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 value exceeds the step size (delta). These errors can accumulate over time, leading to distortion and degradation of the decoded signal quality.
Low Bit Efficiency:
Delta modulation typically uses only one bit per sample to represent the signal, resulting in low bit efficiency compared to more sophisticated modulation schemes. This limitation makes delta modulation less suitable for applications requiring high data rates or efficient spectrum utilization.
Better Alternatives:
In modern wireless communication systems, there are several alternative modulation schemes that offer better performance, robustness to noise, and higher data rates than delta modulation. Techniques such as amplitude modulation (AM), frequency modulation (FM), phase modulation (PM), and various digital modulation schemes (e.g., QPSK, QAM) are commonly used in wireless standards like Wi-Fi, Bluetooth, LTE, and 5G.
Adaptive Techniques:
While adaptive delta modulation (ADM) can improve the performance of delta modulation by dynamically adjusting the step size based on the input signal characteristics, it still suffers from limitations related to noise sensitivity and quantization errors.
Overall, while delta modulation has certain advantages such as simplicity and low complexity, it is not commonly used in typical wireless communication systems due to its limitations in terms of noise sensitivity, quantization errors, and low bit efficiency. More advanced modulation schemes are preferred for achieving higher performance, robustness, and efficiency in wireless communication applications.
MATLAB Code for BER vs SNR for Delta Modulation
clear all;
close all;
clc;
% Parameters
N = 1000000; % Number of bits
SNR_dB = 0:1:20; % SNR in dB
SNR_lin = 10.^(SNR_dB./10); % Linear SNR
delta = 0.1; % Step size for delta modulation
% Generate random binary data
data = randi([0,1],N,1);
% Delta modulation
for k = 1:length(SNR_dB)
% Encode data using delta modulation
encoded_data = zeros(N,1);
for i = 1:N
if i == 1
encoded_data(i) = data(i); % First bit directly encoded
else
prediction = encoded_data(i-1) + delta*2*(randi([0,1])-0.5); % Predictor
if data(i) == 0 % If bit is 0, follow prediction
encoded_data(i) = prediction;
else % If bit is 1, add delta to the prediction
encoded_data(i) = prediction + delta;
end
end
end
% Add noise
noise_power = 1/SNR_lin(k);
noise = sqrt(noise_power) * randn(size(encoded_data));
received_data = encoded_data + noise;
% Decode received data
decoded_data = zeros(N,1);
for i = 1:N
if i == 1
decoded_data(i) = received_data(i); % First bit directly decoded
else
if received_data(i) >= encoded_data(i-1) % If received value is greater than previous, decode as 1
decoded_data(i) = 1;
else % Otherwise, decode as 0
decoded_data(i) = 0;
end
end
end
% Calculate BER
errors = sum(data ~= decoded_data);
BER(k) = errors/N;
end
% Plot BER vs SNR
figure;
semilogy(SNR_dB,BER,'b-o');
grid on;
xlabel('SNR (dB)');
ylabel('Bit Error Rate (BER)');
title('BER vs SNR in Delta Modulation');
Output
