Title :
Weighted Regularized Sparse Recovery Method for Optical Power Monitoring
Author :
Zhang, Jun ; Yu, Zhu Liang ; Li, Yuanqing ; Liu, Gordon Ning ; Gu, Zhenghui
Author_Institution :
Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
Monitoring optical power from the broadened optical spectrum acquired by scanning a low-cost tunable optical filter (TOF) is a practical and challenging task for the management of wavelength-division-multiplexing (WDM) networks. Existing methods, such as least squares (LS) and regularized sparse recovery (RSR) methods, suffer from performance deterioration when the power discrepancies between adjacent channels are significant. In this letter, we formulate it as a maximum a posteriori (MAP) estimation problem under Bayesian framework. A weighted-RSR algorithm is, therefore, proposed to estimate the optimal channel power. Experimental results demonstrate that, using commercial low-cost TOFs, the weighted-RSR can accurately monitor the power of mixed 10-G/40-G WDM channels with 50-GHz channel spacing and 10-dB power difference.
Keywords :
least squares approximations; maximum likelihood estimation; sparse matrices; wavelength division multiplexing; Bayesian framework; broadened optical spectrum; least square methods; low cost tunable optical filter; maximum a posteriori estimation; optical power monitoring; performance deterioration; regularized sparse recovery methods; wavelength division multiplexing networks; weighted regularized sparse recovery method; Adaptive optics; Bayesian methods; Channel estimation; Optical fiber networks; Optical interferometry; Optical signal processing; Tunable circuits and devices; Wavelength division multiplexing; Bayesian inference; optical performance monitoring; tunable optical filter (TOF); wavelength-division-multiplexing (WDM) network;
Journal_Title :
Photonics Technology Letters, IEEE
DOI :
10.1109/LPT.2011.2172597