## 1 Introduction  
The elastic optical networks [1] (EONs), as one of the solutions of the next-generation fiber-optic communication network, have recently attracted a great deal of attention. Optical orthogonal frequency division multiplexing (OOFDM) is considered a promising alternative to the EONs because of its advantages, including its high spectral efficiency, good resistance to chromatic dispersion, as well as its provision of hybrid dynamic bandwidth allocation in both frequency and time domains. IMDD realizations, e.g., directly modulated laser (DML) based OOFDM transceivers [2], are usually applied in cost-sensitive systems, e.g., optical access networks or data centers, because of their simple structure and DSP (digital signal processing) [3]. With adaptively modulated optical orthogonal frequency division multiplexing (AMOOFDM) [4; 5; 6; 7; 8; 9], systems can maximize capacity and achieve flexible bandwidth allocation simultaneously. However, to achieve the bandwidth on demand (BoD) approach, the EONs are expected to be able to dynamically adjust various transmission parameters, e.g., modulation formats, OSNR, spectrum assignments, etc., depending on the adjusting traffic demands and network condition. Negotiations between the transmitters and receivers are vital for adjusting the modulation format or other parameters before resetting the bit rate. To achieve the flexible bandwidth provision, one of the critical functional requirements for intelligent receivers in EONs is the ability to identify the modulation formats and monitor OSNR of received signals without any prior information through the negotiations between transmitters and receivers. Modulation format identification has aroused growing attention from researchers in recent years. [PERSON] investigated and characterized a blind modulation format recognition method by projecting partial received data in Stokes space onto a 2-D plane to plot a converted binary graph [10]. Based on the evaluation of the peak-to-average-power ratio of the incoming data samples, [PERSON] presented a simple novel digital modulation format identification scheme for coherent optical systems after some independent DSP processing at the receiver [11]. [PERSON] proposed a modulation format identification technique based on the extraction and identification of specific features of received signal power distributions in digital coherent receivers, which successfully identified six modulation formats [12]. However, the method described in [10] requires the approximate OSNR and the carrier frequency of the received signals, the method in [11] requires additional hardware components as well as the OSNR of received signals, and the scheme in [12] requires the computation of the received signal power distributions for digital coherent receivers. In recent years, several machine learning-based modulation format identification (MFI) techniques have been proposed both in digital coherent and directly detected receivers [13; 14; 15; 16; 17; 18; 19; 20; 21; 22] for optical communications systems because of their excellent learning ability from data, which can avoid the requirement of pre-information. [PERSON] proposed a deep machine learning method to identify three modulation formats at an accuracy of 100% in a wide optical signal-to-noise ratio range [13]. A simple and cost-effective MFI technique was also proposed by his teams using an artificial neural network based on asynchronous amplitude histogram (AAH) [14]. [PERSON] experimentally demonstrated a cost-effective technique to achieve optical performance monitoring functionalities and enable simultaneous symbol rate and modulation format identification based on artificial neural networks [15]. [PERSON] introduced a novel modulation format identification method based on intensity fluctuation features using support vector machines [16]. [PERSON] utilized an artificial neural network to identify modulation format and a genetic algorithm to simplify the structures of an artificial neural network for directly detected receivers [17].