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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...

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....

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.

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...

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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Quasi-light Storage for Optical Data Packets

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Decision-aided sampling frequency offset compensation for reduced-guard-interval coherent optical OFDM systems.

Wei Wang, Qunbi Zhuge, Mohamed Morsy-Osman

|November 18, 2014

Summary

This study introduces a new algorithm to fix sampling frequency offset in optical OFDM systems. The decision-aided SFO compensation (DA-SFOC) method improves performance and reduces overhead for robust optical fiber transmission.

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Area of Science:

Optical communications
Digital signal processing

Background:

Sampling frequency offset (SFO) causes inter-symbol interference (ISI) in reduced-guard-interval (RGI) coherent optical (CO) OFDM systems.
Existing methods may require significant cyclic prefix (CP) to mitigate SFO.

Purpose of the Study:

To develop a decision-aided algorithm for SFO compensation in RGI CO-OFDM systems.
To reduce the cyclic prefix (CP) requirement while maintaining system performance.

Main Methods:

Derivation of CP requirements to prevent SFO-induced ISI.
Proposal and numerical investigation of a novel decision-aided SFO compensation (DA-SFOC) algorithm.
Experimental verification using a 28 Gbaud polarization division multiplexing (PDM) RGI CO-OFDM testbed.

Main Results:

The proposed DA-SFOC algorithm demonstrates high tolerance to SFO.
The algorithm effectively reduces the CP requirement compared to conventional methods.
Numerical simulations and experimental results confirm the robustness of DA-SFOC against SFO in optical fiber transmission.

Conclusions:

The DA-SFOC algorithm is a highly effective solution for SFO compensation in RGI CO-OFDM systems.
The method offers improved performance and reduced overhead, making it suitable for advanced optical communication systems.
Experimental validation confirms the practical applicability and robustness of the proposed technique.