Machine learning-assisted design and control for period-one microwave photonic sensing signal

• Published in: Optics and laser technology Vol. 180; p. 111449
• Main Authors: Fang, Can, Ruan, Yuxi, Guo, Qinghua, Yu, Yanguang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Laser dynamics; Machine learning; Microwave photonics; Optical feedback effect; Self-mixing effect; Semiconductor laser with optical feedback; Self-mixing effect; Optical feedback effect; Semiconductor laser with optical feedback; Microwave photonics; Machine learning; Laser dynamics
• ISSN: 0030-3992; 1879-2545
• DOI: 10.1016/j.optlastec.2024.111449

Microwave photonic (MWP) sensing and measurement are envisioned to be a promising alternative to the conventional pure electronic or optical solutions. A semiconductor laser (SL) with external optical feedback (EOF) operating in a period-one (P1) dynamic state contributes a new implementation architecture for MWP systems. However, designing such a SL system to generate frequency-modulated MWP sensing signals through traditional Lang–Kobayashi (L–K) equations requires extensive computational effort to derive the system control parameters (SCP), making real-time adjustment of the SCP impossible in cases where it is needed. This paper proposes an effective design approach based on machine learning. A feedforward neural network (FNN), in conjunction with a gradient descent algorithm, is employed to fast and accurately ascertain the SCP, offering a solution readily applicable in the system design. Both simulation and experiment are conducted to validate the proposed approach. •Proposes a machine learning approach to optimize control for period-one state.•Solves computational bottlenecks of Lang–Kobayashi (L–K) equation method.•Uses a neural network with gradient descent to determine control parameters quickly.•Experimental validation confirmed the performance of the proposed method.