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

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...

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...   MATLAB Script for  BER vs. SNR for M-QAM, M-PSK, QPSk, BPSK %Written by Salim Wireless %Visit www.salimwireless.com for study materials on wireless communication %or, if you want to learn how to code in MATLAB clc; clear; close all; % Parameters num_symbols = 1e5; % Number of symbols snr_db = -20:2:20; % Range of SNR values in dB % PSK and QAM orders to be tested psk_orders = [2, 4, 8, 16, 32]; qam_orders = [4, 16, 64, 256]; % Initialize BER arrays ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_orders), length(snr_db)); % BER calculation for each PSK order and SNR value for i = 1:length(psk_orders) psk_order = psk_orders(i); for j = 1:length(snr_db) % Generate random symbols data_symbols = randi([0, psk_order-1], 1, num_symb...

Theoretical BER vs SNR for BPSK

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 Rate (BER) The probability of error (BER) for BPSK is given by a function called the Q-function. The Q-function Q(x) measures the tail probability of the normal distribution, i.e., the probability that a Gaussian random variable exceeds a certain value x.  Formula for BER: BER=Q(...

Constellation Diagrams of ASK, PSK, and FSK

Modulation ASK, FSK & PSK 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 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.  This article will primarily discuss constellation diagrams, as well as what constellation diagrams tell us and the significance of constellation diagrams. Constellation diagrams can often demonstrate how the amplitude and phase of signals or symbols differ. These two characteristics lessen the interference between t...

Theoretical and simulated BER vs. SNR for ASK, FSK, and PSK

  BER vs. SNR denotes how many bits in error are received in a communication process for a particular Signal-to-noise (SNR) ratio. In most cases, SNR is measured in decibel (dB). For a typical communication system, a signal is often affected by two types of noises 1. Additive White Gaussian Noise (AWGN) 2. Rayleigh Fading In the case of additive white Gaussian noise (AWGN), random magnitude is added to the transmitted signal. On the other hand, Rayleigh fading (due to multipath) attenuates the different frequency components of a signal differently. A good signal-to-noise ratio tries to mitigate the effect of noise.  Calculate BER for Binary ASK Modulation The theoretical BER for binary ASK (BASK) in an AWGN channel is given by: BER  = (1/2) * erfc(0.5 * sqrt(SNR_ask));   Enter SNR (dB): Calculate BER BER vs. SNR curves for ASK, FSK, and PSK Calculate BER for Binary FSK Modulation The theoretical BER for binary FSK (BFSK) in a...

OFDM in MATLAB

  MATLAB Script % The code is written by SalimWireless.Com 1. Initialization clc; clear all; close all; 2. Generate Random Bits % Generate random bits numBits = 100; bits = randi([0, 1], 1, numBits); 3. Define Parameters % Define parameters numSubcarriers = 4; % Number of subcarriers numPilotSymbols = 3; % Number of pilot symbols cpLength = ceil(numBits / 4); % Length of cyclic prefix (one-fourth of the data length) 4. Add Cyclic Prefix % Add cyclic prefix dataWithCP = [bits(end - cpLength + 1:end), bits]; 5. Insert Pilot Symbols % Insert pilot symbols pilotSymbols = ones(1, numPilotSymbols); % Example pilot symbols (could be any pattern) dataWithPilots = [pilotSymbols, dataWithCP];   6. Perform OFDM Modulation (IFFT) % Perform OFDM modulation (IFFT) dataMatrix = reshape(dataWithPilots, numSubcarriers, []); ofdmSignal = ifft(dataMatrix, numSubcarriers); ofdmSignal = reshape(ofdmSignal, 1, []); 7. Display the Generated Data % Display the generated data disp("Original Bits:"); ...

Why is Time-bandwidth Product Important?

Time-Bandwidth Product (TBP) The time-bandwidth product (TBP) is defined as: TBP = Δ f ⋅ Δ t Δf (Bandwidth) : The frequency bandwidth of the signal, representing the range of frequencies over which the signal is spread. Δt (Time duration) : The duration for which the signal is significant, i.e., the time interval during which the signal is non-zero. The TBP is a measure of the "spread" of the signal in both time and frequency domains. A higher TBP means the signal is both spread over a larger time period and occupies a wider frequency range.     To calculate the period of a signal with finite bandwidth, Heisenberg’s uncertainty principle plays a vital role where the time-bandwidth product indicates the processing gain of the signal. We apply spread spectrum techniques in wireless communication for various reasons, such as interference resili...