Detect preamble in data
System object™ detects a preamble in an input data sequence. A
preamble is a set of symbols or bits used in packet-based communication systems to
indicate the start of a packet. The preamble detector object finds the location
corresponding to the end of the preamble.
To detect a preamble in an input data sequence:
comm.PreambleDetector object and
set the properties of the object.
step to detect the presence of a
Alternatively, 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
y = step(obj,x) and
y = obj(x)
perform equivalent operations.
prbdet = comm.PreambleDetector creates a preamble detector
prbdet, using the default properties.
prbdet = comm.PreambleDetector(Name,Value) specifies additional
Name,Value pairs. Unspecified properties have
prbdet = comm.PreambleDetector(prb,Name,Value) specifies the
prb in addition to those properties specified by using
prbdet = comm.PreambleDetector('Input','Bit','Detections','First');
Input— Type of input data
Type of input data, specified as
'Bit'. For binary inputs, set this parameter to
'Bit'. For all other inputs, set this parameter to
'Symbol'. Symbol data can be of data type
double while bit data
can, in addition, support the
uint8 data types.
Preamble— Preamble sequence
[1+1i; 1-1i](default) | column vector
Preamble sequence, specified as a real or complex column vector. The
object uses this sequence to detect the presence of the preamble in the
input data. If
preamble must be a binary sequence. If
'Symbol', the preamble can be any real or complex
Complex Number Support: Yes
Threshold— Detection threshold
3(default) | nonnegative scalar
Detection threshold, specified as a nonnegative scalar. When the computed
detection metric is greater than or equal to
the preamble is detected. This property is available when
Input is set to
Detections— Number of preambles to detect
Number of preambles to detect, specified as
'All' — Detects all the preambles in the
input data sequence.
'First' — Detect only the first preamble
in the input data sequence.
|step||Detect preamble in data|
Specify a six-bit preamble.
prb = [1 0 0 1 0 1]';
Create a preamble detector object using preamble
prb and taking bit inputs.
prbdet = comm.PreambleDetector(prb,'Input','Bit');
Generate a binary data sequence containing two preambles and using random bits to represent the data fields.
pkt = [prb; randi([0 1],10,1); prb; randi([0 1],10,1)];
Locate the indices of the two preambles. The indices correspond to the end of the preambles. The detector correctly identified indices 6 and 22 as the end of the two preambles inserted in the sequence.
idx = prbdet(pkt)
idx = 2×1 6 22
Using a preamble sequence with good correlation properties, find the last sample of the preamble for a transmitted data frame in a stream of delayed received data.
Define the preamble with a length of 17 by using the ninth root of the Zadoff-Chu sequence. Generate a transmission signal by concatenating several samples from a random signal, the preamble, and a random signal of 100 samples.
M = 16; % 16-QAM modulation preamble = zadoffChuSeq(9,17); x = randi([0 M-1],100,1); xmod = qammod(x,M,UnitAverage=true); txsig = [xmod(23:30); preamble; xmod];
Create a variable fractional delay System object. Introduce a variable fractional delay of
82.3 samples. To return the full frame when executing the variable fractional delay object, add zero padding at the end of the transmitted signal. Add AWGN to the transmitted signal.
vfd = dsp.VariableFractionalDelay; samplesToDelay = 82.3; txsigdelayed = vfd([txsig; zeros(ceil(samplesToDelay),1)],samplesToDelay); SNR = 40; rxsig = awgn(txsigdelayed,SNR);
Create a preamble detector System object, specifying the preamble, the threshold level, and output of the index for the first detection. For the conditions in the example, setting the threshold to 60% of the total magnitude of the preamble samples finds the correct index for the preamble. Run the preamble detection object, returning the preamble index and detection metric.
thr = 0.6*sum(abs(preamble).^2); preambleDet = comm.PreambleDetector( ... Preamble=preamble, ... Threshold=thr, ... Detections='First'); [idx,detmet] = preambleDet(rxsig); idx
idx = 107
The detection metric is the absolute value of the cross-correlation of the preamble and the input signal. The cross-correlation peak should align with the returned preamble index. To confirm the returned index has identified the preamble, plot the returned cross-correlation values and compare the retuned index value to the peak in the cross-correlation values.
Create a preamble and apply QPSK modulation.
p1 = [0 1 2 3 3 2 1 0]'; p = [p1; p1]; prb = pskmod(p,4,pi/4,'gray');
comm.PreambleDetector object using preamble
prbdet = comm.PreambleDetector(prb)
prbdet = comm.PreambleDetector with properties: Input: 'Symbol' Preamble: [16x1 double] Threshold: 3 Detections: 'All'
Generate a sequence of random symbols. The first sequence represents the last 20 symbols from a previous packet. The second sequence represents the symbols from the current packet.
d1 = randi([0 3],20,1); d2 = randi([0 3],100,1);
Modulate the two sequences.
x1 = pskmod(d1,4,pi/4,'gray'); x2 = pskmod(d2,4,pi/4,'gray');
Create a sequence of modulated symbols consisting of the remnant of the previous packet, the preamble, and the current packet.
y = [x1; prb; x2];
Add white Gaussian noise.
z = awgn(y,10);
Determine the preamble index and the detection metric.
[idx,detmet] = prbdet(z);
Calculate the number of elements in
idx. Because the number of elements is greater than one, the detection threshold is too low.
ans = 80
Display the five largest detection metrics.
detmetSort = sort(detmet,'descend'); detmetSort(1:5)
ans = 5×1 16.3115 13.6900 10.5698 9.1920 8.9706
Increase the threshold and determine the preamble index. The result of 36 corresponds to the sum of the preamble length (16) and the remaining samples in the previous packet (20). This indicates that the preamble has been successfully detected.
prbdet.Threshold = 15; idx = prbdet(z)
idx = 36
When the input data is composed of bits, the preamble detector uses an exact pattern match.
When the input data is composed of symbols, the preamble detector uses a cross-correlation algorithm. A finite impulse response (FIR) filter, in which the coefficients are specified from the preamble, computes the cross-correlation between the input data and the preamble. When a sequence of input samples match the preamble, the filter output reaches its peak. The index of the peak corresponds to the end of the preamble sequence in the input data. See Discrete FIR Filter (Simulink) for further information on the FIR filter algorithm.
The cross-correlation values that are greater than or equal to the specified threshold are reported as peaks.
If the detection threshold is too low, the algorithm will detect false peaks, or, in the extreme case, detect as many detected peaks as there are input samples.
If the detection threshold is too high, the algorithm will miss detecting peaks, or, in the extreme case, detect no peaks.
Consequently, the selection of the detection threshold is critical.
Usage notes and limitations:
See System Objects in MATLAB Code Generation (MATLAB Coder).