Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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. The spectrum...

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...

Time and frequency -Domain Interpretation of Phase-lag Control

Time and frequency -Domain Interpretation of Phase-lag Control

Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any finite,...

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Physics-informed neural Volterra compensation enabling over 2600× efficiency improvement in 12,057-km ultra-long-haul coherent transmission.

Communications engineering·2026

Same author

Integrating fixed and mobile coherent optical access networks for unified broadband services.

Communications engineering·2026

Same author

On the coexistence of distributed fiber optic sensing and IM/DD transmission via digital subcarrier multiplexing.

Optics express·2026

Same author

Leveraging Fresnel reflection of legacy PON to enhance distributed fiber vibration/temperature sensing.

Optics express·2025

Same author

Ultra-low-complexity weight-sharing trigonometric nonlinear equalizer for beyond net-200-Gb/s/λ short-reach optical interconnects: publisher's note.

Optics letters·2025

Same author

Low-complexity cluster-assisting look-up-table-based Volterra decision-feedback equalizer for IM/DD systems: publisher's note.

Optics letters·2025

Search research articles

Related Experiment Video

Updated: May 17, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Low-complexity non-integer fractionally spaced feed-forward equalization with half-symbol-spaced kernel estimation

Qiang Bin, Junwei Zhang, Alan Pak Tao Lau

|May 15, 2026

Summary

A novel feed-forward equalizer (FFE) uses half-symbol-spaced kernel estimation (HSSKE) for efficient high-speed optical communication. This method reduces complexity while maintaining performance, improving receiver sensitivity and bit error rates in PAM-4 systems.

More Related Videos

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Related Experiment Videos

Last Updated: May 17, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Area of Science:

Optical Communications
Signal Processing

Background:

High-speed intensity modulation and direct detection (IM/DD) systems require efficient equalization techniques.
Fractionally spaced equalizers (FS-FFE) are crucial for mitigating signal impairments in modern optical networks.

Purpose of the Study:

To propose a low-complexity non-integer fractionally spaced feed-forward equalizer (FFE) utilizing half-symbol-spaced kernel estimation (HSSKE).
To enable accurate, aliasing-free kernel estimation at 2 samples per symbol (sps) and efficient equalization below 2 sps.

Main Methods:

Implementation of HSSKE for kernel estimation in a 1.2-sps FFE.
Experimental validation on a 100-GBaud/λ PAM-4 system over 1-km and 2-km standard single-mode fibers (SSMFs).
Integration with noise whitening filter (NWF) and maximum likelihood sequence estimation (MLSE) for advanced performance analysis.

Main Results:

The 1.2-sps FFE with HSSKE outperformed the 1.2-sps FFE with symbol-spaced estimation (SSKE).
Achieved equalization performance comparable to conventional 2-sps FFE with over 27% reduction in computational complexity.
Demonstrated 0.9-dB receiver sensitivity improvement and BER reduction below HD-FEC thresholds when combined with NWF and MLSE.

Conclusions:

The proposed HSSKE-based FFE offers a significant reduction in computational complexity for high-speed IM/DD systems.
This approach provides a viable solution for achieving high performance at lower sampling rates, enhancing spectral efficiency.
The integration with NWF and MLSE further boosts performance, meeting stringent forward error correction (FEC) requirements.