Skip to main content
Home Wireless Communication Modulation MATLAB Beamforming Project Ideas MIMO Computer Networks
Electronics Industry Wireless FORUM Work Hub Applications & Games Generate QR for Any Link Sitemap Generator (for Any Website) Play game Electrical & Electronics Calculator Contact Get Newsletter.
☰

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...


 

MATLAB Script for BER vs. SNR for M-QAM, M-PSK, QPSk, BPSK


Output





If M>8, the distance between constellation points is short, and a higher Eb/No (SNR per Bit) ratio is required to reach the desired BER. Although the mapping from the data bits is arbitrary, some data bits are typically used. Every constellation point in the M-PSK constellation has two neighbors, each with an equal chance of making an error. As there are four bits per symbol, BER assumes a one-bit error for every mistake in a character. The demodulator in PSK must be able to calculate the received sinusoid's phase about some reference phase. While using the same bandwidths as ASK, PSK is less prone to errors than ASK. Also, using bandwidth with a significant data rate is more effective.

In the above figure, it is clear that PSK is more robust than QAM in the context of noise resilience. QAM modulations, including 16-QAM, are sensitive to both amplitude and phase errors. As you increase the number of constellation points (e.g., from 16-QAM to 8-PSK), the signal becomes more susceptible to amplitude and phase noise. In contrast, PSK modulations primarily rely on phase information and may be less sensitive to amplitude variations. This can make 8-PSK more robust in this scenario. 

QAM schemes require a higher SNR to achieve the same error rates as PSK schemes with the same number of constellation points. This means that 16-QAM may require a higher SNR than 8-PSK to achieve a satisfactory bit error rate (BER) or symbol error rate (SER). In practical communication systems, achieving the necessary SNR can be challenging, especially in noisy or fading channels.

MATLAB Code for BER vs SNR for m-ary QAM

clc;
clear all;
close all;


% Set parameters
snr_dB = -20:2:20; % SNR values in dB
qam_orders = [4, 16, 64, 256]; % QAM modulation orders

% Loop through each QAM order
for qam_order = qam_orders
% Calculate theoretical BER using berawgn
ber = berawgn(snr_dB, 'qam', qam_order);

% Plot the results
semilogy(snr_dB, ber, 'o-', 'DisplayName', sprintf('%d-QAM', qam_order));
hold on;
end

% Add labels and legend
title('BER vs SNR for Variable QAM');
xlabel('SNR (dB)');
ylabel('Bit Error Rate (BER)');
grid on;
legend('Location', 'best');

Output

 

Fig: BER vs SNR graph for Various QAM
 

Copy the aforementioned MATLAB Code from Here

 

 

MATLAB Code for BER vs SNR for m-ary PSK

clc;
clear all;
close all;

% Parameters
num_symbols = 1e5; % Number of symbols
snr_db = 0:2:20; % Range of SNR values in dB

% PSK orders to be tested
psk_orders = [2, 4, 8, 16, 32];

% Initialize BER arrays
ber_results = zeros(length(psk_orders), length(snr_db));

% BER calculation for each PSK order and SNR value
for i = 1:length(psk_orders)
psk_order = psk_orders(i);

for j = 1:length(snr_db)
% Generate random symbols
data_symbols = randi([0, psk_order-1], 1, num_symbols);

% Modulate symbols to generate signal
modulated_signal = pskmod(data_symbols, psk_order);

% Add AWGN to the signal
snr_linear = 10^(snr_db(j)/10);
received_signal = awgn(modulated_signal, snr_db(j), 'measured');

% Demodulate received signal
demodulated_symbols = pskdemod(received_signal, psk_order);

% Calculate BER
ber_results(i, j) = sum(data_symbols ~= demodulated_symbols) / num_symbols;
end
end

% Plot BER vs. SNR
figure;
semilogy(snr_db, ber_results(1, :), 'o-', 'DisplayName', 'BPSK');
hold on;

for i = 2:length(psk_orders)
semilogy(snr_db, ber_results(i, :), 'o-', 'DisplayName', sprintf('%d-PSK', psk_orders(i)));
end

title('BER vs. SNR for Various PSK Schemes');
xlabel('SNR (dB)');
ylabel('Bit Error Rate (BER)');
legend('Location', 'best');
grid on;
hold off;

Output

 
 
Fig: BER vs SNR graph for various PSK
 

Copy the above code from here



Read more about
[1] BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, and others

People are good at skipping over material they already know!

View Related Topics to







Admin & Author: Salim

profile

  Website: www.salimwireless.com
  Interests: Signal Processing, Telecommunication, 5G Technology, Present & Future Wireless Technologies, Digital Signal Processing, Computer Networks, Millimeter Wave Band Channel, Web Development
  Seeking an opportunity in the Teaching or Electronics & Telecommunication domains.
  Possess M.Tech in Electronic Communication Systems.


Contact Us

Name

Email *

Message *

Popular Posts

Channel Estimation utilizing Decision Feedback Equalizer (DFE)

  Channel estimation using DFE is a similar process to a non-linear equalization process. In DFE (decision feed equalizer), equalization error bits/symbols between the feedforward tabs and feedback taps are calculated continuously. And equalizer's tap weights tap weights are updated correspondingly.  In plain language, the error between the received bits and known training bits is calculated, and tap weights are updated accordingly. The equalizer estimates the channel impulse response (CIR) .  Once we find the channel impulse response or channel information, we can easily retrieve the original message signal from the noisy data. In the communication process, the whole system is modeled as a linear time-invariant (LTI) system. And  y = h*x + n where, y = received signal            x = transmitted signal           n = additive white Gaussian noise [Read more about the Linear time-invariant (LTI) system and convolu...
Read more

MATLAB Codes for Various types of beamforming | Beam Steering, Digital...

Beamforming Techniques MATLAB Codes for Beamforming... The mathematical [↗] and theoretical aspects of beamforming [↗] have already been covered. We'll talk about coding in MATLAB in this tutorial so that you may generate results for different beamforming approaches. Let's go right to the content of the article. In analog beamforming, certain codebooks are employed on the TX and RX sides to select the best beam pairs. Because of their beamforming gains, communication created through the strongest beams from both the TX and RX side enhances spectrum efficiency. Additionally, beamforming gain directly impacts SNR improvement. Wireless communication system capacity = bandwidth*log2(1+SNR) bits/s. Thus, the capacity or overall throughput of the system increases. MATLAB Script %Written by Salim Wireless %Visit www.salimwireless.com for study materials on wireless communication %or, if you want to learn how to code in MATLAB clear all;...
Read more

Theoretical BER vs SNR for BPSK

Let's simplify the explanation for the theoretical Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) for Binary Phase Shift Keying (BPSK) in an Additive White Gaussian Noise (AWGN) channel.  Key Points Fig 1: Constellation Diagrams of BASK, BFSK, and BPSK [↗] BPSK Modulation: Transmits one of two signals: +√Eb ​ or -√Eb , where Eb​ is the energy per bit. These signals represent binary 0 and 1 . AWGN Channel: The channel adds Gaussian noise with zero mean and variance N0/2 (where N0 ​ is the noise power spectral density). Receiver Decision: The receiver decides if the received signal is closer to +√Eb​ (for bit 0) or -√Eb​ (for bit 1) . Bit Error Rate (BER) The probability of error (BER) for BPSK is given by a function called the Q-function. The Q-function Q(x) measures the tail probability of the normal distribution, i.e., the probability that a Gaussian random variable exceeds a certain value x.  Formula for BER: BER=Q(...
Read more

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ... 1. What is Bit Error Rate (BER)? The abbreviation BER stands for bit error rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. It is defined as,  In mathematics, BER = (number of bits received in error / total number of transmitted bits)  On the other hand, SNR refers to the signal-to-noise power ratio. For ease of calculation, we commonly convert it to dB or decibels.   2. What is Signal the signal-to-noise ratio (SNR)? SNR = signal power/noise power (SNR is a ratio of signal power to noise power) SNR (in dB) = 10*log(signal power / noise power) [base 10] For instance, the SNR for a given communication system is 3dB. So, SNR (in ratio) = 10^{SNR (in dB) / 10} = 2 Therefore, in this instance,...
Read more

Constellation Diagrams of ASK, PSK, and FSK

Modulation ASK, FSK & PSK Constellation BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals: +√Eb​ or -√Eb (they differ by 180 degree phase shift), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  This article will primarily discuss constellation diagrams, as well as what constellation diagrams tell us and the significance of constellation diagrams. Constellation diagrams can often demonstrate how the amplitude and phase of signals or symbols differ. These two characteristics lessen the interference between t...
Read more

Constellation Diagram of PSK in Detail

        Fig 1: Constellation Diagram of PSK    In the above figure, the binary bit '1' is represented by S1(t) and the binary bit '0' by S2(t), respectively. So, energy of S1(t) = (√(Eb))2 = Eb So, energy of S2(t) = (-√(Eb))2 = Eb Distance between the signaling points, d12 = 2(√(Eb))   Energy per bit for binary '1' and binary '0'           High-order PSK (e.g., 8 PSK, 16 PSK) can transmit more bits per symbol but is more sensitive to noise. Low-order PSK (e.g., BPSK, QPSK) is less susceptible to noise. PSK modulation can be visualized using a constellation diagram, where each point represents a symbol. In the presence of noise, points may be away from the original positions, making them harder to distinguish.  
Read more

Comparisons among ASK, PSK, and FSK | And the definitions of each

Modulation ASK, FSK & PSK Constellation MATLAB Simulink MATLAB Code Comparisons among ASK, PSK, and FSK    Comparisons among ASK, PSK, and FSK Comparison among ASK,  FSK, and PSK Performance Comparison: 1. Noise Sensitivity:    - ASK is the most sensitive to noise due to its reliance on amplitude variations.    - PSK is less sensitive to noise compared to ASK.    - FSK is relatively more robust against noise, making it suitable for noisy environments. 2. Bandwidth Efficiency:    - PSK is the most bandwidth-efficient, requiring less bandwidth than FSK for the same data rate.    - FSK requires wider bandwidth compared to PSK.    - ASK's bandwidth efficiency lies between FSK and PSK. Bandwidth Calculator for ASK, FSK, and PSK The baud rate represents the number of symbols transmitted per second Select Modulation Type: ASK...
Read more