9796796 - DESIGN OF COMMUNICATION NETWORKS AND SYSTEMS A - Z

The objective of the course is to develop skills in terms of design of communication networks and systems, including VoIP systems, structured cabling, and fiber optic systems. In addition to traditional communication systems, the course aims to provide students with skills needed for designing fifth and sixth generation networks, including Software Defined Network (SDN) / Network Function Virtualization (NFV) paradigms and the application of simple machine learning techniques for network and service management. 

Knowledge and understanding

By exploiting the knowledge acquired, the student will be able to identify the main problems and solutions for the design of a communication network or system.

Applying knowledge and understanding

 At the end of the course, students will be provided with a) skills in terms of designing a Local Area Network (LAN) or a Wide Area Network (WAN) b) knowledge of the legislation related to electronic communications and ICT services c) skills needed for designing fifth and sixth generation networks, including SDN/NFV paradigms and basic knowledge of  machine learning techniques. 

Making judgements

Starting from technical specifications, the student will be able to design communication networks and systems by making proper design choices autonomously. Numerical exercises, computer simulations and the development of design projects will refine the making judgement skill.

Communication skills

The student will improve the technical language and will be able to interact with colleagues of a teamwork to discuss the proper solutions to a specific design problem. To this aim, during the laboratory lessons, students will be grouped in small teams. 

Learning skills

Students can broaden their knowledge through the study of recommended textbooks or scientific papers published on specialized journals.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Attendance is not mandatory, but strongly recommended.

Propagation through guided transmission media. Introduction. Propagation on overhead cables, coaxial cables, twisted pair cables and fiber optic cables.

VoIP systems. Voice signal features and Voice Activity Detection (VAD). The main communication protocols for VoIP. VoIP system architectures and components.

Structured cabling design. Structured cabling. Network topology. Campus Distributor (CD),  Building Distributor (BD), and Floor Distributor (FD). Passive multiservice physical infrastructure.

Access networks. Copper and fiber access networks. Fixed Wireless Access (FWA).

Wide Area Networks. Optical fiber transmission systems. Optical Amplifiers and Regenerators. Dense Wavelength Division Multiplexing (DWDM).

Simulation theory and traffic theory. Introduction to simulation and application to communication networks. Simulation of queueing systems. Analysis and representation of results. Continuous and discrete network traffic models.

Network softwarization. Introduction. Resource virtualization. Network softwarization paradigms.

Machine learning techniques for network and Services Management. Classification of machine learning techniques: Supervised learning, unsupervised learning and reinforcement learning. Examples of machine learning techniques applied for network and service management.

Laboratory. Design of a structured cabling. Implementation of traffic models. Implementation and configuration of SDN networks. Application of machine learning techniques for network and service management.

[1] Digital learning materials provided by the teacher.
[2] Freeman, Telecommunication systems Engineering. J. Wiley and Son
[3] R. Ramaswami, K. Sivarajan, G. and Sasaki, Optical Networks: A Practical Perspective. Morgan Kaufmann
[4] R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. Cambridge, MA, USA.

Crosstalk in twisted pair cables.

Attenuation and dispersion in optical fibers.

Definition and estimate of the confidence interval for evaluating the accuracy of the results.

Virtualization of resources, SaaS, PaaS, and IaaS models.

Definition of Markov Decision Process (MDP).