Using Communications Toolbox™ you can implement fading channels using objects or blocks. Rayleigh and Rician fading channels are useful models of real-world phenomena in wireless communications. These phenomena include multipath scattering effects, time dispersion, and Doppler shifts that arise from relative motion between the transmitter and receiver. This section gives a brief overview of fading channels and describes how to implement them using the toolbox.
This figure depicts direct and major reflected paths between a stationary radio transmitter and a moving receiver. The shaded shapes represent reflectors such as buildings.
The major paths result in the arrival of delayed versions of the signal at the receiver. In addition, the radio signal undergoes scattering on a local scale for each major path. Such local scattering is typically characterized by many reflections by objects near the mobile. These irresolvable components combine at the receiver and cause a phenomenon known as multipath fading. Due to this phenomenon, each major path behaves as a discrete fading path. Typically, the fading process is characterized by a Rayleigh distribution for a non line-of-sight path and a Rician distribution for a line-of-sight path.
The relative motion between the transmitter and receiver causes Doppler shifts. Local scattering typically comes from many angles around the mobile. This scenario causes a range of Doppler shifts, known as the Doppler spectrum. The maximum Doppler shift corresponds to the local scattering components whose direction exactly opposes the trajectory of the mobile.
The channel filter applies path gains to the input signal, Signal in
.
The path gains are configured based on settings chosen in the fading channel object or
block.
These blocks and objects enable you to model SISO or MIMO fading channels.
Tool | SISO | MIMO |
---|---|---|
MATLAB^{®} | ||
Simulink^{®} |
A baseband channel model for multipath propagation scenarios that you implement using objects includes:
N discrete fading paths. Each path has its own delay and average power gain. A channel for which N = 1 is called a frequency-flat fading channel. A channel for which N > 1 is experienced as a frequency-selective fading channel by a signal of sufficiently wide bandwidth.
A Rayleigh or Rician model for each path.
Default channel path modeling using a Jakes Doppler spectrum, with a maximum Doppler shift that can be specified. Other types of Doppler spectra allowed (identical or different for all paths) include: flat, restricted Jakes, asymmetrical Jakes, Gaussian, bi-Gaussian, rounded, and bell.
If the maximum Doppler shift is set to 0 or omitted during the construction of a channel object, then the object models the channel as static. For this configuration, the fading does not evolve with time and the Doppler spectrum specified has no effect on the fading process.
Some additional information about typical values for delays and gains is in Choosing Realistic Channel Property Values.
The Channels block library includes MIMO and SISO fading blocks that can simulate real-world phenomena in mobile communications. These phenomena include multipath scattering effects, in addition to Doppler shifts that arise from relative motion between the transmitter and receiver.
To model a channel that involves both fading and additive white Gaussian noise, use a fading channel block connected in series with the AWGN Channel block, where the fading channel block comes first.
The MIMO Fading Channel and SISO Fading Channel blocks can be set to model Rayleigh or Rician fading distributions of the channel. Based on the type of signal path, choose the fading distribution to use.
Signal Path | Fading Distribution |
---|---|
Direct line-of-sight path from transmitter to receiver | Rician |
One or more major reflected paths from transmitter to receiver | Rayleigh |
In the case of multiple major reflected paths, a single instance of a fading channel block configured for Rayleigh fading distribution can model all of them simultaneously.
Choosing appropriate block parameters for your situation is important. For more information, see Choosing Realistic Channel Property Values, and the MIMO Fading Channel and SISO Fading Channel block reference pages.
You can view the characteristics of a fading channel using channel visualization tools. For more information, see Channel Visualization.
A communications system involving a fading channel usually requires components that compensate for the fading response. Typical approaches to compensate for fading include:
Differential modulation or a one-tap equalizer helps compensate for a frequency-flat fading channel. For information about implementing differential modulation, see the M-DPSK Modulator Baseband block reference page.
An equalizer with multiple taps helps compensate for a frequency-selective fading channel. See Equalization for more information.
The Communications Link with Adaptive Equalization example illustrates why compensating for a fading channel is necessary.
The Rayleigh and Rician multipath fading channel simulators in Communications Toolbox use the band-limited discrete multipath channel model of section 9.1.3.5.2 in [1]. This implementation assumes that the delay power profile and the Doppler spectrum of the channel are separable [1]. The multipath fading channel is therefore modeled as a linear finite impulse-response (FIR) filter. Let $$\left\{{s}_{i}\right\}$$ denote the set of samples at the input to the channel. Then the samples $$\left\{{y}_{i}\right\}$$ at the output of the channel are related to $$\left\{{s}_{i}\right\}$$ through:
$${y}_{i}={\displaystyle \sum _{n=-{N}_{1}}^{{N}_{2}}{s}_{i-n}{g}_{n}}$$
where $$\left\{{g}_{n}\right\}$$ is the set of tap weights given by:
$${g}_{n}={\displaystyle \sum _{k=1}^{K}{a}_{k}\text{sinc}\left[\frac{{\tau}_{k}}{{T}_{s}}-n\right]}\text{,}-{N}_{1}\le n\le {N}_{2}$$
In the equations:
$${T}_{s}$$ is the input sample period to the channel.
$$\left\{{\tau}_{k}\right\}$$, where $$1\le k\le K$$, is the set of path delays. K is the total number of paths in the multipath fading channel.
$$\left\{{a}_{k}\right\}$$, where $$1\le k\le K$$, is the set of complex path gains of the multipath fading channel. These path gains are uncorrelated with each other.
$${N}_{1}$$ and $${N}_{2}$$ are chosen so that $$\left|{g}_{n}\right|$$ is small when n is less than $$-{N}_{1}$$ or greater than $${N}_{2}$$.
Two techniques, filtered Gaussian noise and sum-of-sinusoids, are used to generate the set of complex path gains, $${a}_{k}$$.
Each path gain process $${a}_{k}$$ is generated by the following steps:
Filtered Gaussian Noise Technique
A complex uncorrelated (white) Gaussian process with zero mean and unit variance is generated in discrete time.
The complex Gaussian process is filtered by a Doppler filter with frequency response $$H(f)=\sqrt{S(f)}$$, where $$S(f)$$ denotes the desired Doppler power spectrum.
The filtered complex Gaussian process is interpolated so that its sample period is consistent with the sample period of the input signal. A combination of linear and polyphase interpolation is used.
Sum-of-sinusoids Technique
Mutually uncorrelated Rayleigh fading waveforms are generated using the method described in [2], where i = 1 corresponds to the in-phase component and i = 2 corresponds to the quadrature component.
$$\begin{array}{c}{z}_{k}(t)={\mu}_{k}^{(1)}(t)+j{\mu}_{k}^{(2)}(t),\text{\hspace{1em}}k=1,2,\dots ,K\\ {\mu}_{k}^{(i)}(t)=\sqrt{\frac{2}{{N}_{k}}}{\displaystyle \sum _{n=1}^{{N}_{k}}\mathrm{cos}\left(2\pi {f}_{k,n}^{(i)}t+{\theta}_{k,n}^{(i)}\right)},\text{\hspace{1em}}i=1,2\end{array}$$
Where
N_{k} specifies the number of sinusoids used to model a single path.
$${f}_{k,n}^{(i)}$$ is the discrete Doppler frequency and is calculated for each sinusoid component within a single path.
$${\theta}_{k,n}^{(i)}$$ is the phase of the nth component of $${\mu}_{k}^{(i)}$$ and is an i.i.d. random variable having a uniform distribution over the interval $$(0,2\pi ]$$.
t is the fading process time.
When modeling a Jakes Doppler spectrum, the discrete Doppler frequencies, $${f}_{k,n}^{(i)}$$, with maximum shift f_{max} are given by
$$\begin{array}{c}{f}_{k,n}^{(i)}={f}_{\mathrm{max}}\mathrm{cos}\left({\alpha}_{k,n}^{(i)}\right)\\ ={f}_{\mathrm{max}}\mathrm{cos}\left[\frac{\pi}{2{N}_{k}}\left(n-\frac{1}{2}\right)+{\alpha}_{k,0}^{(i)}\right]\end{array}$$
where
$${\alpha}_{k,0}^{(i)}\triangleq {\left(-1\right)}^{i-1}\frac{\pi}{4{N}_{k}}\cdot \frac{k}{K+2},\text{\hspace{1em}}i=1,\text{\hspace{0.17em}}2\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{and}\text{\hspace{0.17em}}\text{\hspace{0.17em}}k=1,2,\dots ,K$$
To advance the fading process in time, an initial time parameter, t_{init}, is introduced. The fading waveforms become
$${\mu}_{k}^{(i)}(t)=\sqrt{\frac{2}{{N}_{k}}}{\displaystyle \sum _{n=1}^{{N}_{k}}\mathrm{cos}\left(2\pi {f}_{k,n}^{(i)}\left(t+{t}_{init}\right)+{\theta}_{k,n}^{(i)}\right)},\text{\hspace{1em}}i=1,2$$
When t_{init} = 0, the fading process starts at time zero. A positive value of t_{init} advances the fading process relative to time zero while maintaining its continuity.
Channel fading samples are generated using the GMEDS_{1} [2] algorithm.
Calculate Complex Coefficients
The complex process resulting from either technique, $${z}_{k}$$, is scaled to obtain the correct average path gain. In the case of a Rayleigh channel, the fading process is obtained as:
$${a}_{k}=\sqrt{{\Omega}_{k}}{z}_{k}$$
where
$${\Omega}_{k}=E\left[{\left|{a}_{k}\right|}^{2}\right]$$
In the case of a Rician channel, the fading process is obtained as:
$${a}_{k}=\sqrt{{\Omega}_{k}}\left[\frac{{z}_{k}}{\sqrt{{K}_{r,k}+1}}+\sqrt{\frac{{K}_{r,k}}{{K}_{r,k}+1}}{e}^{j\left(2\pi {f}_{d,LOS,k}t+{\theta}_{LOS,k}\right)}\right]$$
where $${K}_{r,k}$$ is the Rician K-factor of the kth path, $${f}_{d,LOS,k}$$ is the Doppler shift of the line-of-sight component of the kth path (in Hz), and $${\theta}_{LOS,k}$$ is the initial phase of the line-of-sight component of the kth path (in rad).
At the input to the band-limited multipath channel model, the transmitted symbols must be oversampled by a factor at least equal to the bandwidth expansion factor introduced by pulse shaping. For example, if sinc pulse shaping is used, for which the bandwidth of the pulse-shaped signal is equal to the symbol rate, then the bandwidth expansion factor is 1, and at least one sample per symbol is required at the input to the channel. If a raised cosine (RC) filter with a factor more than 1 is used, for which the bandwidth of the pulse-shaped signal is equal to twice the symbol rate, then the bandwidth expansion factor is 2, and at least two samples per symbol are required at the input to the channel.
Channel Filter Model Characteristics
The channel model implements a fractional delay (FD) finite impulse response (FIR) bandpass filter with a length of 16 coefficients and with a delay error limited to 1%. For each discrete path, the FIR design is selected by choosing the optimal set of coefficients from among 50 fractional delay (0.02, 0.04, …, 0.98) candidate sets of coefficients.
Each discrete path is rounds to its selected fractional delay. To achieve a group delay bandwidth exceeding 80% and a magnitude bandwidth exceeding 90%, the algorithm selects the FIR coefficient values for each fractional delay, while satisfying the following criteria:
Group delay ripple ≤ 10%
Magnitude ripple ≤ 2 dB
Magnitude bandedge attenuation = 3 dB
The plots show bandwidths that satisfy the design criteria for group delay ripple, magnitude ripple, and magnitude bandedge attenuation.
For additional information, see the article A Matlab-based Object-Oriented Approach to Multipath Fading Channel Simulation at MATLAB Central.
[1] Jeruchim, M. C., Balaban, P., and Shanmugan, K. S., Simulation of Communication Systems, Second Edition, New York, Kluwer Academic/Plenum, 2000.
[2] Pätzold, Matthias, Cheng-Xiang Wang, and Bjorn Olav Hogstand. “Two New Sum-of-Sinusoids-Based Methods for the Efficient Generation of Multiple Uncorrelated Rayleigh Fading Waveforms.” IEEE Transactions on Wireless Communications. Vol. 8, Number 6, 2009, pp. 3122–3131.
Communications Toolbox models a fading channel as a linear FIR filter. Filtering a signal using a fading channel involves these steps:
Create a channel object that describes the channel that you want to use. A channel object is a type of MATLAB variable that contains information about the channel, such as the maximum Doppler shift.
Adjust properties of the channel object, if necessary, to tailor it to your needs. For example, you can change the path delays or average path gains.
Apply the channel object to your signal using calling the object.
This section describes how to define, inspect, and manipulate channel objects. The topics are:
To create a fading channel object suitable for your modeling situation, select one of these System objects.
Function | Object | Situation Modeled |
---|---|---|
Rayleigh fading channel object | One or more major reflected paths | |
Rician fading channel object | One direct line-of-sight path, possibly combined with one or more major reflected paths |
For example, this command creates a channel object representing a Rayleigh fading channel that acts on a signal sampled at 100,000 Hz. The maximum Doppler shift of the channel is 130 Hz.
rayChan1 = comm.RayleighChannel('SampleRate',1e5,... 'MaximumDopplerShift',130); % Rayleigh fading channel object
To learn how to call the rayChan1
fading channel object to filter
the transmitted signal through the channel, see Using Fading Channels.
You can also create another object by duplicating an existing object and then adjust
the properties of the new object, if necessary. To duplicate an object always use the
copy
function such as:
rayChan2 = copy(rayChan1); % Copy rayChan1 to create an independent rayChan2.
instead of rayChan2 = rayChan1
. The copy
command creates a copy of rayChan1
that is independent of
rayChan1
. By contrast, the command rayChan2 =
rayChan1
creates rayChan2
as merely a reference to
rayChan1
, so that rayChan1
and
rayChan2
always have indistinguishable content.
A channel object has numerous properties that record information about the channel model, about the state of a channel that has already filtered a signal, and about the channel operation on a future signal.
You can view the properties in these ways:
To view all properties of a channel object, enter the object name in the Command Window.
You can view a property of a channel object or assign the value to a variable by entering the object name followed by a dot (period), followed by the name of the property.
You can change the writable properties of a channel object in these ways:
To change the default value of a channel object property, enter the desired value in the object creation syntax.
To change the value of a writeable property of a channel object, issue an assignment statement that uses dot notation on the channel object. More specifically, dot notation means an expression that consists of the object name, followed by a dot, followed by the name of the property.
Display Rayleigh Channel Object Properties
Create a Rayleigh channel object. Display the object to show the properties initialized by default and the ones specified when creating the object. Entering rayChan
displays all properties of the channel object. Some of the properties values were assigned when the object was created, while other properties have default values. For more information about specific channel properties, see the reference page for the comm.RayleighChannel
object.
rayChan = comm.RayleighChannel('SampleRate',1e5,'MaximumDopplerShift',130); rayChan % View all the properties
rayChan = comm.RayleighChannel with properties: SampleRate: 100000 PathDelays: 0 AveragePathGains: 0 NormalizePathGains: true MaximumDopplerShift: 130 DopplerSpectrum: [1x1 struct] Show all properties
g = rayChan.AveragePathGains % Retrieve the AveragePathGains property of rayChan
g = 0
Adjust Rician Channel Object Properties
A Rician fading channel object has an additional property that does not appear for a Rayleigh fading channel object, namely, a scalar KFactor
property. For more information about Rician channel properties, see the reference page for the comm.RicianChannel
object.
Change Rician Channel Object Properties
Create a Rician channel object. The default setting for the Visualization
property is 'Off'
. Changing the default setting to 'Impulse response'
generates an impulse response plot of the output signal when the object is called.
ricChan= comm.RicianChannel; % Create object ricChan.Visualization = 'Impulse response' % Enables the impulse response channel visualization
ricChan = comm.RicianChannel with properties: SampleRate: 1 PathDelays: 0 AveragePathGains: 0 NormalizePathGains: true KFactor: 3 DirectPathDopplerShift: 0 DirectPathInitialPhase: 0 MaximumDopplerShift: 1.0000e-03 DopplerSpectrum: [1x1 struct] Show all properties
The output displays a subset of all the properties of the channel object. Select all properties
to see the complete set of properties for ricChan
.
Some properties of a channel object are related to each other such that when one
property's value changes, another property's value must change in some corresponding way
to keep the channel object consistent. For example, if you change the vector length of
PathDelays
, then the value of AveragePathGains
must change so that its vector length equals that of the new value of
PathDelays
. This is because the length of each of the two vectors
equals the number of discrete paths of the channel. For details about linked properties
and an example, see comm.RayleighChannel
or comm.RicianChannel
.
The Doppler spectrum of a channel object is specified through its
DopplerSpectrum
property. The value of this property must be either:
A Doppler object. In this case, the same Doppler spectrum applies to each path of the channel object.
A cell array of Doppler objects of the same length as the
PathDelays
vector property. In this case, the Doppler spectrum of
each path is given by the corresponding Doppler object in the vector.
When the vector length of the PathDelays
property is
increased, the length of DopplerSpectrum
is automatically
increased to match the length of PathDelays
, by appending
Jakes Doppler objects.
If the length of the PathDelays
vector property is
decreased, the length of DopplerSpectrum
is automatically
decreased to match the length of PathDelays
, by removing
the last Doppler object(s).
A Doppler object contains all the properties used to characterize the Doppler
spectrum, with the exception of the maximum Doppler shift, which is a property of the
channel object. This section describes how to create and manipulate Doppler objects, and how
to assign them to the DopplerSpectrum
property of channel
objects.
To create Doppler objects, use the doppler
function. The sole purpose of the doppler
function is to create Doppler objects used to specify the value of
the DopplerSpectrum
property of channel objects. Select from the
following:
doppler('Jakes')
doppler('Flat')
doppler('Rounded', ...)
doppler('Bell', ...)
doppler('Asymmetric Jakes', ...)
doppler('Restricted Jakes', ...)
doppler('Gaussian', ...)
doppler('BiGaussian', ...)
For example, a Gaussian spectrum with a normalized (by the maximum Doppler shift of the channel) standard deviation of 0.1, can be created as:
dopp1 = doppler('Gaussian',0.1);
When creating a Doppler object, consider the following dependencies:
If you assign a single Doppler spectrum structure to
DopplerSpectrum
, all paths have the same specified Doppler
spectrum.
If the FadingTechnique
property is 'Sum of
sinusoids'
, DopplerSpectrum
must be
doppler('Jakes')
;
If you assign a row cell array of different Doppler spectrum structures (which
can be chosen from any of those on the previous list) to
DopplerSpectrum
, each path has the Doppler spectrum
specified by the corresponding structure in the cell array. In this case, the
length of DopplerSpectrum
must be equal to the length of
PathDelays
.
To generate C code, specify DopplerSpectrum
to a single
Doppler spectrum structure.
As in the case of channel objects, Doppler objects can be duplicated using the
copy
function. The command:
dopp2 = copy(dopp1);
creates a Doppler object dopp2
with the same properties as that of
dopp1
. dopp1
and dopp2
are then
separate instances of a Doppler object, in that modifying either one will not affect the
other. Using dopp1 = dopp2
instead will cause both
dopp1
and dopp2
to reference the same instance of
a Doppler object, in that modifying either one will cause the same modification to the
other.
The syntax for viewing and changing Doppler object properties is the same as for the case of channel objects (see Displaying and Changing Object Properties).
Create, change, and view a doppler
object
A rounded Doppler object with default properties is created and displayed, and the third element of its Polynomial
property is modified.
doppRound = doppler('Rounded')
doppRound = struct with fields:
SpectrumType: 'Rounded'
Polynomial: [1 -1.7200 0.7850]
Adjust the third coefficient of the polynomial.
doppRound.Polynomial(3) = 0.825
doppRound = struct with fields:
SpectrumType: 'Rounded'
Polynomial: [1 -1.7200 0.8250]
The property SpectrumType
, which is common to all Doppler objects, is read-only. It is automatically specified at object construction, and cannot be modified. If you wish to use a different Doppler spectrum type, you need to create a new Doppler object of the desired type.
The DopplerSpectrum
property of a channel object can be changed by
assigning to it a Doppler object or a vector of Doppler objects.
This example illustrates how to change the default Jakes Doppler spectrum of a constructed Rayleigh channel object to a flat Doppler spectrum.
Create a Rayleigh channel object
Set the sample rate to 9600 Hz and the maximum Doppler shift to 100 Hz.
rayChan = comm.RayleighChannel('SampleRate',9600, 'MaximumDopplerShift',100)
rayChan = comm.RayleighChannel with properties: SampleRate: 9600 PathDelays: 0 AveragePathGains: 0 NormalizePathGains: true MaximumDopplerShift: 100 DopplerSpectrum: [1x1 struct] Show all properties
rayChan.DopplerSpectrum
ans = struct with fields:
SpectrumType: 'Jakes'
Modify the Doppler spectrum
Create a flat Doppler object and assign it in the rayChan
object.
doppFlat = doppler('Flat')
doppFlat = struct with fields:
SpectrumType: 'Flat'
rayChan.DopplerSpectrum = doppFlat
rayChan = comm.RayleighChannel with properties: SampleRate: 9600 PathDelays: 0 AveragePathGains: 0 NormalizePathGains: true MaximumDopplerShift: 100 DopplerSpectrum: [1x1 struct] Show all properties
rayChan.DopplerSpectrum
ans = struct with fields:
SpectrumType: 'Flat'
This example shows how to change the default Jakes Doppler spectrum of a constructed Rician channel object to a Gaussian Doppler spectrum with normalized standard deviation of 0.3, and subsequently display the DopplerSpectrum
property, and change the value of the normalized standard deviation to 1.1.
Create a Rician channel object
Set the sample rate to 9600 Hz, the maximum Doppler shift to 100 Hz, and K factor to 2.
ricChan = comm.RicianChannel('SampleRate',9600, 'MaximumDopplerShift',100, 'KFactor',2)
ricChan = comm.RicianChannel with properties: SampleRate: 9600 PathDelays: 0 AveragePathGains: 0 NormalizePathGains: true KFactor: 2 DirectPathDopplerShift: 0 DirectPathInitialPhase: 0 MaximumDopplerShift: 100 DopplerSpectrum: [1x1 struct] Show all properties
ricChan.DopplerSpectrum
ans = struct with fields:
SpectrumType: 'Jakes'
Modify the Doppler spectrum
Create a Gaussian Doppler object with normalized standard deviation of 0.3 and assign it in the ricChan
object.
doppGaus = doppler('Gaussian',0.3)
doppGaus = struct with fields:
SpectrumType: 'Gaussian'
NormalizedStandardDeviation: 0.3000
ricChan.DopplerSpectrum = doppGaus
ricChan = comm.RicianChannel with properties: SampleRate: 9600 PathDelays: 0 AveragePathGains: 0 NormalizePathGains: true KFactor: 2 DirectPathDopplerShift: 0 DirectPathInitialPhase: 0 MaximumDopplerShift: 100 DopplerSpectrum: [1x1 struct] Show all properties
ricChan.DopplerSpectrum
ans = struct with fields:
SpectrumType: 'Gaussian'
NormalizedStandardDeviation: 0.3000
ricChan.DopplerSpectrum.NormalizedStandardDeviation = 1.1; ricChan.DopplerSpectrum
ans = struct with fields:
SpectrumType: 'Gaussian'
NormalizedStandardDeviation: 1.1000
This example illustrates how to change the default Jakes Doppler spectrum of a constructed three-path Rayleigh channel object to a cell array of different Doppler spectra, and then change the properties of the Doppler spectrum of the third path.
Create a Rayleigh channel object
Set the sample rate to 9600 Hz, the maximum Doppler shift to 100 Hz, and specify path delays of 0
, 1e-4
, and 2.1e-4 seconds
.
rayChan = comm.RayleighChannel('SampleRate',9600, 'MaximumDopplerShift',100, 'PathDelays',[0 1e-4 2.1e-4])
rayChan = comm.RayleighChannel with properties: SampleRate: 9600 PathDelays: [0 1.0000e-04 2.1000e-04] AveragePathGains: 0 NormalizePathGains: true MaximumDopplerShift: 100 DopplerSpectrum: [1x1 struct] Show all properties
rayChan.DopplerSpectrum
ans = struct with fields:
SpectrumType: 'Jakes'
Modify the Doppler spectrum
Specify the DopplerSpectrum
property as a cell array with an independent Doppler spectrum for each path.
rayChan.DopplerSpectrum = {doppler('Flat') doppler('Flat') doppler('Rounded')}
rayChan = comm.RayleighChannel with properties: SampleRate: 9600 PathDelays: [0 1.0000e-04 2.1000e-04] AveragePathGains: 0 NormalizePathGains: true MaximumDopplerShift: 100 DopplerSpectrum: {[1x1 struct] [1x1 struct] [1x1 struct]} Show all properties
rayChan.DopplerSpectrum{:}
ans = struct with fields:
SpectrumType: 'Flat'
ans = struct with fields:
SpectrumType: 'Flat'
ans = struct with fields:
SpectrumType: 'Rounded'
Polynomial: [1 -1.7200 0.7850]
Change the Polynomial
property for the third path.
rayChan.DopplerSpectrum{3}.Polynomial = [1 -1.21 0.7]
rayChan = comm.RayleighChannel with properties: SampleRate: 9600 PathDelays: [0 1.0000e-04 2.1000e-04] AveragePathGains: 0 NormalizePathGains: true MaximumDopplerShift: 100 DopplerSpectrum: {[1x1 struct] [1x1 struct] [1x1 struct]} Show all properties
rayChan.DopplerSpectrum{:}
ans = struct with fields:
SpectrumType: 'Flat'
ans = struct with fields:
SpectrumType: 'Flat'
ans = struct with fields:
SpectrumType: 'Rounded'
Polynomial: [1 -1.2100 0.7000]
Before you filter a signal using a channel object, make sure that the properties of the channel have suitable values for the situation you want to model. This section offers some guidelines to help you choose realistic values that are appropriate for your modeling needs. The topics are
The syntaxes for viewing and changing values of properties of channel objects are described in Specifying a Fading Channel.
Here are some tips for choosing property values that describe realistic channels:
Path Delays
By convention, the first delay is typically set to zero. The first delay corresponds to the first arriving path.
For indoor environments, path delays after the first are typically between 1e-9 seconds and 1e-7 seconds.
For outdoor environments, path delays after the first are typically between 1e-7 seconds and 1e-5 seconds. Very large delays in this range might correspond, for example, to an area surrounded by mountains.
The ability of a signal to resolve discrete paths is related to its bandwidth. If the difference between the largest and smallest path delays is less than about 1% of the symbol period, then the signal experiences the channel as if it had only one discrete path.
Average Path Gains
The average path gains in the channel object indicate the average power gain of each fading path. In practice, an average path gain value is a large negative dB value. However, computer models typically use average path gains between -20 dB and 0 dB.
The dB values in a vector of average path gains often decay roughly linearly as a function of delay, but the specific delay profile depends on the propagation environment.
To ensure that the expected total power of the combined path gains is 1, you can
normalize path gains via the NormalizePathGains
property of the
channel object.
Maximum Doppler Shifts
Some wireless applications, such as standard GSM (Global System for Mobile Communication) systems, prefer to specify Doppler shifts in terms of the speed of the mobile. If the mobile moves at speed v (m/s), then the maximum Doppler shift is calculated as follows, where f is the transmission carrier frequency in Hertz and c is the speed of light (3e8 m/s).
$${f}_{d}=\frac{vf}{c}$$
Based on this formula in terms of the speed of the mobile, a signal from a moving car on a freeway might experience a maximum Doppler shift of about 80 Hz, while a signal from a moving pedestrian might experience a maximum Doppler shift of about 4 Hz. These figures assume a transmission carrier frequency of 900 MHz.
A maximum Doppler shift of 0 corresponds to a static channel that comes from a Rayleigh or Rician distribution.
K-Factor for Rician Fading Channels
The Rician K-factor specifies the ratio of specular-to-diffuse power for a direct line-of-sight path. The ratio is expressed linearly, not in dB.
For Rician fading, the K-factor is typically between 1 and 10.
A K-factor of 0 corresponds to Rayleigh fading.
Doppler Spectrum Parameters
See the doppler
reference page for the respective
Doppler objects for descriptions of the parameters and their significance.
Tips for configuring a channel object to customize the filtering process:
If your data is partitioned into a series of vectors (that you process within a loop, for example), you can call the channel object multiple times (in each iteration within a loop). The channel's state information is updated and saved after each invocation. The channel output is irrelevant to how the data is partitioned (vector length).
If want the channel output to be repeatable, choose the seed option for the
RandomStream
property of the channel object. To repeat the
output, call the reset
object function to reset both the internal
filters and the internal random number generator.
If you want to model discontinuously transmitted data, set the
FadingTechnique
property to 'Sum of sinusoids'
and the InitialTimeSource
property to 'Input
port'
for the channel object. When calling the object, specify the start
time of each data vector/frame to be processed by the channel via an input.
If you want to normalize the fading process so that the expected value of the
path gains' total power is 1 (the channel does not contribute additional power gain or
loss), set the NormalizePathGains
property of the channel object to
true
.
After you have created a channel object as described in Specifying a Fading Channel, you can call the object to pass a signal through the channel. Provide the signal as an input argument to the channel object. At the end of the filtering operation, the channel object retains its state so that you can find out the final path gains or the total number of samples that the channel has processed by calling the info object function with the object as the input argument.
For an example that illustrates the basic syntax and state retention, see Power of a Faded Signal.
To visualize the characteristics of the channel set the Visualization
property to 'Impulse response'
, 'Frequency response'
,
or 'Doppler spectrum'
. For more information, see Channel Visualization.
This example shows you how to visualize impulse response of a channel.
Create a channel object
While creating the channel object, use name-value pairs to set the Visualization
property
to 'Impulse response'
.
rayChan = comm.RayleighChannel('SampleRate',100000,'MaximumDopplerShift',130,... 'PathDelays',[0 1.5e-5 3.2e-5],'AveragePathGains',[0, -3, -3],... 'Visualization','Impulse response');
Generate a bit stream and create a modulator object. Modulate the bit stream and pass the
modulated DBPSK signal through the channel by calling the channel object.
tx = randi([0 1],500,1); dbspkMod = comm.DBPSKModulator; dpskSig = dbspkMod(tx); y = rayChan(dpskSig);
The impulse response is plotted when the object is called.
These examples use fading channels:
This example plots the power of a faded signal versus sample number. It illustrates the syntax of creating and calling a comm.RayleighChannel
fading channel object and the state retention of the channel object.
rayChan = comm.RayleighChannel('SampleRate',10000,'MaximumDopplerShift',100); sig = j*ones(2000,1); % Signal out = rayChan(sig); % Pass signal through channel. rayChan % Display all properties of the channel object.
rayChan = comm.RayleighChannel with properties: SampleRate: 10000 PathDelays: 0 AveragePathGains: 0 NormalizePathGains: true MaximumDopplerShift: 100 DopplerSpectrum: [1x1 struct] Show all properties
% Plot power of faded signal, versus sample number.
plot(20*log10(abs(out)))
This example creates a frequency-flat Rayleigh fading channel object and calls it to process a DBPSK signal consisting of a single vector. Bit error rate is computed for different values of the signal-to-noise ratio. The fading channel filter is applied before AWGN. This is the recommended sequence to use when you combine fading with AWGN.
Create Rayleigh fading channel, modulator, and demodulator objects
chan = comm.RayleighChannel('SampleRate',1e4,'MaximumDopplerShift',100);
Create DBPSK modulator and demodulator objects with the modulation order set to 2
. Generate DBPSK modulated data and pass it through the channel.
M = 2; % DBPSK modulation order tx = randi([0 M-1],50000,1); % Generate a random bit stream mod = comm.DBPSKModulator; demod = comm.DBPSKDemodulator; dpskSig = mod(tx); fadedSig = chan(dpskSig); % Apply the channel effects
Create an AWGN channel object and an error rate calculator object.
awgnChan = comm.AWGNChannel('NoiseMethod', 'Signal to noise ratio (SNR)'); errorCalc = comm.ErrorRate;
Compute error rate for different values of SNR.
SNR = 0:2:20; % Range of SNR values, in dB. numSNR = length(SNR); berVec = zeros(3, numSNR); % Preallocate a vector for BER results for n = 1:numSNR awgnChan.SNR = SNR(n); rxSig = awgnChan(fadedSig); % Add Gaussian noise rx = demod(rxSig); % Demodulate reset(errorCalc) berVec(:,n) = errorCalc(tx,rx); % Compute error rate. end BER = berVec(1,:);
Compute theoretical performance results, for comparison.
BERtheory = berfading(SNR,'dpsk',M,1);
Plot BER results.
semilogy(SNR,BERtheory,'b-',SNR,BER,'r*'); legend('Theoretical BER','Empirical BER'); xlabel('SNR (dB)'); ylabel('BER'); title('Binary DPSK over Rayleigh Fading Channel');
The value of a channel object's ChannelFilterDelay property is the number of samples by which the output of the channel lags the input. If you compare the input and output data sets directly, you must take the delay into account by using appropriate truncating or padding operations.
The example illustrates a way to account for the delay before computing a bit error rate.
Create DBPSK modulator and demodulator objects with the modulation order set to 2
. Generate DBPSK modulated data and pass it through the channel.
bitRate = 50000;
M = 2; % DQPSK modulation order
mod = comm.DBPSKModulator;
demod = comm.DBPSKDemodulator;
Create Rayleigh fading channel object.
rayChan = comm.RayleighChannel('SampleRate',bitRate,'MaximumDopplerShift',4,... 'PathDelays',[0 0.5/bitRate],'AveragePathGains',[0 -10]); chInfo = info(rayChan); delay = chInfo.ChannelFilterDelay;
Generate random bit stream data. Modulate the data, pass it through the fading channel, and demodulate it.
tx = randi([0 M-1],50000,1); dpskSig = mod(tx); fadedSig = rayChan(dpskSig); rx = demod(fadedSig);
Compute bit error rate, taking delay into account.
errorCalc = comm.ErrorRate('ReceiveDelay', delay);
berVec = step(errorCalc,tx,rx);
ber = berVec(1)
ber = 0.0147
num = berVec(2)
num = 737
This example filters input data through a Rayleigh fading channel within a for
loop. It uses the small data sets from successive iterations to create an animated effect. The Rayleigh fading channel has two discrete major paths. For information on filtering data through a channel multiple times while maintaining continuity from one invocation to the next, see Configure Channel Objects Based on Simulation Needs.
Set up parameters. Specify a bit rate of 50e3
Hz, and a loop iteration count of 125
. Create a QPSK modulator and Rayleigh fading channel objects.
bitRate = 50000; % Data rate is 50 kb/s numTrials = 125; % Number of iterations of loop M = 4; % QPSK modulation order qpskMod = comm.QPSKModulator; rayChan = comm.RayleighChannel('SampleRate',bitRate,'MaximumDopplerShift',4,'PathDelays',[0 2e-5],'AveragePathGains',[0 -9]);
Initialize a scatter plot.
scatterPlot = comm.ConstellationDiagram;
Apply channel in a loop, maintaining continuity. Plot only the current data in each iteration.
for n = 1:numTrials tx = randi([0 M-1],500,1); % Generate random bit stream pskSig = qpskMod(tx); % PSK modulate signal fadedSig = rayChan(pskSig); % Apply channel effects % Plot the new data from this iteration. update(scatterPlot,fadedSig); end
Typically, a path gain in a fading channel changes insignificantly over a period of 1/(100fd) seconds, where fd is the maximum Doppler shift. Because this period corresponds to a very large number of bits in many modern wireless data applications, assessing performance over a statistically significant range of fading entails simulating a prohibitively large amount of data. Quasi-static channel modeling provides a more tractable approach, which you can implement using these steps:
Generate a random channel realization using a maximum Doppler shift of 0.
Process some large number of bits.
Compute error statistics.
Repeat these steps many times to produce a distribution of the performance metric.
This example illustrates the quasi-static channel modeling approach.
Create modulator and demodulator system objects with a modulation order of 4
.
M = 4; dpskMod = comm.DPSKModulator('ModulationOrder', M); dpskDemod = comm.DPSKDemodulator('ModulationOrder', M); numBits = 10000; % Each trial uses 10000 bits. numTrials = 20; % Number of BER computations
Note: In reality, numTrials
would be a large number to get an accurate estimate of outage
probabilities or packet error rate. Use 20
here just to make the example run more quickly.
Create a Rician channel object and set the maximum Doppler shift to zero.
ricianChan = comm.RicianChannel('KFactor',3,'MaximumDopplerShift',0);
Within a for
loop, generate a random bit stream, DPSK modulate the signal, filter the modulated
signal through a Rician fading and AWGN channels, and demodulate the faded signal.
nErrors = zeros(1,numTrials); for n = 1:numTrials tx = randi([0 M-1],numBits,1); % Generate random bit stream dpskSig = dpskMod(tx); % DPSK modulate signal fadedSig = ricianChan(dpskSig); % Apply channel effects rxSig = awgn(fadedSig,15,'measured'); % Add Gaussian noise. rx = dpskDemod(rxSig); % Demodulate.
Compute number of symbol errors. Ignore first sample because of DPSK initial condition.
nErrors(n) = symerr(tx(2:end),rx(2:end))
end
nErrors = 1×20
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nErrors = 1×20
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
While the example runs, the Command Window displays the growing list of symbol error
counts in the vector nErrors
. It also displays the packet error rate at the end. The sample
output below shows a final value of nErrors
and omits intermediate values. Your results
might vary because of randomness in the example.
per = mean(nErrors > 0) % Proportion of packets that had errors
per = 0.0500
As an example to show how the quasi-static channel modeling approach can save computation, consider a wireless local area network (LAN) in which the carrier frequency is 2.4 GHz, mobile speed is 1 m/s, and bit rate is 10 Mb/s. The following expression shows that the channel changes insignificantly over 12,500 bits:
$$\begin{array}{c}\left(\frac{1}{100{f}_{d}}\text{s}\right)\left(\text{10Mb/s}\right)=\left(\frac{c}{100vf}\text{s}\right)\left(10\text{Mb/s}\right)\\ =\frac{3\times {10}^{8}\text{m/s}}{100(1\text{m/s})(2.4\text{GHz})}\left(10\text{Mb/s}\right)\\ =\text{12},500\text{b}\end{array}$$
A traditional Monte Carlo approach for computing the error rate of this system would entail simulating thousands of times the number of bits shown above, perhaps tens of millions of bits. By contrast, a quasi-static channel modeling approach would simulate a few packets at each of about 100 locations to arrive at a spatial distribution of error rates. From this distribution one could determine, for example, how reliable the communication link is for a random location within the indoor space. If each simulation contains 5,000 bits, 100 simulations would process half a million bits in total. This is substantially fewer bits compared to the traditional Monte Carlo approach.
These models include the use of fading channels:
Rayleigh Fading Channel, which illustrates the channel's effect on a QPSK modulated signal