comm.ChannelFilter

Filter signal using multipath gains at specified path delays

Since R2020b

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Description

Use the comm.ChannelFilter System object™ to filter a signal using multipath gains at specified path delays.

To filter a signal using multipath gains:

Create the comm.ChannelFilter object and set its properties.
Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

Creation

Syntax

chanFilt = comm.ChannelFilter

chanFilt = comm.ChannelFilter(Name,Value)

Description

chanFilt = comm.ChannelFilter creates a multipath channel filter System object to filter an input signal with path gains at the specified path delays

chanFilt = comm.ChannelFilter(Name,Value) sets properties using one or more name-value pairs. For example, 'SampleRate',1e6 sets the sampling rate to 1 MHz. Enclose each property name in quotes.

example

Properties

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Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

`SampleRate` — Sample rate
`1` Hz (default) | real positive scalar

Sample rate of the input signal, specified as a real, positive scalar.

Data Types: double

`PathDelays` — Discrete path delays
`0` (default) | real scalar | real vector

Delays of the discrete paths in seconds, specified as a real scalar or vector.

Data Types: double

`FilterDelaySource` — Channel filter delay source
`'Auto'` (default) | `'Custom'`

Channel filter delay source, specified as either 'Auto' or 'Custom'.

Set FilterDelaySource to 'Auto' to specify the channel filter delay as the minimum possible value.
Set FilterDelaySource to 'Custom' to specify the channel filter delay as a custom value. The custom value cannot be smaller than the minimum possible value.

Data Types: char

`FilterDelay` — Channel filter delay
`7` (default) | real non-negative integer scalar

Channel filter delay in samples, specified as a real, non-negative, integer scalar.

Dependencies

To enable this property, set the FilterDelaySource property to 'Custom'. The specified value must be no smaller than the automatically determined channel filter delay when you set FilterDelaySource to 'Auto'.

Data Types: double

`NormalizeChannelOutputs` — Normalize outputs by number of receive antennas
`1` or `true` (default) | `0` or `false`

Normalize outputs by number of receive antennas, specified as a logical 1 (true) or 0 (false).

Data Types: logical

Usage

Syntax

Y = chanFilt(X,G)

Description

Y = chanFilt(X,G) filters input signal X, through a multipath channel with path gains G, at the path delay locations specified by the PathDelays property.

example

Input Arguments

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`X` — Input signal
matrix | `dlarray` object

Input signal, specified as an N_S-by-N_T matrix, or a dlarray (Deep Learning Toolbox) object. N_S is the number of samples and N_T is the number of transmit antennas. For more information, see Array Support.

This object accepts variable-size inputs. After the object is locked, you can change the size of each input channel, but you cannot change the number of channels. For more information, see Variable-Size Signal Support with System Objects.

Data Types: double | single
Complex Number Support: Yes

`G` — Path gain
array

Path gain, specified as an array. For more information, see Array Support.

The input G must be a N_S-by-N_P-by-N_T-by-N_R or 1-by-N_P-by-N_T-by-N_R array, where N_R is the number of receive antennas and N_P is the number of paths as determined by the length of the PathDelays property.

Data Types: double | single
Complex Number Support: Yes

Output Arguments

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`Y` — Channel output
matrix

Channel output, returned as a N_S-by-N_R matrix. The output matches the data type of X. For more information, see Array Support.

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

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Specific to `comm.ChannelFilter`

info Return characteristic information about channel filter

Common to All System Objects

`step`	Run System object algorithm
`release`	Release resources and allow changes to System object property values and input characteristics
`reset`	Reset internal states of System object

Examples

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Explore Spatial Diversity of Channel in Distributed MIMO System

Open Live Script

In a distributed MIMO system, explore spatial diversity by transmitting the same signal from two geographically separated transmitters and combining the received signals at one receiver. Use ray tracing to analyze the propagation paths and gains from each transmitter to receiver.

Perform Ray Tracing

Import buildings data for Chicago into siteviewer from an OpenStreetMap® (osm) file. For more information about the osm file, see [1]. Place two transmitter sites and one receiver site in the city.

sv = siteviewer("Buildings","Chicago.osm"); 
rx = rxsite("Name","Receiver", ...
    "Latitude",41.878543,"Longitude",-87.630599, ...
    "AntennaHeight",1.5);
show(rx)
tx1 = txsite("Name","Transmitter #1", ...
    "Latitude",41.878996,"Longitude",-87.629361); 
show(tx1)
tx2 = txsite("Name","Transmitter #2", ...
    "Latitude",41.880142,"Longitude",-87.630850);
show(tx2)

Perform ray tracing from the transmitter sites to the receiver site. Then, plot the propagation paths. By default, the raytrace function uses the shooting and bouncing rays (SBR) method and calculate paths with up to two reflections.

rays = raytrace([tx1 tx2],rx);
plot([rays{:}])

Many propagation paths from transmitter site to receiver site

Ray tracing finds several ray paths to the receiver from each transmitter. From the map we can visually see the first transmitter is closer to the receiver than the second transmitter. We can also see the first transmitter has more reflected paths to the receiver. Display the propagation delays associated with each transmitter.

pd1 = [rays{1}.PropagationDelay]

pd1 = 1×10
10^-6 ×

    0.3830    0.3839    0.5476    0.6482    0.5485    0.5486    0.6572    0.6945    0.7140    0.8874

pd2 = [rays{2}.PropagationDelay]

pd2 = 1×5
10^-6 ×

    0.5967    0.5973    0.6059    0.6066    0.6255

Construct one channel filter for each transmitter site. Specify a sample rate of 30 MHz and use the minimum delay among the seven rays as the reference of time 0.

chanFilt1 = comm.ChannelFilter( ...
    "SampleRate",30e6, ...
    "PathDelays",pd1-min([pd1, pd2]))

chanFilt1 = 
  comm.ChannelFilter with properties:

                 SampleRate: 30000000
                 PathDelays: [0 8.7598e-10 1.6459e-07 2.6516e-07 1.6553e-07 1.6565e-07 2.7417e-07 3.1151e-07 3.3105e-07 5.0442e-07]
          FilterDelaySource: 'Auto'
    NormalizeChannelOutputs: true

chanFilt2 = comm.ChannelFilter( ...
    "SampleRate",30e6, ...
    "PathDelays",pd2-min([pd1, pd2]))

chanFilt2 = 
  comm.ChannelFilter with properties:

                 SampleRate: 30000000
                 PathDelays: [2.1372e-07 2.1434e-07 2.2294e-07 2.2357e-07 2.4247e-07]
          FilterDelaySource: 'Auto'
    NormalizeChannelOutputs: true

The individual channel filters for the two transmitters yield different filter delay values. Use the info object function of comm.ChannelFilter to show the filter delay of the two channel filters.

fd1 = chanFilt1.info.ChannelFilterDelay

fd1 = 7

fd2 = chanFilt2.info.ChannelFilterDelay

fd2 = 1

The two channel filters must have the same filter delay to combine the channel outputs at the receiver site. Customize the filter delay for each channel filter to use the larger value of the individually computed delay values.

set(chanFilt1,"FilterDelaySource","Custom", ...
    "FilterDelay",max(fd1,fd2));
set(chanFilt2,"FilterDelaySource","Custom", ...
    "FilterDelay",max(fd1,fd2));

Apply Receive Signal Combining

Set up system parameters, assigning only one isotropic antenna at each site.

Nt = 1;    % Number of transmit elements
Ns = 1000; % Samples per frame
M  = 64;   % Modulation order

Retrieve path gains from the computed rays. Assume the sites are static and no Doppler shift is introduced.

pg1  = 10.^(-[rays{1}.PathLoss]/20) .* ...
    exp(1i*[rays{1}.PhaseShift]);
pg2  = 10.^(-[rays{2}.PathLoss]/20) .* ...
    exp(1i*[rays{2}.PhaseShift]);

Generate a frame of random 64-QAM signals. Perform channel filtering for each transmitter site and receive signal combining. The combined 2x1 distributed MIMO channel has a filter delay of max(fd1,fd2).

x  = qammod(randi([0, M-1],Ns,Nt),M);
y = chanFilt1(x,pg1) + chanFilt2(x,pg2);

Appendix

[1] The osm file is downloaded from https://www.openstreetmap.org, which provides access to crowd-sourced map data all over the world. The data is licensed under the Open Data Commons Open Database License (ODbL), https://opendatacommons.org/licenses/odbl/.

Perform Channel Filtering for an LTE 2x2 EVA Profile

Open Live Script

Construct a channel filter object with the LTE Extended Vehicular A model (EVA) delay profile.

chanFilt = comm.ChannelFilter( ...
    'SampleRate', 30.72e6, ...
    'PathDelays', [0 30 150 310 370 710 1090 1730 2510]*1e-9);

Set up system parameters. There are two transmit and receive antennas.

[Nt, Nr] = deal(2);
Ns = 30720;
Np = length(chanFilt.PathDelays);
M  = 256;

Generate random 256-QAM signal and complex path gains.

x = qammod(randi([0, M-1], Ns, Nt), M);
g = complex(rand(Ns, Np, Nt, Nr), rand(Ns, Np, Nt, Nr));

Filter the signal with path gains for the EVA delay profile.

y = chanFilt(x, g);

Reciprocal Downlink and Uplink Transmissions in MIMO Channel

Open Live Script

Using one MIMO channel System object™ and two identically configured channel filter System objects, switch a link-level simulation between 3-by-2 downlink and reciprocal 2-by-3 uplink signal transmissions.

Define system parameters.

modOrder = 256;        % Modulation order
Nant1 = 3;             % Number of 'transmit' antennas
Nant2 = 2;             % Number of 'receive' antennas   
Rs = 1e6;              % Sample rate 
pd = [0 1.5 2.3]*1e-6; % Path delays
frmLen = 1e3;          % Frame length

Create a MIMO channel System object™, configuring it for path gain generation by disabling channel filtering.

chan = comm.MIMOChannel( ...
    'SampleRate',Rs, ...
    'PathDelays',pd, ...
    'AveragePathGains',[1.5 1.2 0.2], ...
    'MaximumDopplerShift',300, ...
    'SpatialCorrelationSpecification','none', ...
    'NumTransmitAntennas',Nant1, ...
    'NumReceiveAntennas',Nant2, ...
    'ChannelFiltering',false, ...
    'NumSamples',frmLen);

Create identical channel filter System objects for both transmission directions: one channel filter for the Nant1-by-Nant2 downlink channel (3 transmit antennas to 2 receive antennas) and a reciprocal channel filter for the Nant2-by-Nant1 uplink channel (2 transmit antennas to 3 receive antennas).

chanFiltDownlink = comm.ChannelFilter( ...
    'SampleRate',Rs, ...
    'PathDelays',pd);
chanFiltUplink = clone(chanFiltDownlink);

Downlink Transmission

Generate random path gains for one frame of the downlink 3-by-2 channel. Pass randomly generated 256-QAM signals through the 3-by-2 downlink channel.

pgDownlink = chan();
x = qammod(randi([0 modOrder-1],frmLen,Nant1),modOrder);
yDL = chanFiltDownlink(x,pgDownlink);

Uplink Transmission

Switch the link direction. Run the channel object to generate another frame of path gains, permuting its 3rd (Tx) and 4th (Rx) dimensions for the reciprocal uplink 2-by-3 channel. Pass randomly generated 256-QAM signals through the 2-by-3 reciprocal uplink channel.

pgUplink = permute(chan(),[1 2 4 3]);
x = qammod(randi([0 modOrder-1],frmLen,Nant2),modOrder);
yUL = chanFiltUplink(x,pgUplink);

Downlink and Uplink Array Dimensions

Show the sizes of the downlink and uplink path gain arrays returned by the MIMI channel object as an $N_{S}$ -by- $N_{P}$ -by- $N_{T}$ -by- $N_{R}$ array.

$N_{S}$ is the number of samples.
$N_{P}$ is the number of path delays.
$N_{T}$ is the number of transmit antennas. Nant1 for downlink and Nant2 for uplink.
$N_{R}$ is the number of receive antennas. Nant2 for downlink and Nant1 for uplink.

size(pgDownlink)

ans = 1×4

        1000           3           3           2

size(pgUplink)

ans = 1×4

        1000           3           2           3

Show the size of the channel output matrices returned by the MIMI channel object as an $N_{S}$ -by- $N_{R}$ matrix. $N_{S}$ is the number of samples. $N_{R}$ is the number of receive antennas.

size(yDL)

ans = 1×2

        1000           2

size(yUL)

ans = 1×2

        1000           3

More About

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Array Support

The comm.ChannelFilter object supports an input signal represented in an array, dlarray (Deep Learning Toolbox), or gpuArray (Parallel Computing Toolbox).

If X is specified as a dlarray, Y is returned as a dlarray object.
If X is a dlarray holding a gpuArray, then G must be a gpuArray.
If X is a dlarray holding an array, then G must be an array.
The number of batch observations (N_B) is an optional dimension that can be added to the input for all supported data types. Variable N_B is not supported. When the N_B dimension is included:
- X — The input dimensions can be an array of up to three dimensions, specified as an N_S-by-N_T-by-N_B array.
- G — If X is a 3-D array, G can be an N_S-by-N_P-by-N_T-by-N_R or N_S-by-N_P-by-N_T-by-N_R-by-N_B array.
- Y — The output is returned as an N_S-by-N_R matrix or an N_S-by-N_R-by-N_B array.

For a list of Communications Toolbox™ features that support dlarray objects, see AI for Wireless.

Algorithms

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Channel Filter Model Characteristics

The channel filter implements a fractional delay (FD) finite impulse response (FIR) bandpass filter with a length of 16 coefficients for each candidate fractional delay at 0, 0.02, 0.04, …, 0.98.

Each discrete path is rounded to its nearest candidate fractional delay, so the delay error limit is 1% of the sample time. To achieve a group delay bandwidth exceeding 80% and a magnitude bandwidth exceeding 90%, the algorithm selects the optimal 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.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

See System Objects in MATLAB Code Generation (MATLAB Coder).

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This System object supports GPU array inputs. For more information, see Accelerate Simulation Using GPUs.

Usage notes and limitations:

To enable GPU processing, input signal (X) and path gains (G) must be gpuArray objects.

Version History

Introduced in R2020b

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R2024b: Add deep learning array support

The comm.ChannelFilter object adds support for dlarray (Deep Learning Toolbox) object processing for deep learning applications.

R2024a: Add GPU array support

The comm.ChannelFilter System object adds support for gpuArray (Parallel Computing Toolbox) object processing to run code on a graphics processing unit (GPU).

comm.ChannelFilter

Description

Creation

Syntax

Description

Properties

SampleRate — Sample rate 1 Hz (default) | real positive scalar

PathDelays — Discrete path delays 0 (default) | real scalar | real vector

FilterDelaySource — Channel filter delay source 'Auto' (default) | 'Custom'

FilterDelay — Channel filter delay 7 (default) | real non-negative integer scalar

Dependencies

NormalizeChannelOutputs — Normalize outputs by number of receive antennas 1 or true (default) | 0 or false

Usage

Syntax

Description

Input Arguments

X — Input signal matrix | dlarray object

G — Path gain array

Output Arguments

Y — Channel output matrix

Object Functions

Specific to comm.ChannelFilter

Common to All System Objects

Examples

Explore Spatial Diversity of Channel in Distributed MIMO System

Perform Channel Filtering for an LTE 2x2 EVA Profile

Reciprocal Downlink and Uplink Transmissions in MIMO Channel

More About

Array Support

Algorithms

Channel Filter Model Characteristics

Extended Capabilities

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

R2024b: Add deep learning array support

R2024a: Add GPU array support

See Also

Objects

`SampleRate` — Sample rate
`1` Hz (default) | real positive scalar

`PathDelays` — Discrete path delays
`0` (default) | real scalar | real vector

`FilterDelaySource` — Channel filter delay source
`'Auto'` (default) | `'Custom'`

`FilterDelay` — Channel filter delay
`7` (default) | real non-negative integer scalar

`NormalizeChannelOutputs` — Normalize outputs by number of receive antennas
`1` or `true` (default) | `0` or `false`

`X` — Input signal
matrix | `dlarray` object

`G` — Path gain
array

`Y` — Channel output
matrix

Specific to `comm.ChannelFilter`

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.