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Bandpass Sampling

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....

Aliasing

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

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Related Experiment Video

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Generation and Coherent Control of Pulsed Quantum Frequency Combs

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Published on: June 8, 2018

Parallel high-speed random bit generation based on wideband chaotic microcomb and wavelet high-pass filtering.

Anran Li, Ning Jiang, Yong Geng

|October 15, 2024

Summary

This study introduces a novel parallel random bit generation (RBG) method using wideband chaotic microcombs. The technique achieves ultra-fast, high-quality random bit streams with enhanced bandwidth and unbiased amplitude distributions.

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Area of Science:

Photonics
Information Security
Signal Processing

Background:

Random bit generation (RBG) is crucial for secure communications and cryptography.
Existing RBG methods face limitations in speed and bandwidth.
Chaotic microcombs offer potential for high-speed signal generation.

Purpose of the Study:

To propose and demonstrate a parallel ultra-fast random bit generation (RBG) scheme.
To enhance the bandwidth and randomness of chaotic signals for RBG.
To achieve high-rate, secure random bit generation using wideband chaotic microcombs.

Main Methods:

Utilizing a wideband chaotic microcomb.
Employing phase modulation and dispersive component broadening for spectrum enhancement.
Applying wavelet high-pass filtering (WHPF) to achieve unbiased probability density functions (PDFs).

Main Results:

Increased effective bandwidth of each comb tooth by over 10-fold.
Achieved high symmetry in PDFs with a skewness coefficient |S| of 0.0026.
Reached a single-channel RBG rate of 200 Gbps.
Confirmed true randomness and orthogonality of generated sequences.
Demonstrated simultaneous generation of dozens of wideband chaotic combs (1500-1600 nm).

Conclusions:

The proposed scheme enables ultra-fast parallel random bit generation.
The method significantly enhances chaotic signal bandwidth and randomness quality.
This approach provides a robust platform for high-security, high-speed random number generation.