BER Analyzer
Analyze bit error rate (BER) performance of communications systems
Description
The BER Analyzer app calculates BER as a function of the energy per bit to noise power spectral density ratio (Eb/N0). Using this app, you can:
Plot theoretical BER vs. Eb/N0 estimates and upper bounds.
Plot BER vs. Eb/N0 using the semianalytic technique. The semianalytic technique estimates BER performance by using a combination of simulation and analysis. Use this technique when the system error rate is small, for example, < 10–6.
Estimate BER performance by using MATLAB® functions or Simulink® models.
Open the BER Analyzer App
MATLAB Toolstrip: On the Apps tab, under Signal Processing and Communications, click the app icon.
MATLAB command prompt: Enter
bertool.
Examples
Theoretical Plot
Generate a theoretical estimate of BER performance for a 16-QAM link in AWGN.
Open the BER Analysis app.
bertool
Specify the Eb/N0
range as 0:10.
Set Modulation type to QAM and
Modulation order to 16.
Plot the BER curve by clicking Plot.
Semianalytic Plot
Use the semianalytic technique to plot the BER for a QPSK link having rectangular pulses.
Open the BER Analysis app.
bertool
On the Semianalytic tab, set these parameters:
Set the Modulation order to
4.Set the Samples per symbol parameter to
8.Set the Transmitted signal and Received signal parameters to
rectpulse(pskmod([0:3 0],4),8). To use the semianalytic technique, the number of symbols must exceed ML, where M is the modulation order and L is the impulse response length. The impulse response is 1, so a minimum of five symbols is required.Specify the Numerator as
ones(8,1)/8. These coefficients specify an ideal integrator having eight samples per symbol.
Plot the BER vs. Eb/N0 curve by clicking Plot.
Monte Carlo Simulation
Simulate BER using a custom MATLAB function.
Open the BER Analysis app.
bertool
On the Monte Carlo tab, specify
Eb/N0 range as
1:.5:6.
To plot estimated BER values, run the simulation by clicking Run.
On the Theoretical tab, specify
Eb/N0 range as
1:6 and set Modulation order to
4.
Enable convolutional coding by selecting the Convolutional check box.
Plot the upper bound of the BER curve by clicking Plot.
Parameters
TheoreticalEb/N0 range — Range of Eb/N0 values over which the BER is evaluated
0:18 (default) | vector
Specifies the range of Eb/N0 values, in dB, over which the BER is evaluated. The values in the range vector must be real.
Example: 5:10
Channel type — Type of channel over which the BER is evaluated
AWGN (default) | Rayleigh | Rician
Specifies the type of channel over which the BER is evaluated.
The Rayleigh and Rician options
correspond to flat fading channels.
Modulation type — Modulation type of the communication link
PSK (default) | DPSK | OQPSK | PAM | QAM | FSK | MSK | CPFSK
Specifies the modulation type of the communication link.
Modulation order — Modulation order of the communication link
2 (default) | 4 | 8 | 16 | 32 | 64
Specifies the modulation order of the communication link.
Differential encoding — Differential encoding of the input data
Off (default) | On
Specifies if the input data sequence is differentially encoded.
Demodulation type — Demodulation type
Coherent | Noncoherent
Specifies if Coherent or Noncoherent demodulation
is used. This parameter is available only when the Modulation
type is FSK or MSK.
Channel coding — Channel coding used in estimating the BER
None (default) | Convolutional | Block
Specifies the type of channel coding used to estimate the theoretical BER.
Synchronization — Synchronization error
Perfect synchronization (default) | Normalized timing error | RMS phase noise level
Specifies the synchronization error in the demodulation process.
This parameter is available only when the Modulation type is PSK and
the Modulation order is 2.
When Synchronization is
Normalized timing error, specify the normalized error as a real number from0to0.5.When Synchronization is
RMS phase noise level, specify the RMS phase noise as a nonnegative real number.
Decision method — Decoding decision method
Hard (default) | Soft
Specify the method used to decode the received data. This parameter is available when either of these conditions exist:
Channel coding is set to Convolutional
Channel coding is set to Block and Coding Type is
General
Trellis — Convolutional code trellis
poly2trellis(7,[171 133]) (default) | structure
Specify the convolutional code trellis as a structure variable.
You can generate this structure by using the poly2trellis function.
The parameter is available only when the Channel coding parameter
is Convolutional.
Coding type — Specify block coding type
General (default) | Hamming | Golay | Reed-Solomon
Specify the block code used in the BER evaluation.
N — Codeword length
positive integer
Specify the codeword length as a positive integer.
K — Message length
positive integer
Specify the message length as a positive integer such that K is less than N.
dmin — Minimum code distance
positive integer
Specify the minimum distance of the (N,K) block code as a positive
integer. This parameter is available when the Coding type is General.
Samples per symbol — Samples per symbol
16 (default) | positive integer
Specify the number of samples per symbol as a positive integer.
Transmitted signal — Transmitted sample sequence
rectpulse(step(comm.BPSKModulator,
[0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0].'), 16) (default) | vector
Specify the transmit sequence as a real or complex column vector.
Data Types: double
Complex Number Support: Yes
Received signal — Received sample sequence
rectpulse(step(comm.BPSKModulator,
[0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0].'), 16) (default) | vector
Specify the received sequence as a real or complex column vector.
Data Types: double
Complex Number Support: Yes
Numerator — Numerator of the receive filter coefficients
ones(16,1)/16 (default) | scalar | vector
Specify the numerator of the receive filter coefficients as a vector.
Denominator — Denominator of the receive filter coefficients
1 (default) | scalar | vector
Specify the denominator of the receive filter coefficients as a vector.
Simulation MATLAB file or Simulink model — File name of the BER simulation
character vector
Specify the name of the MATLAB file or Simulink model containing the simulation code.
BER variable name — Name of the variable containing the BER simulation data
character vector
Specify the name of the MATLAB workspace variable that contains the BER simulation data.
Number of errors — Number of errors measured before simulation stop
100 (default) | positive integer
Specify the number of errors that must be measured before the simulation stops. Typically, 100 measured errors are enough to produce an accurate BER estimate.
Number of bits — Number of bits processed before simulation stop
1e8 (default) | positive integer
Specify the number of bits that must be processed before the simulation stops. This parameter is used to prevent the simulation from running too long.
Note
The Monte Carlo simulation stops when either the number of errors or number of bits threshold is reached.