Design of Communication Networks and Systems

Objectives

The course aims to provide students with the theoretical knowledge and methodological tools required for the design, analysis, and optimization of communication networks and systems, integrating infrastructural aspects, mathematical modeling, and software-based methodologies.

The first part of the course addresses the main aspects related to copper and optical-fiber transmission media, access networks, and structured cabling, with particular attention to the physical and performance characteristics of different transmission media, design criteria, and the main reference standards and regulations.

The second part is devoted to the stochastic modeling of communication systems and their analysis through quantitative methods. Poisson processes, queueing theory, Markov chains and processes, as well as Markov Decision Processes (MDPs) and their extensions to partially observable and multi-agent scenarios, will be introduced. These tools will be used to model, analyze, and optimize the behavior of communication networks and systems.

The course also provides the fundamentals of simulation theory, with particular reference to discrete-event simulation, statistical evaluation of results, and confidence-interval estimation. MATLAB and Python will be used for the modeling, simulation, and analysis of queueing systems and for the implementation of decision-making models applied to communication networks.

Laboratory activities are aimed at putting the theoretical concepts into practice through the design of structured cabling systems, the modeling and simulation of communication networks, and the implementation of communication systems based on advanced stochastic models. These activities will allow students to develop practical skills, result-analysis capabilities, problem-solving skills, and the ability to critically evaluate design solutions.

Students will also be encouraged to collaborate with their peers during practical activities, critically discussing the adopted design solutions and the obtained results, thereby developing transferable skills useful in technical, scientific, and professional contexts.

Knowledge and understanding

By the end of the course, students will know and understand the fundamental principles of copper and optical-fibre transmission media, access networks, and structured cabling. They will also understand methods for the simulation, modelling, and analysis of complex systems, with particular reference to queueing systems, Markov processes, and Markov decision processes applied to telecommunication networks and systems.

Applying knowledge and understanding

By the end of the course, students will be able to design wired and wireless telecommunication networks. In particular, they will be able to design structured cabling at different levels, including floor, building, and campus networks, and to use MATLAB for queueing-system analysis and Python for advanced modelling based on Markov processes and reinforcement-learning techniques.

Making judgements

Students will develop the ability to critically analyse the performance and reliability of telecommunication networks and systems by interpreting the results obtained from project work and simulations. They will also be able to independently select appropriate technological solutions and justify their choices according to technical, economic, and regulatory criteria.

Communication skills

Students will be able to clearly and effectively communicate the results of their analysis and design activities using appropriate specialist technical language. They will also be able to prepare technical reports and design documentation, work effectively in teams, discuss the adopted solutions, and present experimental results.

Learning Skills

Students will acquire the ability to independently consult scientific databases, technical standards, and bibliographic references. They will also be able to broaden their knowledge through reference textbooks, scientific articles, and regulatory documents and to keep up to date with developments in telecommunication technologies.

The course includes both face-to-face lectures (49 hours, 7 CFU) and laboratory sessions (30 hours, 2 CFU), aimed at putting into practice the concepts and methodologies introduced during the theoretical lessons. Laboratory activities may be carried out individually or in groups and focus on the design of structured cabling systems, the modeling and analysis of queueing systems using MATLAB, and the design of communication systems through advanced stochastic modeling techniques and Python-based tools.

These teaching methods are consistent with the learning objectives of the course, which aim both to provide knowledge related to the design and analysis of communication networks and systems and to develop the ability to apply quantitative and computational tools to the modeling, simulation, and optimization of communication systems.

Laboratory activities will also allow students to apply, in design and simulation scenarios, concepts related to queueing systems, Markov processes, and Markov Decision Processes, as well as to critically analyze the obtained results and assess the effectiveness of the proposed solutions.

Should the course be delivered in blended or distance-learning mode, the necessary changes may be introduced with respect to the arrangements described above, in order to ensure compliance with the programme set out in this syllabus.

Essential knowledge:

• Probability theory
• Random variables
• Main aspects related to digital modulations
• Source and channel coding
• Circuit-switching and packet-switching techniques
• Basic concepts of electrical engineering and signal transmission

Important knowledge:

• Basic concepts of statistics
• Basic concepts of telecommunication networks

Useful knowledge:

• Basic knowledge of the object-oriented programming paradigm
• Knowledge of the MATLAB environment
• Knowledge of the Python environment
• Basic notions of algorithms and data structures

Attendance is not mandatory.

However, attendance is strongly recommended, also in view of the highly laboratory-oriented nature of the topics covered.

Detailed Programme

Part 1: Course Introduction (1 hour)

[Lectures: 1 hour – Practice and Labs: 0 hours]

Course objectives, examination arrangements, teaching materials.

Part 2: Copper Transmission Media (6 hours)

[Lectures: 6 hours – Practice and Labs: 0 hours]

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. 

Part 3: Optical Fiber Cables (6 hours)

[Lectures: 6 hours – Practice and Labs: 0 hours]

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. 

Part 4: Access Networks (3 hours)

[Lectures: 3 hours – Practice and Labs: 0 hours]

Copper and fiber access networks. 

Part 5: Structured Cabling (11 hours)

[Lectures: 6 hours – Practice and Labs: 5 hours]

 Standard TIA/EIA 568A, ISO/IEC 11801 and CEI EN 50173. Construction Products Regulation (CPR). Power over Ethernet (PoE). Italian legislation. Article 135-bis. Reference Guidelines.  Design of a structured cabling system.

Part 6: Stochastic Modeling of Communication Systems (19 hours)

[Lectures: 13 hours – Practice and Labs: 6 hours]

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. Overview of MATLAB. Modeling and analysis of queueing systems using MATLAB. Applications to communication networks.

Part 7: Decision Processes and Advanced Stochastic Modeling (17 hours)

[Lectures: 7 hours – Practice and Labs: 10 hours]

Markov Reward Processes (MRPs). Markov Decision Processes (MDPs). Extension to Partially Observable Markov Decision Processes (POMDPs) and multi-agent systems. Sequential optimization techniques for solving MDPs, POMDPs, and multi-agent systems. Overview of Python. Definition of customized MDP components for communication systems, followed by their implementation in Python and solution through sequential optimization algorithms.

Part 8: Simulation Theory (16 hours)

[Lectures: 7 hours – Practice and Labs: 9 hours]

Introduction to simulation. Simulation of queueing systems. Discrete-event simulation. Simulation terminology. Confidence interval estimation. Analysis and representation of results. Histograms and boxplots. Implementation of simulation models in MATLAB. Performance evaluation of communication networks through simulation. Comparison between analytical and simulation results.

Contribution of the Course to the Objectives of the 2030 Agenda for Sustainable Development

The topics covered in the course and the knowledge acquired contribute, directly or indirectly, to the design and development of efficient, resilient, and sustainable communication infrastructures and systems, as well as to the development of advanced skills in information and communication technologies. The modeling, simulation, and optimization methodologies addressed in the course also promote the efficient use of resources and the performance evaluation of communication infrastructures. The course therefore contributes to Goals 4, 9, 11, and 12 of the 2030 Agenda for Sustainable Development.

[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.

[8] A. M. Law, Simulation Modeling and Analysis, 5th ed., McGraw-Hill Education, 2015.

The examination consists of project-based assignments related to different parts of the course programme, integrated with an oral examination. The project assignments may be carried out individually or in groups and must be developed autonomously by the students according to the specifications provided by the lecturer. The resulting project work and related documentation must be submitted to the lecturer in accordance with the procedures indicated during the course and will be subject to a preliminary assessment. Admission to the oral examination is subject to a satisfactory evaluation of the project assignments. During the oral examination, students will be required to discuss the work carried out, explaining the adopted methodologies, the design choices made, and the results obtained. The discussion will be complemented by questions on the theoretical aspects of the course related to the project activities, in order to assess the student’s individual understanding of the topics and ability to critically justify the adopted choices. The overall assessment will take into account the correctness and quality of the proposed solutions, the ability to appropriately apply the methodologies and tools covered during the course, the ability to critically analyze the obtained results, the knowledge of the underlying theoretical aspects, and the quality of the technical language used during the oral discussion.

Learning assessment may also be carried out on-line, should the conditions require it. To ensure equal opportunities and in compliance with current laws, interested students may request a personal interview in order to plan any compensatory and/or dispensatory measures based on educational objectives and specific needs. Students can also contact the CInAP (Centro per I'integrazione Attiva e Partecipata — Servizi per le Disabilita e/o i DSA) referring teacher within their department (https://www.cinap.unict.it/content/referenti).

• Describe the characteristics of balanced-pair cables.
• Define primary line parameters, characteristic impedance, attenuation, and crosstalk.
• Describe the differences between multimode and single-mode fibres, with reference to numerical aperture, attenuation, modal and chromatic dispersion, and the characteristics of an optical-fibre link.
• Define a Poisson process and describe its main properties.
• Analyse M/M/1 and M/M/1/N queueing systems.
• Define discrete-event simulation and describe its main phases.
• Define MRPs and MDPs and describe their main components.