Skip to main content

What is +/- 3dB Frequency Response? Applications ...


 

Remember, for most of the pass-band filters, the magnitude of the desired frequency range varies by 3dB. It is common for most of the pass-band filters.

The term '3dB frequency response' is used frequently to indicate that power has decreased to 50% of its maximum or original amount. However, it also states that the signal voltages have reduced to 0.707 of their highest value. So,

The -3dB comes from either 20 Log (0.707) or 10 Log (0.5).

Viewing the signal in the frequency domain is quite helpful. In electronic amplifiers, the +/-3dB limit is commonly utilized. It makes it clear whether or not the signal is a flat pass-band. You can observe that the signal in the case of pulse shaping is nearly flat along +/-3dB bandwidth.

The phrase "+/-3 dB" originally meant flatness in the above figure, not high- and low-frequency extension. For instance, one could state that "between 100 Hz and 18 kHz, the signal is flat, within +/-3 dB." Accordingly, a device's frequency response graph (i.e., for speakers) would not depart from a straight line between two frequencies by more than 3 dB in either direction.

Ad ---------------------------------------------------------------------

. - - -  - - - - - - - - . Filters

---------------------------------------------------------------------

Particularly in communication applications, continuous gain over a wider bandwidth is necessary. The difference in frequencies between +/-3 dB values is what constitutes the bandpass filter's bandwidth. Growth reasonably stays consistent in this area. Beyond the 3dB barrier, attenuation is significant, increasing the likelihood of information loss. Therefore, when the voltage is reduced from maximum to 0.707Max, or the power is reduced from maximum to half power, the signal's bandwidth is determined.

A less powerful signal than 50% of its original (maximum) power could be more helpful. 10log((P/2)/P) = 10log(0.5) = -3 dB is what we get when we take dB. As a result, at -3dB on the dB scale, half power is reached. Why does 3dB? Has to do with intolerance for a 50% fall in signal strength. It would have been -1.25dB if it had been 25%.

Application of -3dB Frequency Response

All sorts of filters frequently employ the -3dB point (low pass, band pass, high pass...). It only states that the filter only allows half of the power at that frequency to pass.


Q.1. The frequency at which the response is _-3db?

A. In the case of digital filter designing, we often use a bandpass filter to pass frequency components that fall in a particular range (e.g., frequencies that fall between h1 to h2 Hz). Here, the bandpass filter will allow the passing of the frequencies, which range from h1 to h2 Hz; other frequencies will be discarded. The signal is called a flat passband if the magnitude in this frequency range doesn't vary much. In most cases, for filters, it varies between +/- 3db in magnitude. 


Why Signal Amplitude Reduces After Bandpass Filtering

In real-world filters, the amplitude of a signal is often scaled due to unity energy normalization, which is applied to preserve the total signal power. This normalization ensures that the filtered signal maintains the same power as the original but results in a reduction in amplitude.

1. Signal Power Before Filtering

For a sinusoidal signal:

x(t) = A cos(2πfct)

The power P_x of the signal is given by:

Px = (1/T) ∫ |x(t)|² dt = A²/2

2. Bandpass Filter and Unity Energy Normalization

A bandpass filter with a constant gain H(f) over the passband ensures power normalization by scaling the gain such that:

f₁f₂ |H(f)|² df = 1

For a filter with bandwidth B = f₂ - f₁, the gain is:

|H(f)|² = 1/B

The filter scales the signal by 1/√B to normalize the power.

3. Effect on Signal Amplitude

After filtering, the power of the filtered signal is the same as the original, but the amplitude is reduced. For sinusoidal signals:

Py = Px = A²/2

The amplitude of the filtered signal Ay is scaled as:

Ay = A × √(1/B)

4. Example: Amplitude Halving

Consider a sinusoidal signal:

x(t) = cos(2π × 1000t)

If the filter has a bandwidth B = 2, the amplitude of the filtered signal becomes:

Ay = A × 1/√2 ≈ 0.707A

Thus, the amplitude is reduced by approximately 29.3%.

If filter bandwidth B = 4, then the amplitude of the filtered signal reduces to 50%, and so on.


5. Why This Happens

Real-world filters are designed to prioritize power preservation rather than amplitude. This normalization ensures the filter does not artificially boost or reduce the signal's power. 

 

Read also about 

[1] MATLAB Code for understanding +/- 3 dB Frequency Response of a Bandpass Filter

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

MATLAB code for MSK

 Copy the MATLAB Code from here % The code is developed by SalimWireless.com clc; clear; close all; % Define a bit sequence bitSeq = [0, 1, 0, 0, 1, 1, 1, 0, 0, 1]; % Perform MSK modulation [modSignal, timeVec] = modulateMSK(bitSeq, 10, 10, 10000); % Plot the modulated signal subplot(2,1,1); samples = 1:numel(bitSeq); stem(samples, bitSeq); title('Original message signal'); xlabel('Time (s)'); ylabel('Amplitude'); % Plot the modulated signal subplot(2,1,2); samples = 1:10000; plot(samples / 10000, modSignal(1:10000)); title('MSK modulated signal'); xlabel('Time (s)'); ylabel('Amplitude'); % Perform MSK demodulation demodBits = demodMSK(modSignal, 10, 10, 10000); % Function to perform MSK modulation function [signal, timeVec] = modulateMSK(bits, carrierFreq, baudRate, sampleFreq) % Converts a binary bit sequence into an MSK-modulated signal % Inputs: % bits - Binary input sequence % carrierFreq - Carri...

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, ... 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.   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 s...

Constellation Diagrams of ASK, PSK, and FSK

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 one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals: +√Eb​ or -√Eb (they differ by 180 degree phase shift), where Eb​ is the energy per bit. These signals represent binary 0 and 1.    Simulator for BASK, BPSK, and BFSK Constellation Diagrams SNR (dB): 15 Add AWGN Noise Modulation Type BPSK BFSK ...

Fundamentals of Channel Estimation

Channel Estimation Techniques Channel Estimation is an auto-regressive process that may be performed with a number of iterations. There are commonly three types of channel estimation approaches. 1. Pilot estimation  2. Blind estimation  3. Semi-blind estimation. For Channel Estimation,  CIR [↗] is used. The amplitudes of the impulses decrease over time and are not correlated. For example, y(n) = h(n) * x(n) + w(n) where y(n) is the received signal, x(n) is the sent signal, and w(n) is the additive white gaussian noise At the next stage, h(n+1) = a*h(n) + w(n) The channel coefficient will be modified as stated above at the subsequent stage. The scaling factor "a" determines the impulse's amplitude, whereas "h(n+1)" represents the channel coefficient at the following stage. Pilot Estimation Method To understand how a communication medium is currently behaving, a channel estimate is necessary. In order to monitor a channel's behavior in practice communication ...

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...

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.  Fig: Rayleigh Fading due to multi-paths Let's explore wireless communication under two common noise scenarios: AWGN (Additive White Gaussian Noise) and Rayleigh fading. y = h*x + n ... (i) Symbol '*' represents convolution. 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 fa...

Constellation Diagram of FSK in Detail

  Binary bits '0' and '1' can be mapped to 'j' and '1' to '1', respectively, for Baseband Binary Frequency Shift Keying (BFSK) . Signals are in phase here. These bits can be mapped into baseband representation for a number of uses, including power spectral density (PSD) calculations. For passband BFSK transmission, we can modulate signal 'j' with a lower carrier frequency and signal '1' with a higher carrier frequency while transmitting over a wireless channel. Let's assume we are transmitting carrier signal fc1 for the transmission of binary bit '1' and carrier signal fc2 for the transmission of binary bit '0'. Simulator for 2-FSK Constellation Diagram Simulator for 2-FSK Constellation Diagram SNR (dB): 15 Add AWGN Noise Run Simulation ...

Gaussian minimum shift keying (GMSK)

Dive into the fascinating world of GMSK modulation, where continuous phase modulation and spectral efficiency come together for robust communication systems! Core Process of GMSK Modulation Phase Accumulation (Integration of Filtered Signal) After applying Gaussian filtering to the Non-Return-to-Zero (NRZ) signal, we integrate the smoothed NRZ signal over time to produce a continuous phase signal: θ(t) = ∫ 0 t m filtered (τ) dτ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Modulation The next step involves using the phase signal to modulate a high-frequency carrier wave: s(t) = cos(2πf c t + θ(t)) Here, f c is the carrier frequency, and s(t) represents the continuous-phase modulated carrier wave. Quadrature Modulation (Optional) ...