Ultra-High Complexity Optical VCSEL Chaos Generation Based on Reservoir Computing and Logistic Map Cascade for Multiple Applications
In this paper, we propose and numerically investigate an ultra-high complexity vertical-cavity surface emitting laser (VCSEL) optical feedback chaotic (RCLCOF) system based on Reservoir Computing (RC) and logistic map cascade, which is considered for integration into the DSP chip. By simultaneously utilizing the high-dimensional mapping capability of RC and the complex nonlinear transformation of logistic map, the proposed system's complexity is significantly enhanced. Specifically, the maximum Permutation Entropy (PE), Spectral Entropy (SE) and bandwidth are 0.999, 0.915 and 40.491GHz, respectively. Furthermore, the time delay signatures (TDSs) are effectively concealed. To clarify, the RC model in the proposed system does not require training, and all connection weights (Win, WR and Wout) are randomly generated. Meanwhile, the complexity enhancement process is based on post-processing, which is relatively flexible and controllable. These conditions are very beneficial for practical implementation. Subsequently, the effects of different parameters on chaotic complexity are explored in detail and the optimal range is determined. Finally, we investigate the application of the RCLCOF system in random number generation, secure key distribution and optical computing, and all confirmed to achieve satisfactory results. In summary, the proposed RCLCOF system has the advantages of high complexity, high integration and low cost, which is expected to be applied in various fields.
Authors
- Yuefeng Ji (ORCID: https://orcid.org/0000-0002-6618-272X)
- Hongxiang Wang (ORCID: https://orcid.org/0000-0003-4138-5376)
- Pengjin Zhu (ORCID: https://orcid.org/0009-0008-9571-735X)
Institutions
- Beijing University of Posts and Telecommunications (CN)
Publication Details
- Journal
- Journal of Lightwave Technology
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1109/jlt.2025.3563496
- Citations
- 1
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- National Natural Science Foundation of China