MPSK Demodulator Baseband
Demodulate PSKmodulated data
Libraries:
Communications Toolbox /
Modulation /
Digital Baseband Modulation /
PSK
Communications Toolbox HDL Support /
Modulation /
PM
Description
The MPSK Demodulator Baseband block demodulates a baseband representation of a PSKmodulated signal. The modulation order, M, is equivalent to the number of points in the signal constellation and is determined by the Mary number parameter. The block accepts scalar or column vector input signals.
Examples
Modulate and Demodulate 8PSK Signal
Generate and demodulate a noisy 8PSK signal.
Open the doc_8psk_model
model. The model generates random data using the Random Integer Generator block. To modulate the random data, the model uses the MPSK Modulator Baseband block with a modulation order of 8 and a constellation order set to Gray. Subsequently, the modulated data passes through an additive white Gaussian noise channel (AWGN Channel block). The model displays the noisy constellation through the Constellation Diagram block. To demodulate this noisy signal, it employs the MPSK Demodulator Baseband block with the same modulation and constellation order as the modulator. Finally, the model computes the error statistics using the Error Rate Calculation.
Run the model.
Collect the error statistics in a vector, ErrorVec
. Observe that the number of symbol errors is zero when Eb/No is 15 dB.
Number of symbol errors = 0
Change the Eb/No of the AWGN Channel block from 15 dB to 5 dB. The constellation diagram shows the increase in the noise.
Because of the increase in the noise level, the number of symbol errors is greater than zero.
Number of symbol errors = 21
GrayCoded MPSK Modulation Error Rate in AWGN Channel Using Simulink
This example uses the doc_gray_code
to compute bit error rates (BER) and symbol error rates (SER) for MPSK modulation. The theoretical error rate performance of MPSK modulation in AWGN is compared to the error rate performance for Graycoded symbol mapping and to the error rate performance of binarycoded symbol mapping.
The Random Integer Generator block serves as the source, producing a sequence of integers. The Integer to Bit Converter block converts each integer into a corresponding binary representation. The MPSK Modulator Baseband block in the doc_gray_code
model:
Accepts binaryvalued inputs that represent integers in the range [0, (M  1], where M is the modulation order.
Maps binary representations to constellation points using a Graycoded ordering.
Produces unitmagnitude complex phasor outputs, with evenly spaced phases in the range [0, (2 (M  1) / M)].
The AWGN Channel block adds white Gaussian noise to the modulated data. The MPSK Demodulator Baseband block demodulates the noisy data. The Bit to Integer Converter block converts each binary representation to a corresponding integer. Then two separate Error Rate Calculation blocks calculate the error rates of the demodulated data. The block labeled SER Calculation compares the integer data to compute the symbol error rate statistics and the block labeled BER Calculation compares the bits data to compute the bit error rate statistics. The output of the Error Rate Calculation block is a threeelement vector containing the calculated error rate, the number of errors observed, and the amount of data processed.
To reduce simulation run time and ensure that the statistics of the errors remain stable as the Eb/N0 ratio increases, the model is configured to run until 100 errors occur or until 1e8 bits have been transmitted.
The model initializes variables used to configure block parameters by using the PreLoadFcn
callback function. For more information, see Model Callbacks (Simulink).
Produce Error Rate Curves
Compute the theoretical BER for nondifferential 8PSK in AWGN over a range of Eb/N0 values by using the
function. Simulate the berawgn
doc_gray_code
model with Graycoded symbol mapping over the same range of Eb/N0 values.
Compare Gray coding with binary coding, by modifying the MPSK Modulator Baseband and MPSK Demodulator Baseband blocks to set the Constellation ordering parameter to Binary
instead of Gray
. Simulate the doc_gray_code
model with binarycoded symbol mapping over the same range of Eb/N0 values.
Plot the results by using the semilogy
function. The Graycoded system achieves better error rate performance than the binarycoded system. Further, the Graycoded error rate aligns with the theoretical error rate statistics.
Ports
Input
In_1 — Input signal
scalar  vector
Input port accepting a baseband representation of a PSKmodulated signal.
Data Types: single
 double
 int8
 int16
 int32
 uint8
 uint16
 uint32
 Boolean
Output
Out_1 — Output signal
scalar  vector
Output signal, returned as a scalar or vector. The output is a demodulated version of the PSKmodulated signal.
Data Types: single
 double
 fixed point
Parameters
To edit block parameters interactively, use the Property Inspector. From the Simulink^{®} Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.
Mary number — Modulation order of the PSK constellation
8
(default)  scalar
Specify the modulation order as a positive integer power of two.
Example: 2
 16
Output type — Output signal data type
Integer
(default)  Bit
Specify the elements of the input signal as integers or bits. If
Output type is Bit
, the
number of samples per frame is an integer multiple of the number of bits per
symbol, log_{2}(M).
Decision type — Demodulator output
Hard decision
(default)  Loglikelihood ratio
 Approximate loglikelihood ratio
Specify the demodulator output to be hard decision, loglikelihood ratio
(LLR), or approximate LLR. The LLR and approximate LLR outputs are used with
error decoders that support softdecision inputs such as a Viterbi Decoder, to achieve
superior performance. This parameter is available when Output
type is Bit
.
See Phase Modulation for algorithm
details. The output values for Loglikelihood
ratio
and Approximate loglikelihood
ratio
decision types are of the same data type as the
input values
Noise variance source — Source of noise variance
Dialog
(default)  Port
Specify the source of the noise variance estimate. This parameter is
available when Decision type is
Loglikelihood ratio
or
Approximate loglikelihood ratio
.
To specify the noise variance from the dialog box, select
Dialog
.To input the noise variance from an input port, select
Port
.
Noise variance — Estimate of noise variance
1
(default)  positive scalar
Specify the estimate of the noise variance as a positive scalar. This
parameter is available when Noise variance source is
Dialog
.
This parameter is tunable in all simulation modes. If you use the Simulink Coder™ rapid simulation (RSIM) target to build an RSIM executable, then you can tune the parameter without recompiling the model. Avoiding recompilation is useful for Monte Carlo simulations in which you run the simulation multiple times (perhaps on multiple computers) with different amounts of noise.
Note
The exact LLR algorithm computes exponentials using finite precision arithmetic. Computation of exponentials with very large positive or negative magnitudes might yield:
Inf
orInf
if the noise variance is a very large valueNaN
if both the noise variance and signal power are very small values
When the output returns any of these values, try using the approximate LLR algorithm because it does not compute exponentials.
Constellation ordering — Symbol mapping
Gray
(default)  Binary
 Userdefined
Specify how the integer or group of log_{2}(M) bits is mapped to the corresponding symbol.
When Constellation ordering is set to
Gray
, the output symbol is mapped to the input signal using a Grayencoded signal constellation.When Constellation ordering is set to
Binary
, the modulated symbol is exp(jϕ+j2πm/M), where ϕ is the phase offset in radians, m is the integer output such that 0 ≤ m ≤ M – 1, and M is the modulation order.When Constellation ordering is
Userdefined
, specify a vector of size M, which has unique integer values in the range [0, M–1]. The first element of this vector corresponds to the constellation point having a value of e^{jϕ} with subsequent elements running counterclockwise.
Example: [0 3 2 1]
Constellation mapping — Userdefined symbol mapping
[0:7]
(default)  vector
Specify the order in which input integers are mapped to output integers.
The parameter is available when Constellation ordering
is Userdefined
, and must be a row or column
vector of size M having unique integer values in the
range [0, M – 1].
The first element of this vector corresponds to the constellation point at 0 + Phase offset angle, with subsequent elements running counterclockwise. The last element corresponds to the 2π/M + Phase offset constellation point.
Phase offset (rad) — Phase offset in radians
pi/8
(default)  scalar
Specify, in radians, the phase offset of the initial constellation as a real scalar.
Example: pi/4
Output data type — Output data type
Inherit via internal
rule
(default)  Smallest unsigned integer
 double
 single
 int8
 uint8
 int16
 uint16
 int32
 uint32
Specify the data type of the demodulated output signal.
The Data Type Assistant helps you set data attributes. To use the Data Type Assistant, click Show data type assistant . For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Block Characteristics
Data Types 

Multidimensional Signals 

VariableSize Signals 

^{a} M = 2, 4, 8 only. ^{b} Fixedpoint inputs must be signed. ^{c} When ASIC/FPGA is selected in the Hardware Implementation Pane, output is ufix(1) for bit outputs, and ufix(ceil(log2(M))) for integer outputs. 
Algorithms
HardDecision BPSK Demodulation
The signal preprocessing required for BPSK demodulation depends on the configuration.
This figure shows the harddecision BPSK demodulation signal diagram for the trivial phase offset (multiple of π/2) configuration.
This figure shows the harddecision BPSK demodulation floatingpoint signal diagram for the nontrivial phase offset configuration.
This figure shows the harddecision BPSK demodulation fixedpoint signal diagram for the nontrivial phase offset configuration.
HardDecision QPSK Demodulation
The signal preprocessing required for QPSK demodulation depends on the configuration.
This figure shows the harddecision QPSK demodulation signal diagram for the trivial phase offset (odd multiple of π/4) configuration.
This figure shows the harddecision QPSK demodulation floatingpoint signal diagram for the nontrivial phase offset configuration.
This figure shows the harddecision QPSK demodulation fixedpoint signal diagram for the nontrivial phase offset configuration.
HardDecision HigherOrder PSK
The signal preprocessing required for higher order PSK demodulation depends on the configuration.
This figure shows the harddecision 8PSK demodulation signal diagram for the trivial phase offset (odd multiple of π/8) configuration.
This figure shows the harddecision 8PSK demodulation fixedpoint signal diagram for trivial phase offset (odd multiple of π/8) configuration.
This figure shows the harddecision MPSK demodulation floatingpoint signal diagram for the nontrivial phase offset configuration.
For M > 8, to improve speed and implementation costs, no derotation arithmetic is performed for trivial case (specifically, when phase offset is 0, π/2, π, or 3π/2).
Also, for M > 8, only double
and
single
input types are supported.
LogLikelihood Ratio and Approximate LogLikelihood Ratio
The exact LLR and approximate LLR algorithms (softdecision) are described in Phase Modulation.
References
[1] Proakis, John G. Digital Communications. 4th ed. New York: McGraw Hill, 2001.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
HDL Code Generation
Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.
HDL Coder™ provides additional configuration options that affect HDL implementation and synthesized logic.
This block has one default HDL architecture.
ConstrainedOutputPipeline  Number of registers to place at
the outputs by moving existing delays within your design. Distributed
pipelining does not redistribute these registers. The default is

InputPipeline  Number of input pipeline stages
to insert in the generated code. Distributed pipelining and constrained
output pipelining can move these registers. The default is

OutputPipeline  Number of output pipeline stages
to insert in the generated code. Distributed pipelining and constrained
output pipelining can move these registers. The default is

Version History
Introduced before R2006a
See Also
Blocks
Objects
Functions
Topics
MATLAB Command
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
 América Latina (Español)
 Canada (English)
 United States (English)
Europe
 Belgium (English)
 Denmark (English)
 Deutschland (Deutsch)
 España (Español)
 Finland (English)
 France (Français)
 Ireland (English)
 Italia (Italiano)
 Luxembourg (English)
 Netherlands (English)
 Norway (English)
 Österreich (Deutsch)
 Portugal (English)
 Sweden (English)
 Switzerland
 United Kingdom (English)