Low-complexity FPGA-accelerated NN-based adaptive equalizer for 100 Gb/s IMDD PON

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dc.contributor.authorRoshanshomal, Ehsanen
dc.contributor.authorMurphy, Stephen L.en
dc.contributor.authorAyat, S. Omiden
dc.contributor.authorJamali, Faribaen
dc.contributor.authorTownsend, Paul D.en
dc.contributor.authorAntony, Cleitusen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-10-10T15:38:28Z
dc.date.available2025-10-10T15:38:28Z
dc.date.issued2025-09-03en
dc.description.abstractWe demonstrate a low-complexity, field-programable gate array (FPGA)-based adaptive neural network equalizer to mitigate nonlinear impairments caused by semiconductor optical amplifier (SOA) gain saturation in a 100 Gb/s intensity modulation with direct detection (IMDD) passive optical network (PON). The proposed equalizer employs a 32-tap feedforward neural network (FFNN) for multi-symbol detection. This approach incorporates both offline training and adaptive learning techniques to ensure real-time adaptability. To enhance FPGA efficiency, the model is quantized to an 8-bit fixed-point format, and the FFNN core is parallelized to achieve a 100 Gb/s throughput. Experimental results show a dynamic range of 27.8 dB and a sensitivity of -22.8 dBm. This approach improves real-time digital signal processing and establishes a foundation for future machine learning-based solutions in next-generation PON systems, addressing key performance challenges.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2276-P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRoshanshomal, E., Murphy, S. L., Ayat, S. O., Jamali, F., Townsend, P. D. and Antony, C. (2025) 'Low-complexity FPGA-accelerated NN-based adaptive equalizer for 100 Gb/s IMDD PON', 2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Barcelona, Spain, 26-29 May 2025, pp. 1-5. https://doi.org/10.1109/ICMLCN64995.2025.11140557en
dc.identifier.doi10.1109/icmlcn64995.2025.11140557en
dc.identifier.endpage5en
dc.identifier.isbn979-8-3315-2042-7en
dc.identifier.isbn979-8-3315-2043-4en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/18019
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Barcelona, Spain, 26-29 May 2025en
dc.rights© 2025, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectNeural networken
dc.subjectFPGAen
dc.subjectEqualizeren
dc.subjectPassive optical networken
dc.titleLow-complexity FPGA-accelerated NN-based adaptive equalizer for 100 Gb/s IMDD PONen
dc.typeConference itemen
dc.typeproceedings-articleen
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