# GPON PLOAMd Message Analysis Using Supervised Neural Networks
## 2 Related Work  
The GPON protocol is a following protocol, which replaces old technologies providing services such as internet connectivity, cable television (CATV), and others. Passive optical networks are still under research and development, and, hence, experiments and protocol upgrades are being conducted [21]. The newest version of this protocol family is next-generation passive optical network stage 2 (NG-PON2), which is capable of symmetrical 40 Gbps transfer owing to time and wavelength division multiplexing (TWDM) [22]. There is also an experimental study attempting to extend a long-range GPON with dense wavelength division multiplexing (DWDM) over 135 km [23]. The ITU-T also has a competitor in the PON standard, which is defined by the Institute of Electrical and Electronics Engineers (IEEE) as the Ethernet passive optical network (EPON), which uses standard Ethernet frames instead of frames based on asynchronous transfer mode (ATM) cells [24]. The newest research defines EPON standards with bitrates up to 100 Gbps [25; 26].  
Artificial intelligence (AI) and machine learning (ML) algorithms are used to solve various problems of current networks [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37]. In a given location, the minimal GPON deployment cost is achieved by choosing optimal routing information, type of aggregating equipment and position of network elements. This is an NP-hard problem, and the optimal solution can be approximated using ant colony optimization (ACO) [38]. Traffic analysis using current methods (e.g., port-based and payload-based) shows various disadvantages, but the analysis using ML methods could be a potential replacement, even for encrypted data traffic [39; 40]. ML may also help with parameter tuning for a GPON rule-based expert system used for the problem of root cause localization [41].  
The authors of [42; 43] presented an artificial intelligence modeling for future and intelligent elastic optical networks (EONs) as a review. We can also mention the article by [PERSON] et al. [44], which provides a comprehensive study about machine learning for optical networks. [PERSON] et al. [45] proposed the novel machine learning-based equalization algorithm for intensity modulation and direct detection PON (IM-DD PON) and extended the capacity of PON from 50 Gbps per \(\lambda\) to 100 Gbps per \(\lambda\) over 25 km standard single-mode fiber (SSMF). Further, [PERSON] et al. [46] proposed using neural network (NN)-based equalizer to mitigate both the linear and nonlinear distortions in IM-DD 100 Gbps per \(\lambda\) PON based on 20G-class optical and electrical devices.  
Neural networks based on various types of recurrent layers help to solve difficult problems (e.g., speech recognition, natural language processing, sequence analysis, etc.). Therefore, researchers investigate performance differences of these recurrent layers in certain applications. [PERSON] et al. [47] evaluated the performance of a bidirectional a recurrent neural network (BiRNN), a gated recurrent unit (GRU), and a LSTM used for TV script generation. The results of this paper show the best performance of all tested layers has the LSTM layer. Similarly, [PERSON] et al. [48] evaluated the performance of various recurrent layers for a reservoir flow forecast, and the LSTM cells show the most accurate predictions. On the other hand, [PERSON] et al. [49] evaluated various recurrent layers for speech recognition and proposes a GRU-based network as the best. The network has slightly worse accuracy compared to the LSTM-based network, but the number of parameters in the GRU is much lower making GRU layer preferable for speech recognition.