Skip to main content

Flat fading vs. Frequency Selective fading

Frequency Selective Fading

Frequency-selective fading occurs when multiple copies of a transmitted signal arrive at the receiver with different propagation delays, typically due to multipath propagation with varying path lengths. In channel modeling, a tap or cluster represents a group of multipath components (MPCs) arriving at approximately the same time. While there may be theoretically an infinite number of taps, practical mobile communication systems consider a finite number.

The frequency response of the channel varies because the different delayed paths cause constructive and destructive interference at different frequencies. Modeling this effect can be done using a linear, time-variant (LTV), and causal system description of the mobile radio channel.

Flat Fading vs Frequency Selective Fading

Flat fading occurs when the signal bandwidth is smaller than the channel's coherence bandwidth, meaning all frequency components experience similar fading. Narrowband signals are typically affected by flat fading, and diversity reception or error-correction coding is used to mitigate its effects.

Frequency-selective fading occurs when the signal bandwidth exceeds the channel's coherence bandwidth (or equivalently, when the symbol duration is smaller than the channel's delay spread). In this case, different frequency components of the signal experience different levels of fading, leading to inter-symbol interference (ISI) unless equalization techniques are applied.


Summary

In the case of frequency-selective fading, multipath propagation occurs, and each multipath component is affected by different fading characteristics. The signal reaches the receiver through multiple paths in such a way that one symbol interferes with another. The root cause of this interference is that multiple delayed copies of the same signal arrive at the receiver with delays that exceed the symbol duration. As a result, the current symbol interferes with the subsequent symbol, leading to inter-symbol interference (ISI).

In the case of flat fading, this phenomenon does not occur because the signal bandwidth is much smaller than the channel’s coherence bandwidth. In other words, the channel response remains approximately constant (flat) over the entire signal bandwidth. You can think of it as the channel remaining relatively unchanged over a certain duration, while the symbol duration is much shorter than the time over which the channel varies significantly, preventing ISI.


Further Reading

  1. Frequency Selective Fading vs Flat Fading in MATLAB



Contact Us

Name

Email *

Message *

Popular Posts

PSD Calculation with FFT: MATLAB Tutorial for Signal Analysis

  Implementation Steps 1. FFT Computes the Frequency Content of a Signal FFT converts a time-domain signal to the frequency domain. If: The signal is sampled at rate $f_s$ You compute an $N_{\text{FFT}}$-point FFT Then each FFT bin corresponds to a frequency resolution of: $$\Delta f = \frac{f_s}{N_{\text{FFT}}}$$ So the FFT gives you accurate frequency content, assuming the signal is stationary and adequately sampled (Nyquist criterion met).  2. Magnitude Squared Gives Power (Not Amplitude) $$P[k] = |X[k]|^2$$ This gives power at each frequency bin, not just amplitude. It represents how much energy is present at each frequency. It's a key step for PSD.  3. Normalization Makes the PSD Physically Meaningful The equation: $$\text{PSD}[k] = \frac{|X[k]|^2}{N_{\text{FFT}} \cdot f_s \cdot U}$$ is derived from first principles and ensures that the u...

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSK, BPSK (with Simulation)

🧮 MATLAB Code for BPSK, M-ary PSK, and M-ary QAM Together 🧮 MATLAB Code for M-ary QAM 🧮 MATLAB Code for M-ary PSK 📚 Further Reading MATLAB Script for BER vs. SNR for M-QAM, M-PSK, QPSK, BPSK % Written by Salim Wireless clc; clear; close all; snr_db = -5:2:25; psk_orders = [2, 4, 8, 16, 32]; qam_orders = [4, 16, 64, 256]; ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_orders), length(snr_db)); for i = 1:length(psk_orders) ber_psk_results(i, :) = berawgn(snr_db, 'psk', psk_orders(i), 'nondiff'); end for i = 1:length(qam_orders) ber_qam_results(i, :) = berawgn(snr_db, 'qam', qam_orders(i)); end figure; semilogy(snr_db, ber_psk_results(1, :), 'o-', 'LineWidth', 1.5, 'DisplayName', 'BPSK'); hold on; for i = 2:length(psk_orders) semilogy(snr_db, ber_psk_results(i, :), 'o-', 'DisplayName', sprintf('%d-PSK', psk_or...

Theoretical BER vs SNR for BPSK

Theoretical Bit Error Rate (BER) vs Signal-to-Noise Ratio (SNR) for BPSK in AWGN Channel 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 Rat...

MUSIC Algorithm Explained (with MATLAB + Simulator)

Practical Implementation of the MUSIC Algorithm The focus is on how the algorithm works computationally , not just theory, and it explains the denominator (a H E n E n H a) mathematically and intuitively. 1. Introduction The MUSIC (Multiple Signal Classification) algorithm is a high-resolution method used in signal processing and array processing to estimate the Direction of Arrival (DOA) of signals received by a sensor array. Unlike classical beamforming methods, MUSIC uses eigenvector decomposition of the covariance matrix to separate the signal subspace and noise subspace , allowing it to achieve much higher angular resolution. In practical implementations, MUSIC works by: Simulating or collecting array signals Computing the covariance matrix Performing eigenvalue decomposition Separating signal and noise subspaces Scanning possible angles using a steering vector Constructing a pseudo-spectrum where peaks indicate signal directions 2. Signal Mo...

Power Spectral Density Calculation Using FFT in MATLAB

📘 📘 Overview 🧮 🧮 Steps to calculate 💻 🧮 MATLAB Codes 📚 📚 Further Reading Power spectral density (PSD) tells us how the power of a signal is distributed across different frequency components, whereas Fourier Magnitude gives you the amplitude (or strength) of each frequency component in the signal. Steps to calculate the PSD of a signal Firstly, calculate the fast Fourier transform (FFT) of a signal. Then, calculate the Fourier magnitude (absolute value) of the signal. Square the Fourier magnitude to get the power spectrum. To calculate the Power Spectral Density (PSD), divide the squared magnitude by the product of the sampling frequency (fs) and the total number of samples (N). Formula: PSD = |FFT|^2 / (fs * N) Sampling frequency (fs): The rate at which the continuous-time signal is sampled (in Hz). ...

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 ...

MATLAB Code for MUSIC

  MATLAB Code clc; clear; close all ; %% Step 1: Define Parameters M = 8; % Number of array sensors d = 0.5; % Sensor spacing (lambda/2) K = 2; % Number of signals N = 200; % Number of snapshots theta = [-20 30]; % True signal angles (degrees) SNR = 10; % Signal-to-noise ratio (dB) fprintf( 'Step 1: Parameters Initialized\n' ); %% Step 2: Generate Signal Sources t = 1:N; s1 = exp(1j*2*pi*0.05*t); s2 = exp(1j*2*pi*0.1*t); S = [s1; s2]; figure; plot(real(S(1,:))) title( 'Signal 1 (Real Part)' ) xlabel( 'Samples' ) ylabel( 'Amplitude' ) figure; plot(real(S(2,:))) title( 'Signal 2 (Real Part)' ) xlabel( 'Samples' ) ylabel( 'Amplitude' ) fprintf( 'Step 2: Source Signals Generated\n' ); %% Step 3: Construct Steering Matrix A = zeros(M,K); for k = 1:K A(:,k) = exp(-1j*2*pi*d*(0:M-1)'*sin(theta(k)*pi/180)); end fprintf( 'Step 3: Steering Matr...

Direction of Arrival (DoA) Online Simulator (using MUSIC)

Interactive DOA Simulator X-axis XY angle (deg): 45 XZ angle (deg): 30 Noise: 0.05 Y-axis XY angle (deg): 60 YZ angle (deg): 45 Noise: 0.05 Z-axis XZ angle (deg): 60 YZ angle (deg): 30 Noise: 0.05 Estimated DOA (deg): 0 Simulation Workflow and Mathematical Background This simulator demonstrates Direction of Arrival (DOA) estimation using three-axis sensor signals (X, Y, Z), Maximal Ratio Combining (MRC) , and the MUSIC algorithm . It allows interactive control of signal angles and noise for teaching purposes. 1. Signal Generation A pure sinewave signal of frequency f is projected onto three axes using user-defined angles in different planes: X-axis: θ XY , θ XZ Y-axis: θ XY , θ YZ Z-axis: θ XZ , θ YZ Mathematically, for each time sample t : x(t) = s(t) * cos(θ_xy_x) * cos(θ_xz_x) + n_x(t) y(t) = s(t) * sin(θ_xy_y) * cos(θ_yz_y) + n_y(t) z(t) = s(t) * sin(θ_xz_z) * sin(θ_yz_z) + n_z(t) wh...