mirror of https://github.com/pvnis/srsRAN_4G.git
Added Matlab model for PRACH detection probability test
Ismael Gomez
9 years ago
parent
d3ce6e83f0
commit
71d7151b1b
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%% PRACH Detection Conformance Test
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clear
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numSubframes = 50; % Number of subframes frames to simulate at each SNR
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SNRdB = linspace(-12,-4,7); % SNR points to simulate
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foffset = 270.0; % Frequency offset in Hertz
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%% UE Configuration
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% User Equipment (UE) settings are specified in the structure |ue|.
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ue.NULRB = 6; % 6 Resource Blocks
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ue.DuplexMode = 'FDD'; % Frequency Division Duplexing (FDD)
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ue.CyclicPrefixUL = 'Normal'; % Normal cyclic prefix length
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ue.NTxAnts = 1; % Number of transmission antennas
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%% PRACH Configuration
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prach.Format = 0; % PRACH format: TS36.104, Table 8.4.2.1-1
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prach.SeqIdx = 22; % Logical sequence index: TS36.141, Table A.6-1
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prach.CyclicShiftIdx = 1; % Cyclic shift index: TS36.141, Table A.6-1
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prach.HighSpeed = 0; % Normal mode: TS36.104, Table 8.4.2.1-1
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prach.FreqOffset = 0; % Default frequency location
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prach.PreambleIdx = 32; % Preamble index: TS36.141, Table A.6-1
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info = ltePRACHInfo(ue, prach); % PRACH information
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%% Propagation Channel Configuration
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% Configure the propagation channel model using a structure |chcfg| as per
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% TS36.104, Table 8.4.2.1-1 [ <#9 1> ].
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chcfg.NRxAnts = 1; % Number of receive antenna
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chcfg.DelayProfile = 'EPA'; % Delay profile
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chcfg.DopplerFreq = 70.0; % Doppler frequency
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chcfg.MIMOCorrelation = 'Low'; % MIMO correlation
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chcfg.Seed = 1; % Channel seed
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chcfg.NTerms = 16; % Oscillators used in fading model
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chcfg.ModelType = 'GMEDS'; % Rayleigh fading model type
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chcfg.InitPhase = 'Random'; % Random initial phases
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chcfg.NormalizePathGains = 'On'; % Normalize delay profile power
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chcfg.NormalizeTxAnts = 'On'; % Normalize for transmit antennas
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chcfg.SamplingRate = info.SamplingRate; % Sampling rate
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%% Loop for SNR Values
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% Initialize the random number generator stream
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rng('default');
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% Initialize variables storing probability of detection at each SNR
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pDetection = zeros(2,length(SNRdB));
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for nSNR = 1:length(SNRdB)
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% Scale noise to ensure the desired SNR after SC-FDMA demodulation
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ulinfo = lteSCFDMAInfo(ue);
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SNR = 10^(SNRdB(nSNR)/20);
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N = 1/(SNR*sqrt(double(ulinfo.Nfft)))/sqrt(2.0);
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% Detected preamble count
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detectedCount = 0;
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detectedCount_srs = 0;
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% Loop for each subframe
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for nsf = 1:numSubframes
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% PRACH transmission
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ue.NSubframe = mod(nsf-1, 10);
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ue.NFrame = fix((nsf-1)/10);
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% Set PRACH timing offset in us as per TS36.141, Figure 8.4.1.4.2-2
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prach.TimingOffset = info.BaseOffset + ue.NSubframe/10.0;
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prach.TimingOffset = 0;
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% Generate transmit wave
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txwave = ltePRACH(ue, prach);
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% Channel modeling
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chcfg.InitTime = (nsf-1)/1000;
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%[rxwave, fadinginfo] = lteFadingChannel(chcfg, ...
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% [txwave; zeros(25, 1)]);
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rxwave=txwave;
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% Add noise
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noise = N*complex(randn(size(rxwave)), randn(size(rxwave)));
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rxwave = rxwave + noise;
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% Remove the implementation delay of the channel modeling
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%rxwave = rxwave((fadinginfo.ChannelFilterDelay + 1):1920, :);
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% Apply frequency offset
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%t = ((0:size(rxwave, 1)-1)/chcfg.SamplingRate).';
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%rxwave = rxwave .* repmat(exp(1i*2*pi*foffset*t), ...
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% 1, size(rxwave, 2));
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% PRACH detection for all cell preamble indices
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[detected, offsets] = ltePRACHDetect(ue, prach, rxwave, (0:63).');
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txwave_srs = srslte_prach(ue, prach);
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plot(abs(txwave-txwave_srs))
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[detected_srs] = srslte_prach_detect(ue, prach, rxwave);
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% Test for preamble detection
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if (length(detected)==1)
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% Test for correct preamble detection
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if (detected==prach.PreambleIdx)
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detectedCount = detectedCount + 1; % Detected preamble
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% Calculate timing estimation error. The true offset is
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% PRACH offset plus channel delay
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% trueOffset = prach.TimingOffset/1e6 + 310e-9;
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% measuredOffset = offsets(1)/chcfg.SamplingRate;
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% timingerror = abs(measuredOffset-trueOffset);
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%
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% % Test for acceptable timing error
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% if (timingerror<=2.08e-6)
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% detectedCount = detectedCount + 1; % Detected preamble
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% else
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% disp('Timing error');
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% end
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else
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disp('Detected incorrect preamble');
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end
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else
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disp('Detected multiple or zero preambles');
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end
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if (length(detected_srs)==1 && detected_srs==prach.PreambleIdx)
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detectedCount_srs = detectedCount_srs + 1;
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end
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end % of subframe loop
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% Compute final detection probability for this SNR
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pDetection(1,nSNR) = detectedCount/numSubframes;
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pDetection(2,nSNR) = detectedCount_srs/numSubframes;
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end % of SNR loop
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%% Analysis
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plot(SNRdB, pDetection)
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legend('Matlab','srsLTE')
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grid on
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xlabel('SNR (dB)')
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ylabel('Pdet')
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