comm.CPMDemodulator

Demodulate signal using CPM method and Viterbi algorithm

Description

The comm.CPMDemodulator System object™ demodulates an input signal that was modulated using the continuous phase modulation (CPM) method. The input is a baseband representation of the modulated signal. For more information about the demodulation and filtering applied, see Algorithms.

To demodulate a signal that was modulated using the CPM method:

1. Create the comm.CPMDemodulator object and set its properties.

2. 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

cpmdemod = comm.CPMDemodulator

cpmdemod = comm.CPMDemodulator(Name=Value)

cpmdemod = comm.CPMDemodulator(M,Name=Value)

Description

cpmdemod = comm.CPMDemodulator creates a demodulator System object to demodulate input CPM signals using the Viterbi algorithm.

cpmdemod = comm.CPMDemodulator(Name=Value) sets properties using one or more name-value arguments. For example, comm.CPMDemodulator(SymbolMapping='Gray') configures the object with gray-coded symbol ordering for the modulated symbols.

cpmdemod = comm.CPMDemodulator(M,Name=Value) sets the ModulationOrder property to M and optional name-value arguments.

Properties

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.

ModulationOrder — Modulation order

4 (default) | power of two scalar

Modulation order, specified as a power-of-two scalar. The modulation order M = 2^k specifies number of points in the symbol alphabet. k is a positive integer indicating the number of bits per symbol.

BitOutput — Option to output data as bits

0 or false (default) | 1 or true

Option to output data as bits, specified as 0 (false) or 1 (true).

• Set this property to false to output data as integers.

• Set this property to true to output data as bits.

For more information, see Integer-Valued and Binary-Valued Output Signals.

DecisionMethod — Demodulation decision method

'Hard decision' (default) | 'Approximate log-likelihood ratio'

Demodulation decision method, specified as 'Hard decision' or 'Approximate log-likelihood ratio'.

• When you set this property to 'Hard decision', the output Y is returned as a column vector with element values of 0 or 1. The output data type is specified by OutputDataType.

• When you set this property to 'Approximate log-likelihood ratio', the output data type is the same as the input X. For the approximate log-likelihood ratio, the object generates positive values for 0s and negative values for 1s.

For more information, see Algorithms.

Dependencies

This property applies when you set BitOutput to true.

VarianceSource — Source of noise variance

'Property' (default) | 'Input port'

Source of noise variance, specified as 'Property' or 'Input port'.

• When you set this property to 'Property', you set the noise variance by using the Variance property.

• When you set this property to 'Input port', you set the noise variance by using the nvar input argument.

Dependencies

This property applies when you set BitOutput to true and DecisionMethod to 'Approximate log-likelihood ratio'.

Variance — Noise variance

1 (default) | positive scalar

Noise variance, specified as a positive scalar.

Tunable: Yes

Dependencies

This property applies when you set BitOutput to true, VarianceSource to 'Property', and DecisionMethod to 'Approximate log-likelihood ratio'.

Data Types: double

SymbolMapping — Symbol mapping

'Binary' (default) | 'Gray'

Symbol mapping, specified as 'Binary' or 'Gray'. This property determines how each integer maps to a group of output bits.

• Set this property to 'Binary' to map symbols using binary-coded ordering.

• Set this property to 'Gray' to map symbols using Gray-coded ordering.

Dependencies

To enable this property, set the BitOutput property to true.

ModulationIndex — Modulation index {h_i}

0.5 (default) | nonnegative scalar | column vector

Modulation index {h_i}, specified as a nonnegative scalar or column vector. The modulator operates in multi-h. For more information, see CPM Demodulation.

Data Types: double

FrequencyPulse — Type of frequency pulse shaping

'Rectangular' (default) | 'Raised Cosine' | 'Spectral Raised Cosine' | 'Gaussian' | 'Tamed FM'

Type of frequency pulse shaping used by the modulator to smooth the phase transitions of the modulated signal, specified as 'Rectangular', 'Raised Cosine', 'Spectral Raised Cosine', 'Gaussian', or 'Tamed FM'. For more information, see Pulse Shape Filtering.

MainLobeDuration — Main lobe duration

1 (default) | positive integer

Main lobe duration of the largest lobe in the spectral raised cosine pulse, specified as a positive integer representing the number of symbol intervals used by the demodulator to pulse-shape the modulated signal.

Dependencies

To enable this property, set the FrequencyPulse property to 'Spectral Raised Cosine'.

Data Types: double

RolloffFactor — Roll-off factor

0.2 (default) | scalar in the range [0, 1]

Roll-off factor of the spectral raised cosine pulse, specified as a scalar in the range [0, 1].

Dependencies

To enable this property, set the FrequencyPulse property to 'Spectral Raised Cosine'.

Data Types: double

BandwidthTimeProduct — Product of bandwidth and symbol time of Gaussian pulse shape

0.3 (default) | positive scalar

Product of the bandwidth and symbol time of the Gaussian pulse shape, specified as a positive scalar. Use BandwidthTimeProduct to reduce the bandwidth, at the expense of increased intersymbol interference.

Dependencies

To enable this property, set the FrequencyPulse property to 'Gaussian'.

Data Types: double

PulseLength — Length of frequency pulse shape

1 (default) | positive integer

Length of the frequency pulse shape in symbol intervals, specified as a positive integer. For more information on the frequency pulse length, refer to LT in Pulse Shape Filtering.

Data Types: double

SymbolPrehistory — Symbol prehistory

1 (default) | scalar | vector

Symbol prehistory, specified as scalar or vector with odd integer elements. Values must be in the range [–(M – 1), (M – 1)]. M is the modulation order specified by ModulationOrder. The SymbolPrehistory property defines the data symbols used by the modulator before the first call of the object, in reverse chronological order.

• A scalar value expands to a vector of length PulseLength – 1.

• For a vector, the length must be PulseLength – 1.

Data Types: double

InitialPhaseOffset — Initial phase offset

0 (default) | scalar

Initial phase offset in radians, specified as a scalar. This parameter value is initial phase offset of the modulated waveform.

SamplesPerSymbol — Symbol sampling rate

8 (default) | positive integer

Symbol sampling rate, specified as a positive integer. This property specifies the input symbol downsampling factor for each output sample.

Tip

To accurately model nonbinary pulse shapes, specifically pulse shapes other than rectangular, you should set the symbol sampling rate to values greater than 4.

Data Types: double

TracebackDepth — Traceback depth for Viterbi algorithm

16 (default) | positive integer

Traceback depth for the Viterbi algorithm, specified as a positive integer representing the number of trellis branches that the Viterbi algorithm uses to construct each traceback path. The value of this property is also the output delay and the number of zero symbols that precede the first meaningful demodulated symbol in the output. For more information, see Traceback Depth and Output Delays.

Data Types: double

OutputDataType — Data type of output

'double' (default) | 'single' | 'logical' | 'int8' | 'int16' | 'int32' | 'uint8' | 'uint16' | 'uint32'

Data type of the output, specified as 'double', 'int8', 'int16', 'int32', 'int32', 'uint8', 'uint16', 'uint32', or 'logical'.

• When you set the BitOutput property to false, you can set the output to double-precision, single-precision, or signed-integer data types.

• When you set the BitOutput property to true, you can set the output to double-precision, single-precision, signed-integer, unsigned-integer, or logical data types.

Dependencies

This property applies when you set BitOutput to false or when you set BitOutput to true and DecisionMethod to 'Hard decision'.

Usage

Syntax

Y = cpmdemod(X)

Description

Y = cpmdemod(X) demodulates the input signal by using the CPM method.

Input Arguments

X — CPM-modulated signal

column vector

CPM-modulated signal, specified as a column vector with a length equal to an integer multiple of the SamplesPerSymbol.

This object accepts variable-size inputs. After the object is locked, you can change the frame size (number of rows) of the signal during simulation. For more information, see Variable-Size Signals in Code.

Data Types: double | single
Complex Number Support: Yes

nvar — Noise variance

positive scalar

Noise variance, specified as a positive scalar value.

Dependencies

This property applies when you set BitOutput to true, VarianceSource to 'Input port', and DecisionMethod to 'Approximate log-likelihood ratio'.

Data Types: double | single

Output Arguments

Y — Demodulated output signal

column vector | matrix

Demodulated output signal, returned as a column vector or matrix. The output signal has a delay equal to the TracebackDepth property value.

For more information, see Integer-Valued and Binary-Valued Output Signals and Traceback Depth and Output Delays.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32 | logical

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)

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

CPM Modulate and Demodulate Signal with Gray Mapping and Bit Inputs

Create CPM modulator, and CPM demodulator System objects.

    cpmmodulator = comm.CPMModulator(8, ...
        'BitInput',true, ...
        'SymbolMapping','Gray');
    cpmdemodulator = comm.CPMDemodulator(8, ...
        'BitOutput',true, ...
        'SymbolMapping','Gray');

Create an error rate calculator System object™, that accounts for the delay caused by the Viterbi algorithm.

    delay = log2(cpmdemodulator.ModulationOrder) ...
        * cpmdemodulator.TracebackDepth;
    errorRate = comm.ErrorRate('ReceiveDelay',delay);

Transmit 100 3-bit words and print the error rate results.

    for counter = 1:100
        data = randi([0 1],300,1);
        modSignal = cpmmodulator(data);
        noisySignal = awgn(modSignal,0);
        receivedData = cpmdemodulator(noisySignal);
        errorStats = errorRate(data,receivedData);
    end
    fprintf('Error rate = %f\nNumber of errors = %d\n', ...
      errorStats(1),errorStats(2))

Error rate = 0.004474
Number of errors = 134

Apply GFSK Modulation and Demodulation

Using the comm.CPMModulator and comm.CPMDemodulator System objects, apply Gaussian frequency-shift keying (GFSK) modulation and demodulation to random bit data.

Create a GFSK modulator and demodulator pair.

gfskMod = comm.CPMModulator( ...
    ModulationOrder=2, ...
    FrequencyPulse='Gaussian', ...
    BandwidthTimeProduct=0.5, ...
    ModulationIndex=1, ...
    BitInput=true);
gfskDemod = comm.CPMDemodulator( ...
    ModulationOrder=2, ...
    FrequencyPulse='Gaussian', ...
    BandwidthTimeProduct=0.5, ...
    ModulationIndex=1, ...
    BitOutput=true);

Generate random bit data and apply GFSK modulation. Plot the eye diagram of the modulated signal traces.

numSym = 100;
x = randi([0 1],numSym*gfskMod.SamplesPerSymbol,1);
y = gfskMod(x);
eyediagram(y,16)

Demodulate the GFSK-modulated data. To verify that the demodulated signal data is equal to the original data, account for the delay introduced by the Gaussian filtering in the GFSK modulation and demodulation processes.

z = gfskDemod(y);
delay = finddelay(x,z);
isequal(x(1:end-delay),z(delay+1:end))

ans = logical
   1

More About

Integer-Valued and Binary-Valued Output Signals

When you set BitOutput to false:

• The object outputs an integer column vector of length equal to N/N_SPS. The output values are odd integers in the range [–(M–1), (M–1)].

• You can set the OutputDataType property to 'double', 'int8', 'int16', or 'int32'.

When you set BitOutput to true:

• The object outputs a binary column vector of length equal to k×(N/N_SPS).

• In bit output mode, the object follows this process:

  1. Map each demodulated symbol to an odd integer L in the range [–(M–1), (M–1)].

  2. Map L to the nonnegative integer (L + M–1)/2.

  3. Map each nonnegative integer to a k-length binary word using binary-coded ordering or Gray-coded ordering, as specified by the SymbolMapping property.

• You can set the OutputDataType property to 'double', 'single', 'logical', 'int8', 'int16', 'int32', 'uint8', 'uint16', or 'uint32'.

N is the number of input baseband modulated symbols in the input signal (specifically, the length of the input signal).

N_SPS represents the value of the SamplesPerSymbol property.

M is the modulation order, as specified by the ModulationOrder property. The modulation order, M = 2^k specifies the number of points in the symbol alphabet, where k is a positive integer indicating the number of bits per symbol.

Traceback Depth and Output Delays

The traceback depth, D, is the number of trellis branches used to construct each traceback path in the trellis description of the modulation scheme for demodulation using the Viterbi algorithm.

Determine the optimal setting for traceback depth by calculating the minimum squared Euclidean distance. Alternatively, you can use a common heuristic based on the number of states. Specifically, the five-times-the-constraint-length rule, which approximates the traceback depth as D=5log₂(numStates).

For a binary raised cosine pulse shape with a pulse length of 3 and h=2/3, applying this rule D=5log₂(3×2²) ~ 18 gives a result that is close to the optimum value of 20.

The Viterbi algorithm processing results in a delay preceding the first meaningful demodulated value in the output.

The length of the delay vector, which consists of zero-valued symbols prepended to the output signal, equals the value of the TracebackDepth property.

Algorithms

CPM Demodulation

CPM demodulation processing consists of a correlator followed by a maximum-likelihood sequence estimation (MLSE) detector that searches the paths through the state trellis for the minimum Euclidean distance path. When the modulation index is rational (h = m / p), a finite number of phase states exist in the symbol. The implementation uses the Viterbi algorithm to perform MLSE detection. The input to the demodulator is a baseband representation of the modulated signal.

{h_i} is a sequence of modulation indices that moves cyclically through a set of indices {h₀, h₁, h₂, …,h_H-1}.

• h_i = m_i / p_i is the modulation index in proper rational form.

• m_i is the numerator of the modulation index.

• p_i is the denominator of the modulation index.

• m_i and p_i are relatively prime positive numbers.

• The least common multiple (LCM) of {p₀, p₁, p₂, …,p_H-1} is denoted as p.

• h_i= m'_i / p.

{h_i} determines the number of phase states,

$$numPhaseStates=\left\{\begin{array}{l}p,\text{for}\text{all}\text{even}m{\text{'}}_i\\ 2p,\text{for}\text{any}\text{odd}m{\text{'}}_i\end{array}\right\}\text{,}$$

and affects the number of trellis states,

numStates = numPhaseStates×M^(L–1),

• L is the pulse length.

• M is the modulation order.

CPM Method

Continuous phase modulation includes a convolutional encoder, a symbol mapper, and a modulator.

The output of the modulator is a baseband representation of the modulated signal:

$$\begin{array}{l}s(t)=\exp \left[j2\pi {\displaystyle \sum _{i=0}^n{\alpha }_i{h}_{i}q(t-iT)}\right]\text{ and}\\ nT<t<(n+1)T,\end{array}$$

where:

• {α_i} is a sequence of M-ary data symbols selected from the alphabet ±1, ±3, ±(M–1).

• M must have the form 2^k for some positive integer k, where M is the modulation order and specifies the size of the symbol alphabet.

• {h_i} is a sequence of modulation indices. h_i moves cyclically through a set of indices {h₀, h₁, h₂, ..., h_H-1}.

  • When H=1, only one modulation index exists, h₀, which is denoted as h. The phase shift over a symbol is π × h.

  • When h_i varies from interval to interval, the modulator operates in multi-h. To ensure a finite number of phase states, h_i must be a rational number.

Pulse Shape Filtering

The CPM method uses pulse shaping to smooth the phase transitions of the modulated signal. The function q(t) is the phase response obtained from the frequency pulse, g(t), through this relation: $$q(t)={\displaystyle {\int }_{-\infty }^{t}g(t)dt}$$.

The specified frequency pulse shape corresponds to these pulse shape expressions for g(t).

Pulse Shape	Expression
Rectangular	$$g(t)=\{\begin{array}{cc}\frac{1}{2LT},& 0\le t\le LT\\ 0& \text{otherwise}\end{array}$$
Raised cosine	$$g(t)=\{\begin{array}{cc}\frac{1}{2LT}\left[1-\cos \left(\frac{2\pi t}{LT}\right)\right],& 0\le t\le LT\\ 0& \text{otherwise}\end{array}$$
Spectral raised cosine	$$g(t)=\frac{1}{L_{\text{main}}T}\frac{\sin \left(\frac{2\pi t}{L_{\text{main}}T}\right)}{\frac{2\pi t}{L_{\text{main}}T}}\frac{\cos \left(\beta \frac{2\pi t}{L_{\text{main}}T}\right)}{1-{\left(\frac{4\beta }{L_{\text{main}}T}t\right)}^2},0\le \beta \le 1$$
Gaussian	$$\begin{array}{c}g(t)=\frac{1}{2T}\left\{Q\left[2\pi B_b\frac{t-\frac{T}{2}}{\sqrt{\ln 2}}\right]-Q\left[2\pi B_b\frac{t+\frac{T}{2}}{\sqrt{\ln 2}}\right]\right\},\text{where}\\ Q(t)={\displaystyle {\int }_t^{\infty }\frac{1}{\sqrt{2\pi }}{e}^{-{\tau }^2/2}d\tau }\end{array}$$
Tamed FM (tamed frequency modulation)	$$\begin{array}{l}g(t)=\frac{1}{8}\left[g_0(t-T)+2g_0(t)+g_0(t+T)\right],\text{where}\\ {\text{g}}_0(t)\approx \frac{1}{T}\left[\frac{\sin (\frac{\pi t}{T})}{\frac{\pi t}{T}}-\frac{{\pi }^2}{24}\frac{2\sin \left(\frac{\pi t}{T}\right)-\frac{2\pi t}{T}\cos \left(\frac{\pi t}{T}\right)-{\left(\frac{\pi t}{T}\right)}^2\sin \left(\frac{\pi t}{T}\right)}{{\left(\frac{\pi t}{T}\right)}^3}\right]\end{array}$$

• L_main is the main lobe pulse duration in symbol intervals.

• β is the roll-off factor of the spectral raised cosine.

• B_b is the product of the bandwidth and the Gaussian pulse.

• The duration of the pulse, LT, is the pulse length in symbol intervals. As defined by the expressions, the spectral raised cosine, Gaussian, and tamed FM pulse shapes have infinite length. For all practical purposes, LT specifies the truncated finite length.

• T is the symbol durations.

• Q(t) is the complementary cumulative distribution function.

For more information on pulse shape filtering, see [1].

Hard Versus Soft Decision Demodulation

The demodulator uses a trellis representation of CPM. It offers demodulation by

• Hard decision — The demodulator generates hard decision (0s and 1s) using the Viterbi algorithm.

  The Viterbi algorithm finds the most likely sequence and instead of maximizing the likelihood function for each bit it estimates several bits at once as described in [1].

• Soft decision — The demodulator generates soft decision log-likelihood ratios (positive values for 0s and negative values for 1s) using the maximum log maximum a-posteriori probability (max-log-MAP) algorithm.

  The forward and backward path metric calculations implement the BCJR algorithm with a sliding window. The BCJR algorithm produces a soft estimate for each bit by considering the incoming bits as a maximum a- posteriori probability (MAP) detection problem as described in [2] and [3] and uses max(a_i) for the logarithmic approximation.

This equation shows the branch metric calculation:

$${\gamma }_k(s,s\text{'})=\frac{2}{\sigma }{\displaystyle \sum _{n=1}^{sps}real(y_{kn})real(x_{kn})+imag(y_{kn})imag(x_{kn})}$$

• y_k is the k^th received symbol.

• x_k is one of the possible k^th transmitted symbols.

• s is the current state of the branch metric.

• s' is the previous state of the branch metric.

• σ is the noise variance.

References

[1] Anderson, John B., Tor Aulin, and Carl-Erik Sundberg. Digital Phase Modulation. New York: Plenum Press, 1986.

[2] Benedetto, S., G. Montorsi, D. Divsalar, and F. Pollara. "A Soft-Input Soft-Output Maximum A Posterior (MAP) Module to Decode Parallel and Serial Concatenated Codes." Jet Propulsion Lab TDA Progress Report (November 1996): 42–127.

[3] Viterbi, A.J. “An Intuitive Justification and a Simplified Implementation of the MAP Decoder for Convolutional Codes.” IEEE^® Journal on Selected Areas in Communications 16, no. 2 (February 1998): 260–64. https://doi.org/10.1109/49.661114.

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).

Version History

Introduced in R2012a

R2024a: Soft-decision decoding

When you set DecisionMethod to 'Approximate log-likelihood ratio', the demodulator generates soft decision log-likelihood ratios (positive values for 0s and negative values for 1s) using the maximum log maximum a-posteriori probability (max-log-MAP) algorithm.

R2023b: Variable-size support

This support enables you to vary the length of input signal each time you call the object. For more information, see input signal X.