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Omkar Tripathy,
Rosalin Pradhan,
Bibhu Prasad Ganthia,
- Student, Department of Electrical Engineering, Capital Engineering College, Khordha, Odisha, India
- Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
- Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
Abstract
This paper proposes a novel self-adaptive neuromorphic opto-electronic transceiver architecture designed to enhance the intelligence, adaptability, and efficiency of next-generation optical communication networks. The proposed system integrates neuromorphic computing principles with photonic signal processing to enable real-time learning, dynamic resource allocation, and autonomous compensation of channel impairments such as dispersion, nonlinearities, and noise. Unlike conventional transceivers, the developed model employs spiking neural networks embedded within opto-electronic circuits to mimic biological learning behavior, allowing continuous adaptation to varying network conditions without external control. A hybrid architecture combining optical front-end components and electronic neuromorphic processors is formulated, where optical signals are directly mapped into neural spike domains for efficient processing. Simulation results demonstrate significant improvements in bit error rate, spectral efficiency, and energy consumption compared to traditional DSP-based optical systems. Furthermore, the system exhibits strong robustness against dynamic traffic fluctuations and environmental disturbances, making it suitable for future cognitive and autonomous communication infrastructures. The suggested design minimizes dependence on traditional digital signal processing blocks, thereby reducing processing latency and hardware complexity. It also facilitates scalability for high-capacity networks and smooth integration with new technologies like 6G and edge computing. The proposed framework opens new directions for integrating artificial intelligence with photonic technologies, paving the way for intelligent, self-optimizing optical networks.
Keywords: Neuromorphic Opto-Electronics, Optical Communication, Cognitive Networks, Photonic Signal Processing, Spiking Neural Networks, Adaptive Transceivers
Omkar Tripathy, Rosalin Pradhan, Bibhu Prasad Ganthia. Graphene–Perovskite Hybrid Opto-Electronic Modulators for Ultra-Low Power Optical Communication. Trends in Opto-electro & Optical Communication. 2026; 16(01):-.
Omkar Tripathy, Rosalin Pradhan, Bibhu Prasad Ganthia. Graphene–Perovskite Hybrid Opto-Electronic Modulators for Ultra-Low Power Optical Communication. Trends in Opto-electro & Optical Communication. 2026; 16(01):-. Available from: https://journals.stmjournals.com/toeoc/article=2026/view=240722
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Trends in Opto-electro & Optical Communication
| Volume | 16 |
| 01 | |
| Received | 15/04/2026 |
| Accepted | 15/04/2026 |
| Published | 23/04/2026 |
| Publication Time | 8 Days |
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