From “Rethinking Ring and Young” in 2011 to proposing NGFI (xhaul) in 2014, the RAN revolutionary path to meet ambitious 5G demands has been charted out. Traditional fronthaul solutions fell short both in required bandwidth and architecture flexibility. Next generation fronthaul interface (NGFI, aka xhaul) proposed by China Mobile targeting a packet-based, traffic-dependent, and antenna scale-independent interface will be central to the 5G RAN revolution. This paper presents its latest progress. Specifically, a two-level NGFI architecture will be highlighted, and the function split options with associated requirements in, e.g. latency, bandwidth and synchronization will be presented. In addition, challenges, as well as the potential solutions for NGFI realization will be discussed.

Multi-access edge computing (MEC) is capable of meeting the challenging requirements of next-generation networks, e.g. , 6G, as a benefit of providing computing and caching capabilities in the close proximity of the users. However, the traditional MEC architecture relies on specialized hardware and its bespoke software functions are closely integrated with the hardware, hence it is too rigid for supporting the rapidly evolving scenarios in the face of the demanding requirements of 6G. As a remedy, we conceive the compelling concept of open-source cellular networking and intrinsically amalgamate it with MEC, which is defined by open-source software running on general-purpose hardware platforms. Specifically, an open-source MEC (OS-MEC) scheme is presented relying on a pair of core principles: the decoupling of the MEC functions and resources from each other with the aid of network function virtualization (NFV); as well as the reconfiguration of the disaggregated MEC functions and resources into customized edge instances. This philosophy allows operators to adaptively customize their users’ networks. Then, we develop improved networking functions for OS-MEC decoupling and discuss both its key components as well as the process of OS-MEC reconfiguration. The typical use cases of the proposed OS-MEC scheme are characterized with the aid of a small-scale test network. Finally, we discuss some of the potential open-source-related technical challenges when facing 6G.

Unmanned aerial vehicle (UAV)-assisted multiaccess edge computing (MEC) networks can effectively broaden the application scope of the Internet of Things (IoT) in complex scenarios such as maritime operations, military communications, and emergency commands. However, uncertain factors, such as weather changes and temporary airspace control, pose great challenges to UAV flight safety. Obstacles resulting from these uncertain factors may intersect with UAVs with preplanned flight paths, leading to accidents. Therefore, generating the optimal flight trajectory to avoid these obstacles is key in the successful operation of this fuzzy system. In this paper, we present a heuristic trajectory generation scheme for complex offshore environments that can generate optimal trajectories according to complex terrain conditions and avoid uncertain obstacles. First, we build a complex terrain model based on a three-dimensional (3D) offshore environment to simulate the conditions in UAV-assisted MEC networks. Second, we propose a network performance optimization objective function that is based on UAV characteristics. Third, we improve the existing ant colony optimization (ACO) algorithm by introducing chaotic mapping, polarizing the pheromone recording rule and implementing a simulated annealing screening mechanism to efficiently generate trajectories. Finally, we design an efficient obstacle avoidance algorithm for different combinations of obstacle regions. The simulation results show that our proposed trajectory generation scheme can efficiently avoid obstacles and significantly improve the total trajectory loss rate compared with that of baseline schemes.

With the advent of mobile networks, we are witnessing an unprecedented shift in the landscape of mobile network services, evolving from traditional voice calls to advanced AI services. This paper delves into the intricacies of this evolution, particularly emphasizing the deep integration of AI Agents into 6G networks. Despite recent researches in using large language model (LLM) and artificial intelligence (AI) agent for network automation, the fundamental mobile AI agent use cases, their network requirements, potential network architecture and enabling technologies for supporting the pervasive AI agents in 6G ear are largely unexplored. In this article, we present an in-depth analysis of typical mobile AI agent use cases in 6G, consisting of AI agent-based 6G network automation, handheld personalized agents, connected robotics and autonomous systems, and wearable AI agent. Then, we elucidate a novel system architecture that supports identified use cases. The article also addresses core aspects of enabling technologies, including 6G agent and application agent collaboration, efficient model and memory management, coordinated agent-to-agent communication and support of multi-modal data transmission. Finally, two future research directions: security and privacy protection and AI agent tailored communication are discussed. This article lays a foundation for understanding the role of 6G in realizing the full potential of AI agents in various applications.

In semantic communication, artificial intelligence (AI) technologies are used to comprehend and anticipate dynamic fluctuations within multi-access edge computing (MEC) networks, thereby improving the utilization of wireless communication network resources. This communication paradigm shift is expected to address numerous intricate edge resource optimization challenges in the 6th-generation mobile network (6G) era. Within the forthcoming space–air–ground–sea integrated network framework, semantic data optimization and dynamic resource allocation are poised to enable cloud–edge–terminal collaborative task processing to be ultra-low latency, ultra-high reliability, and ultra-efficient. The efficient semantic communication-enabled MEC network architecture can be applied to applications across diverse scenarios, including intelligent transportation, smart healthcare, smart manufacturing, and smart homes. In this paper, we first investigate the architecture and main challenges of the semantic communication-enabled MEC network and then propose an edge computing resource optimization scheme for the Internet of vehicles (IoV) that is based on semantic communication. The simulation results indicate that semantic communication technology can significantly diminish transmission latency and network energy consumption in the intelligent IoV.

As one of the important complementary technologies of the fifth-generation (5G) wireless communication and beyond, mobile device-to-device (D2D) edge caching and computing can effectively reduce the pressure on backbone networks and improve the user experience. Specific content can be pre-cached on the user devices based on personalized content placement strategies, and the cached content can be fetched by neighboring devices in the same D2D network. However, when multiple devices simultaneously fetch content from the same device, collisions will occur and reduce communication efficiency. In this paper, we design the content fetching strategies based on an actor-critic deep reinforcement learning (DRL) architecture, which can adjust the content fetching collision rate to adapt to different application scenarios. First, the optimization problem is formulated with the goal of minimizing the collision rate to improve the throughput, and a general actor-critic DRL algorithm is used to improve the content fetching strategy. Second, by optimizing the network architecture and reward function, the two-level actor-critic algorithm is improved to effectively manage the collision rate and transmission power. Furthermore, to balance the conflict between the collision rate and device energy consumption, the related reward values are weighted in the reward function to optimize the energy efficiency. The simulation results show that the content fetching collision rate based on the improved two-level actor-critic algorithm decreases significantly compared with that of the baseline algorithms, and the network energy consumption can be optimized by adjusting the weight factors.