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.

1. 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)]);

2. 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’]);

3. 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.

1. Time-Domain Waveform:

Figure;

Plot (real (rxWaveform));

Title (‘Received 5G NR Waveform – Time Domain’);

Xlabel (‘Sample’);

Ylabel (‘Amplitude’);

2. 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)’);

3. 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:

1. 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)]);

2. 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)]);

3. 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’);

4. 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)]);

5. 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)’);

6. 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’);

7. 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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