## 1 Introduction  
In the past 10 years, considerable attention has been given to optical network technology. Traditional wavelength division multiplexing (WDM) optical networks with fixed wavelength and a single modulation format are facing challenges. To satisfy the exponential growth of global communication, optical fiber communication systems are envisioned to be flexible, large capacity, long transmission distance, and high spectral utilization. [PERSON] et al. proposed the concept of spectrum sliced elastic optical network (EON) with a large capacity, high flexibility, strong scalability, and adaption to various network service forms [1]. In particular, EON based on orthogonal frequency division multiplexing (OFDM) provides an important idea for solving the spectrum flexibility problem of optical networks and has attracted extensive interest. In EONs, the unit of spectral resources is no longer fixed wavelength, but the smaller frequency gap. According to network operating demands, the EONs will be capable of dynamically implementing signal of different modulation formats (MFs), data rates, and error correction protocols to maximize the bandwidth and energy efficiency of the network [2]. Dynamic variation of transmission parameters in EONs poses an important challenge to digital signal processing (DSP) algorithms employed in the optical receivers. Underthe condition of modulation format switching, it is preferred to make the receivers have the ability of automatically identifying the signal modulation format, hence DSP algorithms can be deployed to achieve the optimum detection performance accordingly. Modulation format identification (MFI) technique is indispensable for re-configurable digital coherent receivers used in EONs. It is imperative to have appropriate monitoring mechanisms across the networks to acquire precise and real-time information about the quality of transmission links and health of optical signals [3; 4]. Benefiting from the rapid advancement of DSP technology, the digital coherent receiver can compensate almost all linear impairments, resulting in the transmission performance being mainly determined by optical signal-to-noise ratio (OSNR) [5]. OSNR is one of the most noteworthy parameters in coherent optical networks because of its direct relation with BER. OSNR information is also vital for automatic fault detection and diagnosis, as well as for in-service characterization of signal quality [6]. In recent years, researchers have utilized various technologies to achieve MFI such as signal cumulant and signal power distribution-based method [7; 8], peak-to-average-power ratio of received data samples-based methods [9; 10], Stokes space-based methods [11; 12; 13; 14; 15], and machine learning-based methods [16; 17; 18; 19]. Various OSNR estimation techniques for coherent detection systems have been proposed recently including statistical moments [20], error vector magnitude [21], delay-line interferometer [22; 23], Stokes parameters [24], Golay sequences [25], offset filtering, and optical power measurement [26], radio frequency (RF) spectrum [27], and amplitude noise correlation [28] based methods. The above techniques have a single function and can only implement MFI or OSNR estimation. A cost-effective multi-parameter estimation in next-generation EONs is attractive. [PERSON] et al. propose a method combining optical signal-to-noise ratio (OSNR) monitoring and MFI based on signals' amplitude histograms with the use of deep neural networks [29]. The method utilizes amplitude information of the received signals and is insensitive to phase noises. However, the method cannot distinguish MFs with same amplitude distribution such as QPSK and 8 PSK. [PERSON] et al. propose an intelligent constellation diagram analysis algorithm using convolutional neural network to implement both MFI and OSNR estimation [30]. Based on the characteristics of neural networks and supervised-learning, these two methods both need a large number of signal samples as training data.