Skip to main content

Coherence Bandwidth and Coherence Time (with MATLAB + Simulator)



For Doppler Delay or Multi-path Delay

Coherence time
Tcoh ∝ 1 / vmax (For slow fading, coherence time Tcoh is greater than the signaling interval.)

Coherence bandwidth
Wcoh ∝ 1 / Ï„max (For frequency-flat fading, coherence bandwidth Wcoh is greater than the signaling bandwidth.)


Where:

  • Tcoh = coherence time
  • Wcoh = coherence bandwidth
  • vmax = maximum Doppler frequency (or maximum Doppler shift)
  • Ï„max = maximum excess delay (maximum time delay spread)

Notes:

  • The notation vmax−1 and Ï„max−1 indicate inverse proportionality.
  • Doppler spread refers to the range of frequency shifts caused by relative motion, determining Tcoh.
  • Delay spread (or multipath delay spread) determines Wcoh.
  • Frequency-flat fading occurs when Wcoh is greater than the signaling bandwidth.

Coherence Bandwidth

Coherence bandwidth is a concept in wireless communication and signal processing that relates to the frequency range over which a wireless channel remains approximately constant in terms of its characteristics.

Coherence bandwidth is inversely related to the delay spread time (e.g., RMS delay spread). The coherence bandwidth is related to the delay spread of the channel, which is a measure of the time it takes for signals to traverse the channel due to multipath. The two are related by the following approximation:

Coherence Bandwidth ≈ 1/(delay spread time)

Or, Coherence Bandwidth ≈ 1/(root-mean-square delay spread time)

(Coherence bandwidth in Hertz)

For instance, if the root-mean-square delay spread is 500 ns (i.e., {1/(2*10^6)} seconds), the coherence bandwidth is approximately 2 MHz (1 / 500e-9) in a household indoor environment.

For narrowband approximation:

Coherence Bandwidth ≈ 1/root-mean-square delay spread time

 

Frequency Auto-correlation Function

The frequency auto-correlation function RH(Δf) of the channel transfer function H(f) is defined as:

RH(Δf) = E{H(f)H*(f+Δf)}

E{⋅} denotes the expectation operator, and H*(f) is the complex conjugate of H(f).

  • The Coherence Bandwidth (B_c) is precisely derived from this function. It's defined as the maximum Δf for which R_H(Δf) remains above a certain threshold (e.g., 0.5 or 1/e of its peak value).

  • This threshold indicates the point where the channel response at f and f + Δf becomes sufficiently "uncorrelated" or "different" that they can no longer be considered "coherently" related.

 

Coherence Bandwidth Definition

The coherence bandwidth is often defined as the frequency separation Δf over which the auto-correlation function RH(Δf) drops to a certain fraction of its maximum value, typically 0.5 or 1/e:

Bc ≈ 1 / (β * Tm)

Where Tm is the root-mean-square delay spread time, which characterizes the extent of multi-path propagation, and β is a constant (e.g., 5 for a 0.5 correlation drop or 2π for other definitions).

 

Coherence Time

Coherence time represents the duration for which the channel conditions remain approximately constant. It describes the amount of time during which the wireless channel's characteristics, including phase, amplitude, and delay, can be considered relatively stable.

The relationship between coherence time and Doppler spread is inverse. A larger Doppler spread (faster change) corresponds to a shorter coherence time and vice versa.

 

Time Auto-correlation Function

The time auto-correlation function Rh(Δt) of the channel impulse response h(t) is defined as:

Rh(Δt) = E{h(t)h*(t+Δt)}

E{⋅} denotes the expectation operator, and h*(t) is the complex conjugate of h(t).

  • The Coherence Time (T_c) is precisely derived from this function. It's defined as the maximum Δt for which R_H(Δt) remains above a certain threshold (e.g., 0.5 or 1/e of its peak value).

  • This threshold indicates the point where the channel response at t and t + Δt becomes sufficiently "uncorrelated" or "different" that they can no longer be considered "coherently" related.

 

Coherence Time Definition

The coherence time is often defined as the time lag Δt over which the auto-correlation function Rh(Δt) drops to a certain fraction of its maximum value, typically 0.5 or 1/e:

Tc ≈ 1 / fD

Where fD is the Doppler spread, which characterizes the rate of change of the channel due to relative motion.

If a vehicle is moving at 30 m/s and the carrier frequency is 2 GHz:

fD = v * f / c = (30 * 2 * 10^9) / (3 * 10^8) = 200 Hz

So, the coherence time using the general approximation is 1 / (200) = 5 ms (approx).

 

MATLAB Code Example for Approximating Coherence Time and Bandwidth

% The code is written by SalimWireless.Com
clc;
clear all;
close all;

% Example: Define Doppler Spread and RMS Delay Spread directly for approximation
% (In a real scenario, these would be measured or estimated from channel data)

% --- Parameters for Coherence Time ---
vehicle_speed = 30; % m/s
carrier_frequency = 2e9; % Hz (2 GHz)
speed_of_light = 3e8; % m/s

doppler_spread = (vehicle_speed * carrier_frequency) / speed_of_light; % Hz

% Approximate Coherence Time (Tc ~ 1/fD)
coherence_time_approx = 1 / doppler_spread; % seconds

disp(['Doppler Spread (fD): ', num2str(doppler_spread), ' Hz']);
disp(['Approximate Coherence Time (Tc): ', num2str(coherence_time_approx), ' seconds']);

% --- Parameters for Coherence Bandwidth ---
rms_delay_spread = 500e-9; % seconds (e.g., 500 ns)

% Approximate Coherence Bandwidth (Bc ~ 1/rms_delay_spread, using a common factor like 1/5)
% A typical constant 'beta' is often used, for example, 5 for 0.5 correlation drop
beta_for_Bc = 5;
coherence_bandwidth_approx = 1 / (beta_for_Bc * rms_delay_spread); % Hz

disp(['RMS Delay Spread (Tm): ', num2str(rms_delay_spread), ' seconds']);
disp(['Approximate Coherence Bandwidth (Bc): ', num2str(coherence_bandwidth_approx), ' Hz']);
 

Output from the MATLAB Code above

Doppler Spread (fD): 200 Hz

Approximate Coherence Time (Tc): 0.005 seconds

RMS Delay Spread (Tm): 5e-07 seconds

Approximate Coherence Bandwidth (Bc): 400000 Hz

 

Relationship between Coherence Time and Doppler Spread, and Coherence Bandwidth and Delay Spread

The coherence time of a wireless channel is inversely proportional to its Doppler spread. Doppler spread refers to the range of Doppler shifts experienced by different multipath components, indicating how rapidly the channel changes due to motion.

Conversely, the coherence bandwidth of a wireless channel is inversely proportional to its delay spread. Delay spread refers to the time difference between the arrival of the first and last significant multipath components of a signal, indicating the frequency selectivity of the channel.

The relationship between coherence time (Tc) and Doppler spread (fD) can be approximated using the formula:

Tc ≈ 1 / fD

And for coherence bandwidth (Bc) and delay spread (Tm):

Bc ≈ 1 / (β ⋅ Tm)

Where β is a factor depending on the specific characteristics of the wireless environment and the correlation level considered, typically ranging from 1 to 5 or 2π.

 

MATLAB Code Example for Coherence Time Approximation (based on Doppler Spread)

% The code is written by SalimWireless.Com
clc;
clear all;
close all;

% Define parameters for Doppler Spread
vehicle_speed = 30; % m/s
carrier_frequency = 2e9; % Hz (2 GHz)
speed_of_light = 3e8; % m/s

% Calculate Doppler spread
doppler_spread = (vehicle_speed * carrier_frequency) / speed_of_light;

% Calculate coherence time using approximation
coherence_time = 1 / doppler_spread;

% Display coherence time
disp(['Calculated Doppler Spread (fD): ', num2str(doppler_spread), ' Hz']);
disp(['Approximate Coherence Time (Tc): ', num2str(coherence_time), ' seconds']);
 

Output from the MATLAB Code above

Calculated Doppler Spread (fD): 200 Hz

Approximate Coherence Time (Tc): 0.005 seconds

 

Further Reading

  1. Relationship Between Fading, Coherence Time, and Coherence Bandwidth
  2. Coherence Bandwidth Online Simulator
     
  3. Example of coherence time in wireless communication
  4. Further study on time-bandwidth product
  5. Slow fading vs. fast fading
  6. Flat fading vs. Frequency Selective fading 

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

Theoretical BER vs SNR for binary ASK, FSK, and PSK with MATLAB Code + Simulator

📘 Overview & Theory 🧮 MATLAB Codes 📚 Further Reading Bit Error Rate (BER) Equations BER formulas for ASK, FSK, and PSK modulation schemes. ASK BER = 0.5 × erfc(0.5 × √SNR) FSK BER = 0.5 × erfc(√(SNR / 2)) PSK BER = 0.5 × erfc(√SNR) Theoretical BER vs SNR for Amplitude Shift Keying (ASK) The theoretical Bit Error Rate (BER) for binary ASK depends on how binary bits are mapped to signal amplitudes. For typical cases: If bits are mapped to 1 and -1, the BER is: BER = Q(√(2 × SNR)) If bits are mapped to 0 and 1, the BER becomes: BER = Q(√(SNR / 2)) Where: Q(x) is the Q-function: Q(x) = 0.5 × erfc(x / √2) SNR : Signal-to-Noise Ratio N₀ : Noise Power Spectral Density Understanding the Q-F...

Simulation of ASK, FSK, and PSK using MATLAB Simulink (with Online Simulator)

📘 Overview 🧮 How to use MATLAB Simulink 🧮 Simulation of ASK using MATLAB Simulink 🧮 Simulation of FSK using MATLAB Simulink 🧮 Simulation of PSK using MATLAB Simulink 🧮 Simulator for ASK, FSK, and PSK 🧮 Digital Signal Processing Simulator 📚 Further Reading ASK, FSK & PSK HomePage MATLAB Simulation Simulation of Amplitude Shift Keying (ASK) using MATLAB Simulink In Simulink, we pick different components/elements from MATLAB Simulink Library. Then we connect the components and perform a particular operation. Result A sine wave source, a pulse generator, a product block, a mux, and a scope are shown in the diagram above. The pulse generator generates the '1' and '0' bit sequences. Sine wave sources produce a specific amplitude and frequency. The scope displays the modulated signal as well as the original bit sequence created by the pulse generator. Mux i...

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 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 figure of merit for a...

MATLAB Codes for Various types of beamforming | Beam Steering, Digital...

📘 How Beamforming Improves SNR 🧮 MATLAB Code 📚 Further Reading 📂 Other Topics on Beamforming in MATLAB ... MIMO / Massive MIMO Beamforming Techniques Beamforming Techniques MATLAB Codes for Beamforming... How Beamforming Improves SNR The mathematical [↗] and theoretical aspects of beamforming [↗] have already been covered. We'll talk about coding in MATLAB in this tutorial so that you may generate results for different beamforming approaches. Let's go right to the content of the article. In analog beamforming, certain codebooks are employed on the TX and RX sides to select the best beam pairs. Because of their beamforming gains, communication created through the strongest beams from both the TX and RX side enhances spectrum efficiency. Additionally, beamforming gain directly impacts SNR improvement. [Read more about Beamforming and How it improves SNR] Wireless...

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). ...

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Interactive Modulation Simulators Visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator More Topics Simulator for Binary ASK Modulation Digital Message Bits Carrier Freq (Hz) Sampling Rate (...

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 Amplitude Shift Frequency Shift Phase Shift Live Simulator ASK, FSK & PSK HomePage MATLAB Code MATLAB Code for ASK Modulation and Demodulation COPY % 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); binar...

Constellation Diagrams of ASK, PSK, and FSK (with MATLAB Code + Simulator)

Constellation Diagrams: ASK, FSK, and PSK Comprehensive guide to signal space representation, including interactive simulators and MATLAB implementations. 📘 Overview 🧮 Simulator ⚖️ Theory 📚 Resources Definitions Constellation Tool Key Points MATLAB Code 📂 Other Topics: M-ary PSK & QAM Diagrams ▼ 🧮 Simulator for M-ary PSK Constellation 🧮 Simulator for M-ary QAM Constellation BASK (Binary ASK) Modulation Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1. BFSK (Binary FSK) Modulation Transmits on...