The aim of the course is to provide students with theoretical and practical knowledge for the design, implementation, and analysis of telecommunication systems. The first part focuses on copper and fiber optic transmission media, structured cabling, access networks, and wide-area transmission systems. The second part is devoted to stochastic modeling and simulation of communication systems, equipping students with advanced tools to analyze, design, and optimize networks and services through quantitative methods and practical applications developed with software environments (MATLAB and Python).  

Knowledge and understanding

By leveraging the acquired knowledge, students will be able to identify the main challenges and solutions for the design of a telecommunication network or system. 

Applying knowledge and understanding

Students will be able to apply the acquired knowledge to design structured cabling at different levels (floor, building, campus). They will be capable of using MATLAB for the analysis of queueing systems and Python for advanced modeling with Markov processes and reinforcement learning, applying these tools to communication networks.

Making judgements

Students will develop the ability to autonomously and consciously select the most suitable technological solutions for the design and optimization of telecommunication networks. They will also be able to critically assess the results obtained through practical exercises and simulations, justifying their choices based on technical, economic, and regulatory criteria. 

Communication skills

The course will enable students to strengthen their technical-specialist language and to communicate their analyses and design outcomes clearly and effectively. Laboratory activities in small groups will foster teamwork skills, the ability to discuss design solutions, and engagement in professional contexts. 

Learning skills

Students will acquire the ability to independently broaden their knowledge through the study of reference texts, scientific articles, and regulatory documents. They will be able to stay up to date with the evolution of telecommunication technologies and to develop skills useful for tackling advanced topics in the field in the future. 

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.

Frontal lessons

Copper transmission media (4h). Remind on transmission lines. Twisted pair cables: Description. Primary line constants. Characteristic impedance. Kilometric attenuation. Effects of temperature. Crosstalk. Nomenclature for the cables. Categories and classes. 

Fiber optic cables (6h). Definition. Step-index and graded-index fiber. Multimode and single-mode fiber. Numerical aperture. Kilometric attenuation. Optical windows. Modal and chromatic dispersion. Bandwidth of a fiber link. Standard fibers. Classes. Optical connectors. Optical transmitters, optical receivers, optical amplifiers and Regenerators. DWDM (Dense Wavelength Division Multiplexing) e CWDM (Coarse Wavelength Division Multiplexing). 

Voice calls on wired networks (2h). Time Division Multiplexing (TDM) hierarchy for digital telephony. SS7 signaling. 

Access networks (3h). Copper and fiber access networks. 

Structured cabling (6h). Standard TIA/EIA 568A, ISO/IEC 11801 e CEI EN 50173. Construction Products Regulation (CPR). Power over Ethernet (PoE). Italian legislation. Article 135-bis. Reference Guidelines. 

Stochastic modeling of communication systems (21h). Poisson processes. Introduction to queueing theory. Discrete-time Markov chains. Birth-Death processes. M/M/1 systems. M/M/1/N systems. M/M/N systems. M/M/N/N systems. Markov reward processes (MRPs). Markov decision processes (MDPs). Extension to partially observable (PO) and multi-agent (MA) MDPs. Reinforcement learning (RL)-based techniques for solving MDPs, POMDPs, and MAMDPs.

Simulation theory (7h). Introduction to simulation. Simulation of queueing systems. Discrete-event simulation. Terminology related to simulation. Confidence interval estimation. Output analysis and representation of results. Histograms and boxplots.

Laboratory

Design of a structured cabling (5h).

Application of Queueing Systems in Network Design (10h). MATLAB overview. Design of queueing systems in the MATLAB environment. Applications to communication networks. 

Design of communication systems using advanced stochastic modeling (15h). Python overview. Definition of the MDPs components customized for communication systems, followed by their implementation in Python and solving them using Reinforcement Learning (RL)-based algorithms. 

[1] Digital learning materials provided by the teacher.

[2] R. L. Freeman, Telecommunication Systems Engineering. New York, NY, USA: J. Wiley and Sons.

[3] R. Ramaswami, K. Sivarajan, and G. Sasaki, Optical Networks: A Practical Perspective. San Francisco, CA, USA: Morgan Kaufmann.

[4] S. Gai, P. Nicoletti, and G. Montessoro, Reti locali. Dal cablaggio all’internetworking, 2nd ed. Torino, Italy: Telecom Italia, 1995.

[5] F. Callegati, W. Cerroni, and C. Raffaelli, Traffic Engineering: A Practical Approach. Cham, Switzerland: Springer, 2023. ISBN: 978-3-031-09588-7.

[6] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA, USA: MIT Press.

[7] S. V. Albrecht, F. Christianos, and L. Schäfer, Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. Cambridge, MA, USA: MIT Press, 2024.

Ethernet cable categories.

Access networks.

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

M/M/N queueing systems.

Definition of a Markov Decision Process (MDP).