Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement,...
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2025-01-01
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| author | Luís C. B. Silva Pablo R. N. Marciano Maria J. Pontes Maxwell E. Monteiro Paulo S. B. André Marcelo E. V. Segatto |
| author_facet | Luís C. B. Silva Pablo R. N. Marciano Maria J. Pontes Maxwell E. Monteiro Paulo S. B. André Marcelo E. V. Segatto |
| author_sort | Luís C. B. Silva |
| collection | DOAJ |
| description | The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks. |
| format | Article |
| id | doaj-art-ae05d3c02475499590e55d015a2661ef |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Photonics |
| spelling | doaj-art-ae05d3c02475499590e55d015a2661ef2025-01-24T13:46:17ZengMDPI AGPhotonics2304-67322025-01-011213910.3390/photonics12010039Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A ReviewLuís C. B. Silva0Pablo R. N. Marciano1Maria J. Pontes2Maxwell E. Monteiro3Paulo S. B. André4Marcelo E. V. Segatto5Department of Electrical Engineering, Federal University of Espírito Santo, Fernando Ferrari Avenue, Vitória 29075-910, BrazilDepartment of Electrical Engineering, Federal University of Espírito Santo, Fernando Ferrari Avenue, Vitória 29075-910, BrazilDepartment of Electrical Engineering, Federal University of Espírito Santo, Fernando Ferrari Avenue, Vitória 29075-910, BrazilFederal Institute of Espírito Santo (IFES), Serra 29166-630, BrazilDepartment of Electrical and Computer Engineering, Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, PortugalDepartment of Electrical Engineering, Federal University of Espírito Santo, Fernando Ferrari Avenue, Vitória 29075-910, BrazilThe demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks.https://www.mdpi.com/2304-6732/12/1/39artificial intelligenceequalizationintegrated circuitsliterature reviewoptical communicationsphotonic neural networks |
| spellingShingle | Luís C. B. Silva Pablo R. N. Marciano Maria J. Pontes Maxwell E. Monteiro Paulo S. B. André Marcelo E. V. Segatto Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review Photonics artificial intelligence equalization integrated circuits literature review optical communications photonic neural networks |
| title | Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review |
| title_full | Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review |
| title_fullStr | Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review |
| title_full_unstemmed | Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review |
| title_short | Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review |
| title_sort | integrated photonic neural networks for equalizing optical communication signals a review |
| topic | artificial intelligence equalization integrated circuits literature review optical communications photonic neural networks |
| url | https://www.mdpi.com/2304-6732/12/1/39 |
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