comm.MER

Measure modulation error ratio

Description

The comm.MER (modulation error ratio) object measures the signal-to-noise ratio (SNR) in digital modulation applications. You can use MER measurements to determine system performance in communications applications. For example, determining whether a DVB-T system conforms to applicable radio transmission standards requires accurate MER measurements. The block measures all outputs in dB.

To measure modulation error ratio:

Define and set up your MER object. See Construction.
Call step to measure the modulation error ratio according to the properties of comm.MER. The behavior of step is specific to each object in the toolbox.

Note

Starting in R2016b, instead of using the step method to perform the operation defined by the System object™, you can call the object with arguments, as if it were a function. For example, y = step(obj,x) and y = obj(x) perform equivalent operations.

Construction

MER = comm.MER creates a modulation error ratio (MER) System object, MER. This object measures the signal-to-noise ratio (SNR) in digital modulation applications.

MER = comm.MER(Name,Value) creates an MER object with each specified property set to the specified value. You can specify additional name-value pair arguments in any order as (Name1,Value1,...,NameN,ValueN).

Example: MER = comm.MER('ReferenceSignalSource','Estimated from reference constellation') creates an object, MER, that measures the MER of a received signal by using a reference constellation.

Properties

`ReferenceSignalSource`	Reference signal source Reference signal source, specified as either `'Input port'` (default) or `'Estimated from reference constellation'`. To provide an explicit reference signal against which the input signal is measured, set this property to `'Input port'`. To measure the MER of the input signal against a reference constellation, set this property to `'Estimated from reference constellation'`.
`ReferenceConstellation`	Reference constellation Reference constellation, specified as a vector. This property is available when the `ReferenceSignalSource` property is `'Estimated from reference constellation'`. The default is `[0.7071 - 0.7071i; -0.7071 - 0.7071i; -0.7071 + 0.7071i; 0.7071 + 0.7071i]`, which corresponds to a standard QPSK constellation. You can derive constellation points by using modulation functions or objects. For example, to derive the reference constellation for a 16-QAM signal, you can use `qammod(0:15,16)`.
`MeasurementIntervalSource`	Measurement interval source Measurement interval source, specified as one of the following: `'Input length'` (default), `'Entire history'`, `'Custom'`, or `'Custom with periodic reset'`. This property affects the RMS and maximum MER outputs only. To calculate MER using only the current samples, set this property to `'Input length'`. To calculate MER for all samples, set this property to `'Entire history'`. To calculate MER over an interval you specify and to use a sliding window, set this property to `'Custom'`. To calculate MER over an interval you specify and to reset the object each time the measurement interval is filled, set this property to `'Custom with periodic reset'`.
`MeasurementInterval`	Measurement interval Measurement interval over which the MER is calculated, specified in samples as a real positive integer. This property is available when `MeasurementIntervalSource` is `'Custom'` or `'Custom with periodic reset'`. The default is `100`.
`AveragingDimensions`	Averaging dimensions Averaging dimensions, specified as a positive integer or row vector of positive integers. This property determines the dimensions over which the averaging is performed. For example, to average across the rows, set this property to `2`. The default is `1`. The object supports variable-size inputs over the dimensions in which the averaging takes place. However, the input size for the nonaveraged dimensions must remain constant between `step` calls. For example, if the input has size `[4 3 2]` and `Averaging dimensions` is `[1 3]`, the output size is `[1 3 1]`, and the second dimension must remain fixed at `3`.
`MinimumMEROutputPort`	Minimum MER measurement output port Minimum MER measurement output port, specified as a logical scalar. To create an output port for minimum MER measurements, set this property to `true`. The default is `false`.
`XPercentileMEROutputPort`	X-percentile MER measurement output port X-percentile MER measurement output port, specified as a logical scalar. To create an output port for X-percentile MER measurements, set this property to `true`. The X-percentile MER measurements persist until you reset the object. These measurements are calculated by using all of the input frames since the last reset. The default is `false`.
`XPercentileValue`	X-percentile value X-percentile value above which X% of the MER measurements fall, specified as a real scalar from `0` to `100`. This property applies when `XPercentileMEROutputPort` is `true`. The default is `95`.
`SymbolCountOutputPort`	Symbol count output port Symbol count output port, specified as a logical scalar. To output the number of accumulated symbols used to calculate the X-percentile MER measurements, set this property to `true`. This property is available when `XPercentileMEROutputPort` property is `true`. The default is `false`.

Methods

reset	Reset states of MER measurement object
step	Measure modulation error ratio

Common to All System Objects
`release`	Allow System object property value changes

Examples

collapse all

Measure MER of Noisy 16-QAM Modulated Signal

Open Live Script

Create an MER object which outputs minimum MER, 90-percentile MER, and the number of symbols.

mer = comm.MER('MinimumMEROutputPort',true, ...
    'XPercentileMEROutputPort',true,'XPercentileValue',90,...
    'SymbolCountOutputPort',true);

Generate random data. Apply 16-QAM modulation having unit average power. Pass the signal through an AWGN channel.

data = randi([0 15],1000,1);
refsym = qammod(data,16,'UnitAveragePower',true);
rxsym = awgn(refsym,20);

Determine the RMS, minimum, and 90th percentile MER values.

[MERdB,MinMER,PercentileMER,NumSym] = mer(refsym,rxsym)

MERdB = 20.1071

MinMER = 11.4248

PercentileMER = 16.5850

NumSym = 1000

Measure MER Using Reference Constellation

Open Live Script

Generate random data symbols, and apply 8-PSK modulation.

d = randi([0 7],2000,1);
txSig = pskmod(d,8,pi/8);

Pass the modulated signal through an AWGN channel.

rxSig = awgn(txSig,30);

Create an MER object. Measure the MER using the transmitted signal as the reference.

mer = comm.MER;
mer1 = mer(txSig,rxSig);

Release the MER object. Set the object to use a reference constellation for making MER measurements.

release(mer)
mer.ReferenceSignalSource = 'Estimated from reference constellation';
mer.ReferenceConstellation = pskmod(0:7,8,pi/8);

Measure the MER using only the received signal as an input. Verify that it matches the result obtained with a reference signal.

mer2 = mer(rxSig);
[mer1 mer2]

ans = 1×2

   30.0271   30.0271

Measure MER Using Custom Measurement Interval

Open Live Script

Measure the MER of a noisy 8-PSK signal using two types of custom measurement intervals. Display the results.

Set the number of frames, M, and the number of subframes per frame, K.

M = 2;
K = 5;

Set the number of symbols in a subframe. Calculate the corresponding frame length.

sfLen = 100;
frmLen = K*sfLen

frmLen = 500

Create an MER object. Configure the object to use a custom measurement interval equal to the frame length.

mer1 = comm.MER('MeasurementIntervalSource','Custom', ...
    'MeasurementInterval',frmLen);

Configure the object to measure MER using an 8-PSK reference constellation.

mer1.ReferenceSignalSource = 'Estimated from reference constellation';
mer1.ReferenceConstellation = pskmod(0:7,8,pi/8);

Create an MER object, and configure it use a 500-symbol measurement interval with a periodic reset. Configure the object to measure MER using an 8-PSK reference constellation.

mer2 = comm.MER('MeasurementIntervalSource','Custom with periodic reset', ...
    'MeasurementInterval',frmLen);
mer2.ReferenceSignalSource = 'Estimated from reference constellation';
mer2.ReferenceConstellation = pskmod(0:7,8,pi/8);

Initialize the MER and signal-to-noise arrays.

merNoReset = zeros(K,M);
merReset = zeros(K,M);
snrdB = zeros(K,M);

Measure the MER for a noisy 8-PSK signal using both objects. The SNR is increases by 1 dB from subframe to subframe. For merNoReset, the 500 most recent symbols are used to compute the estimate. In this case, a sliding window is used so that an entire data frame is used as the basis for the estimate. For merReset, the symbols are cleared each time a new frame is encountered.

for m = 1:M
    for k = 1:K
        data = randi([0 7],sfLen,1);
        txSig = pskmod(data,8,pi/8);
        snrdB(k,m) = k+(m-1)*K+7;
        rxSig = awgn(txSig,snrdB(k,m));
        merNoReset(k,m) = mer1(rxSig);
        merReset(k,m) = mer2(rxSig);
    end
end

Display the MER measured using the two approaches. The windowing used in the first case provides an averaging across the subframes. In the second case, the MER object resets after the first frame so that the calculated MER values more accurately reflect the current SNR.

stairs(snrdB(:),[merNoReset(:) merReset(:)])
xlabel('SNR (dB)')
ylabel('MER (%)')
legend('No Reset','Periodic Reset')

Measure MER Across Different Dimensions

Open Live Script

Create OFDM modulator and demodulator objects.

ofdmmod = comm.OFDMModulator('FFTLength',32,'NumSymbols',4);
ofdmdemod = comm.OFDMDemodulator('FFTLength',32,'NumSymbols',4);

Determine the number of subcarriers and symbols in the OFDM signal.

ofdmDims = info(ofdmmod);
numSC = ofdmDims.DataInputSize(1)

numSC = 21

numSym = ofdmDims.DataInputSize(2)

numSym = 4

Generate random symbols and apply QPSK modulation.

msg = randi([0 3],numSC,numSym);
modSig = pskmod(msg,4,pi/4);

OFDM modulate the QPSK signal. Pass the signal through an AWGN channel. Demodulate the noisy signal.

txSig = ofdmmod(modSig);
rxSig = awgn(txSig,10,'measured');
demodSig = ofdmdemod(rxSig);

Create an MER object, where the result is averaged over the subcarriers. Measure the MER. There are four entries corresponding to each of the 4 OFDM symbols.

mer = comm.MER('AveragingDimensions',1);
modErrorRatio = mer(demodSig,modSig)

modErrorRatio = 1×4

   11.2338   12.5315   12.8882   12.7015

Overwrite the MER object, where the result is averaged over the OFDM symbols. Measure the MER. There are 21 entries corresponding to each of the 21 subcarriers.

mer = comm.MER('AveragingDimensions',2);
modErrorRatio = mer(demodSig,modSig)

modErrorRatio = 21×1

   10.8054
   14.9655
   14.5721
   13.6024
   13.0132
   12.1391
   10.4012
    9.5017
    8.8055
   13.3824
      ⋮

Measure the MER and average over both the subcarriers and the OFDM symbols.

mer = comm.MER('AveragingDimensions',[1 2]);
modErrorRatio = mer(demodSig,modSig)

modErrorRatio = 12.2884

Algorithms

MER is a measure of the SNR in a modulated signal calculated in dB. The MER over N symbols is

$MER = 10 \cdot \log_{10} (\frac{\sum_{n = 1}^{N} (I_{k}^{2} + Q_{k}^{2})}{\sum_{n = 1}^{N} (e_{k})}) dB,$

The MER for the kth symbol is

$M E R_{k} = 10 * \log_{10} (\frac{\frac{1}{N} \sum_{n = 1}^{N} (I_{k}^{2} + Q_{k}^{2})}{e_{k}}) dB .$

The minimum MER represents the minimum MER value in a burst, or

$M E R_{\min} = \min_{k \in [1, ..., N]} {M E R_{k}},$

where:

e_k = $e_{k} = {(I_{k} - {\tilde{I}}_{k})}^{2} + {(Q_{k} - {\tilde{Q}}_{k})}^{2}$
I_k = In-phase measurement of the kth symbol in the burst
Q_k = Quadrature phase measurement of the kth symbol in the burst
I_k and Q_k represent ideal (reference) values. ${\tilde{I}}_{k}$ and ${\tilde{Q}}_{k}$ represent measured (received) symbols.

The block computes the X-percentile MER by creating a histogram of all the incoming MER_k values. The output provides the MER value above which X% of the MER values fall.

Documentation

comm.MER

Description

Construction

Properties

Methods

Examples

Measure MER of Noisy 16-QAM Modulated Signal

Measure MER Using Reference Constellation

Measure MER Using Custom Measurement Interval

Measure MER Across Different Dimensions

Algorithms

Extended Capabilities

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

See Also

Communications Toolbox Documentation

Support

Documentation

comm.MER

Description

Construction

Properties

Methods

Examples

Measure MER of Noisy 16-QAM Modulated Signal

Measure MER Using Reference Constellation

Measure MER Using Custom Measurement Interval

Measure MER Across Different Dimensions

Algorithms

Extended Capabilities

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

See Also

Communications Toolbox Documentation

Support

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