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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....

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...

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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...

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...

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.

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Related Experiment Video

Updated: Jun 22, 2025

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Low complexity sub-baud rate sampling reception in a bandwidth-limited IM/DD system.

Shenmao Zhang, Xinyu Chang, Xiaoxiao Dai

|July 1, 2024

Summary

This study introduces a novel sub-baud rate sampling method for intensity modulation and direct detection systems, significantly reducing digital signal processing complexity. This innovation achieves over 60% complexity reduction, crucial for high-speed optical communications.

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Area of Science:

Optical Communications
Digital Signal Processing

Background:

Short-reach optical communication systems face bandwidth limitations.
Digital signal processing (DSP) complexity is a critical challenge in intensity modulation and direct detection (IM/DD) systems.

Purpose of the Study:

To propose a novel approach for reducing DSP complexity in IM/DD systems.
To enable efficient operation under severe bandwidth limitations.

Main Methods:

A sub-baud rate sampling reception method using a polyphase feedforward equalizer-based maximum likelihood sequence estimation (PFFE-MLSE).
Operating at a sampling rate of 0.6 samples per symbol, eliminating the need for resampling.
Achieving over 60% complexity reduction compared to traditional FFE-MLSE.

Main Results:

Demonstrated feasibility through an offline experiment transmitting a 100-Gbaud on-off keying (OOK) signal over a 5-km single-mode fiber (SMF) link.
Achieved bit error ratio (BER) meeting the KP4-forward error correction (KP4-FEC) threshold in the optical back-to-back (OBTB) scenario.
Met the 7% hard-decision FEC (HD-FEC) threshold in the 5-km SMF transmission.

Conclusions:

The proposed PFFE-MLSE sub-baud rate sampling method effectively reduces DSP complexity in IM/DD systems.
This approach is suitable for short-reach optical communication systems with bandwidth constraints.
The method meets FEC thresholds, demonstrating its practical viability.