MATLAB Program for 16QAM Modulation and Demodulation Implementation

Below is a MATLAB program written to implement 16QAM modulation and demodulation. % 16QAM Modulation and Demodulation Program % Generate random input bits for 16QAM modulation input_bits = randi([0 1], 1, 1000); % Perform 16QAM modulation using the generated bits modulated_signal = qammod(input_bits, 16); % Add Additive White Gaussian Noise (AWGN) with 10dB SNR noisy_signal = awgn(modulated_signal, 10); % Demodulate the received signal using 16QAM demodulation demodulated_bits = qamdemod(noisy_signal, 16); % Calculate the Bit Error Rate (BER) error_rate = sum(input_bits ~= demodulated_bits) / length(input_bits); % Display the calculated error rate disp(['Bit Error Rate: ', num2str(error_rate)]); The above program implements 16QAM modulation and demodulation functionality using MATLAB. The program begins by generating a random sequence of input bits using the randi() function. This input signal is then modulated using 16QAM modulation through the qammod() function, which maps the binary data to complex constellation points representing 16 different phase and amplitude combinations. The program then adds Additive White Gaussian Noise (AWGN) using the awgn() function with a specified signal-to-noise ratio of 10dB, simulating noise interference that occurs during signal transmission in real-world communication channels. The demodulation process is performed using the qamdemod() function, which reverses the modulation process by mapping the received constellation points back to binary data. Finally, the program calculates the Bit Error Rate (BER) by comparing the original input bits with the demodulated bits, providing a quantitative measure of the system's performance under noisy conditions. The BER calculation uses element-wise comparison and normalization by the total number of bits. Through this program, you can understand the implementation methodology of 16QAM modulation and demodulation, and you can modify and optimize the program according to specific requirements. The implementation demonstrates key digital communication concepts including constellation mapping, noise modeling, and error rate analysis.