A presentation in English aiming to discuss the definition behind the autonomic working of 5G network projects. It is based on the layered reference model of the brain, the autonomy view from the IBM (International Business Machines Corporation) and the cognition from the point of view of Qusay H. Mahmoud. In addition, the ARIB, the BWAC, the XIA and the Horizon 2020 projects are summarized.

1. Jefferson David Santos e Silva - 734
jeffersondss@mtel.inatel.br
Mestrado em Telecomunicações
1ºSemestre , 2018
Approaches for Intelligent
Working in 5G Networks
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2. INTRODUCTION
• Cognitive systems are able of dynamically alter their
functionality and/or topology in accordance with the changing
needs of their users;
• Autonomics systems are a collection of elements that manage
their internal behavior and their relationships with other
autonomic elements;
• The cognitive working mimics the brain according to a layered
reference model of the brain composed of seven layers;
• In order to achieve such performance, virtualization techniques,
artificial intelligence and machine learning are being considered
as enabling technologies of the 5G.
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3. THE NATURAL INTELLIGENT
SYSTEM MODEL OF THE BRAIN
• A cognitive system must be self-aware, situation-aware and acts
adaptively based on goals;
• An autonomic system must present the properties of self-
management, self-configuration, self-optimization, self-healing
and self-protection;
• The layers are: sensation, memory, perception, action, meta-,
and higher cognitive.
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4. THE NATURAL INTELLIGENT
SYSTEM MODEL OF THE BRAIN
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Fig. 1. The Natural Intelligent System Model of the brain.

5. THE NATURAL INTELLIGENT
SYSTEM MODEL OF THE BRAIN
• Willingness- and time-driven information may be based on
humans needs or regulatory policies;
• Event-driven information may be based on external data
collected from the network or the environment.
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6. PROJECTS RELATED TO 5G
• The project ARIB (Association of Radio Industries and Businesses)
“2020 and beyond” Ad Hoc;
• The Broadband Wireless Access & Applications Center (BWAC);
• The eXpressive Internet Architecture (XIA);
• The Horizon 2020 Program.
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7. THE PROJECT ARIB
• It presents a conceptual view of the mobile network consisted of
three layers;
• The upper layer includes application and services that can be
delivered to the individual users, enterprises and mobile network
operators;
• The middle layer represents a centralized control platform
composed of software modules for network control;
• The bottom layer is related to the end-to-end data transmission.
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8. THE PROJECT ARIB
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Fig. 2. The conceptual view in three layers of the mobile network.

9. THE PROJECT ARIB
• Through a SDN (Software Defined Network) controller, the
messages between the upper and the bottom layers are
managed;
• The functioning of the network model is not well detailed;
• The ARIB group predict a heterogeneous environment with multi-
RAT and centralized or distributed control.
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10. THE PROJECT ARIB
• Distributed control represents unilateral decisions based on only
individual nodes knowledge and affects local parameters;
• Centralized control regards an entire network issue;
• The ARIB project may show a cognitive behavior since decisions
and actions can be made individually or collectively.
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11. THE BWAC CENTER
• Cognitive Radio (CR) is standardized as being comprised by a set
of cognitive algorithms;
• An ideal CR is an agent that perceives the user’s situation to
offer assistance;
• It presents a working that imitates the brain since it
distinguishes between the operating scenarios and chases the
best performance even in a mutable environment.
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12. THE BWAC CENTER
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Fig. 3. Block-diagram of the CR.

13. THE XIA PROJECT
• Contracts between applications and the network contains
principal types that express their intent to use specific
functionalities;
• By principal it refers to the recipient of a packet;
• By type it refers to the contract and to the communication style;
• The evolvability in XIA is addressed trough the use of fallback;
• This solution includes multiple destination address fields in the
packet header.
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14. THE XIA PROJECT
• The security is increased with the use of XID’s (XIA Identifier)
inside the principal type;
• XID’s are used to assert the security above the nodes and the
contents;
• XIA chooses optimally the style of communication and brings new
ways of routing decisions besides the IP-based routing;
• XIA shows the properties of autonomic computing of self-
configuration and self-protection;
• It does not fulfill the cognitive networks requirements.
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15. THE HORIZON 2020
• It uses NFV, SDN and Network Slicing (NS) as enablers;
• The ideas take advantage from the IBM autonomic computing
vision;
• Besides the MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) a
managed system of sensors, actuators and the domain of
environment are added to permit the network to change its
response;
• This adaptation brings the property of self-adaptation.
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16. THE HORIZON 2020
• It uses Machine Learning based on mathematical algorithms or
statistical methods;
• Machine Learning with the MAPE-K will bring “making decision”
properties besides the autonomic ones.
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17. CONCLUSION
• The less dependent on human inputs a project is for changing its
behavior, the more cognitive the working of a 5G related project
will be;
• All the approaches bring scalability and dynamism;
• The virtualization techniques such as NFV, SDN and NS improve
the scalability;
• The computing algorithms used in CR and Machine Learning assist
the network in changing itself;
• Future works may detail better the functioning of individual
projects.
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18. REFERENCES
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• Janig, W. Integrative Action of the Autonomic Nervous System: Neurobiology of
Homeostasis. Cambridge University Press, pp. 35-84, June 2008.
• Kephart, J. Chess, D. The Vision of Autonomic Computing. IEEE Computer Magazine, vol.
36, no. 1, pp. 41-50, Jan 2003.
• Mahmoud, Q. Cognitive Networks Towards Self-Aware Networks. John Wiley and Sons, Ltd
2007. Chapter 1-9.
• ITU – International Telecommunications Union. (2018, May 16). Draft New Report ITU-R M.
[IMT-2020.TECH PERF REQ] – Minimum Requirements Related to Technical Performance
for IMT-2020 Radio Interface(s). Retrieved from https://www.itu.int/md/R15-SG05-C-
0040/en.
• 5G Americas. (2018, May 16). 5G Network Transformation December 2017. Retrieved from
http://www.5gamericas.org/files/3815/1310/3919/5G_Network_Transformation_Final.pd
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19. REFERENCES
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• Wang, Y. et al. A layered reference model of the brain (LRMB). IEEE Transactions on
Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 36, no. 2, pp. 124-
133, March 2006.http://ieeexplore.ieee.org/document/7763264/. Acessado em 01 de
outubro de 2017.
• ARIB 2020 and Beyond Ad Hoc Group. Mobile Communications Systems for 2020 and
Beyond. White Paper, version 1.0.0, October 2014.
• H., Asadi et al. Metacognitive Radio Engine Design and Standardization. IEEE Journal on
Selected Areas in Communications, vol. 33, no. 4, pp. 711-724, April 2015.
• Anand, A. et al. XIA: an Architecture for an Evolvable and Trustworthy Internet.
Proceedings of the 10th ACM Workshop on Hot Topics in Networks. pp. 1-6, November
2011. [doi>10.1145/2070562.2070564]
• 5G PPP Network Management & Quality of Service Working Group. (2018, June 13).
Cognitive Network Management for 5G The Path Towards the Development and
Deployment of Cognitive Networking. Retrieved from https://5g-ppp.eu/cognative-
network-management-for-5g/.

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