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MATLAB Code for ASK, FSK, and PSK (with Online Simulator)


MATLAB Code for ASK, FSK, and PSK

Comprehensive implementation of digital modulation and demodulation techniques with simulation results.

📘 Theory 📡 ASK Code 📶 FSK Code 🎚️ PSK Code 🕹️ Simulator 📚 Further Reading



MATLAB Code for ASK Modulation and Demodulation

% The code is written by SalimWireless.Com clc; clear all; close all; % Parameters Tb = 1; fc = 10; N_bits = 10; Fs = 100 * fc; Ts = 1/Fs; samples_per_bit = Fs * Tb; rng(10); binary_data = randi([0, 1], 1, N_bits); t_overall = 0:Ts:(N_bits * Tb) - Ts; message_signal_overall = zeros(1, length(t_overall)); ask_signal_overall = zeros(1, length(t_overall)); carrier_template = sqrt(2/Tb) * sin(2*pi*fc*(0:Ts:Tb-Ts)); for i = 1:N_bits current_bit = binary_data(i); t_segment_indices = ((i-1)*samples_per_bit + 1) : (i*samples_per_bit); if current_bit == 1 message_segment = ones(1, samples_per_bit); else message_segment = zeros(1, samples_per_bit); end ask_segment = carrier_template .* message_segment; message_signal_overall(t_segment_indices) = message_segment; ask_signal_overall(t_segment_indices) = ask_segment; end
ASK Output

Fig 1: ASK Modulation and Demodulation

MATLAB Code for FSK Modulation and Demodulation

% The code is written by SalimWireless.Com clc; clear; close all; % Parameters Fs = 1000; fc1 = 20; fc2 = 50; Tb = 1; num_bits = 10; Ts = 1/Fs; samples_per_bit = Fs * Tb; rng(20); bits = randi([0, 1], 1, num_bits); t_bit = 0:Ts:Tb-Ts; modulated_signal = []; for bit = bits if bit == 0 modulated_signal = [modulated_signal sin(2*pi*fc1*t_bit)]; else modulated_signal = [modulated_signal sin(2*pi*fc2*t_bit)]; end end
FSK Output

Fig 2: FSK Modulation and Demodulation

MATLAB Code for PSK Modulation and Demodulation

% The code is written by SalimWireless.Com clc; clear all; close all; carrier_frequency = 10; bit_duration = 1; sampling_frequency = 1000; Ts = 1/sampling_frequency; samples_per_bit = sampling_frequency * bit_duration; rng(30); bit_stream = randi([0, 1], 1, 8); t_bit = 0:Ts:bit_duration-Ts; psk_signal = []; for bit = bit_stream if bit == 0 psk_signal = [psk_signal sin(2*pi*carrier_frequency*t_bit + 0)]; else psk_signal = [psk_signal sin(2*pi*carrier_frequency*t_bit + pi)]; end end
PSK Output

Fig 3: PSK Modulation and Demodulation

Understanding the MATLAB Implementation

Key Variables

  • Fs: Sampling frequency (must be > 2x carrier frequency).
  • Tb: Bit duration (defines how long each symbol lasts).
  • rng(10): Ensures your random bit generation is reproducible.

The Logic

The code uses a for loop to iterate through the bitstream. Depending on the bit value (0 or 1), it selects the appropriate carrier frequency or phase, effectively performing Hard-Decision Mapping.

Common MATLAB Errors & Fixes

1. "Undefined function or variable": Ensure you are running the script in the same folder where your variables are saved. Use clc; clear all; at the top.

2. Aliasing in Plots: If your waveforms look "jagged," increase the Fs (Sampling Frequency) to at least 10 times the carrier frequency.

3. Toolboxes: These codes run on base MATLAB, but for advanced BER analysis, you may need the Communication Toolbox.

Interactive Modulation Simulator

Launch the web-based tool to simulate digital modulation techniques.

Simulator Launch Simulator →

Modulation Parameter Changed Noise Immunity Complexity
ASK Amplitude Low (Highly susceptible) Simplest
FSK Frequency High (Robust) Moderate
PSK Phase Very High Highest (Requires Coherent Detection)

From Simulation to Hardware

While these MATLAB scripts simulate digital modulation in a vacuum, real-world deployment requires considering AWGN (Additive White Gaussian Noise). If you are moving to hardware like USRP (Universal Software Radio Peripheral) or RTL-SDR, you must implement Pulse Shaping (like Root-Raised Cosine) to limit bandwidth occupancy. Want to read more about raised cosine filter? Click here.

Frequently Asked Questions

Which is better: ASK, FSK, or PSK?

PSK is generally superior for high-speed data because it is the most bandwidth-efficient and noise-resistant, though it requires more complex receiver hardware.

How do I calculate BER in MATLAB?

You can use the biterr function in MATLAB to compare the transmitted bitstream with the demodulated bitstream to calculate the Bit Error Rate.


📚 Further Reading

BER vs SNR Main Page > Modulation Techniques Main Page >
DSP Simulations Main Page >

Effect of Noise (AWGN) on ASK, FSK, and PSK

The modulated signal, x(t), is propagated through a physical communication medium—such as a wireless interface or fiber-optic cabling—where it is subject to various channel impairments. Consequently, the resulting received signal, y(t), constitutes a degraded representation of the original transmission.

This relationship is mathematically characterized by the general received signal model in the presence of fading and noise:

y(t) = h(t) · x(t) + n(t)

Where:

  • x(t) denotes the transmitted modulated signal;
  • h(t) represents the multiplicative fading characteristics of the channel;
  • n(t) signifies the Additive White Gaussian Noise (AWGN) superimposed on the signal.

Read More: Effect of AWGN on ASK→   Effect of AWGN on FSK→   Effect of AWGN on PSK→   GET MATLAB Code 


Effect of Rayleigh Fading on ASK, FSK, and PSK

This technical analysis details the lifecycle of a signal within a digital communication system, focusing on the transition from the message signal to final recovery. To facilitate long-distance transmission, information is modulated onto high-frequency carriers. However, as the signal traverses a physical medium, it encounters significant degradation modeled by the following time-domain relationship:

y(t) = [h(t)] ∗ s(t) + w(t)

In this model, h(t) represents the Channel Impulse Response—specifically Rayleigh Fading—while w(t) signifies Additive White Gaussian Noise (AWGN). The text distinguishes between these two impairments: AWGN is a constant thermal noise that reduces the signal-to-noise ratio (SNR) across all frequencies, whereas Rayleigh Fading is a stochastic process caused by multipath propagation. In urban or indoor environments, signal reflections create "deep fades," resulting in rapid fluctuations of signal strength.

The performance impact is most visible in the Bit Error Rate (BER). While BER in an AWGN channel decreases exponentially with higher power, fading channels exhibit a much slower, linear decay. For instance, at 0 dB SNR using BPSK modulation, the BER effectively doubles from 0.078 (AWGN) to 0.16 (Rayleigh).

To combat these effects, systems employ Equalization to reverse channel distortion and Diversity Techniques (spatial, temporal, or frequency) to ensure redundancy.

Modern Outlook: Current industry standards in 5G, IoT, and V2X communication are shifting from traditional linear filters toward Deep Learning (DL) models. These AI-driven approaches offer superior Channel State Information (CSI) prediction, allowing for higher spectral efficiency and lower latency in 6G research and satellite infrastructures.

Read More: Impact of Rayleigh Fading on BPSK

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