In MATLAB, conducting a 5G simulation is a little bit complicated, but with proper procedure and assistance, it is possible to attain in an efficient manner. To guide you throughout the process, we provide step-by-step procedures for performing an effective 5G simulation and evaluation of data in MATLAB:
Step 1: Create 5G Waveform
By using MATLAB’s 5G Toolbox, you ought to develop a 5G waveform initially. PDSCH (Physical Downlink Shared Channel), creating the carrier and other frameworks are involved.
% define carrier configuration
Carrier = nrCarrierConfig;
carrier.NSizeGrid = 52; % Number of resource blocks
carrier.SubcarrierSpacing = 30; % Subcarrier spacing in kHz
% define PDSCH configuration
Pdsch = nrPDSCHConfig;
pdsch.Modulation = ‘QPSK’; % Modulation scheme
pdsch.NumLayers = 1; % Number of transmission layers
pdsch.PRBSet = 0:carrier.NSizeGrid-1; % PRB allocation
% generate waveform
Waveconfig = nrWavegenConfig;
waveconfig.Carriers = carrier;
Waveconfig. PDSCH = pdsch;
% Generate the waveform
[Waveform, info] = nrWaveformGenerator (waveconfig);
% Display waveform information
Disp (info);
Step 2: Simulate Channel Impacts
And then, by means of channel, simulate the transmission of the waveform. Diverse channel degradations like interruptions, noise and fading are encompassed in this process.
% define TDL channel model
Channel = nrTDLChannel;
channel.DelayProfile = ‘TDL-C’;
channel.DelaySpread = 100e-9; % 100 ns delay spread
channel.MaximumDopplerShift = 5; % 5 Hz Doppler shift
channel.SampleRate = info.SampleRate;
% Pass the waveform through the channel
RxWaveform = channel (waveform);
add AWGN noise
Snr = 30; % Signal-to-noise ratio in dB
RxWaveform = awgn (rxWaveform, snr, ‘measured’);
Step 3: Receive and Operate the Signal
To decode the transferred data, receive the signal and operate it after the transmission function.
% Synchronize the received waveform
[RxGrid, nSlotOffset] = nrSynchronize (rxWaveform, carrier, ‘SNR’, snr);
% Channel estimation
EstChannelGrid = nrChannelEstimate (rxGrid, carrier, pdsch);
% Decode the PDSCH
PdschRx = nrPDSCHDecode (rxGrid, estChannelGrid, carrier, pdsch);
% Compute bit error rate (BER)
TxBits = Randi ([0 1], length (pdschRx)*2, 1); % Assuming QPSK
RxBits = qamdemod (pdschRx, 4, ‘OutputType’, ‘bit’);
Ber = biterr (txBits, rxBits) / length (txBits);
Disp ([‘Bit Error Rate (BER): ‘num2str (ber)]);
Step 4: Evaluate Performance Metrics
Different performance metrics like SNR (Signal-to-Noise Ratio), throughput and BER (Bit Error Rate) have to be evaluated at last.
- Bit Error Rate (BER):
Throughout the time duration of research, this BER metric assesses the amount of bit errors divided by the total amount of transmitted bits.
% Calculate BER
[~, ber] = biterr (txBits, rxBits);
disp ([‘Bit Error Rate (BER): ‘, num2str(ber)]);
- Signal-to-Noise Ratio (SNR):
Regarding the background noise, SNR evaluates the signal capabilities.
% Estimate SNR from the received signal
SnrEstimate = snr (rxWaveform);
Disp ([‘Estimated SNR: ‘num2str (snrEstimate), ‘ dB’]);
- Throughput:
In a given period of time, throughput metric analyzes how many units of data a system can process.
% calculate throughput
Total Bits = length (rxBits);
Error Bits = sum (rxBits ~= txBits);
Throughput = (total Bits – error Bits) / total Bits * info.SampleRate;
disp ([‘Throughput: ‘, num2str(throughput), ‘ bits/sec’]);
Step 5: Visualize Results
To acquire in-depth interpretation on performance of the system, visualization of findings is very beneficial.
- Time-Domain Waveform:
Figure;
Plot (real (rxWaveform));
Title (‘Received 5G NR Waveform – Time Domain’);
Xlabel (‘Sample’);
Ylabel (‘Amplitude’);
- Frequency-Domain Spectrum:
NFFT = 2048;
FreqDomain = 20*log10 (abs (fftshift (fft (rxWaveform, NFFT))));
f = linspace (-0.5, 0.5, NFFT) * info.SampleRate;
Figure;
Plot (f/1e6, freqDomain);
Title (‘Received 5G NR Waveform – Frequency Domain’);
Xlabel (‘Frequency (MHz)’);
Ylabel (‘Magnitude (dB)’);
- Resource Grid:
Resource Grid = nrResourceGrid (carrier);
PdschIndices = pdschIndices (carrier, pdsch);
PdschSymbols = nrPDSCH (carrier, pdsch, randi ([0 1], pdsch.ModulationOrder * numel (pdschIndices), 1));
ResourceGrid (pdschIndices) = pdschSymbols;
Figure;
Imagesc (abs (resourceGrid));
Title (‘5G NR Resource Grid’);
Xlabel (‘OFDM Symbol’);
Ylabel (‘Subcarrier’);
Colorbar;
How is Matlab used in telecommunication engineering?
In telecommunication engineering, the MATLAB tool is widely used due to its flexibility and efficient capacity for evaluating data and developing techniques. Some of the numerous applications of MATLAB in the area of telecommunication engineering are suggested by us:
- Signal Processing
Major Applications:
- Modulation and Demodulation: Diverse modulation policies such as OFDM, QAM and QPSK have to be applied and evaluated.
- Filter Design: It is required to model and examine digital and analog filters such as IIR and FIR.
- Fourier analysis: For frequency domain analysis, carry out Fourier transforms like IFFT or FFT.
Instance:
% Generate a QPSK signal
Data = Randi ([0 3], 1000, 1);
QpskMod = comm.QPSKModulator (‘BitInput’, false);
ModSignal = qpskMod (data);
% add AWGN noise
NoisySignal = awgn (modSignal, 20, ‘measured’);
% Demodulate the signal
QpskMod = comm.QPSKModulator (‘Bit Output’, false);
DemodData = qpskDemod (noisySignal);
% Calculate the Bit Error Rate (BER)
[NumErrors, ber] = biterr (data, demodData);
Disp ([‘Bit Error Rate (BER): ‘, num2str(ber)]);
- Communication Systems Simulation
Major Applications:
- End-to-End System Simulation: Encompassing channel, transmitter and receiver, simulate extensive communication systems.
- Channel Modeling: Different types of channels like Rician, AWGN and Rayleigh could be designed.
- MIMO Systems: Specifically for spatial multiplexing and variations, simulate MIMO (Multiple Input Multiple Output) systems.
Instance:
% define system parameters
M = 16; % QAM order
k = log2 (M); % Bits per symbol
NSym = 1000; % Number of symbols
% Generate random data
Data = Randi ([0 M-1], nSym, 1);
% modulate data using 16-QAM
Mod Data = qammod (data, M, ‘UnitAveragePower’, true);
% Pass through Rayleigh channel
Channel = comm.RayleighChannel (‘SampleRate’, 1e6, ‘MaximumDopplerShift’, 30);
RxData = channel (modData);
% add AWGN noise
RxData = awgn (rxData, 20, ‘measured’);
% demodulate data
DemodData = qamdemod (rxData, M, ‘UnitAveragePower’, true);
% Calculate BER
[NumErrors, ber] = biterr (data, demodData);
Disp ([‘Bit Error Rate (BER): ‘, num2str(ber)]);
- Wireless Communication
Major Applications: 5G NR Waveform Generation
- 5G and LTE Simulation: On 5G, simulate physical layer and system-level perspectives of 5G and LTE networks.
- Beamforming: As regards antenna rays, develop and evaluate beamforming techniques.
- Spectrum Analysis: The spectrums of wireless signal should be examined and create spectrum sensing techniques.
Instance:
% define carrier configuration
Carrier = nrCarrierConfig;
carrier.NSizeGrid = 52;
carrier.SubcarrierSpacing = 30;
% define PDSCH configuration
Pdsch = nrPDSCHConfig;
pdsch.Modulation = ‘QPSK’;
pdsch.NumLayers = 1;
pdsch.PRBSet = 0:carrier.NSizeGrid-1;
% generate waveform
Waveconfig = nrWavegenConfig;
waveconfig.Carriers = carrier;
Waveconfig. PDSCH = pdsch;
[Waveform, info] = nrWaveformGenerator (waveconfig);
% Plot the time-domain waveform
Figure;
Plot (real (waveform));
Title (‘5G NR Waveform – Time Domain’);
Xlabel (‘Sample’);
Ylabel (‘Amplitude’);
- Error Correction Coding
Major Applications:
- Encoding and Decoding: Multiple error correction codes such as LDPC, BCH, Hamming and Turbo codes must be executed and evaluated.
- Performance Analysis: Based on different channel scenarios, assess the performance of various coding policies.
Instance: LDPC Coding
% define LDPC parameters
LdpcEncoder = comm.LDPCEncoder;
LdpcDecoder = comm.LDPCDecoder;
Modulator = comm.BPSKModulator;
Demodulator = comm.BPSKModulator (‘Decision Method’, ‘Log-likelihood ratio’, ‘Variance’, 1e-10);
% Generate random binary data
Data = Randi ([0 1], 64800, 1)
% encode data
Encoded Data = ldpcEncoder (data);
% Modulate encoded data
ModSignal = modulator (encodedData);
% Pass through AWGN channel
RxSignal = awgn (modSignal, 1);
% Demodulate received signal
RxLLR = demodulator (rxSignal);
% decode data
DecodedData = ldpcDecoder (rxLLR);
% Calculate BER
[NumErrors, ber] = biterr (data, decodedData);
Disp ([‘Bit Error Rate (BER): ‘num2str (ber)]);
- Antenna Design and Analysis
Major Applications:
- Antenna Array Design: For various applications, model and explore antenna arrays.
- Beamforming Analysis: Beamforming techniques have to be simulated and analyzed.
- Radiation Pattern Analysis: Radiation models of antennas should be evaluated and illustrated.
Instance: Antenna Array Design
% Design a linear antenna array
NumElements = 8;
ElementSpacing = 0.5; % Half-wavelength spacing
Array = phased.ULA (‘NumElements’, numElements, ‘ElementSpacing’, elementSpacing);
% Define steering vector
SteeringVector = phased. SteeringVector (‘Sensor Array’, array, ‘Propagation Speed’, physicist (‘Light Speed’));
% define angles
Angles = -90:90;
% calculate response
Response = steeringVector (1e9, angles);
% Plot response
Figure;
Plot (angles, mag2db (abs (response)));
Title (‘Antenna Array Response’);
Xlabel (‘Angle (degrees)’);
Ylabel (‘Magnitude (dB)’);
- Network Simulation
Major Applications:
- Network Protocol Design: Considering the wired and wireless networks, develop and simulate network protocols.
- Traffic Analysis: Network traffic should be explored and enhance the network performance.
- QoS Analysis: Especially for various network setups, assess QoS (Quality of Service) standards.
Instance: Simple Network Simulation
% Create a network model
Network = rand (10, 10); % Random 10×10 adjacency matrix
G = digraph (network);
% Plot the network
Figure;
Plot (G);
Title (‘Network Topology’);
- Digital Communication Systems
Major Applications:
- System Design and Simulation: End-to-end digital communication systems required to be created and simulated.
- Performance Analysis: Considering the diverse modulation and coding policies, evaluate the performance.
- Channel Estimation and Equalization: Channel estimation and equalization algorithms must be established.
Instance: OFDM System Simulation
% System parameters
NumSubcarriers = 64;
CpLen = 16; % Cyclic prefix length
ModOrder = 16; % 16-QAM
% Generate random data
Data = Randi ([0 modOrder-1], numSubcarriers, 1);
% modulate data using 16-QAM
ModData = qammod (data, modOrder);
% IFFT to generate OFDM symbols
OfdmSymbols = ifft (modData);
% Add cyclic prefix
TxSignal = [ofdmSymbols (end-cpLen+1: end); ofdmSymbols];
% Pass through AWGN channel
RxSignal = awgn (txSignal, 20, ‘measured’);
% Remove cyclic prefix
RxOfdmSymbols = rxSignal (cpLen+1: end);
% FFT to recover transmitted data
RxData = fft (rxOfdmSymbols);
% demodulate data
DemodData = qamdemod (rxData, modOrder);
% Calculate BER
[NumErrors, ber] = biterr (data, demodData);
Disp ([‘Bit Error Rate (BER): ‘, num2str(ber)]);
5G Simulation In MATLAB
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