Related Experiment Video
Updated: Mar 12, 2026

09:43
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
Published on: March 20, 2017
10.4K
Common phase error estimation in coherent optical OFDM systems using best-fit bounding box.
Optics Express
|November 10, 2016
Summary
We introduce a novel best-fit bounding box method for phase error estimation in coherent optical OFDM systems. This approach improves spectral efficiency and reduces complexity compared to existing methods.
Area of Science:
- Optical Communications
- Signal Processing
- Image Processing Applications
Background:
- Coherent optical Orthogonal Frequency Division Multiplexing (OFDM) systems are susceptible to phase errors.
- Accurate phase error estimation is crucial for maintaining system performance and spectral efficiency.
- Existing methods like pilot-aided and blind phase searching have limitations in accuracy or complexity.
Purpose of the Study:
- To investigate and characterize a new best-fit bounding box method for phase error estimation.
- To adapt techniques from image processing (2-D convex hull) for optical communication signal processing.
- To evaluate the proposed method's performance against established techniques.
Main Methods:
- Calculating the 2-D convex hull of the received signal constellation.
- Performing root mean square error analysis.
- Analyzing laser linewidth tolerance, noise tolerance, and computation complexity through simulations and experiments.
Main Results:
- The best-fit bounding box method achieves significantly improved spectral efficiency.
- System performance is comparable to the pilot-aided method.
- The proposed method demonstrates good estimation accuracy and reduced computational complexity compared to blind phase searching.
Conclusions:
- The best-fit bounding box method offers a promising alternative for phase error estimation in coherent optical OFDM systems.
- It balances spectral efficiency, accuracy, and computational load effectively.
- The adaptation of image processing techniques shows potential for optical communication advancements.
Related Concept Videos
Linear Approximation in Frequency Domain
414
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....
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....
414
Linear Approximation in Time Domain
388
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
388
Propagation of Uncertainty from Systematic Error
1.5K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.5K
Propagation of Uncertainty from Random Error
2.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.1K
Margin of Error
8.0K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
8.0K
Expected Frequencies in Goodness-of-Fit Tests
8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
8.8K

