Skip to main content
Home Wireless Communication Modulation MATLAB Beamforming Project Ideas MIMO Computer Networks Lab 🚀

Delta Modulation & Demodulation


Delta Modulation & Demodulation Technique



Another name for delta modulation is a 1-bit quantizer. As a result, compared to PCM or DPCM, less bandwidth is needed here.


We know that bandwidth (BW),


BW = nfs/2 .........(1)




Where n = number of bits per sample


          fs = Frequency of Sampling





To avoid the cause of under-sampling, fs cannot be decreased in the above equation 1 to decrease bandwidth (BW). To retrieve the intended signal at the receiver side, we must keep our sample frequency at least two times the frequency of the message signal.



Alternatively, fs > 2fm



In this case, fm stands for message signal frequency, which is often the highest frequency available in message transmission.




However, in delta modulation, the bandwidth will be reduced to the smallest amount feasible by picking the lowest possible value of n, i.e. 1 bit/sample.


Assume that Rb = nfs is the data rate.


As a result, Rb = fs (if n=1 bit/sample)


So, in the delta modulation scheme, we can say,



Bit rate = Pulse rate = Sampling rate



Because we're only allocating 1 bit/sample, the number of levels is L = 2^(1) = 2. In general, the highest level is represented by '+∆', while the lowest level is represented by '-∆'. From the quantizer value we decide whether the sampling bit is '1' or '0'.










In delta modulation, we actually accomplish the following:



We compare the current sample value to the prior sample value in this modulation. When the difference (also known as "error") value exceeds the threshold value, the value is detected as "1." In the same way, if it goes below the threshold value, it will be '0'.







Diagram:











                                                                       Fig: Delta Modulation



Here, the input of the quantizer,


e(nTs) = m(nTs) – m^(nTs)


Where, m(nTs) = current sample

m^(nTs) = previous sample

The difference between the current sample value and the previous sample value (or, e(nTs)) is the quantizer's input. The modulated signal is represented as bit '1' if the difference value is greater than the threshold value (say, 0 Volt); otherwise, it is represented as bit '0'.


With the use of diagrams, we'll now discuss delta modulation (DM) and demodulation at the receiver side.



Delta Demodulation


Assume there are two levels (due to the one-bit quantizer) or that the quantizer step value is '+∆' and '–∆' on the negative side. '+∆' indicates a higher level, whereas '-∆' indicates a lower level.


Take a look at the quantizer diagram below. If the difference (or error value) between the current sample value and the prior sample value exceeds the threshold value, the sample will be converted to bit '1' (For your convenience, let's say, the threshold is 0 Volt). If the above-mentioned difference value is between 0 and + ∆ Volt, we convert it to bit '1'. Similarly, we translate to bit '0'  for values between 0 and - ∆ Volt.




Diagram of DM Quantizer:








DM Encoder:









DM Decoder at receiver side:








In decoding process, at t=0, sample value = 0

At, t = Ts, sample value = 0+∆ = +∆

      t = 2Ts, sample value = +∆ +∆ = +2∆

      t = 3Ts, sample value = +2∆ +∆ = +3∆

      t = 4Ts, sample value = +3∆ -∆ = +2∆

      t = 5Ts, sample value = +2∆ -∆ = +∆


Whenever the signal reaches the receiver it was 0, at t=0 & t< Ts; At t=Ts, we receive +∆. Now, the summation of the present sample value and previous sample value (which is '0' at the start) equals 0 +∆= +∆; At t=2Ts,  the sum of the current sample value and previous sample value = +∆ +∆ = +2∆ and so on (as shown in the above chart).

MATLAB Code for Delta Modulation and Demodulation

 
 
 

 
                                                                 (Get MATLAB Code)

Read also about

[1] MATLAB Code for Delta Modulation and Demodulation

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

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, the signal power i

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 FSK PSK Baud Rate (Hz):

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

Modulation Constellation Diagrams BER vs. SNR 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 %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 clc; clear; close all; % Parameters num_symbols = 1e5; % Number of symbols snr_db = -20:2:20; % Range of SNR values in dB % PSK orders to be tested psk_orders = [2, 4, 8, 16, 32]; % QAM orders to be tested qam_orders = [4, 16, 64, 256]; % Initialize BER arrays ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_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]

FFT Magnitude and Phase Spectrum using MATLAB

MATLAB Code clc; clear; close all; % Parameters fs = 100;           % Sampling frequency t = 0:1/fs:1-1/fs;  % Time vector % Signal definition x = cos(2*pi*15*t - pi/4) - sin(2*pi*40*t); % Compute Fourier Transform y = fft(x); z = fftshift(y); % Frequency vector ly = length(y); f = (-ly/2:ly/2-1)/ly*fs; % Compute phase phase = angle(z); % Plot magnitude of the Fourier Transform figure; subplot(2, 1, 1); stem(f, abs(z), 'b'); xlabel('Frequency (Hz)'); ylabel('|y|'); title('Magnitude of Fourier Transform'); grid on; % Plot phase of the Fourier Transform subplot(2, 1, 2); stem(f, phase, 'b'); xlabel('Frequency (Hz)'); ylabel('Phase (radians)'); title('Phase of Fourier Transform'); grid on;   Output  Copy the MATLAB Code from here % The code is written by SalimWireless.Com clc; clear; close all; % Parameters fs = 100; % Sampling frequency t = 0:1/fs:1-1/fs; % Time vector % Signal definition x = cos(2*pi*15*t -

Difference between AWGN and Rayleigh Fading

Wireless Signal Processing Gaussian and Rayleigh Distribution Difference between AWGN and Rayleigh Fading 1. Introduction Rayleigh fading coefficients and AWGN, or additive white gaussian noise [↗] , are two distinct factors that affect a wireless communication channel. In mathematics, we can express it in that way.  Let's explore wireless communication under two common noise scenarios: AWGN (Additive White Gaussian Noise) and Rayleigh fading. y = hx + n ... (i) The transmitted signal  x  is multiplied by the channel coefficient or channel impulse response (h)  in the equation above, and the symbol  "n"  stands for the white Gaussian noise that is added to the signal through any type of channel (here, it is a wireless channel or wireless medium). Due to multi-paths the channel impulse response (h) changes. And multi-paths cause Rayleigh fading. 2. Additive White Gaussian Noise (AWGN) The mathematical effect involves adding Gauss

Channel Impulse Response (CIR)

Channel Impulse Response (CIR) Wireless Signal Processing CIR, Doppler Shift & Gaussian Random Variable  The Channel Impulse Response (CIR) is a concept primarily used in the field of telecommunications and signal processing. It provides information about how a communication channel responds to an impulse signal.   What is the Channel Impulse Response (CIR) ? It describes the behavior of a communication channel in response to an impulse signal. In signal processing,  an impulse signal has zero amplitude at all other times and amplitude  ∞ at time 0 for the signal. Using a Dirac Delta function, we can approximate this.  ...(i) δ( t) now has a very intriguing characteristic. The answer is 1 when the Fourier Transform of  δ( t) is calculated. As a result, all frequencies are responded to equally by  δ (t). This is crucial since we never know which frequencies a system will affect when examining an unidentified one. Since it can test the system for all freq

MATLAB Code for Pulse Amplitude Modulation (PAM) and Demodulation

  Pulse Amplitude Modulation (PAM) & Demodulation MATLAB Script clc; clear all; close all; fm= 10; % frequency of the message signal fc= 100; % frequency of the carrier signal fs=1000*fm; % (=100KHz) sampling frequency (where 1000 is the upsampling factor) t=0:1/fs:1; % sampling rate of (1/fs = 100 kHz) m=1*cos(2*pi*fm*t); % Message signal with period 2*pi*fm (sinusoidal wave signal) c=0.5*square(2*pi*fc*t)+0.5; % square wave with period 2*pi*fc s=m.*c; % modulated signal (multiplication of element by element) subplot(4,1,1); plot(t,m); title('Message signal'); xlabel ('Time'); ylabel('Amplitude'); subplot(4,1,2); plot(t,c); title('Carrier signal'); xlabel('Time'); ylabel('Amplitude'); subplot(4,1,3); plot(t,s); title('Modulated signal'); xlabel('Time'); ylabel('Amplitude'); %demdulated d=s.*c; % At receiver, received signal is multiplied by carrier signal filter=fir1(200,fm/fs,'low'); % low-pass FIR fi