Automatic reference optical spectrum retrieval method for ultra-high resolution optical spectrum distortion analysis utilizing integrated machine learning techniques

作者：Lu, Hongwei; Cui, Sheng^*; Ke, Changjian; Liu, Deming

来源：Optics Express, 2017, 25(26): 32491-32503.

DOI：10.1364/OE.25.032491

• MODULATION FORMAT IDENTIFICATION
• DIGITAL COHERENT RECEIVERS
• STIMULATED BRILLOUIN-SCATTERING
• NEURAL-NETWORKS
• SIGNALS
• RECOGNITION

Optical spectrum (OS) is a vital characteristic of optical signals. Ultra-high resolution (UHR) OS can provide more detailed and accurate information for optical performance monitoring and optical link quality diagnosis. By comparing actual signal UHR-OS observed at in-line monitoring points with the theoretical ideal ones, various signal distortions can be readily identified and more accurately estimated. But in the future flexible heterogeneous optical networks optical signals with different symbol rates, modulation formats and pulse shaping schemes may coexist in the same system. Hence the ideal reference OS of the channel to be monitored can't be assumed to be fixed or known in advance. It may also be impossible to undertake a reference OS measurement at or near the transmitter as the route path may be dynamically generated. To solve this problem we proposed an automatic ideal reference optical spectrum retrieval (OSR) method according to the actually observed ones. The OSR method can tolerate large OS distortions due to non-ideal optical links or transmitters by the integration of two machine learning techniques, namely unsupervised principle component analysis (PCA) and supervised multiclass support vector machines (SVMs) for feature extraction and UHR-OS classification, respectively. Extensive simulations conducted for nine types of optical signals commonly used show that this method performs very well in the presence of various significant distortions caused by non-ideal optical links or transmitters.

• 出版日期2017-12-25
• 单位华中科技大学