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Related Concept Videos

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...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...

Parallel Resonance

Parallel Resonance

The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:

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.

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....

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Related Experiment Video

Updated: Jan 16, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

Simultaneous RF self-interference cancellation and high-frequency wideband instantaneous reception using optical

Rongguang Feng, Meiling Yang, Yumo Tian

|October 1, 2025

Summary

This summary is machine-generated.

This study introduces optical frequency comb (OFC)-based undersampling for advanced radio-frequency self-interference cancellation (RF SIC), enabling wider bandwidth reception. An optical path matching criterion ensures successful signal recovery for in-band full-duplex systems.

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Published on: February 6, 2014

Related Experiment Videos

Last Updated: Jan 16, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Area of Science:

Optical Engineering
Wireless Communications
Signal Processing

Background:

Continuous-wave (CW) radio-frequency self-interference cancellation (RF SIC) is crucial for in-band full-duplex (IBFD) technology.
Existing methods struggle with instantaneous reception of high-frequency wideband signals.
Misaligned optical pulse phases in optical frequency comb (OFC)-based reception can cause cancellation failures.

Purpose of the Study:

To propose an OFC-based undersampling technique for enhanced wideband RF SIC and signal reception.
To introduce a novel optical path matching criterion for OFC-based reception systems.
To extend RF SIC theory to OFC-based systems and validate a new transmission architecture.

Main Methods:

Developed an optical path matching criterion for OFC-based reception.
Designed a wavelength-division multiplexing common-path transmission architecture.
Employed OFC-based undersampling for wideband RF SIC.

Main Results:

Achieved RF SIC depths of at least 41.5 dB for single-tone signals and over 26 dB for QPSK signals (60 Msps/s).
Successfully recovered the signal of interest (SOI) constellation with an error vector magnitude (EVM) below 16.8%.
Demonstrated effectiveness across the 48 MHz to 9.98 GHz frequency band.

Conclusions:

The proposed OFC-based undersampling and optical path matching criterion significantly enhance wideband RF SIC.
The novel transmission architecture effectively meets optical and electrical path matching requirements.
The method meets 3GPP standards for QPSK signal reception, advancing IBFD capabilities.