gusucode.com > wlan工具箱matlab源码程序 > wlan/wlanexamples/SpectralEmissionMask10MHzExample.m
%% 802.11p Spectral Emission Mask Testing
%
% This example shows how to perform spectrum emission mask tests for an
% IEEE(R) 802.11p(TM) transmitted waveform.
% Copyright 2015-2016 The MathWorks, Inc.
%% Introduction
% IEEE 802.11p [ <#11 2> ] is an approved amendment to the IEEE 802.11
% standard to enable support for wireless access in vehicular environments
% (WAVE). Using the half-clocked mode with a 10 MHz channel bandwidth, it
% operates at the 5.85-5.925 GHz bands for which additional spectral
% emission masks are defined [ Annex D of <#11 1> ].
%
% This example shows how spectral mask measurements can be performed on a
% transmitted waveform. The waveform is generated with
% WLAN System Toolbox(TM) for simplicity, but a waveform captured with a
% spectrum analyzer could be used as well.
%
% A waveform consisting of three 10 MHz IEEE 802.11p packets separated by a
% 32 microsecond gap is generated. Random data is used for each packet and
% 16QAM modulation is used. The baseband waveform is upsampled and filtered
% to reduce the out of band emissions thereby meeting the spectral mask
% requirements. A high power amplifier (HPA) model is used, which
% introduces inband distortion and spectral regrowth. The spectral emission
% mask measurement is performed on the upsampled waveform after the high
% power amplifier modeling. The test schematic is illustrated in the
% following diagram:
%
% <<SpectralEmissionMaskDiagram.png>>
%% IEEE 802.11p non-HT Packet Configuration
% In this example, an IEEE 802.11p waveform consisting of multiple non-HT
% format packets is generated. Format parameters of the non-HT waveform are
% described using a non-HT format configuration object. The object is
% created using the <matlab:doc('wlanNonHTConfig') wlanNonHTConfig>
% function. In this example, the object is configured for a 10 MHz
% bandwidth operation as used by IEEE 802.11p.
cfgNHT = wlanNonHTConfig; % Create packet configuration
cfgNHT.ChannelBandwidth = 'CBW10'; % 10 MHz
cfgNHT.MCS = 4; % Modulation 16QAM, rate-1/2
cfgNHT.PSDULength = 1000; % PSDU length in bytes
%% Baseband Waveform Generation
% The waveform generator can be configured to generate one or more packets
% and add an idle time between each packet. In this example three packets
% with a 32 microsecond idle period will be created. Random bits for all
% packets |data| are created and passed as an argument to
% <matlab:doc('wlanWaveformGenerator') wlanWaveformGenerator> along with
% the non-HT packet configuration object |cfgNHT| and additional waveform
% generation parameters. |cfgNHT| configures the waveform generator to
% create the IEEE 802.11p Non-HT waveform.
% Set random stream for repeatability of results
s = rng(98765);
% Generate a multi-packet waveform
idleTime = 32e-6; % 32 microsecond idle time between packets
numPackets = 3; % Generate 3 packets
% Create random data; PSDULength is in bytes
data = randi([0 1], cfgNHT.PSDULength*8*numPackets, 1);
genWaveform = wlanWaveformGenerator(data, cfgNHT, ...
'NumPackets', numPackets,...
'IdleTime', idleTime);
% Get the sampling rate of the waveform
fs = helperSampleRate(cfgNHT);
disp(['Baseband sampling rate: ' num2str(fs/1e6) ' Msps']);
%% Oversampling and Filtering
% Spectral filtering is used to reduce the out of band spectral emissions
% due to the implicit rectangular pulse shaping in the OFDM modulation,
% and spectral regrowth caused by the high power amplifier in an RF chain.
% To model the effect of a high power amplifier on the waveform and view
% the out of band spectral emissions the waveform must be oversampled. In
% this example the waveform is oversampled with an interpolation filter
% which also acts as a spectral filter. This allows the waveform to meet
% spectral mask requirements. The waveform is oversampled and filtered
% using <matlab:doc('dsp.FIRInterpolator') dsp.FIRInterpolator>.
% Oversample the waveform
osf = 3; % Oversampling factor
filterLen = 100; % Filter length
r = 50; % Design parameter for Chebyshev window (attenuation, dB)
% Generate filter coefficients and interpolate
coeffs = osf.*firnyquist(filterLen, osf, chebwin(filterLen+1, r));
coeffs = coeffs(1:end-1); % Remove trailing zero
interpolationFilter = dsp.FIRInterpolator(osf, 'Numerator', coeffs);
filtWaveform = interpolationFilter([genWaveform; zeros(filterLen/2,1)]);
% Plot the magnitude and phase response of the filter applied after
% oversampling
h = fvtool(interpolationFilter);
h.Analysis = 'freq'; % Plot magnitude and phase responses
h.FS = osf*fs; % Set sampling rate
h.NormalizedFrequency = 'off'; % Plot responses against frequency
%% High Power Amplifier Modeling
% Within an RF chain, the high power amplifier is a necessary component
% that also introduces nonlinear behavior in the form of inband distortion
% and spectral regrowth. The Rapp model is used to simulate power
% amplifiers for wireless LAN applications. The Rapp model causes AM/AM
% distortion and is modeled with <matlab:doc('comm.MemorylessNonlinearity')
% comm.MemorylessNonlinearity>. The high power amplifier is backed-off to
% operate below the saturation point to reduce distortion. The backoff is
% controlled by the variable |hpaBackoff|.
hpaBackoff = 6; % dB
% Create and configure a memoryless nonlinearity to model the amplifier
nonLinearity = comm.MemorylessNonlinearity;
nonLinearity.Method = 'Rapp model';
nonLinearity.Smoothness = 3; % p parameter
nonLinearity.LinearGain = -hpaBackoff; % dB
% Apply the model to the transmit waveform
txWaveform = nonLinearity(filtWaveform);
%% Transmit Spectrum Emission Mask Measurement
%
% Stations are classified according to the allowed maximum transmit powers
% (in mW). For the four different classes of stations, four different
% spectral emission masks are defined [ Annex D of <#11 1>]. The spectral
% masks are defined relative to the peak power spectral density (PSD).
%
% In this example the spectrum emission mask of the transmitted
% waveform after high power amplifier modeling is measured for a Class
% A station.
% IEEE Std 802.11-2012 Annex D.2.3, Table D-5: Class A STA
dBrLimits = [-40 -40 -28 -20 -10 0 0 -10 -20 -28 -40 -40];
fLimits = [-Inf -15 -10 -5.5 -5 -4.5 4.5 5 5.5 10 15 Inf];
%%
% A time gated spectral measurement of the Non-HT Data field is used for
% the transmitter spectrum emission mask test [ <#11 3> ]. The Non-HT Data
% field of each packet is extracted from the upsampled |txWaveform| using
% the start index of each packet. The extracted Non-HT Data fields are
% concatenated in preparation for measurement.
% Indices for accessing each field within the time-domain packet
ind = wlanFieldIndices(cfgNHT);
startIdx = osf*(ind.NonHTData(1)-1)+1; % Upsampled start of Non-HT Data
endIdx = osf*ind.NonHTData(2); % Upsampled end of Non-HT Data
idleNSamps = osf*idleTime/(1/fs); % Upsampled idle time samples
perPktLength = endIdx + idleNSamps;
idx = zeros(endIdx-startIdx+1, numPackets);
for i = 1:numPackets
% Start of packet in txWaveform, accounting for the filter delay
pktOffset = (i-1)*perPktLength+filterLen/2;
% Indices of NonHT Data in txWaveform
idx(:,i) = pktOffset+(startIdx:endIdx);
end
% Select the Data field for the individual packets
gatedNHTDataTx = txWaveform(idx(:),:);
%%
% The plot generated by the helper function |helperSpectralMaskTest|
% overlays the required spectral mask with the measured PSD. It checks the
% transmitted PSD levels to be within the specified mask levels and
% displays a pass/fail status after the test.
% Evaluate the PSD and check for compliance
helperSpectralMaskTest(gatedNHTDataTx, fs, osf, dBrLimits, fLimits);
% Restore default stream
rng(s);
%% Conclusion and Further Exploration
% The transmit spectral mask for Class A Stations at the 5.85-5.925 GHz
% bands for a 10 MHz channel spacing is shown in this example. It is also
% shown how the peak spectral density of the transmitted signal falls
% within the spectral mask to satisfy regulatory restrictions. A similar
% result can be generated for the 5 MHz channel spacing.
%
% The high power amplifier model and the spectral filtering affect the
% out-of-band emissions in the spectral mask plot. For different station
% classes with higher relative dB values, try using different filters or
% filter lengths and/or increase the backoff for lower emissions.
%
% For information on other transmitter measurements like modulation
% accuracy and spectral flatness, refer to the following example:
%
% * <VHTTransmitterMeasurementsExample.html 802.11ac Transmitter
% Modulation Accuracy and Spectral Emission Testing>
%% Appendix
% This example uses the following helper functions:
%
% * <matlab:edit('helperSpectralMaskTest.m') helperSpectralMaskTest.m>
% * <matlab:edit('helperSampleRate.m') helperSampleRate.m>
%% Selected Bibliography
% # IEEE Std 802.11-2012: IEEE Standard for Information technology -
% Telecommunications and information exchange between systems - Local and
% metropolitan area networks - Specific requirements, Part 11: Wireless LAN
% Medium Access Control (MAC) and Physical Layer (PHY) Specifications,
% IEEE, New York, NY, USA, 1999-2013.
% # IEEE Std 802.11p-2010: IEEE Standard for Information technology -
% Telecommunications and information exchange between systems - Local and
% metropolitan area networks - Specific requirements, Part 11: Wireless LAN
% Medium Access Control (MAC) and Physical Layer (PHY) Specifications,
% Amendment 6: Wireless Access in Vehicular Environments, IEEE, New York,
% NY, USA, 2010.
% # Archambault, Jerry, and Shravan Surineni. "IEEE 802.11 spectral
% measurements using vector signal analyzers." RF Design 27.6 (2004):
% 38-49.
displayEndOfDemoMessage(mfilename)