## 1 Introduction  
It is expected that 5G networks will provide 1000 times more data rate than they do today and will reduce up to 90% of the consumed energy per service [1]. In addition to this, 5G networks will have to support more than 250 Gb/s/km\({}^{2}\) in dense-urban areas, with devices' density in the order of several hundred--or even thousands--per km\({}^{2}\)[2]. Moreover, many new mobile services are emerging in 5G mobile networks, such as Augmented/Virtual Reality (AR/VR), autonomous driving, and industrial automation. These new services are characterized by restricted requirements in terms of data rate, end-to-end latency, and reliability [1]. In conclusion, the capabilities of 5G will extend far beyond those of the current LTE networks. In the 5G Non-Standalone (NSA) architecture, the 5G New Radio (NR) is introduced and connected to the existing Long Term Evolution (LTE) Evolved Packet Core (EPC) via the SI-U interface [3]. Operators will deploy 5G cells and depend entirely on the existing Long Term Evolution (LTE) network for all control functions and add-on services [4]. While in 5G Standalone (SA) architecture, operators deploy new radio access network as well as new 5G core (5 GC) network, providing the user with end-to-end 5G experience. The 5 GC architecture is designed to be a cloud-native, as it will use Network Function Virtualization (NFV) and Software Defined Network (SDN) technologies. Such technologies will be natively built into the 5G SA Packet Core architecture [5]. Moreover, in the 5 GC, the IP Multimedia Subsystem (IMS) is still used to provide the voice service, i.e., Voice over 5G (Vo5G) [6]. To this end, the new 5G architecture (SA 5G and NSA 5G) will utilize the IMS in the core network [6; 7; 8]. Coping with the 5G requirements and delivering a high level of service, reducing the voice call failures in the IMS network becomes crucial. Artificial Intelligent (AI) and Machine Learning (ML) are introduced as promising solutions to deal with the voice call failures in the IMS network and core network generally [9]. In this work, we focus on the NSA architecture option three [10]. Since NSA has the same core network architecture as Voice Over LTE (VoLTE), we worked on call failures present in the VoLTE. However, the same applies to the 5G NSA core network. The IP Multimedia Subsystem (IMS) framework is the core network in the Voice Over LTE service [11]; it provides Internet Protocol (IP) telecommunication services. The Session Initiation Protocol (SIP) is used for establishing the sessions, modifying the sessions, and terminating the sessions [12]. The SIP protocol is designed to provide independent services for the underlying transport protocol so that SIP applications can run over the Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Transport Layer Security (TLS), or any other network protocols. Any problem that occurs inside any underlying transport protocol should lead consecutively to a SIP failure response [13; 14; 15]. Hence, we settled our focus on the SIP protocol to obtain the highest exposure for problems that occur inside the network [16]. Artificial Intelligence (AI) and Machine Learning (ML) are recently introduced in the mobile network to analyze the big data available to retrieve interesting and informative information [17]. Analyzing big data enables mobile network operators to make intelligent real-time decisions in various services. Machine learning can be efficiently used to model different technical problems of the next 5G networks, such as large-scale MIMOs, device-to-device (D2D) networks, and heterogeneous networks constituted by femtocells and small cells [18]. Moreover, machine learning is introduced as an automated solution to deal with network and call failures. Failure management represents a cornerstone in mobile networks to avoid service disruptions and to satisfy customers' Quality of Service (QoS) [19].