The code below is an example of 2 X 2 MIMO.   MATLAB Code clc; clear all; close all; % MIMO System Parameters numTx = 2; % Number of transmit antennas numRx = 2; % Number of receive antennas numSymbols = 50; % Number of symbols to transmit % Generate random binary data data = randi([0, 1], numTx, numSymbols); % PSK Modulation (BPSK) modulatedSymbols = 1 - 2 * data; % BPSK modulation: '0' maps to +1, '1' maps to -1 % Precoding (Identity Matrix for demonstration) precodingMatrix = eye(numTx); % Transmit Signal transmitSignal = precodingMatrix * modulatedSymbols; % Channel (Rayleigh Fading Channel) H = (randn(numRx, numTx) + 1i * randn(numRx, numTx)) / sqrt(2); % Rayleigh fading channel % Received Signal receivedSignal = H * transmitSignal; % Demodulation demodulatedSymbols = H' * receivedSignal; % PSK Demodulation (BPSK) receivedData = demodulatedSymbols < 0; % Decision threshold at 0 Output       Fig 1: Original Message Bits             Fig 2: Received Message

  In Rayleigh fading, the channel coefficients tend to have a Rayleigh distribution, which is characterized by a random phase and magnitude with an exponential distribution. This means the magnitude of the channel coefficient follows an exponential distribution with a mean of 1. In Rician fading, there is a dominant line-of-sight component in addition to the scattered components. The channel coefficients in Rician fading can indeed tend towards 1, especially when the line-of-sight component is strong. When the line-of-sight component dominates, the Rician fading channel behaves more deterministically, and the channel coefficients may tend towards the value of the line-of-sight component, which could be close to 1.   MATLAB Script clc; clear all; close all; % Define parameters numSamples = 1000; % Number of samples K_factor = 5; % K-factor for Rician fading SNR_dB = 20; % Signal-to-noise ratio (in dB) % Generate complex Gaussian random variable for Rayleigh fading channel h_rayleigh = (

  MATLAB Script % The code is written by SalimWireless.Com clc; clear all; close all; % Generate random bits numBits = 100; bits = randi([0, 1], 1, numBits); % Define parameters numSubcarriers = 4; % Number of subcarriers numPilotSymbols = 3; % Number of pilot symbols cpLength = ceil(numBits / 4); % Length of cyclic prefix (one-fourth of the data length) % Add cyclic prefix dataWithCP = [bits(end - cpLength + 1:end), bits]; % Insert pilot symbols pilotSymbols = ones(1, numPilotSymbols); % Example pilot symbols (could be any pattern) dataWithPilots = [pilotSymbols, dataWithCP]; % Perform OFDM modulation (IFFT) dataMatrix = reshape(dataWithPilots, numSubcarriers, []); ofdmSignal = ifft(dataMatrix, numSubcarriers); ofdmSignal = reshape(ofdmSignal, 1, []); % Display the generated data disp("Original Bits:"); disp(bits); disp("Data with Cyclic Prefix and Pilots:"); disp(dataWithPilots); disp("OFDM Signal:"); disp(ofdmSignal); %%%%%%%%%%%%%%%%%%%%%%%%%%% Demodul

  Alamouti's Space-Time Block Coding (STBC) is a technique used in MIMO wireless communication systems to achieve diversity gain without requiring channel knowledge at the transmitter. It involves transmitting multiple copies of the same symbols over multiple antennas with specific phase relationships. This allows the receiver to combine the signals effectively and recover the transmitted symbols even in the presence of fading. The Alamouti precoding matrix is constructed based on the Alamouti code, which defines the phase relationships between the symbols transmitted from different antennas over two consecutive time slots. For a 2x1 MIMO system (two transmit antennas and one receive antenna), the Alamouti precoding matrix is as follows: Precoding Matrix=[s1  −s2∗;  s2   s1∗] Where:     s1 and s2 are the symbols to be transmitted from the two antennas in the current time slot.     s1∗​ and s2∗​ are the complex conjugates of s1​ and s2​ respectively. This matrix ensures that the sy

MATLAB Script % This code is written by SalimWirelss.Com clc; clear all; close all; M = 4; % Number of levels after quantization / size of signal constellation k = log2(M); % Number of bits per symbol rng(1) % Assaining the value of seed integer N = 1000000; % Number of bits to process % Initialize SNR values and BER array snr_dB = -20:1:20; % SNR values in dB BER = zeros(size(snr_dB)); for snr_idx = 1:length(snr_dB) snrdB = snr_dB(snr_idx); InputBits = randi([0 1], 1, N); % Generating randon bits InputSymbol_matrix = reshape(InputBits, length(InputBits)/k, k); % Reshape data into binary k-tuples, k = log2(M) InputSymbols_decimal = bi2de(InputSymbol_matrix); % Convert binary to decimal for n = 1:N/k if InputSymbols_decimal(n) == 0 QAM(n) = complex(1,1); elseif InputSymbols_decimal(n) == 1 QAM(n) = complex(-1,1); elseif InputSymbols_decimal(n) == 2 QAM(n) = complex(1,-1); else QAM(n) = complex(-1,-1); end end % Transmission of 4QAM data over AWGN channel Y = awgn(QAM,

MATLAB Script  % This code is written by SalimWirelss.Com clc; clear all; close all; M = 4; % Number of levels after quantization / size of signal constellation k = log2(M); % Number of bits per symbol rng(10) % Assaining the value of seed integer N = 10000; % Number of bits to process % Initialize SNR values and BER array snr_dB = 0:1:20; % SNR values in dB BER = zeros(size(snr_dB)); for snr_idx = 1:length(snr_dB) snrdB = snr_dB(snr_idx); InputBits = randi([0 1], 1, N); % Generating randon bits InputSymbol_matrix = reshape(InputBits, length(InputBits)/k, k); % Reshape data into binary k-tuples, k = log2(M) InputSymbols_decimal = bi2de(InputSymbol_matrix); % Convert binary to decimal for n = 1:N/k if InputSymbols_decimal(n) == 0 QAM(n) = complex(1,1); elseif InputSymbols_decimal(n) == 1 QAM(n) = complex(-1,1); elseif InputSymbols_decimal(n) == 2 QAM(n) = complex(1,-1); else QAM(n) = complex(-1,-1); end end % Transmission of 4QAM data over AWGN channel Y = awgn(QAM, s

  MATLAB Code clc; clear all; close all; % Define filter parameters fs = 1000; % Sampling frequency (Hz) fpass1 = 50; % Lower cutoff frequency (Hz) fpass2 = 200; % Upper cutoff frequency (Hz) filterOrder = 4; % Filter order % Calculate normalized cutoff frequencies nyquistFreq = fs / 2; normFpass1 = fpass1 / nyquistFreq; normFpass2 = fpass2 / nyquistFreq; % Design the bandpass filter filter = designfilt('bandpassiir', ... 'FilterOrder', filterOrder, ... 'HalfPowerFrequency1', normFpass1, ... 'HalfPowerFrequency2', normFpass2, ... 'DesignMethod', 'butter'); % Visualize the filter frequency response fvtool(filter, 'Analysis', 'freq'); % Print filter coefficients disp('Filter Coefficients:'); disp(filter.Coefficients);   Output          Fig 1: Magnitude response and phase response of a filter with the bandpass filter order of 4 Fig 2: Magnitude response and phase response of a filter with the bandpass filter order

  Delta modulation and demodulation [↗] processes are pretty simple. It uses a 1-bit quantizer, or there are 2^(1) = two quantization levels. In this encoding technique, we compare the succeeded sample with the previous sample. If it is greater than the previous sample, we assign 1. Otherwise, we assign 0. Here, we encode the modulated signal like this. However, this modulation scheme is susceptible to noise. So Delta modulation (DM) is not commonly used in typical wireless communication systems for several reasons: Noise Sensitivity:  Delta modulation is highly sensitive to noise due to its reliance on small changes (delta) in the input signal. In wireless communication systems, especially in environments with high levels of noise and interference, delta modulation may result in poor performance and low signal fidelity. Quantization Errors:  Delta modulation suffers from quantization errors, which occur when the difference between the input signal and the predicted va