Skip to main content

A Brief Discussion of Filters

 

Low Pass Filter

In most cases, filters extract the required frequency from a signal. Low-pass filters only permit frequencies falling below and attenuating frequencies above the cutoff frequency.

Low-pass filters generally have a form where the output decays at higher frequencies.

Low-Pass Filter Transfer Function

Let’s take the following simple transfer function:

\[ H(z) = \frac{1}{1 + 0.5z^{-1}} \]

Analysis of the Transfer Function

The denominator suggests it’s a low-pass filter because it has a single pole and the gain decreases as frequency increases (in the discrete-time case, this is for higher \(\omega\)).

At low frequencies, the magnitude will be close to 1, but as the frequency increases, the magnitude will drop.

Fig: Low Pass Filter

A low pass filter's cutoff frequency is calculated as

Cut off frequency = 1 / 2*pi*R*C


High Pass Filter

All frequencies in a signal above the high pass filter's cutoff frequency can pass through the high pass filter. High-pass filters generally have a form where the output increases at higher frequencies.

High-Pass Filter Transfer Function

Now consider the following transfer function:

\[ H(z) = \frac{z^{-1}}{1 + 0.5z^{-1}} \]

Analysis of the Transfer Function

The numerator has z^{-1}, suggesting a high-pass filter because it includes a term that shifts the response at low frequencies, while passing higher frequencies more easily.

By examining the transfer function, you can classify the filter's behavior in terms of its frequency response.

Fig: High Pass Filter

A high pass filter's cutoff frequency is calculated as

Cut off frequency = 1 / 2*pi*R1*C1


Band Pass Filter

A band pass filter is a device that permits frequencies that fall within a specific frequency range. Both frequencies inside and outside of the field are attenuated. Band-pass filters will show a peak or resonance at a specific frequency.

Bandpass Filter Transfer Function

Now consider the following transfer function for a bandpass filter:

\[ H(z) = \frac{z^{-1} - z^{-2}}{1 + 0.5z^{-1} + 0.25z^{-2}} \]

Analysis of the Transfer Function

The numerator contains the terms z^{-1} and z^{-2}, suggesting that the filter allows a band of frequencies to pass while attenuating lower and higher frequencies.

The filter achieves this by having a zero at a specific frequency, which creates a notch at the desired frequency, thus passing a band of frequencies in the middle.

By examining the transfer function, you can classify the filter's behavior in terms of its frequency response. This includes its ability to pass signals within a specific frequency band and attenuate others outside that range.

Fig: Band Pass Filter
All frequencies above (1/2*pi*R1*C1) and below (1/2*pi*R2*C2) are passed by the band pass filter shown in the figure above.

Keep in mind that a bandpass filter is a combination of a high pass and a low pass filter.


Further Reading


People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

📘 Overview of BER and SNR 🧮 Online Simulator for BER calculation of m-ary QAM and m-ary PSK 🧮 MATLAB Code for BER calculation of M-ary QAM, M-ary PSK, QPSK, BPSK, ... 📚 Further Reading 📂 View Other Topics on M-ary QAM, M-ary PSK, QPSK ... 🧮 Online Simulator for Constellation Diagram of m-ary QAM 🧮 Online Simulator for Constellation Diagram of m-ary PSK 🧮 MATLAB Code for BER calculation of ASK, FSK, and PSK 🧮 MATLAB Code for BER calculation of Alamouti Scheme 🧮 Different approaches to calculate BER vs SNR 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. BER = (number of bits received in error) / (total number of tran...

Constellation Diagrams of ASK, PSK, and FSK

📘 Overview of Energy per Bit (Eb / N0) 🧮 Online Simulator for constellation diagrams of ASK, FSK, and PSK 🧮 Theory behind Constellation Diagrams of ASK, FSK, and PSK 🧮 MATLAB Codes for Constellation Diagrams of ASK, FSK, and PSK 📚 Further Reading 📂 Other Topics on Constellation Diagrams of ASK, PSK, and FSK ... 🧮 Simulator for constellation diagrams of m-ary PSK 🧮 Simulator for constellation diagrams of m-ary QAM 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...

MATLAB Code for ASK, FSK, and PSK

📘 Overview & Theory 🧮 MATLAB Code for ASK 🧮 MATLAB Code for FSK 🧮 MATLAB Code for PSK 🧮 Simulator for binary ASK, FSK, and PSK Modulations 📚 Further Reading ASK, FSK & PSK HomePage MATLAB Code MATLAB Code for ASK Modulation and Demodulation % The code is written by SalimWireless.Com % Clear previous data and plots clc; clear all; close all; % Parameters Tb = 1; % Bit duration (s) fc = 10; % Carrier frequency (Hz) N_bits = 10; % Number of bits Fs = 100 * fc; % Sampling frequency (ensure at least 2*fc, more for better representation) Ts = 1/Fs; % Sampling interval samples_per_bit = Fs * Tb; % Number of samples per bit duration % Generate random binary data rng(10); % Set random seed for reproducibility binary_data = randi([0, 1], 1, N_bits); % Generate random binary data (0 or 1) % Initialize arrays for continuous signals t_overall = 0:Ts:(N_bits...

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

📘 Overview 🧮 Simulator for calculating BER 🧮 MATLAB Codes for calculating theoretical BER 🧮 MATLAB Codes for calculating simulated BER 📚 Further Reading BER vs. SNR denotes how many bits in error are received for a given signal-to-noise ratio, typically measured in dB. Common noise types in wireless systems: 1. Additive White Gaussian Noise (AWGN) 2. Rayleigh Fading AWGN adds random noise; Rayleigh fading attenuates the signal variably. A good SNR helps reduce these effects. Simulator for calculating BER vs SNR for binary ASK, FSK, and PSK Calculate BER for Binary ASK Modulation Enter SNR (dB): Calculate BER Calculate BER for Binary FSK Modulation Enter SNR (dB): Calculate BER Calculate BER for Binary PSK Modulation Enter SNR (dB): Calculate BER BER vs. SNR Curves MATLAB Code for Theoretical BER % The code is written by SalimWireless.Com clc; clear; close all; % SNR va...

Comparisons among ASK, PSK, and FSK | And the definitions of each

📘 Comparisons among ASK, FSK, and PSK 🧮 Online Simulator for calculating Bandwidth of ASK, FSK, and PSK 🧮 MATLAB Code for BER vs. SNR Analysis of ASK, FSK, and PSK 📚 Further Reading 📂 View Other Topics on Comparisons among ASK, PSK, and FSK ... 🧮 Comparisons of Noise Sensitivity, Bandwidth, Complexity, etc. 🧮 MATLAB Code for Constellation Diagrams of ASK, FSK, and PSK 🧮 Online Simulator for ASK, FSK, and PSK Generation 🧮 Online Simulator for ASK, FSK, and PSK Constellation 🧮 Some Questions and Answers 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 Parameters ASK FSK PSK Variable Characteristics Amplitude Frequency ...

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

🧮 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; num_symbols = 1e5; snr_db = -20:2:20; 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) psk_order = psk_orders(i); for j = 1:length(snr_db) data_symbols = randi([0, psk_order-1], 1, num_symbols); modulated_signal = pskmod(data_symbols, psk_order, pi/psk_order); received_signal = awgn(modulated_signal, snr_db(j), 'measured'); demodulated_symbols = pskdemod(received_signal, psk_order, pi/psk_order); ber_psk_results(i, j) = sum(data_symbols ~= demodulated_symbols) / num_symbols; end end for i...

MATLAB Code for Constellation Diagrams of ASK, FSK, and PSK

📘 Overview & Theory 🧮 MATLAB Code for Constellation Diagrams of ASK, FSK, and PSK 🧮 Online Simulator for Constellation diagrams of ASK, FSK, and PSK 📚 Further Reading   MATLAB Script % The code is developed by SalimWireless.Com clc; clear; close all; % Parameters numSymbols = 1000; % Number of symbols to simulate symbolIndices = randi([0 1], numSymbols, 1); % Random binary symbols (0 or 1) % ASK Modulation (BASK) askAmplitude = [0, 1]; % Amplitudes for binary ASK askSymbols = askAmplitude(symbolIndices + 1); % Modulated BASK symbols % FSK Modulation (Modified BFSK with 90-degree offset) fs = 100; % Sampling frequency symbolDuration = 1; % Symbol duration in seconds t = linspace(0, symbolDuration, fs*symbolDuration); fBase = 1; % Base frequency frequencies = [fBase, fBase]; % Same frequency for both % Generate FSK symbols with 90° phase offset fskSymbols = arrayfun(@(idx) ...     cos(2*pi*frequencies(1)*t) * (1-idx) + ...     ...

Robust Signal Detection with Prefix and Postfix

  MATLAB Code clc; clear; close all; % Parameters fs = 1000; % Sampling frequency msgLength = 100; % Length of the message pnLength = 50; % Length of PN sequence silenceLength = 20; % Length of silence before and after lagAmount = 50; % Amount of lag (can be negative for lead) threshold = 0.5; % Threshold for correlation peak detection % Generate Unique PN Sequences pnPrefix = 2 * (randi([0, 1], 1, pnLength) - 0.5); pnPostfix = 2 * (randi([0, 1], 1, pnLength) - 0.5); % Generate Message originalMessage = (randi([0, 1], 1, msgLength)); message = 2*originalMessage - 1; % Construct Dataframe dataframe = [pnPrefix, message, pnPostfix]; % Introduce Lag or Lead if lagAmount > 0 %laggedFrame = [zeros(1, lagAmount), dataframe(1:end - lagAmount)]; laggedFrame = [zeros(1, lagAmount), dataframe]; else laggedFrame = [dataframe(-lagAmount + 1:end), zeros(1, -lagAmount)]; end % Correlation with PN Sequences corrPrefix = xcorr(laggedFrame, pnPrefix); corrPostfix = xcorr(laggedFrame, pnPostfi...