Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers (2309.15747v2)
Abstract: End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems. Such approach may require a differentiable channel model, thus hindering the optimization of links based on directly modulated lasers (DMLs). This is due to the DML behavior in the large-signal regime, for which no analytical solution is available. In this paper, this problem is addressed by developing and comparing differentiable machine learning-based surrogate models. The models are quantitatively assessed in terms of root mean square error and training/testing time. Once the models are trained, the surrogates are then tested in a numerical equalization setup, resembling a practical end-to-end scenario. Based on the numerical investigation conducted, the convolutional attention transformer is shown to outperform the other models considered.
- J. Huang, C. Li, R. Lu, L. Li, and Z. Cao, “Beyond the 100 Gbaud Directly Modulated Laser for Short Reach Applications,” J. Semicond., vol. 42, no. 4, p. 041306, 2021.
- S. Yamaoka, N.-P. P. Diamantopoulos, H. Nishi, T. Fujii, K. Takeda, T. Hiraki et al., “Uncooled 100-GBaud Directly Modulated Membrane Lasers on SiC Substrate,” J. Lightwave Technol., vol. 41, no. 11, pp. 3389–3396, 2023.
- W.-H. Huang, H.-M. Nguyen, C.-W. Wang, M.-C. Chan, C.-C. Wei et al., “Nonlinear Equalization Based on Artificial Neural Network in DML-Based OFDM Transmission Systems,” J. Lightwave Technol., vol. 39, no. 1, pp. 73–82, 2021.
- B. Karanov, M. Chagnon, V. Aref, D. Lavery, P. Bayvel, and L. Schmalen, “Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model,” in Optical Fiber Communications Conference and Exhibition (OFC), 2020, p. Th2A.48.
- M. Srinivasan, J. Song, A. Grabowski, K. Szczerba, H. K. Iversen et al., “End-to-End Learning for VCSEL-Based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities,” J. Lightwave Technol., vol. 41, no. 11, pp. 3261–3277, 2023.
- N. H. Zhu, Z. Shi, Z. K. Zhang, Y. M. Zhang, C. W. Zou et al., “Directly Modulated Semiconductor Lasers,” IEEE J. Sel. Top. Quantum Electron., vol. 24, no. 1, p. 1500219, 2018.
- M. P. Yankov, O. Jovanovic, D. Zibar, and F. D. Ros, “Recent Advances in Constellation Optimization for Fiber-Optic Channels,” in European Conference on Optical Communication (ECOC), 2022, p. Mo3D.4.
- D. Wang, Z. Zhang, M. Zhang, M. Fu, J. Li et al., “The Role of Digital Twin in Optical Communication: Fault Management, Hardware Configuration, and Transmission Simulation,” IEEE Commun. Mag., vol. 59, no. 1, pp. 133–139, 2021.
- S. Hernandez, C. Peucheret, O. Jovanovic, F. D. Ros, and D. Zibar, “Data-Driven Modeling of Directly-Modulated Lasers,” in European Conference on Optical Communication (ECOC), 2023, p. M.A.3.3.
- J. Cartledge and R. Srinivasan, “Extraction of dfb laser rate equation parameters for system simulation purposes,” J. Lightwave Technol., vol. 15, no. 5, pp. 852–860, 1997.
- S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen et al., “Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting,” Adv. Neural Inf. Process, vol. 32, p. 471, 2019.