The incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architectures, as well as the enabling technologies required to integrate AI across the cloud, core network (CN), radio access network (RAN), and terminals. It begins by examining the necessity of embedding AI into 6G networks, making it a native capability. The analysis then outlines potential evolutionary paths for the RAN architecture and proposes an end-to-end AI-driven framework. Additionally, key technologies such as cross-domain AI collaboration, native computing, and native security mechanisms are discussed. The study identifies potential use cases, including embodied intelligence, wearable devices, and generative AI, which offer valuable insights into fostering collaboration within the AI-driven ecosystem and highlight new revenue model opportunities and challenges. The paper concludes with a forward-looking perspective on the convergence of AI and 6G technology.

The integration of Unmanned Aerial Vehicles (UAVs) and Low Earth Orbit (LEO) satellites has become attractive for Internet of Things (IoT) task processing, as it can overcome obstacles in terrestrial network coverage, such as those in oceans or desert areas. However, it lacks a collaborative approach for allocating the communication and computing resources among UAVs and LEO satellites and optimizing the hovering point of UAVs to prolong their endurance. In this paper, we investigate energy-efficient resource allocation in LEO-assisted UAV networks for the Internet of Things. A novel optimization algorithm, that Jointly IoT tasks’ Offloading decision, UAVs’ Region selection, Hovering point chosen, and Communication and Computing resource allocation (ORHCC), is proposed to optimize UAV trajectories and hovering points, enhancing endurance and minimizing energy consumption. In particular, the UAVs’ region selection and IoT tasks offloading are under the Dueling Deep Q-Network (DuDQN) framework, the Hovering point chosen and Communication and Computing resource allocation via the convex solution. The results show that the proposed ORHCC reduces 12.5% and 20.76% energy consumption compared with the PPO and greedy baseline, respectively.

The evolution of communication architecture shifts towards virtualization and cloud-native network functions, setting the stage for the flexibility and integration of emerging technologies. Artificial Intelligence (AI) and Machine Learning (ML) as intrinsic elements in network design are some of the crucial visions and requirements for 6G. This paper, from the perspective of O-RAN, explores how current network architectures should evolve towards the integration of communication and intelligence in 6G. It begins with a comprehensive analysis and comparison of the AI-related work conducted by various standard organizations. Building on this, an end-to-end AI integration framework is proposed, which leverages AI technologies, data services, and digital twin (DT) technologies to achieve an integrated intelligent 6G communication system. After that, the key enabling technologies for cross-domain AI, service-based RAN, programmable RAN and digital twins are discussed. At last, the paper analyzes the challenges and opportunities for O-RAN evolution.

Artificial Intelligence (AI) integration in communication is crucial for 6G. It optimizes terrestrial communication and computing resource usage in the Internet of Things (IoT) using AI techniques, such as supervised learning for data analysis and reinforcement learning for resource allocation. However, in remote areas, i.e., oceans and deserts, IoT devices lose connection due to limited terrestrial coverage. Low Earth Orbit Satellite (LEOS) offers low-latency, high-bandwidth access in these unconnected regions. However, power and computing limitations on both IoT devices and LEOSs present challenges for continuous service. To this end, we present an LEOS-assisted Open Radio Access Network (RAN) Architecture (LO-RAN) where a RAN Intelligence Controller (RIC) is integrated to provide AI abilities. We formulate a joint Offloading decision, Path selection, and Resource allocation problem (OPR) to minimize the weighted energy consumption and latency of LO-RAN. We proposed a Joint Optimization for the Offloading decision, Path selection, and Resource allocation (JOOPR) algorithm. It selects contact and processing LEOSs for path selection, uses Proximal Policy Optimization (PPO) for offloading decisions, and applies Karush-Kuhn-Tucker (KKT) to solve resource allocation. The outputs from path selection and resource allocation contribute to the reward that feeds into the PPO. We conduct numerical simulations to compare the proposed JOOPR with the state-of-the-art approaches. The results show that JOOPR reduces energy consumption and latency by at most 28.75% and 33.01%, respectively.

The development of 6G networks is driving the need for innovative resource orchestration solutions to meet the diverse and dynamic demands of the next-generation applications (APPs). As the number of connected devices increases, traditional network management approaches are insufficient to handle the complex and multiresource requirements of 6G, which include communication, computation, and storage capabilities across multidomain environments. To address these challenges, we introduce HyOrch, a novel approach for 6G-driven resource orchestration that employs hypergraph theory to model the intricate interactions and dependencies among network elements across multiple domains. HyOrch enables a comprehensive representation of these complexities, facilitating efficient resource orchestration across end devices (EDs), edge servers (ESs), and cloud servers (CSs). It employs a hierarchical distributed resource allocation mechanism that dynamically allocates resources based on real-time availability and application-specific requirements, ensuring optimal performance across the entire network. To validate the effectiveness of HyOrch, we conducted evaluations on a real-world testbed with both virtual and physical devices. The results show that HyOrch significantly outperforms existing approaches, improving resource efficiency by 31.54%–42.13% and reducing delay by 9.77%–39.12%, demonstrating its capability to address the evolving challenges of 6G network orchestration.

Radio Access Network (RAN) evolves from a dedicated RAN to a virtual RAN and Open RAN (O-RAN). The evolution is required with an Artificial Intelligence (AI) service for future 6G applications. The AI is also embedded into the network to represent as AI-native RAN which is one of the focuses of the next-generation RAN. In particular, O-RAN is in advance of intelligent RAN, it provides a cloud-based and open architecture for wireless networks, separates traditional RAN components, designs open interfaces, and introduces the RAN Intelligent Controller (RIC) to enhance wireless networks’ scalability, flexibility, and intelligence. However, integrating AI into O-RAN presents challenges, including alignment with other standards and performance limitations and integration cost in diverse vendors. This article discusses the role of O-RAN in 6G networks. We first analyze the requirements of emerging applications and introduce the AI-native O-RAN for 6G. We then address the question, “How open is open?” and outline the RIC evaluation path for 6G. In addition, we explore the potential alignment between 3GPP and O-RAN. Finally, we identify several promising O-RAN research directions to inspire and guide future studies.