# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 2 Related Work  
With the introduction of DOCSIS 3.1, the new cable modem communication standard, the speed of transmission has been significantly increased. This is achieved by using Orthogonal Frequency-Division Multiplexing (OFDM) and higher modulation order. OFDM enables carrier configuration per each cable modem and unlocks flexibility for throughput optimization, which was not the case in the previous standard (DOCSIS 3.0). This opportunity is recognized by [9; 10]. [PERSON] et al. proposed an algorithm for dynamic carrier configuration for each individual modem based on machine learning, tested it on 10 real case datasets and proved better results [9]. [PERSON] et al. carried out similar research but proposed their own algorithm for profile management [10]. Both approaches relied on data collected from the network, proving the importance of the data platform. [PERSON] et al. focused on proactive network maintenance. They applied unsupervised machine learning to group cable modems. Based on the similarity of the received signal and cable modem location, they attempted to estimate whether the problem was inside or outside the house [11]. [PERSON] et al. focused on the self-interference phenomenon in DOCSIS 3.1. In full-duplex communication, signals in different directions distort each other. Since the amount and nature of interference are known in advance, they proposed a self-estimation technique to minimize the impact of the mentioned phenomenon and proved it in laboratory conditions [12; 13]. New internet trends show a higher demand in upstream communication. While DOCSIS 3.1 is still the top-notch standard in HFC networks, it still has asymmetrical links. There are several papers that propose an extension of this standard to achieve symmetric communication [14; 15]. [PERSON] et al. analyzed a full-duplex extension to DOCSIS 3.1 and gave an overview of challenges grouped by the physical, MAC (Media Access Control) domain, and system layer [14], while [PERSON] et al. proposed a full-duplex standard that attempts to solve all the challenges and simulate the proposed protocol in laboratory conditions [15]. Obviously, most papers in the area of HFC networks focus on improving the performance of communication either on the detection or correction of problems of specific parts/elements of the HFC network. In this paper, we focus on the mechanism to detect and localize points of failure regardless of the network device type. However, the problem of failure detection and/or localization is important aspect of any communication network technology. Thus, in this section, we also give an overview of the related work regarding failure detection and/or localization in wired communication networks. A very detailed survey regarding the network monitoring aspects is given in [16]. An important part of any network monitoring system is efficient fault management that comprises detection and localization [16]. The importance of knowledge about network topology for efficient failure detection and localization is emphasized in [16]. Failure detection and localization in optical networks is hot-topic area because these networks usually represent backbone and core of service provider communication network infrastructure [17; 18; 19; 20; 21; 22]. Two types of failures can be observed, hard and soft failures. Hard failures represent complete malfunction of the device or link (for example, fiber cut), while the soft failures represent the degradation of performance (for example, component aging) [17]. Hard failures impact the network performance immediately and are easier to detect. However, the soft failures are important as well, as they gradually degrade the overall network performance. In our paper, the focus is on hard failures, but the big data platform and collected data can be used for soft failures detection as well; however, this aspect is not within the scope of this paper. Typically, machine learning and neural networks are used for soft failure detection in optical networks [17; 18; 19].