Modeling and Testing an LTE RF Receiver

Model Description

The figure below shows the main parts of this example. An LTE waveform is generated using the LTE Toolbox. This waveform is filtered and transmitted through a propagation channel before feeding it to the RF receiver model implemented with RF Blockset. This model is based on commercially available parts. EVM figures are then provided for the output of the RF receiver.

This example is implemented using MATLAB® and Simulink®, which interact at runtime. The functional partition is shown in the figure below

The MATLAB script implements the simulation test-bench, and the Simulink model is the device under test (DUT). LTE frames are streamed between the test-bench and the DUT.

Generate LTE Waveform

In this section we generate the LTE waveform using the LTE Toolbox. We use the reference measurement channel (RMC) R.6 as defined in TS 36.101 [ 1 ]. This RMC specifies a 25 resource elements (REs) bandwidth, equivalent to 5 MHz. A 64 QAM modulation is used. All REs are allocated. Additionally, OCNG noise is enabled in unused REs.

Only one frame is generated. This frame will then be repeated a number of times to perform the EVM measurements.

% Configuration TS 36.101 25 REs (5 MHz), 64-QAM, full allocation
rmc = lteRMCDL('R.6');
rmc.OCNGPDSCHEnable = 'On';

% Create eNodeB transmission with fixed PDSCH data
rng(2);                 % Fixed random seed (arbitrary)
data = randi([0 1], sum(rmc.PDSCH.TrBlkSizes),1);

% Generate 1 frame, to be repeated to simulate a total of N frames
[tx, ~, info] = lteRMCDLTool(rmc, data); % 1 frame

% Calculate the sampling period and the length of the frame.
SamplePeriod = 1/info.SamplingRate;
FrameLength = length(tx);

Initialize Simulation Components

This section initializes some of the simulation components:

% Band limiting interpolation filter
FiltOrd = 32;
h = firpm(FiltOrd,[0 2.25e6*2*SamplePeriod 2.7e6*2*SamplePeriod 1],[1 1 0 0]);
FilterDelaySamples = FiltOrd/2; % filter group delay

% Propagation model
SNRdB = 57;                 % Es/Noc in dB
NocdBm = -98;               % Noc in dBm/15kHz
NocdBW = NocdBm - 30;       % Noc in dBW/15kHz57
SNR = 10^(SNRdB/10);        % linear Es/Noc
Es = SNR*(10^(NocdBW/10));  % linear Es per RE
FFTLength = info.Nfft;
SymbolPower = Es/double(FFTLength);

% Number of simulation frames N>=1
N = 3;

% Preallocate vectors for results for N-1 frames
% EVM is not measured in the first frame to avoid transient effects
evmpeak = zeros(N-1,1);   % Preallocation for results
evmrms = zeros(N-1,1);    % Preallocation for results

Load RF Blockset Testbench

This section loads the Simulink model shown below. This model includes the following components:

% Specify and open Simulink model
model = 'RFLTEReceiverModel';
disp('Starting Simulink');
open_system(model);

Starting Simulink

RF Receiver Model

The RF receiver model includes the elements shown below

The RF demodulator includes the following components and is shown below:

Simulate Frames

This section simulates the specified number of frames. This is done in two stages:

The reason for splitting the processing in these two stages is to simplify the code. During the processing of the first frame we need to take into account the delay of the band limiting filter. This is not the case for subsequent frames, since the filter state is maintained between frames. Therefore, the length of the first frame has to be increased slightly to take into account the delay introduced by the filter.

Simulate First LTE Frame

As mentioned for the first simulated frame we need to increase the length of the signal fed to the Simulink model to compensate for the delay introduced by the filter. Next we launch the simulation of the Simulink model without loading any initial state. After processing the first frame with the Simulink model, its state (xFinal) is stored and assigned to xInitial for the next time the model is run.

The output of the Simulink model is stored in the variable rx, which is available in the workspace. Any delays introduced to this signal are removed after performing synchronization. The EVM is measured on the resulting waveform.

% Generate test data for RF receiver
time = (0:FrameLength+FilterDelaySamples)*SamplePeriod;
% Append to the end of the frame enough samples to compensate for the delay
% of the filter
txWaveform = timeseries([tx; tx(1:FilterDelaySamples+1)],time);

% Simulate RF Blockset model of RF RX
set_param(model, 'LoadInitialState', 'off');
disp('Simulating LTE frame 1 ...');
sim(model, time(end));
% Save the final state of the model in xInitial for next frame processing
xInitial = xFinal;

% Synchronize to received waveform
Offset = lteDLFrameOffset(rmc,squeeze(rx),'TestEVM');
% In this case Offset = FilterDelaySamples therefore the following
% frames do not require synchronization

Simulating LTE frame 1 ...

Simulate Successive LTE Frames

Now the rest of the frames can be simulated. First, the model state is set using the value stored in xInitial at the output of the previous iteration.

% Load state after execution of previous frame. Since we are repeating the
% same frame the model state will be the same after every frame execution.
set_param(model, 'LoadInitialState', 'on', 'InitialState','xInitial');
% Modify input vector to take into account the delay of the bandlimiting
% filter
RepeatFrame = [tx(FilterDelaySamples+1:end); tx(1:FilterDelaySamples+1)];
EVMalg.EnablePlotting = 'Off';
cec.PilotAverage = 'TestEVM';

for n = 2:N % for all remaining frames
    % Generate data
    time = ( (n-1)*FrameLength+(0:FrameLength) + FilterDelaySamples)*SamplePeriod;
    txWaveform = timeseries(RepeatFrame,time);

    % Execute Simulink RF Blockset testbench
    disp(['Simulating LTE frame ',num2str(n),' ...']);
    sim(model, time(end));
    xInitial = xFinal; % Save model state

    % Compute and display EVM measurements
    evmmeas = hPDSCHEVM(rmc,cec,squeeze(rx),EVMalg);
    evmpeak(n-1) = evmmeas.Peak;
    evmrms(n-1) = evmmeas.RMS;
end

Simulating LTE frame 2 ...
Low edge EVM, subframe 0: 3.100%
High edge EVM, subframe 0: 3.125%
Low edge EVM, subframe 1: 2.838%
High edge EVM, subframe 1: 2.858%
Low edge EVM, subframe 2: 2.965%
High edge EVM, subframe 2: 2.976%
Low edge EVM, subframe 3: 3.435%
High edge EVM, subframe 3: 3.442%
Low edge EVM, subframe 4: 2.949%
High edge EVM, subframe 4: 2.968%
Low edge EVM, subframe 6: 3.189%
High edge EVM, subframe 6: 3.189%
Low edge EVM, subframe 7: 3.156%
High edge EVM, subframe 7: 3.162%
Low edge EVM, subframe 8: 3.136%
High edge EVM, subframe 8: 3.137%
Low edge EVM, subframe 9: 3.050%
High edge EVM, subframe 9: 3.052%
Averaged low edge EVM, frame 0: 3.095%
Averaged high edge EVM, frame 0: 3.105%
Averaged EVM frame 0: 3.105%
Averaged overall EVM: 3.105%
Simulating LTE frame 3 ...
Low edge EVM, subframe 0: 2.979%
High edge EVM, subframe 0: 2.997%
Low edge EVM, subframe 1: 2.762%
High edge EVM, subframe 1: 2.774%
Low edge EVM, subframe 2: 3.019%
High edge EVM, subframe 2: 3.027%
Low edge EVM, subframe 3: 3.419%
High edge EVM, subframe 3: 3.413%
Low edge EVM, subframe 4: 2.918%
High edge EVM, subframe 4: 2.934%
Low edge EVM, subframe 6: 3.087%
High edge EVM, subframe 6: 3.091%
Low edge EVM, subframe 7: 3.177%
High edge EVM, subframe 7: 3.182%
Low edge EVM, subframe 8: 3.113%
High edge EVM, subframe 8: 3.113%
Low edge EVM, subframe 9: 3.044%
High edge EVM, subframe 9: 3.050%
Averaged low edge EVM, frame 0: 3.063%
Averaged high edge EVM, frame 0: 3.070%
Averaged EVM frame 0: 3.070%
Averaged overall EVM: 3.070%

According to TS 36.104 [ 2 ], the maximum EVM when the constellation is 64-QAM is 8%. As the overall EVM, around 3%, is lower than 8%, this measurement falls within the requirements of TS 36.104 [ 2 ].

Visualize Measured EVM

This section plots the measured peak and RMS EVM for each simulated frame.

figure;
plot((2:N), 100*evmpeak,'o-')
title('EVM peak %');
xlabel('Number of frames');
figure;
plot((2:N), 100*evmrms,'o-');
title('EVM RMS %');
xlabel('Number of frames');

Cleaning Up

Close the Simulink model and remove the generated files.

bdclose(model);
clear([model,'_acc']);

Appendix

This example uses the following helper functions:

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