SearcharxivSearch

arXiv subjects

Xiaojuan Yan

Publications and source records attributed to Xiaojuan Yan.

2 recordsLinked to original sources

Self-heterodyne spectroscopy via a non-uniformly spaced frequency comb

Frequency comb spectroscopy has significantly advanced molecular spectroscopy across scientific research and diverse applications. Among its key performance metrics especially for time-resolved studies, sensitivity and measurement speed are paramount. However, a long-standing compromise between these parameters arises from the need for noise reduction. Here, we introduce a comb spectroscopy system that overcomes this limitation using a single frequency comb of non-uniformly spaced modes. The comb is generated using an extremely simple setup, composed of a continuous-wave (CW) fiber laser and a single-sideband phase modulator (SSM). Our approach delivers optical-to-radio-frequency conversion comparable to dual-comb spectroscopy (DCS) but through a simplified self-heterodyning architecture. By leveraging the intrinsic mutual coherence of the comb, this design achieves a noise-equivalent absorption coefficient (NEA) of 5.0*10^(-6) Hz^(-1/2)--an order-of-magnitude improvement over state-of-the-art DCS, coupled with long-term stability. The system resolves weak molecular overtone spectra on nanosecond timescales, in a single-shot measurement, at a signal-to-noise ratio of 128. This integration of high sensitivity, resolution, and speed resolves the core trade-off that has long constrained time-resolved spectroscopic analysis.

physics.optics

Genetic Algorithm Optimized Support Vector Machine in NOMA-Based Satellite Networks with Imperfect CSI

With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade the judgment on users' channel state information (CSI) accuracy, thus affecting the user pairing processing and suppressing the superiority of the NOMA scheme. Inspired by the advantages of machine learning (ML) algorithms, we propose an improved support vector machine (SVM) scheme to reduce the inappropriate user pairing risks and enhance the performance of NOMA based satellite networks with imperfect CSI. Particularly, a genetic algorithm (GA) is employed to optimize the regularization and kernel parameters of the SVM, which effectively improves the classification accuracy of the proposed scheme. Simulations are provided to demonstrate that the performance of the proposed method is better than that with random user paring strategy, especially in the scenario with a large number of users.

eess.SP