The deep integration of artificial intelligence (AI) and networks promotes the transformation of 6th generation mobile communication technology (6G) from conventional data transmission to an AI-native network architecture. This paper discusses the mutual empowerment between 6G and AI, and systematically analyzes the standardization progress of “AIassisted 6G architecture” and “6Garchitectureassisted AI” within the 3GPP Release 20 project for 6G network architecture. Furthermore, it compares domestic and international technical evolution routes.

This paper analyzes the connotation and core vision of Mobile AI, summarizes the core requirements and constructs a hierarchical indicator system for ToC smart life, ToB smart production and ToG urban governance scenarios, and further proposes key technologies of Mobile AI from the perspectives of network and AI. Finally, the development path and suggestions of Mobile AI are presented, providing reference for the high-quality development of 6G intelligence-communication integration.

The rapid development of various location-based services and the emerging 6G technologies impose increasingly stringent requirements on the performance of wireless positioning technologies. AI algorithms enable positioning techniques to break through the performance bottlenecks of traditional methods, giving rise to more advanced paradigms. First, the principles and limitations of traditional methods are elaborated. Then, a systematic review of the evolution of mainstream wireless positioning algorithms toward intelligence is presented. Finally, the enabling role of AI-empowered wireless positioning technologies for the 6G technology framework is analyzed, and the emerging challenges and opportunities in this field are discussed.

The deep integration of artificial intelligence and communications has emerged as one of the core driving forces advancing wireless communication systems. This paper analyzes the background and motivation of low-overhead channel state information (CSI) acquisition in 6G systems, and proposes low-overhead CSI acquisition methods for 6G systems accordingly, encompassing two categories of schemes: CSI reconstruction based on sparse reference signals and multi-scenario CSI prediction based on a single reference signal. The effectiveness of the proposed schemes is validated through simulations, and their prospects for standardization, along with the critical issues that remain to be addressed, are further discussed.

Edge-end collaboration, leveraging the advantages of computing power sinking, layered model deployment, terminal cooperative scheduling and unified resource management, can effectively support new generation smart terminals with limited computing resources to accomplish complex intelligent collaborative tasks. This paper systematically reviews the research in the field of edge-end collaboration. It focuses on core technologies including model splitting, terminal-side model lightweighting, and joint communication-computing resource management, and summarizes the existing bottlenecks and research challenges in this domain. On this basis, combined with key 6G technologies such as computing-network integration, integrated sensing and communication, and wireless intelligence, the evolution directions of edge-end collaboration empowered by 6G are prospected, so as to provide references for followup research on edge-end collaboration.

The evolution of the 6G core network toward endogenous intelligence has become a consensus within the industry. However, existing research still exhibits fragmented characteristics regarding multi-agent architectures, intent processing, and the management of Artificial Intelligence (AI) capabilities. Building upon current advancements in 6G architecture standardization research, this study proposes a multi-agent collaboration and AI capability lifecycle management architecture tailored for 6G core networks. This architecture introduces two types of network-level AI agents, namely planning agents and execution agents, with task-level collaboration among heterogeneous agents implemented through dedicated agent-specific interfaces. Simultaneously, the architecture incorporates AI control functionalities that enable unified management of AI capability registration, model training, validation and database integration, provisioning and deployment, operational monitoring, retraining, and anomaly-based rollback.

With the development of perception and intelligent applications in connected vehicles, vehicle terminals continue to generate vast amounts of local data. Federated learning provides an effective approach for conducting multi-vehicle collaborative training whilst safeguarding data privacy, whilst the low-latency, high-reliability communication and edge computing capabilities of the 6th Generation Mobile Communication Technology (6G) networks further provide the network and computational power required for large-scale vehicle collaborative learning. However, traditional federated learning is typically designed for time-invariant data distributions and struggles to adapt to the dynamic environment characterized by vehicle movement and constantly changing data acquisition conditions. To address this, we propose A Data and Resource Co-sensing Federated Continual Learning Algorithm tailored for mobile connected vehicles. By optimizing terminal scheduling, the algorithm enhances the participation of data from new categories, whilst drift-aware adaptive aggregation dynamically adjusts the update step size of the global model. Experimental results demonstrate that the proposed algorithm accelerates the recovery of model performance following concept drift, achieving a final accuracy approximately 2.5 per cent higher than that of bandwidth-priority scheduling and 8.2 per cent higher than that of a fixed learning rate scheme.

Sixth-generation (6G) mobile communication networks are evolving toward ubiquitous connectivity, pervasive intelligence, and the convergence of diverse application scenarios, making security risks increasingly complex. An integrated security risk analysis framework comprising three categories—the evolution of inherited risks, AI-induced risks, and risks introduced by emerging technologies—is developed to examine the evolution of existing risks in 6G scenarios, the derivative risks arising from the involvement of artificial intelligence in network operations, and the incremental risks introduced by emerging technologies such as digital twins. The analysis indicates that 6G security should be designed in tandem with the network architecture and intelligence mechanisms to strengthen dynamic defense and built-in security capabilities. The findings can provide a reference for research on 6G network architectures, security design, and standardization.

To address the increasingly complex service requirements and operating environments of 6G wireless networks, conventional task-specific models face limitations in cross-task adaptation and domain knowledge integration. Large models, with their capabilities in knowledge representation and task generalization, offer a promising paradigm for advancing wireless network intelligence. This paper reviews recent advances in foundation model development for wireless networks, wireless task applications, network agents, and capability evaluation. Furthermore, through a case study on user experience diagnosis in wireless networks, we demonstrate an application paradigm in which large models collaborate with domain knowledge and specialized tools to complete complex tasks. Finally, this paper discusses key challenges, including trustworthy closed-loop operation, cross-scenario transfer, evaluation frameworks, and the industrial ecosystem.

This paper proposes an AI-based cross-module optimization framework that integrates modulation and precoding collaborative design alongside intelligent control strategies, aiming to achieve joint optimization of the physical layer. The proposed framework breaks down traditional inter-module information barriers and supports globally optimal end-to-end performance. To validate the effectiveness of the proposed scheme, evaluations are conducted in both single-user and multi-user scenarios. Simulation results demonstrate that the scheme achieves advantages in terms of key performance metrics such as block error rate and throughput.

The 6th Generation Mobile Communication Technology (6G) imposes high requirements on spectral efficiency and connection density, making Demodulation Reference Signal (DMRS) pilot overhead a critical factor limiting system performance. Reducing DMRS density releases resources for data transmission and provides great room for port multiplexing, but it also exposes the receiver to challenges such as degraded channel estimation accuracy and colored noise. Toward 6G, 3GPP RAN1 has initiated the “low-density DMRS with AI receiver” study, which leverages Artificial Intelligence (AI) to enhance the receiver’s signal processing capability under low-density DMRS and proposes seven AI receiver architectures in total. From the perspective of AI receiver taxonomy, this paper first reviews recent advances in four directions: AI-based channel estimation, MIMO detection, constellation demapping, and multi-module joint optimization. It then systematically summarizes the definitions, taxonomy, advantages, and limitations of the seven AI receiver architectures, and on this basis conducts link-level simulation verification for representative architectures. The simulation results show that AI receivers still maintain superior decoding performance under low-density DMRS.