Course: 2019/2020

Simulation and optimization of communication systems

(18229)

Students are expected to have completed

No requirements.

The student should aquire the following competences:
- Acquire the capacity to design, analyze and optimize signal processing algorithms that perform the main functions of a digital receiver (modulation, synchronization, channel estimation / equalization, detection, decoding).
- Acquire the capacity to design and analyze complex communication systems that combine several classes of signal processing algorithms.
- Acquire the capacity to design optimization problems, define their complexity, and obtain a solution using computing tools and signal processing algorithms.
At the end of the course the student will be able to:
- To handle with ease the mathematical and numerical tools necessary to design, analyze and optimize the elements of a communications system (modulation, synchronization, channel estimation / equalization, detection, coding / decoding).
- To understand, design, analyze and evaluate complex communication systems that combine several kinds of signal processing algorithms.
- To be able to solve practical problems in the design of communication systems using analytical methods and simulation.

Description of contents: programme

Topic 1: Introduction
1.2. Review of the elements that compose a communication system. Block diagram of a communication system.
1.3. Introduction to the parameter estimation a quality factors for communication systems.
1.4. Characterization of the channel in a communication system. Path loss models and small scale models. Classification of the communication channels. Coherence bandwidth and coherence time.
Topic 2: Simulation of communication systems
2.1. Deterministic and random signals. Simulation of random signals according to their statistics. Uniform, gaussian, Rayleigh and Ricean distributions. Use of these distributions for the simulation of communication systems.
2.2. Basic concepts for the simulation of communications systems, baseband equivalent, decimate and interpolation.
2.3. Simulation of linear invariant systems (filters), non-linear (amplifiers) and variants in real time.
2.4. Definition and simulation of the mobile channel.
Topic 3: Estimation of the probability of error
3.1. Parameters that determined the probability of error in a communications system. Modulation, interference and degrees of freedom.
3.2. Mont Carlo method for the estimation of the probability of error. Quality of the estimator.
3.3. Alternative methods for the estimation of the probability of error.
Topic 4: Optimization
4.1. Introduction to the optimization of the parameters that determine a communication system.
4.2. Convex optimization. Use of the CVX tool.
4.3. Non-convex optimization. NP-hard problem

Learning activities and methodology

Theoretical lessons and problems
The lessons are composed of theory and practical examples with the aim of providing a better understanding.
Lab practices
Simulation of the practical cases described during the theoretical lessons.
Practica case.
A practical case in the framework of the cellular communications is proposed for simulation and analysis.

Assessment System

- % end-of-term-examination 0
- % of continuous assessment (assigments, laboratory, practicals...) 100

Basic Bibliography

- A. Goldsmith. Wireless Communications. Cambridge University Press. 2005
- Convex Optimization . Stephen Boyd and Lieven Vandenberghe. Cambridge University Press. 2004
- Jeruchim et al. . Simulation of Communications Systems. Plenum. 1984
- T.S. Rappaport. Wireless Communications . Prentice Hall . 1996

- Stephen Boyd and Lieven Vandenberghe · Convex optimization (CVX) : https://web.stanford.edu/~boyd/cvxbook/

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