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Related Concept Videos

Bandpass Sampling01:17

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...
Upsampling01:22

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...
Aliasing01:18

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 signal...
Passive Filters01:27

Passive Filters

Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
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Properties of Fourier Transform I01:21

Properties of Fourier Transform I

The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
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Downsampling01:20

Downsampling

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Related Experiment Video

Updated: Jun 8, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Spectral efficiency limits of pre-filtered modulation formats.

Yi Cai1, Jin-Xing Cai, Alexei Pilipetskii

  • 1Tyco Electronics Subsea Communications LLC, Eatontown, NJ 07724, USA. ycai@subcom.com

Optics Express
|October 14, 2010
PubMed
Summary

Pre-filtering optical fiber communication systems creates symbol correlation, enhancing spectral efficiency (SE) limits. This memory effect in modulation boosts performance beyond original memoryless formats.

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Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

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Last Updated: Jun 8, 2026

Quasi-light Storage for Optical Data Packets
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Published on: February 6, 2014

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06:42

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Published on: June 8, 2018

Area of Science:

  • Optical Communications
  • Signal Processing
  • Information Theory

Background:

  • Optical fiber communication systems face limitations in spectral efficiency (SE).
  • Memoryless modulation formats are standard but have inherent SE constraints.
  • Pre-filtering is a technique used to modify signal bandwidth.

Purpose of the Study:

  • To investigate the spectral efficiency (SE) limit of pre-filtered modulation in optical fiber systems.
  • To analyze the impact of pre-filtering induced symbol correlation on SE.
  • To compare SE limits of memoryless versus correlated modulations.

Main Methods:

  • Evaluating SE limits for modulation formats with varying numbers (L) of correlated symbols.
  • Assessing bandwidth reduction to 50% of the original signal bandwidth.
  • Analyzing the asymptotic behavior of SE limits as L approaches infinity.

Main Results:

  • Pre-filtering induces symbol correlation, creating modulation with memory.
  • Modulation with memory exhibits a higher SE limit compared to original memoryless modulation.
  • SE limits of correlated modulations approach the SE limit of 50% pre-filtered formats as L increases.
  • The SE limit of L correlated symbols provides a lower bound for the pre-filtered format's SE limit.

Conclusions:

  • Symbol correlation via pre-filtering is a viable method to enhance spectral efficiency in optical communications.
  • Modulation formats with memory offer superior SE performance compared to traditional memoryless formats.
  • The study quantifies the relationship between symbol correlation and achievable SE limits.