MATLAB Examples

PUCCH2 CQI BLER Conformance Test

This example shows how to use the LTE System Toolbox™ to measures the Channel Quality Indicator (CQI) Block Error Rate (BLER). This indicates the probability of incorrectly decoding the CQI information. The CQI BLER performance requirements are defined in TS36.104 Section 8.3.3.1 [ 1 ].

Contents

Introduction

This example uses a simulation length of 10 subframes. This value has been chosen to speed up the simulation. A larger value should be chosen to obtain more accurate results. The probability of erroneous ACK detection is calculated for a number of SNR points. The target defined in TS36.104 Section 8.3.3.1 [ 1 ] for 1.4 MHz bandwidth (6RBs) and a single transmit antenna is a CQI BLER of 1% (i.e. probability of erroneous block detection P = 0.01) at an SNR of -3.9dB. The test is defined for 1 transmit antenna.

numSubframes = 10;                          % Number of subframes
SNRdB = [-9.9 -7.9 -5.9 -3.9 -1.9];         % SNR range
NTxAnts = 1;                                % Number of transmit antennas

UE Configuration

ue = struct;                                % UE config structure
ue.NULRB = 6;                               % 6 resource blocks
ue.CyclicPrefixUL = 'Normal';               % Normal cyclic prefix
ue.Hopping = 'Off';                         % No frequency hopping
ue.NCellID = 9;
ue.RNTI = 1;                                % Radio network temporary id
ue.NTxAnts = NTxAnts;

PUCCH 2 Configuration

% Empty hybrid ACK vector is used for Physical Uplink Control Channel
% (PUCCH) 2
ACK = [];

pucch = struct; % PUCCH config structure
% Vector of PUCCH resource indices, one per transmission antenna. This is
% the n2pucch parameter
pucch.ResourceIdx = 0:ue.NTxAnts-1;
% Set the size of resources allocated to PUCCH format 2
pucch.ResourceSize = 0;
% Number of cyclic shifts used for PUCCH format 1 in resource blocks with a
% mixture of formats 1 and 2. This is the N1cs parameter
pucch.CyclicShifts = 0;

Propagation Channel Configuration

Configure the channel model with the parameters specified in the tests described in TS36.104 Section 8.3.3.1 [ 1 ].

channel = struct;                   % Channel config structure
channel.NRxAnts = 2;                % Number of receive antennas
channel.DelayProfile = 'ETU';       % Channel delay profile
channel.DopplerFreq = 70.0;         % Doppler frequency in Hz
channel.MIMOCorrelation = 'Low';    % Low MIMO correlation
channel.NTerms = 16;                % Oscillators used in fading model
channel.ModelType = 'GMEDS';        % Rayleigh fading model type
channel.Seed = 3;                   % Random number generator seed
channel.InitPhase = 'Random';       % Random initial phases
channel.NormalizePathGains = 'On';  % Normalize delay profile power
channel.NormalizeTxAnts = 'On';     % Normalize for transmit antennas

% SC-FDMA modulation information: required to get the sampling rate
info = lteSCFDMAInfo(ue);
channel.SamplingRate = info.SamplingRate;   % Channel sampling rate

Channel Estimator Configuration

The channel estimator is configured using a structure cec. Here cubic interpolation will be used with an averaging window of 12-by-1 Resource Elements (REs). This configures the channel estimator to use a special mode which ensures the ability to despread and orthogonalize the different overlapping PUCCH transmissions.

cec = struct;                     % Channel estimation config structure
cec.PilotAverage = 'UserDefined'; % Type of pilot averaging
cec.FreqWindow = 12;              % Frequency averaging window in REs (special mode)
cec.TimeWindow = 1;               % Time averaging window in REs (Special mode)
cec.InterpType = 'cubic';         % Cubic interpolation

Simulation Loop for Configured SNR Points

For each SNR point the loop below calculates the probability of successful ACK detection using information obtained from NSubframes consecutive subframes. The following operations are performed for each subframe and SNR values:

  • Create an empty resource grid
  • Generate and map PUCCH 2 and its Demodulation Reference Signal (DRS) to the resource grid
  • SC-FDMA modulation
  • Send the modulated signal through the channel
  • Receiver synchronization
  • SC-FDMA demodulation
  • Channel estimation
  • Minimum Mean Squared Error (MMSE) equalization
  • PUCCH 2 demodulation/decoding
  • Record decoding failures
  • PUCCH 2 DRS decoding. This is not required as part of this test but is included to illustrate the steps involved
% Preallocate memory for probability of detection vector
BLER = zeros(size(SNRdB));
for nSNR = 1:length(SNRdB)

    % Detection failures counter
    failCount = 0;

    % Noise configuration
    SNR = 10^(SNRdB(nSNR)/20);              % Convert dB to linear
    % The noise added before SC-FDMA demodulation will be amplified by the
    % IFFT. The amplification is the square root of the size of the IFFT.
    % To achieve the desired SNR after demodulation the noise power is
    % normalized by this value. In addition, because real and imaginary
    % parts of the noise are created separately before being combined into
    % complex additive white Gaussian noise, the noise amplitude must be
    % scaled by 1/sqrt(2*ue.NTxAnts) so the generated noise power is 1.
    N = 1/(SNR*sqrt(double(info.Nfft)))/sqrt(2.0*ue.NTxAnts);
    % Set the type of random number generator and its seed to the default
    % value
    rng('default');

    % Loop for subframes
    offsetused = 0;
    for nsf = 1:numSubframes

        % Create resource grid
        ue.NSubframe = mod(nsf-1, 10);   % Subframe number
        reGrid = lteULResourceGrid(ue);  % Resource grid

        % Create PUCCH 2 and its DRS
        CQI = randi([0 1], 4, 1);             % Generate 4 CQI bits to send
        % Encode CQI bits to produce 20 bits
        coded = lteUCIEncode(CQI);
        pucch2Sym = ltePUCCH2(ue, pucch, coded);     % PUCCH 2 modulation
        pucch2DRSSym = ltePUCCH2DRS(ue, pucch, ACK); % PUCCH 2 DRS creation

        % Generate indices for PUCCH 2 and its DRS
        pucch2Indices = ltePUCCH2Indices(ue, pucch);
        pucch2DRSIndices = ltePUCCH2DRSIndices(ue, pucch);

        % Map PUCCH 2 and its DRS to the resource grid
        reGrid(pucch2Indices) = pucch2Sym;
        reGrid(pucch2DRSIndices) = pucch2DRSSym;

        % SC-FDMA modulation
        txwave = lteSCFDMAModulate(ue, reGrid);

        % Channel state information: set the init time to the correct value
        % to guarantee continuity of the fading waveform
        channel.InitTime = (nsf-1)/1000;

        % Channel modeling
        % The additional 25 samples added to the end of the waveform are to
        % cover the range of delays expected from the channel modeling (a
        % combination of implementation delay and channel delay spread)
        rxwave = lteFadingChannel(channel, [txwave;zeros(25, ue.NTxAnts)]);

        % Add noise at receiver
        noise = N*complex(randn(size(rxwave)), randn(size(rxwave)));
        rxwave = rxwave + noise;

        % Receiver

        % Synchronization
        % An offset within the range of delays expected from the channel
        % modeling (a combination of implementation delay and channel
        % delay spread) indicates success
        [offset, rxACK] = lteULFrameOffsetPUCCH2( ...
            ue, pucch, rxwave, length(ACK));
        if (offset<25)
            offsetused = offset;
        end

        % SC-FDMA demodulation
        rxgrid = lteSCFDMADemodulate(ue, rxwave(1+offsetused:end, :));

        % Channel estimation
        [H, n0] = lteULChannelEstimatePUCCH2(ue, pucch, cec, rxgrid, rxACK);

        % Extract REs corresponding to the PUCCH 2 from the given subframe
        % across all receive antennas and channel estimates
        [pucch2Rx, pucch2H] = lteExtractResources(pucch2Indices, rxgrid, H);

        % MMSE Equalization
        eqgrid = lteULResourceGrid(ue);
        eqgrid(pucch2Indices) = lteEqualizeMMSE(pucch2Rx, pucch2H, n0);

        % PUCCH 2 demodulation
        rxBits = ltePUCCH2Decode(ue, pucch, eqgrid(pucch2Indices));

        % PUCCH 2 decoding
        decoded = lteUCIDecode(rxBits, length(CQI));

        % Record any decoding failures
        if (sum(decoded~=CQI)~=0)
            failCount = failCount + 1;
        end

        % Perform PUCCH 2 DRS decoding. This is not required as part of
        % this test, but illustrates the steps involved.

        % Extract REs corresponding to the PUCCH 2 DRS from the given
        % subframe across all receive antennas and channel estimates
        [drsRx, drsH] = lteExtractResources(pucch2DRSIndices, rxgrid, H);

        % PUCCH 2 DRS Equalization
        eqgrid(pucch2DRSIndices) = lteEqualizeMMSE(drsRx, drsH, n0);

        % PUCCH 2 DRS decoding
        rxACK = ltePUCCH2DRSDecode( ...
            ue, pucch, length(ACK), eqgrid(pucch2DRSIndices));

    end

    % Probability of erroneous block detection
    BLER(nSNR) = (failCount/numSubframes);

end

Results

plot(SNRdB, BLER, 'b-o', 'LineWidth', 2, 'MarkerSize', 7);
hold on;
plot(-3.9, 0.01, 'rx', 'LineWidth', 2, 'MarkerSize', 7);
xlabel('SNR (dB)');
ylabel('CQI BLER');
title('CQI missed detection test (TS36.104 Section 8.3.3.1)');
axis([SNRdB(1)-0.1 SNRdB(end)+0.1 -0.05 0.4]);
legend('simulated performance', 'target');

Selected Bibliography

  1. 3GPP TS 36.104 "Base Station (BS) radio transmission and reception"