simu_fading

The function generates a sample from a system with Rayleigh flat-fading channels.

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Xialu Liu

Nonlinear Time Series Analysis

Ruey Tsay

Rong Chen

simu_fading function

<dl><dt>nobs</dt>
<dd>sample size.</dd>
<dt>par</dt>
<dd>a list with following components: <code>HH</code> is the state coefficient matrix; <code>WW</code>, <code>WW*t(WW)</code> is the state innovation covariance matrix;
<code>VV</code>, <code>VV*t(VV)</code> is the observation noise covariance matrix; <code>GG</code> is the observation model.</dd></dl>

Arguments

Simulate Signals from A System with Rayleigh Flat-Fading Channels — simu_fading

<dl>

<dt>nobs</dt>
<dd>sample size.</dd>


<dt>par</dt>
<dd>a list with following components: <code>HH</code> is the state coefficient matrix; <code>WW</code>, <code>WW*t(WW)</code> is the state innovation covariance matrix;
<code>VV</code>, <code>VV*t(VV)</code> is the observation noise covariance matrix; <code>GG</code> is the observation model.</dd>

</dl>

simu_fading: Simulate Signals from A System with Rayleigh Flat-Fading Channels

Description

Usage

Arguments

Examples