With the large-scale deployment of 5G commercialization, the concept and technologies of 6G (Sixth Generation) have attracted significant attention ^[1][2][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][9]. 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][10]. 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][19] to the Evolved Packet Core (EPC) ^[8][20], 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][21], 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][5], 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][6]. 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][7]. 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][8], 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]}[22,23,24,25,26]. Most existing works have analyzed the challenges of the 6G communication system and proposed new architectural solutions for the 6G network ^[19][20][21][27,28,29]. Yuanzhe Li et al. ^[21][29] 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][30] introduced basic models for integrated satellite terrestrial networks. To achieve seamless global wireless signal coverage, Chao Wang et al. ^[23][31] 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][32] 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][33] 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][34] 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][35] 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 12, 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][36]. The边缘核心网络中每个 two-level structures of each NF in the Edge Core Network are elaborated in the right part of FigureNF 的两级结构在图 1.2 For instance, the 的右侧部分进行了详细说明。例如，AMF（访问和管理功能）[14]负责移动管理和访问控制，它们由用于用户身份验证，会话管理，安全性和策略控制的微服务组成。例如，移动微服务负责跟踪用户设备 (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 ） 的位置并管理不同网络单元之间的切换。同样，SMF（会话管理功能）[14]负责高效的会话管理。它包含微服务，例如统计信息和报告、会话管理、流量控制和用户数据管理。这些微服务共同有助于确保会话的顺利建立和维护。此外，AUSF（身份验证服务器功能）[14]在用户身份验证和安全操作中起着至关重要的作用。它由多个微服务组成，包括 (Authentication Server Function) ^[29] plays a crucial role in user authentication and security operations. It consists of several microservices, including UE Authentication, 身份验证、UE Authorization, Security Policy Management, and User Data Management. These microservices work together to authenticate users, manage security policies, and handle user-related data.授权、安全策略管理和用户数据管理。这些微服务协同工作以对用户进行身份验证、管理安全策略和处理与用户相关的数据。

4. Network Intelligence网络智能

With the exponential proliferation of mobile devices and data, the seamless integration and rapid development of AI and 随着移动设备和数据的指数级增长，人工智能和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 的无缝集成和快速发展获得了广泛的认可。6G的关键特征是网络内生智能的出现，通常被称为人工智能网络，可以自主感知、分析和做出最佳决策[37]。研究表明，人工智能支持的6G networks will gradually be applied to major network issues, including advanced radio interfaces, intelligent traffic control, security protection, management, and coordination ^[31]. 网络将逐步应用于重大网络问题，包括先进的无线电接口、智能流量控制、安全防护、管理和协调[38]。Khattak et al. ^[32] believe that the AI-enabled 等人[39]认为，人工智能支持的6G networks will also have significant impacts on all other related vertical fields, such as the mobile health applications supported by 网络也将对所有其他相关的垂直领域产生重大影响，例如6G, which will change human life. Cai et al. ^[33] proposed a 支持的移动健康应用，这将改变人类的生活。Cai等人[40]提出了6G mailbox theory to enable distributed algorithm embedding for network intelligence. 邮箱理论，以实现网络智能的分布式算法嵌入。 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 和网络的结合比6G时代更早出现。当时，网络智能主要表现为自动网络管理和编排，以及利用机器学习和大数据分析的网络优化。如图3所示，3GPP working group has been promoting the standardization process of 工作组一直在推动5G Network Intelligence. 网络智能的标准化进程。R8-R10 proposed an intelligent strategy known as the Self-Organizing Network (SON) ^[34]. 提出了一种称为自组织网络（SON）的智能策略[41]。R15 introduced the 引入了5G Network Data Analytics Function (NWDAF) and R16 defined a centralized architecture for 5G big data analysis services ^[35]. 网络数据分析功能（NWDAF），R16为5G大数据分析服务定义了集中式架构[42]。R17 and 和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.设计了分层智能网络架构，进一步推进了网络智能的应用和标准化[43]。然而，尽管这些努力促进了行业的应用和标准化，但它们与真正的网络智能之间仍然存在相当大的差距，在部署灵活性和可扩展性方面存在局限性。 It is obvious that the approach of utilizing AI to merely ``patch'' network operations and management in 显然，利用人工智能在5G networks is no longer viable. 网络中仅仅“修补”网络运营和管理的方法不再可行。Nasralla et al. ^[37] and 等人[44]和Azari et al. ^[38] introduced the technological evolution from 等人[45]从网络应用的不同垂直领域介绍了从5G to 到6G from different vertical fields of network applications, which laid the foundation for our thinking on the evolution of generations. Lv,的技术演进，这为我们思考世代演进奠定了基础。Lv， Z. et al. ^[39][46] studied研究了网络架构的演进和前景，并提出了包含整体网络虚拟化和网络智能 the evolution and prospects of network architecture and proposed a conceptual architecture for 6G networks that encompasses holistic network virtualization and network intelligence (AI).（AI） 的 6G 网络概念架构。