BPSK Demodulator Baseband
Demodulate BPSKmodulated data
Libraries:
Communications Toolbox /
Modulation /
Digital Baseband Modulation /
PSK
Communications Toolbox HDL Support /
Modulation /
PM
Description
The BPSK Demodulator Baseband block demodulates a signal that was modulated using the binary phase
shift keying method. The input is a baseband representation of the modulated signal.
This block accepts a scalar or column vector input signal. The input signal must be a
discretetime complex signal. The block maps the points exp(jθ) or exp(jθ) to 0 and 1, respectively. The Phase offset
(rad)
parameter specifies the value of θ.
Examples
Cyclic Redundancy Check of Noisy BPSK Data Frames in Simulink
Use a CRC code to detect frame errors in a noisy BPSK signal.
In the cm_ex_crc_noisy_bpsk_frames
model, the CRC generator and detector pair use a standard CRC4 polynomial, . The length of the CRC is 4 bits as determined by the degree of the polynomial. The number of checksums per frame is 1, so the full transmission frame has one CRC appended at the end.
A binary signal frame gets a CRC code appended to the end of the frame. BPSK modulation is applied to the signal and the signal passes through an AWGN channel. The signal is demodulated, and then a CRC syndrome detector removes the CRC and calculates the CRC errors.
Generate 12bit frames of binary data and append CRC bits. Based on the degree of the polynomial, 4 bits are appended to each frame. Apply BPSK modulation and pass the signal through an AWGN channel. Demodulate and use the CRC detector to determine if the frame is in error.
The results of the CRC detection are compared to a BER calculation.
Number of bit errors detected: 6 Number of crc errors detected: 7
Extended Examples
Ports
Input
In — BPSKmodulated signal
scalar  vector  matrix
BPSKmodulated signal, specified as a scalar, vector, or matrix. When
this input is a matrix, each column is treated as an independent
channel. This port is unnamed until the Var
port is
enabled. When the noise variance or signal power result in computations
involving extreme positive or negative magnitudes, see BPSK Soft Demodulation for
demodulation decision type considerations.
Data Types: double
 single
 fixed point
Complex Number Support: Yes
Var — Noise Variance
positive scalar  vector of positive values
Noise variance, specified as a positive scalar or vector of positive values. When the noise variance or signal power result in computations involving extreme positive or negative magnitudes, see BPSK Soft Demodulation for demodulation decision type considerations.
Dependencies
To enable this parameter, set the Noise variance
source parameter to
Port
.
Data Types: double
Output
Out — Demodulated signal
scalar  vector
Demodulated signal, returned as a scalar or vector. If the output is a scalar, the value is an integer. If the output is a vector, it is an integervalued or binaryvalued vector.
Data Types: single
 double
 int8
 int16
 int32
 uint8
 uint16
 uint32
 Boolean
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.
MainDecision type — Decision type
Hard decision
(default)  Loglikelihood ratio
 Approximate loglikelihood ratio
Decision type used during demodulation, specified as Hard
decision
, Loglikelihood ratio
or Approximate loglikelihood ratio
. For more
information, see BPSK HardDecision Demodulation and BPSK Soft Demodulation.
The output matches the data type of the input values when
Decision type is set to Loglikelihood
ratio
or Approximate loglikelihood
ratio
.
Noise variance source — Noise variance source
Dialog
(default)  Port
Noise variance source, specified as Dialog
or
Port
.
Dialog
— The noise variance is set using theNoise variance
parameter.Port
— The noise variance is set using theVar
input port.
Noise variance — Noise Variance
1
(default)  positive scalar  vector of positive values
Noise variance, specified as a positive scalar or vector of positive values.
When specified as a scalar, that value is used on all elements in the input signal.
When specified as a vector, the vector length must be equal to the number of columns in the input signal. Each noise variance vector element is applied to its corresponding column in the input signal.
When the noise variance or signal power result in computations involving extreme positive or negative magnitudes, see BPSK Soft Demodulation for demodulation decision type considerations.
This parameter is tunable in normal mode, accelerator mode and rapid accelerator mode. 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. This is useful for Monte Carlo simulations, in which you run the simulation multiple times (perhaps on multiple computers) with different amounts of noise.
Tunable: Yes
Dependencies
To enable this parameter, set the Decision type
parameter to set to either
Loglikelihood ratio
or
Approximate loglikelihood ratio
and set
the Noise variance source parameter to
Dialog
.
Phase offset (rad) — Phase of zeroth point
0
(default)  realvalued scalar
Phase of the zeroth point, specified as a realvalued scalar. Units are in radians.
Example: pi/4
Output data type — Output data type
Inherit via internal
rule
(default)  Smallest unsigned integer
 double
 single
 ...
Output data type, specified as one of these options
When you set the Decision type parameter to Hard decision:
Inherit via internal rule
— The block inherits the output data type from the input port. If the input is a floatingpoint type (single
ordouble
), the output data type is the same as the input data type. If the input data type is fixedpoint, the output data type works as if you set this parameter toSmallest unsigned integer
.Smallest unsigned integer
— The block selects the output data type based on the settings used in the Hardware Implementation pane of the Configuration Parameters dialog box of the model. If you selectASIC/FPGA
in the Hardware Implementation pane, the output data type is the ideal minimum onebit size, that is,ufix(1)
. For all other selections, the output data type is an unsigned integer with the smallest available word length large enough to fit one bit, usually corresponding to the size of a character (for example,uint8
).double
single
int8
uint8
int16
uint16
int32
uint32
boolean
When you set the Decision type parameter to Loglikelihood ratio or Approximate loglikelihood ratio — The block inherits the output data type matches the data type of the input.
Derotate factor — Derotate factor
Same word length as
input
(default)  Specify word length
Derotate factor, specified as Same word length as
input
or Specify word
length
.
Dependencies
This parameter applies only when the input is fixedpoint and the Phase offset (rad) parameter is not a multiple of π/2.
Block Characteristics
Data Types 

Multidimensional Signals 

VariableSize Signals 

^{a} Fixedpoint inputs must be signed. ^{b} ufix(1) only at the output when ASIC/FPGA is selected in the Hardware Implementation Pane. 
More About
BPSK HardDecision Demodulation
When applying hard demodulation, the input signal type and phase offset are considered.
This figure shows the hard decision BPSK demodulator for a floatingpoint or fixedpoint signal and trivial phase offset (multiple of π/2)
This figure shows the hard decision BPSK demodulator for a floatingpoint signal and nontrivial phase offset
This figure shows the hard decision BPSK demodulator for a fixedpoint signal and nontrivial phase offset
BPSK Soft Demodulation
For soft demodulation, two softdecision loglikelihood ratio (LLR) algorithms are available: exact LLR and approximate LLR. The exact LLR algorithm is more accurate but has slower execution speed than the approximate LLR algorithm. For further description of these algorithms, see the Hard vs. SoftDecision Demodulation topic.
Note
The exact LLR algorithm computes exponentials using finite precision arithmetic. For computations involving very large positive or negative magnitudes, the exact LLR algorithm yields:
Inf
orInf
if the noise variance is a very large valueNaN
if the noise variance and signal power are both very small values
The approximate LLR algorithm does not compute exponentials. You can avoid
Inf
, Inf
, and NaN
results by using
the approximate LLR algorithm.
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
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