e.g.
swarm intelligence, neural networks and genetic
algorithms). Finding the most energy efficient route in a
network can reduce its energy footprint and improve
performance. E.g. Kyriakopoulos et al. (2016).
Slide 8

9. 3. Strategy, Marketing and general business operations
• Prediction of demand and network traffic
• Prediction of churn
• Prediction of where network improvements would benefit customers most
• Virtual digital assistants
• Streamlining inbound data and responses by customer representatives
• New natural language processing services
• Fraud detection
• Cybersecurity applications
E.g. Mata et al (2018)
Most of these applications are not unique to telecoms and not the focus of this paper.
Where can AI help?
Applications identified in Literature review
Slide 9

10. 4. Other network industries
The paper analyzes AI applications also in other network industries (Electricity, Gas, Water, Transport).
• Quality measurement and prediction: Some authors integrate AI in quality modelling (Water,
Gas). E.g. Chau (2006).
• Smart grid operations: In Electricity this is understood as demand and traffic management
using new sensor placed on all levels of the network. In many countries it is yet unclear how the
data will be transported. In some countries utilities roll-out out their own telecoms network in
order to do so. E.g. Ramchurn et al. (2012)
• Forecasting Water levels: An author uses of AI to forecast water resources and to optimize
water reservoir management. E.g. Wang (2009).
• Some author also propose a AI algorithms to implement autonomous intelligent agents in
Urban Traffic control that can respond to traffic conditions in real time. E.g. Roozemond (2001)
• Failure detection and predictive Maintenance: Some authors propose AI techniques for
diagnosis and prediction of gas turbines maintenance. E.g. Zhang et al. (2016).
Other industries overall seem to be less committed to AI applications than the telecommunications
industry when looking at the applications discussed in the literature.
Where can AI help?
Applications identified in Literature review
Slide 10

11. • Only few interviews in North America and Europe concluded for now.
• Simple AI-ES is implemented for decades. E.g. decision support.
• First interviews show that there are currently only few more transformational AI-ML applications
already in operation on the market and often only since a short time. But
• Lot of enthusiasm and activity
• Many operators see the industry undergo strong transformation in the coming years
• Most operators are aware of the challenges and are acting proactively
• Especially large operators seem to invest strongly in AI
• Trust seems to be a major issue. Machine Learning algorithms such as Neural Networks cannot be
“understood” by humans. Without trust by the humans operating the algorithms cannot be
implemented. Necessarily a long step by step process to the roll-out. Trials alone may take 1-2 years.
• This may be one of the reasons why there are not more AI applications already in the market.
5. Preliminary conclusions 1/3
Slide 11

12. • While the applications analyzed in the literature concern many different potential uses of AI, the
focus of the industry seems to lie on core network planning and operational issues such as failure
detection and reparation in order to improve performance for customers. This seems to be also
the focus of major equipment in view of 5G.
• As such it can be expected that AI will amplify the effects of the roll-out of new technologies such
as 5G and to increase the performance of existing ones.
• Some operators also use AI-ML already today to automate the design the network. Optimal
network design may take into account many factors such at the of antennas, height of buildings,
thickness of walls, etc., etc. This is no longer an academic discussion!
• Across network industries it seems that the telecommunications industry is taking the lead and is
being currently most transformed by AI. It is possible that the circumstance that the transport of
information is part of the core business of telecommunications operators, facilitates this
development.
5. Preliminary conclusions 2/3
Slide 12

13. • AI - both as ES and increasingly also ML – is extensively used in customer care. Some operators
combine this with natural language processing (NLP)
• There seem to be also AI use-cases to provide totally new services and products for customers
(based on new data markets). A interesting case is for instance where operators can use anonymized
data of their mobile customers to provide intelligence for third parties. For instance, operators can
estimate and predict the number of vehicles that would pass a projected new tunnel based on
device location data. Such new business opportunity do not seem to be a priority for the market
currently.
• Way forward: further interviews are planned. Recently discussed AI applications with operators
include forms of software defined networks (SDNs), of cognitive networks, of (5G) spectrum sharing
and in-cell device-to-device communication.
5. Preliminary conclusions 3/3
Slide 13

14. Q & A
Slide 14

15. • Mata, J., De Miguel, I., Duran, R. J., Merayo, N., Singh, S. K., Jukan, A., & Chamania, M. (2018). Artificial
intelligence (AI) methods in optical networks: A comprehensive survey. Optical Switching and
Networking
• Qi, J., Wu, F., Li, L., & Shu, H. (2007). Artificial intelligence applications in the telecommunications
industry. Expert Systems, 24(4), 271-291
Main references
Slide 15
Other references
Chau, K. W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine pollution bulletin, 52(7), 726-733.
Kyriakopoulos, G.I. Papadimitriou, P. Nicopolitidis, E. Varvarigos, Energyefficient lightpath establishment in backbone optical networks based on ant
colony optimization, J. Lightwave Technol. 34 (23) (2016) 5534–5541
Martinelli, N. Andriolli, P. Castoldi, I. Cerutti, Genetic approach for optimizing the placement of all-optical regenerators in WSON, J. Opt. Commun.
Netw. 6 (11) (2014) 1028–1037
Mata, I. de Miguel, R.J. Durán, J.C. Aguado, N. Merayo, L. Ruiz, P. Fernández, R.M. Lorenzo, E.J. Abril, A SVM approach for lightpath QoT estimation in
optical transport networks, in: 2017 IEEE International Conference on Big Data (IEEE BigData), 2017, pp. 4713–4715
Morales, M. Ruiz, L. Gifre, L.M. Contreras, V. López, L. Velasco, Virtual network topology adaptability based on data analytics for traffic prediction, J.
Opt. Commun. Netw. 9 (1) (2017) A35–A45
Ramchurn, S. D., Vytelingum, P., Rogers, A., & Jennings, N. R. (2012). Putting the'smarts' into the smart grid: a grand challenge for artificial
intelligence. Communications of the ACM, 55(4), 86-97
Roozemond, D. A. (2001). Using intelligent agents for pro-active, real-time urban intersection control. European Journal of Operational Research,
131(2), 293-301.
Wang, W. C., Chau, K. W., Cheng, C. T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting
monthly discharge time series. Journal of hydrology, 374(3-4), 294-306.
Zibar, L.H.H. de Carvalho, M. Piels, A. Doberstein, J. Diniz, B. Nebendahl, C. Franciscangelis, J. Estaran, H. Haisch, N.G. Gonzalez, et al., Application of
machine learning techniques for amplitude and phase noise characterization, J. Lightwave Technol. 33 (7) (2015) 1333–1343
Zhang, W. Hou, L. Guo, S. Wang, Y. Sun, X. Yang, Failure recovery solutions using cognitive mechanisms for software defined optical networks, in:
2016 15th International Conference on Optical Communications and Networks