Blind modulation format identification using decision tree twin support vector machine in optical communication system

Abstract

This paper proposes a method of blind modulation format identification using decision tree twin support vector machine classifier trained with the features extracted from the high-order cumulant and cyclic spectrum after compressed sensing in order to solve the problem of identification efficiency and computing speed and reduce the performance requirements of the sampling system in low optical signal to noise ratio. By reconstructing the feature parameters of fourth-order, eighth-order cumulants and cyclic spectrum under the theory of compressed sensing, and introducing the decision tree twin support vector machine classifier to achieve high-precision classification, the different modulation formats of amplitude shift keying, multiple phase-shift keying and multiple quadrature amplitude modulation are effectively identified. Simulation analysis of the influence of identification accuracy and identification time improves the identification performance and achieves the purpose of identifying more signals with fewer feature parameters. The results indicate that the average identification accuracy of the optical modulation format signals can be achieved over 94% when the OSNR is -5dB, and the identification time is 4.3 times higher than the standard SVM. Owing to its excellent performance, this method can be employed in the next generation optical transport network for auto-adaption real-time modulation format identification.