Related Experiment Video
Updated: Oct 2, 2025
09:43
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
Published on: March 20, 2017
10.0K
Efficient utilization of Hough transform and orthogonal-triangular decomposition for optical wireless modulation
Applied Optics
|February 24, 2022
Summary
This study introduces two novel optical wireless modulation format recognition schemes using image processing techniques like Hough transform and orthogonal-triangular decomposition. These methods achieve high accuracy even at low optical signal-to-noise ratios.
Area of Science:
- Optical Wireless Communication
- Signal Processing
- Machine Learning
Background:
- Modulation format recognition (MFR) is crucial for optical wireless communication systems.
- Existing MFR techniques may struggle with performance degradation at low optical signal-to-noise ratios (OSNRs).
Purpose of the Study:
- To propose and evaluate two new schemes for optical wireless MFR.
- To assess the effectiveness of these schemes across various modulation formats and OSNR levels.
Main Methods:
- Constellation diagrams of optical signals were generated as images for seven modulation formats (2/4/8/16-PSK, 8/16/32-QAM) at OSNRs from 5 to 30 dB.
- Two schemes were developed: one using Hough Transform (HT) on images, and another combining orthogonal-triangular decomposition (OTD) with HT.
- Deep learning classifiers (AlexNet, VGG16, VGG19) were employed for MFR.
Main Results:
- Both proposed schemes generated unique signatures for constellation diagrams, enhancing pattern distinguishability.
- High accuracy in MFR was achieved by both schemes, particularly at lower OSNR values.
- The study analyzed scheme efficiency across different OSNR levels and modulation formats.
Conclusions:
- The proposed OTD and HT-based schemes offer robust optical wireless modulation format recognition.
- These methods demonstrate significant potential for improving MFR performance in challenging, low-OSNR environments.
- The use of deep learning classifiers further enhances the accuracy and reliability of the proposed recognition schemes.
Related Concept Videos
01:03IR Frequency Region: Fingerprint Region
1.2K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.2K
01:24IR Frequency Region: X–H Stretching
1.1K
In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of 2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
1.1K
01:08IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations
1.2K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.2K