Lecture

Statistical Signal Processing for Wireless Communications

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In course

COM-500: Statistical signal and data processing through applications

Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numeri ...

Description

This lecture introduces statistical signal processing tools for wireless communications, covering spread spectrum and narrow band communications, spectral analysis, ultra-wide band communications, and non-parametric spectrum estimation. It explores pulse coding, spectrum distribution, periodogram interpretation, and resolution, with practical Matlab exercises. The lecture also delves into harmonic signals, line spectra, and parametric spectrum estimation using the Yule Walker method. It concludes with smooth spectrum modeling, AR processes, and heart rate variability analysis through spectral estimation.

Instructor

Andrea Ridolfi

I am a professor of Signal Processing and Communication Technologies at Bern University of Applied Sciences. Since 2004 I hold a lecturer position at EPFL, teaching “Mathematical Principles of Signal Processing” (Doctoral School, 2004 – 2011), “Statistical Signal and Data Processing through Applications” (Master Program, (2004 – ongoing), and Signal Processing and Machine Learning for Digital Humanities (Master, 2017 – 2019, co-taught with Mathieu Salzmann). Previously, I have been working as Project Manager and R&D Engineer at EPFL (2011-2014), coordinating the LCAV activities within the NSF – Nanotera project Opensense, and as Project Manager and R&D Engineer with the biomedical signal processing group at CSEM (2006-2011).

Official source

Related concepts (32)

Spectral density estimation

In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density (also known as the power spectral density) of a signal from a sequence of time samples of the signal. Intuitively speaking, the spectral density characterizes the frequency content of the signal. One purpose of estimating the spectral density is to detect any periodicities in the data, by observing peaks at the frequencies corresponding to these periodicities.

Spectral density

The power spectrum of a time series describes the distribution of power into frequency components composing that signal. According to Fourier analysis, any physical signal can be decomposed into a number of discrete frequencies, or a spectrum of frequencies over a continuous range. The statistical average of a certain signal or sort of signal (including noise) as analyzed in terms of its frequency content, is called its spectrum.

Least-squares spectral analysis

Least-squares spectral analysis (LSSA) is a method of estimating a frequency spectrum based on a least-squares fit of sinusoids to data samples, similar to Fourier analysis. Fourier analysis, the most used spectral method in science, generally boosts long-periodic noise in the long and gapped records; LSSA mitigates such problems. Unlike in Fourier analysis, data need not be equally spaced to use LSSA.

Music school

A music school is an educational institution specialized in the study, training, and research of music. Such an institution can also be known as a school of music, music academy, music faculty, college of music, music department (of a larger institution), conservatory, conservatorium or conservatoire (kənˈsəːrvətwɑːr , kɔ̃sɛʁvatwaʁ). Instruction consists of training in the performance of musical instruments, singing, musical composition, conducting, musicianship, as well as academic and research fields such as musicology, music history and music theory.

Music

Music is generally defined as the art of arranging sound to create some combination of form, harmony, melody, rhythm, or otherwise expressive content. Definitions of music vary depending on culture, though it is an aspect of all human societies and a cultural universal. While scholars agree that music is defined by a few specific elements, there is no consensus on their precise definitions. The creation of music is commonly divided into musical composition, musical improvisation, and musical performance, though the topic itself extends into academic disciplines, criticism, philosophy, and psychology.

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