{ "cells": [ { "cell_type": "markdown", "metadata": { "nbsphinx": "hidden" }, "source": [ "# Periodic Signals\n", "\n", "*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Comunications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convolution of a Periodic with an Aperiodic Signal\n", "\n", "The convolution $y(t) = x(t) * h(t)$ of a periodic signal $x(t)$ with an aperiodic signal $h(t)$ results in a periodic signal. This implies for instance that the output signal $y(t)$ of a linear time-invariant (LTI) system with aperiodic impulse response $h(t)$ is periodic for a periodic input signal $x(t)$. This is shown in the following.\n", "\n", "The [spectrum $X(j \\omega)$ of a periodic signal](spectrum.ipynb#Fourier-Transform) $x(t)$ is given as\n", "\n", "\\begin{equation}\n", "X(j \\omega) = X_0(j \\omega) \\cdot {\\bot \\!\\! \\bot \\!\\! \\bot} \\left( \\frac{\\omega T_\\text{p}}{2 \\pi} \\right) \n", "\\end{equation}\n", "\n", "where $X_0(j \\omega) = \\mathcal{F} \\{ x_0(t) \\}$ denotes the Fourier transform of one period $x_0(t)$ of the periodic signal. The spectrum $Y(j \\omega) = \\mathcal{F} \\{ x(t) * h(t) \\} = X(j \\omega) \\cdot H(j \\omega)$ is computed by introducing the spectrum $X(j \\omega)$ of the periodic signal and $H(j \\omega)$ of the aperiodic signal (e.g. impulse response) \n", "\n", "\\begin{align}\n", "Y(j \\omega) &= X_0(j \\omega) \\cdot {\\bot \\!\\! \\bot \\!\\! \\bot} \\left( \\frac{\\omega T_\\text{p}}{2 \\pi} \\right) \\cdot H(j \\omega) \\\\\n", "&= \\frac{2 \\pi}{T_\\text{p}} \\sum_{\\mu = - \\infty}^{\\infty} X_0 \\left( j \\, \\mu \\frac{2 \\pi}{T_\\text{p}} \\right) \\cdot\n", "H \\left( j \\, \\mu \\frac{2 \\pi}{T_\\text{p}} \\right) \\cdot \\delta \\left( \\omega - \\mu \\frac{2 \\pi}{T_\\text{p}} \\right)\n", "\\end{align}\n", "\n", "where the [definition of the Dirac comb](spectrum.ipynb#The-Dirac-Comb) and the multiplication property of the Dirac impulse was used to derive the last equality. The last equality shows that the spectrum $Y(j \\omega)$ is a line spectrum. From this it can be concluded that the signal $y(t)$ has to be periodic. Hence, the convolution of a periodic with an aperiodic signal results in a periodic signal. \n", "\n", "The Fourier series expansion of the convolution $y(t) = x(t) * h(t)$ can be deduced by considering the [relation between the spectrum of a periodic signal and the Fourier series](fourier_series.ipynb#Relation-between-Spectrum-and-Fourier-Series)\n", "\n", "\\begin{equation}\n", "y(t) = \\frac{1}{T_\\text{p}} \\sum_{n = - \\infty}^{\\infty} X_0 \\left( j \\, n \\frac{2 \\pi}{T_\\text{p}} \\right) \\cdot\n", "H \\left( j \\, n \\frac{2 \\pi}{T_\\text{p}} \\right) e^{j n \\frac{2 \\pi}{T_\\text{p}} t}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": { "nbsphinx": "hidden" }, "source": [ "**Copyright**\n", "\n", "This notebook is provided as [Open Educational Resource](https://en.wikipedia.org/wiki/Open_educational_resources). Feel free to use the notebook for your own purposes. The text is licensed under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), the code of the IPython examples under the [MIT license](https://opensource.org/licenses/MIT). Please attribute the work as follows: *Sascha Spors, Continuous- and Discrete-Time Signals and Systems - Theory and Computational Examples*." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }