Skip to main content

Convolution in LTI Wireless Communication Systems

 

LTI Systems

LTI Systems and Convolution

An LTI system is a system that is both linear (obeys the superposition principle) and time-invariant (its behavior doesn't change over time).

The output y(t) of an LTI system is determined by the convolution of the input signal x(t) with the system's unique impulse response h(t). Convolution is a mathematical operation expressed as:

y(t) = ∫-∞ x(Ï„) h(t - Ï„) dÏ„

1. For a Discrete-Time System

In wireless communication, the signal reaches the receiver thru different multi-paths. And they are nothing but time-delayed versions of the same transmitted signal. You always find a relationship between received and transmitted signals is

y = h * x + n 

which denotes that the transmitted signal 'x' is convolved with the channel coefficients ('h') of the particular channel. Because here time-shifted version of the initially transmitted signal is overlapped with the channel coefficients because the time-delayed version of 'x' reaches the receiver at a different time or creates a delay. So, it is necessary to consider all delayed versions of the same signal for a better approximation of transmitted symbols or bits.

We receive multiple impulse responses for a particular input signal delta or unit impulse transmission. 

i.e., the particular input
x[n] = Î´[n]

Produces the output 
y[n] = h[n]

**h[n] = ..., h[-2], h[-1], h[0], h[1], h[2], .... (impulse responses due to multipath etc.,)


So the general input is going to be

x[n] = x[k]δ[n-k]     (on an interval of -infinity to +infinity)

will thus produce the output

y[n] = x[k]h[n-k]     (on an interval of -infinity to +infinity)

Which is also termed a 'convolution sum.'


2. For a Continuous-Time System

For a typical wireless communication system, x is the transmitted data signal, and h is the channel impulse response. And their convolution is represented by this.





The LTI system is modeled considering the original signal convolved with the channel impulse response. On the other hand, on the receiver side, the signal is retrieved by using equalizers. That estimates the originally transmitted signal from the training bits / symbols.
 

Further Reading 



Contact Us

Name

Email *

Message *

Popular Posts

MIMO Channel Matrix | Rank and Condition Number

MIMO / Massive MIMO MIMO Channel Matrix | Rank and Condition...   The channel matrix in wireless communication is a matrix that describes the impact of the channel on the transmitted signal. The channel matrix can be used to model the effects of the atmospheric or underwater environment on the signal, such as the absorption, reflection or scattering of the signal by surrounding objects. When addressing multi-antenna communication, the term "channel matrix" is used. Let's assume that only one TX and one RX are in communication and there's no surrounding object. Here, in our case, we can apply the proper threshold condition to a received signal and get the original transmitted signal at the RX side. However, in real-world situations, we see signal path blockage, reflections, etc.,  (NLOS paths [↗]) more frequently. The obstruction is typically caused by building walls, etc. Multi-antenna communication was introduced to address this issue. It makes diversity app...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...(MATLAB Code + Simulator)

Bit Error Rate (BER) & SNR Guide Analyze communication system performance with our interactive simulators and MATLAB tools. 📘 Theory 🧮 Simulators 💻 MATLAB Code 📚 Resources BER Definition SNR Formula BER Calculator MATLAB Comparison 📂 Explore M-ary QAM, PSK, and QPSK Topics ▼ 🧮 Constellation Simulator: M-ary QAM 🧮 Constellation Simulator: M-ary PSK 🧮 BER calculation for ASK, FSK, and PSK 🧮 Approaches to BER vs SNR Calculation What is Bit Error Rate (BER)? The BER indicates how many corrupted bits are received compared to the total number of bits sent. It is the primary figur...

UGC NET Electronic Science Previous Year Question Papers with Solutions

Home / Engineering & Other Exams / UGC NET 2026 PYQ ⬇️ Download Papers and Solutions 📋 Exam Pattern 💡 Preparation Tips ❓ FAQs 📊 Exam Highlights: Electronic Science (88) Feature Details Junior Research Fellowship (JRF) ₹37,000 + HRA per month Eligibility M.Sc/M.Tech in Electronics (55%) Validity of Certificate JRF (3 Years) | Lectureship (Lifetime) 📥 Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading 📂 View All Question Papers June 2025 - Question Paper Download PDF June 2025 - Solved Paper + Explanation ...

How to Mount Google Drive in Google Colab

How to Mount Google Drive in Google Colab Google Colab provides temporary storage during a session. Any files stored in the /content directory will be deleted when the runtime disconnects. To store datasets, trained models, and results permanently, it is recommended to mount your Google Drive in Colab. Mounting Google Drive allows your notebook to access files directly from your Drive and save outputs there so they remain available even after the Colab session ends. Step 1: Import the Drive Module First import the Google Colab drive module. from google.colab import drive Step 2: Mount Google Drive Run the following command to mount your Google Drive. from google.colab import drive drive.mount('/content/drive') After running the command: A link will appear in the output. Click the link and log in to your Google account. Copy the authentication code provided. Paste the code back into the notebook. Or, a Google authentication page will a...

Wiener Filter in MATLAB

  MATLAB Code  % Wiener Filter Based on Wiener-Hopf Equation % This script demonstrates how to apply the Wiener filter to recover % a reference signal from a noisy signal using the Wiener-Hopf equation. % The filter minimizes the mean squared error between the noisy signal and the reference signal. clear; close all; clc; % Signal Parameters fs = 4000; % Sampling frequency (Hz) T = 1; % Total recording time (seconds) L = T * fs; % Signal length (samples) tt = (0:L-1) / fs; % Time vector ff = (0:L-1) * fs / L; % Frequency vector % Generate Reference Signal (a sinusoid) y = sin(2 * pi * 120 * tt); % Reference sinusoidal signal y = y(:); % Ensure column vector % Create Noisy Signal by Adding Gaussian Noise x = 0.50 * randn(L, 1) + y; % Noisy signal x = x(:); % Ensure column vector % Define Filter Order (Number of Coefficients) N = 200; % Apply Wiener Filter using custom function [xest, b, MSE] = wienerFilt(x, y, N); % Plot Results figure; subplot(411); plot(tt, x, 'k'), hold on, p...

Overmodulation & Distortion in AM

Overmodulation in AM and How It Causes Distortion 1. AM Signal Equation s(t) = A c [1 + μ m(t)] cos(2Ï€ f c t) A c = carrier amplitude m(t) = normalized modulating signal (|m(t)| ≤ 1) μ = modulation index 2. Modulation Index μ = A m / A c - Normal AM: 0 < μ ≤ 1 → no distortion - Overmodulation: μ > 1 → distortion occurs 3. Envelope and Overmodulation A(t) = A c [1 + μ m(t)] - For undistorted AM: 1 + μ m(t) ≥ 0 at all times - If μ > 1: 1 + μ m(t) < 0 at negative peaks → carrier flips Example: Let m(t) = cos(2Ï€ f m t), A c = 1 V, μ = 1.2 Minimum envelope: A min = A c [1 - 1.2] = -0.2 V Negative amplitude → envelope crosses zero → 180° phase flip 4. Mathematical Consequence -A c cos(θ) = A c cos(θ + Ï€) This phase reversal is what causes distortion in the demodulated signal. 5. Instantaneous AM Signal s...

Phase Modulation (PM) & Demodulation

Advanced Analysis of Phase Modulation Phase Modulation (PM): Theoretical Foundations and Spectral Dynamics 1. Analytical Characterization Phase Modulation (PM) is a subset of Angle Modulation , where the information residing in the message signal \( m(t) \) is mapped linearly onto the instantaneous phase of a high-frequency carrier. Unlike Amplitude Modulation (AM), PM is a non-linear modulation process, resulting in an expansion of the signal bandwidth into an infinite dimensional Hilbert space. \[ S_{PM}(t) = A_c \cos\left[ 2\pi f_c t + \phi(t) \right] = A_c \cos\left[ 2\pi f_c t + K_p m(t) \right] \] The instantaneous frequency \( f_i(t) \) is defined as the time derivative of the total angle: \[ f_i(t) = \frac{1}{2\pi} \frac{d\theta_i(t)}{dt} = f_c + \frac{K_p}{2\pi} \frac{dm(t)}{dt} \] ...

QPSK Online Simulator (Signal Generation)

Simulator for QPSK Modulation Quadrature (4-PSK) Bitstream (Even length) Carrier Freq (Hz) Samples Per Symbol Run QPSK Simulation The Math Behind QPSK Quadrature Phase Shift Keying (QPSK) is a form of digital modulation that transmits two bits per symbol by changing the phase of a carrier wave. s(t) = A cos(2Ï€f c t + θ n ) Phase (θ n ): Each pair of bits (dibit) corresponds to a specific phase shift. In Gray coding, we use: "00" → Ï€/4 (45°) "01" → 3Ï€/4 (135°) "11" → 5Ï€/4 (225°) "10" → 7Ï€/4 (315°) Efficiency: Since 4 phases are used, QPSK carries double the data of BPSK in the same bandwidth. ...