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Lu, L.; Liu, C.; Zhang, C.; Hu, Z.; Lin, S.; Liu, Z.; Zhang, M.; Liu, X.; Chen, J. 6G Core Network Based on Intelligent Decision Making. Encyclopedia. Available online: https://encyclopedia.pub/entry/48153 (accessed on 19 May 2024).
Lu L, Liu C, Zhang C, Hu Z, Lin S, Liu Z, et al. 6G Core Network Based on Intelligent Decision Making. Encyclopedia. Available at: https://encyclopedia.pub/entry/48153. Accessed May 19, 2024.
Lu, Lu, Chao Liu, Chunhong Zhang, Zheng Hu, Shangjing Lin, Zihao Liu, Meng Zhang, Xinshu Liu, Jinhao Chen. "6G Core Network Based on Intelligent Decision Making" Encyclopedia, https://encyclopedia.pub/entry/48153 (accessed May 19, 2024).
Lu, L., Liu, C., Zhang, C., Hu, Z., Lin, S., Liu, Z., Zhang, M., Liu, X., & Chen, J. (2023, August 17). 6G Core Network Based on Intelligent Decision Making. In Encyclopedia. https://encyclopedia.pub/entry/48153
Lu, Lu, et al. "6G Core Network Based on Intelligent Decision Making." Encyclopedia. Web. 17 August, 2023.
6G Core Network Based on Intelligent Decision Making
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This entry is adapted from the peer-reviewed paper 10.3390/electronics12153255

The rapid progress of 6G mobile communication technologies has sparked considerable research interest due to the escalating complexity of network service demands and the increasingly diversified nature of application scenarios. As such, the core architectural framework of 6G faces a pressing and multifaceted set of challenges that necessitate sophisticated and forward-thinking approaches to resolution.

6G core network self-evolution reinforcement learning

1. Introduction

With the large-scale deployment of 5G commercialization, the concept and technologies of 6G (Sixth Generation) have attracted significant attention [1][2]. The 6G mobile communication system is envisioned to be an intelligent infrastructure for universal connection of exponential growth heterogeneous devices of massive application scenarios, such as smart cities, multi-sensory extended reality, and tactile internet, etc. The goal of 6G is to establish a seamless global coverage network across space, air, oceans, and land with high transmission rate, low end-to-end latency, and customized service provisioning [1].
The 6G system primarily distinguishes itself from 5G by its endogenous intelligence capability supported by AI (Artificial Intelligence) to meet the constantly changing requirements of users and applications [3]. AI algorithms are ubiquitous from the cloud to the edge and are applied in many aspects of 6G, such as resource management, service orchestration, network security, and semantic communications [4]. However, it is still unclear how the AI can benefit the 6G core network architecture.
The above morphing of network components is designed and evolved by the pure manual effort of expert knowledge and industrial consensus, typically recorded in a series of 3GPP protocols, which is expensive and time-consuming. The previous generations of core networks were generally designed by domain experts under the assumption that the user’s requirements were predefined and predictable [5]. Meanwhile, the design process was often characterized by long research cycles and significant investments in manpower. The resulted fixed-defined architecture of core networks struggled to effectively accommodate the dynamic nature of evolving user demands [6]. Therefore, this design philosophy of core network architecture makes it hard to guarantee the diverse and dynamic customized 6G services anywhere and anytime.
Therefore, a 6G core network capable of automatically morphing its architecture according to the changing scenarios is promising. A 6G core network will explore new architecture without being restricted by traditional design paradigms. Intuitively, AI technology is the natural choice for automatically evolving the architecture of the 6G core network. However, to the best of our knowledge, little thought has been given to how AI can empower the 6G core network architecture with self-evolution capabilities.

2. The Evolution of Core Networks Preceding 5G

The mobile communication system has undergone five generations of evolution, resulting in significant changes in core network architectures. For example, the 4G transitioned from the Universal Mobile Telecommunications System (UMTS) [7] to the Evolved Packet Core (EPC) [8], a pivotal change that separated the user plan and control plan, enhancing the network’s flexibility and reliability. Subsequently, the 5G core network adopted a Service-Based System Architecture (SBA) and embraced Network Function Virtualization (NFV) [9], enabling the core network to evolve and innovate while providing flexible and scalable network services and operations.
Compared to 4G, 5G has not only emerged new functional components, such as the Network Slice Selection Function (NSSF) [10], but also eliminated obsolete components, such as the circuit-switched (CS) domain, which was discontinued in the 4G EPC core network with the introduction of the all-IP network [11]. Additionally, some network components in 4G have undergone separation or have merged, exemplified by the transformation of the Mobility Management Entity (MME) in 4G. Its functions were dispersed into various network functions, such as AMF (Access and Mobility Management Function), SMF (Session Management Function), and AUSF (Authentication Server Function) in 5G [12]. Furthermore, there has been a change in the interactions between network components in 4G, as seen in the adoption of a service bus framework between multiple Network Functions (NFs) in the 5G core network control plane [13], replacing the point-to-point mechanisms utilized in the 4G EPC.

3. 6G Core Network

In comparison to the 5G core network, the 6G core network architecture should be redesigned to achieve a powerful, flexible, and intelligent network [14][15][16][17][18]. Most existing works have analyzed the challenges of the 6G communication system and proposed new architectural solutions for the 6G network [19][20][21]. Yuanzhe Li et al. [21] proposed a cognitive service architecture for the 6G core network, inspired by the nervous system of the octopus, to enhance the core network and meet the increasing quality of service requirements and complex scenarios. Xinran Fang et al. [22] introduced basic models for integrated satellite terrestrial networks. To achieve seamless global wireless signal coverage, Chao Wang et al. [23] proposed a 6G-supported space-air-ground-sea integrated network (SAGSIN) architecture. Through reconsidering the 5G Service Based Architecture (SBA) functional split, Marius Corici et al. [24] proposed a new “Organic 6G Network” concept and a new service architecture for 6G core networks based on advancements in the software services adopted. Zhang et al. [25] introduced a novel concept called “intellicise”, which describes the integration of next-generation networking technologies and AI in wireless networks. The “intellicise” network actively takes systematic entropy reduction as the global optimization objective, adaptively reshapes information systems, and ultimately endows itself with inherent intelligence and simplicity. Maier et al. [26] introduced an ESPN architecture that leverages artificial-intelligence-enhanced computing to explore the flourishing development of multisensory Extended Reality (XR) experiences within multiverse cross-reality environments in the context of 6G. Notably, Cai Lin et al. [27] proposed a Self-Evolution and Transformation (SET) architecture, where a protocol control agent is deployed in each network entity to handle flow/packet level control. This agent can assemble, configure, and exchange protocol functions, thereby enabling the protocol to change and self-evolve.
As shown in Figure 1, the 6G core network adopts a two-layer topology with Edge Core networks and Cloud Core Networks. The Edge Core Networks sink to the edge of the network and act like a peripheral control plane for special domains or usage scenarios. The Cloud Core Network plays the role of the central brain to coordinate multiple Edge Core Networks and no longer directly participates in communication. Each layer of the core network is composed of a set of Network Functions (NF). For each NF, there is a two-level service structure. That is, the microservices serve as the fine-grained units and are then composed to form coarse-grained Network Functions (NFs), catering to diverse communication requirements. With the composition capability of different service granularity, the 6G core network would provide connectivity and support to users under various application scenarios. The integrated terrestrial, aerial, and maritime networks in 6G, built upon the foundation of terrestrial cellular mobile networks and deeply integrated with broadband satellite communications, offer extensive coverage, flexible deployment, and efficient broadcasting capabilities. To enable information exchange and sharing between different networks and to provide customized communication services to a variety of users, these networks also require seamless integration with other heterogeneous networks [28].
Figure 1. Schematic diagram of the 6G core network architecture. The dashed oval box on the right depicts the Service Base Architecture of the Network Functions (NFs) in the Edge Core Network. In each NF, the two-level hierarchical structure, microservices, and NFs are also plotted.
The two-level structures of each NF in the Edge Core Network are elaborated in the right part of Figure 1. For instance, the AMF (Access and Management Function)[29] is responsible for mobility management and access control, which are composed of microservices for User Authentication, Session Management, Security, and Policy Control. For example, the Mobility Microservice is responsible for tracking the location of user equipment (UE) and managing handovers between different network cells. Similarly, the SMF (Session Management Function) [29] is responsible for efficient session management. It incorporates microservices such as Statistics and Reporting, Session Management, Traffic Control, and User Data Management. These microservices collectively contribute to ensuring smooth session establishment and maintenance. Furthermore, the AUSF (Authentication Server Function) [29] plays a crucial role in user authentication and security operations. It consists of several microservices, including UE Authentication, UE Authorization, Security Policy Management, and User Data Management. These microservices work together to authenticate users, manage security policies, and handle user-related data.

4. Network Intelligence

With the exponential proliferation of mobile devices and data, the seamless integration and rapid development of AI and 6G have gained widespread recognition. The key characteristic of 6G is the emergence of network endogenous intelligence, often referred to as AI-enabled networks, which can perceive, analyze, and make optimal decisions autonomously [30]. Research has shown that AI-enabled 6G networks will gradually be applied to major network issues, including advanced radio interfaces, intelligent traffic control, security protection, management, and coordination [31]. Khattak et al. [32] believe that the AI-enabled 6G networks will also have significant impacts on all other related vertical fields, such as the mobile health applications supported by 6G, which will change human life. Cai et al. [33] proposed a 6G mailbox theory to enable distributed algorithm embedding for network intelligence. 
The combination of AI and the network emerged earlier than the 6G era. At that time, network intelligence was primarily manifested through automatic network management and orchestration, as well as network optimization leveraging machine learning and big data analysis. As shown in Figure 2, the 3GPP working group has been promoting the standardization process of 5G Network Intelligence. R8-R10 proposed an intelligent strategy known as the Self-Organizing Network (SON) [34]. R15 introduced the 5G Network Data Analytics Function (NWDAF) and R16 defined a centralized architecture for 5G big data analysis services [35]. R17 and R18 designed a layered intelligent network architecture to further advance the application and standardization of Network Intelligence [36]. However, although these efforts have promoted the application and standardization of the industry, there is still a considerable gap between them and the true network intelligence, with limitations in deployment flexibility and scalability.
 Figure 2. Enhanced and refined diagram of Self-Organizing Network (SON) technology and standardization evolution of network intelligence.
It is obvious that the approach of utilizing AI to merely ``patch'' network operations and management in 5G networks is no longer viable. Nasralla et al. [37] and Azari et al. [38] introduced the technological evolution from 5G to 6G from different vertical fields of network applications, which laid the foundation for our thinking on the evolution of generations. Lv, Z. et al. [39] studied the evolution and prospects of network architecture and proposed a conceptual architecture for 6G networks that encompasses holistic network virtualization and network intelligence (AI).

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Subjects: Telecommunications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Lu Lu
,
Chao Liu
,
Chunhong Zhang
,
Zheng Hu
,
Shangjing Lin
,
Zihao Liu
,
Meng Zhang
,
Xinshu Liu
,
Jinhao Chen
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Update Date: 21 Aug 2023
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