Sixth generation (6G) mobile networks promise unprecedented connectivity with ultra-high data rates, near-zero latency, and high reliability for applications like autonomous systems and extended reality. However, managing diverse resources, communication, computing, and caching (3C), poses significant challenges. Artificial intelligence (AI) is the key to automating and optimizing resource management in 6G. This review compares traditional model-based methods with adaptive data-driven approaches, covering computing and caching resource management alongside radio resources within the edge-cloud continuum. Crucially, the paper examines big AI models (BAIM) and the shift toward agentic AI for holistic, autonomous network automation. To address the opacity of these complex models, we highlight explainable AI (XAI) and digital twins (DTs) as an essential, combined trust and validation layer. Together, they ensure transparency, mitigate the computational overhead of real-time explanations, and enable safe training for critical functions like network slicing and multi-access edge computing (MEC) computation offloading. Finally, key challenges and future research directions for AI integration in next-generation wireless networks are outlined.

The rapid proliferation of heterogeneous services, such as Ultra-Reliable Low-Latency Communication (URLLC), enhanced mobile broadband (eMBB), and Massive Machine Type Communications (mMTC), requires 5G/B5G networks to support dynamic and scalable resource allocation frameworks. This paper presents a Context-Aware Resource Orchestration (CARO) framework, an optimization-based approach that integrates multi-access edge computing (MEC) and network slicing to coordinate resource allocation across diverse service requirements. CARO employs a modular orchestration stack comprising a Slice Orchestrator for slice admission and prioritization, a MEC Controller for host selection and resource allocation, an SDN Controller for path selection, an NFV Orchestrator and VNF Manager for VNF lifecycle management, and a Physical Tier for dynamic infrastructure execution. By leveraging software-defined networking and network function virtualization, CARO dynamically prioritizes slices, selects MEC servers, and enforces QoS-aware policies, adapting to real-time network conditions and slice-aware workload consolidation. Simulation results from a 5G MEC scenario suggest that CARO can reduce average service delay for URLLC services and improve resource utilization compared with static allocation baselines. These results indicate that the proposed framework offers a practical way to balance scalability, adaptability, and operational efficiency in next-generation networks, while keeping orchestration overhead manageable.

We study analytically the generation of breather voltages in a nonlinear electrical transmission line when the dissipative effects are taken into consideration. Focusing on the case of weak dissipation, we apply the reductive perturbation technique in the semidiscrete limit to show that the propagation of weakly nonlinear modulated waves in the network system is modeled by a distributed nonlinear Schrödinger equation. The baseband modulational instability, a phenomenon responsible of simultaneously formation of both soliton and rogue wave, is investigated and the analytical expression for the modulational gain spectrum is derived. Under the condition of the baseband modulational instability, we derive approximate analytical localized wave solutions of model equation. Based on those approximate solutions, we prove that our network system support the propagation of breathers embedded on a vanishing/nonvanishing continuous wave background. We show that, despite the presence of dissipative elements, the propagation of electrical breathers remains possible in the case of weak dissipations. More interestingly, our results show that in the domain where the network may exhibit baseband modulational instability, effects of dissipative losses in the shunt branch are dominated by those in series branch. Also, some of obtained approximate solutions are found to be useful for describing the compression of breather waves propagating in the network under consideration. The numerical results perfectly match the analytical predictions.

Non-Terrestrial Networks (NTN) are considered pivotal for the development of 6G, aiming to provide ubiquitous and continuous mobile broadband coverage. With ongoing standardisation efforts by 3GPP, 5G NTN promises seamless connection moving between terrestrial and satellite networks, using existing or next-generation smartphone devices. This paper focuses on the integration of NTN, particularly Low Earth Orbit (LEO) constellations, for 5G NR services. We explore the latest developments in direct satellite-to-device communication and the significant challenge posed by interference. We assess the performance of 5G NTN by simulating and analysing the downlink performance of LEO constellations providing continuous service to moving User Equipment (UE) in unserved and remote areas, addressing the challenges of achieving higher data rates and managing signaling interference. Based on our baseline Link Budget calculation result, higher Effective Isotropic Radiated Power (EIRP) and additional spectrum bandwidth are essential for enhancing data rates and user coverage. However, given the limited availability of spectrum, increasing EIRP becomes a more practical option to increase data rates, which in turn increases the risk of interference. Our simulation findings indicate that LEO constellations can provide continuous coverage for moving UEs, supporting low data rate services such as text, voice and Internet of Things (IoT), but the limited spectrum bandwidth and interference proved to be some of the main challenges for achieving higher data rates. Interference management strategies, such as reducing sidelobe power and employing advanced technologies such as beamforming [1] and beam-hopping [2], are critical to mitigate interference and improve performance.

The exponential growth of computation-intensive mobile applications calls for intelligent resource management in multi-access edge computing (MEC) networks under realistic user mobility. However, mobility-induced dynamics couple wireless quality, queue evolution, and offloading decisions, which can degrade performance when using mobility-agnostic designs. This paper proposes a unified framework for joint task offloading and resource allocation in SDN-enabled MEC systems. We formulate the problem as a Markov decision process with explicit velocity-based mobility modeling and develop a Mobility-aware Hierarchical Deep Deterministic Policy Gradient (MH-DDPG) algorithm. The hierarchical policy decomposes the hybrid discrete–continuous action space: a high-level module selects edge servers, while a low-level module allocates continuous resources, and a coordination-enhanced hierarchical attention mechanism promotes coherent decisions across the two levels. We further introduce mobility-adaptive prioritized experience replay to account for mobility-driven distribution shifts during training. Theoretical analysis establishes convergence guarantees under the two-timescale stochastic approximation framework, while adaptive weight adjustment facilitates trade-off navigation across operating regions of the multi-objective space. Extensive evaluations on two real-world urban mobility traces (MDT-NJUST and T-Drive) show consistent improvements in latency, energy consumption, and task success rate over seven representative baselines, with stable learning dynamics under dynamic network conditions.

In Multi-access Edge Computing (MEC) environments, session continuity during user mobility remains a pressing challenge due to decentralized infrastructure and high-throughput, latency-sensitive applications. Existing mobility protocols often rely on stateful mechanisms or centralized control, leading to increased signaling overhead, limited scalability, and vulnerability to performance degradation in dynamic networks. This paper introduces the Server Search and Select Algorithm Protocol (SSSAP), a lightweight, UDP-based handover protocol tailored for MEC deployments. The protocol is an extension of our previous work on a handover Server Search and Selection Algorithm (SSSA). SSSAP enables seamless session redirection through a three-phase signaling scheme (pre-handover, handover initiation, and handover termination), preserving service continuity without coupling session state to transport layers. The protocol’s design features extensible headers for multi-metric evaluation and future security adaptation while maintaining minimal dependency on intermediary control nodes. Through extensive simulation and testing, we have validated the SS-SAP efficiency across user equipment nodes and MEC servers. Results demonstrate high handover success rates, low-session setup delays, and balanced server load distribution. SSSAP achieves superior performance in mobility robustness, packet loss mitigation, and integration simplicity. The research outcomes position SSSAP as a scalable and application-agnostic mobility protocol for MEC systems, especially in vehicular and high-mobility scenarios.

This paper proposes an efficient trust-aware authentication and task offloading scheme for Multi-Access Edge Computing (MEC) using the Zero Trust Security (ZTS) principles. The proposed method uses a dual fuzzy logic system to evaluate the trustworthiness of edge servers. Devices connected to the edge servers are authenticated using identity, biometrics and Physical Unclonable Function (PUF) measures. After authentication, tasks can be offloaded from the devices to the most trustworthy edge server. The proposed scheme also considers the resource constraints of the edge servers and aims to minimise the overall task completion time. The experimental results show that the proposed scheme outperforms existing schemes regarding authentication accuracy, task completion time, and energy consumption.

Network slicing is a key enabler for 5G and beyond networks that permits operators to provide scalable, flexible, and dedicated networks over a common physical infrastructure. To cope with the rising demand for ultrareliable and low-latency communication (URLLC) in beyond 5G networks, the provision of dedicated secure networks closer to the users is essential. Multiaccess edge computing (MEC) is a promising technology that provides data and computational resources closer to mobile users. However, MEC servers are resource-constrained, and offering dedicated service-specific network slices at the edge in a highly dynamic and mobile environment is challenging. Network slicing and MEC are being evolved by two different standardization bodies that limit their integration and raise mobility challenges that deserve more attention. We propose a cloud-native microservices architecture for network slice mobility management in MEC that permits each MEC slice to be distributed as stateless and independently deployable microservices. The proposal separates the MEC slice operational data and the user context, as each network function in a MEC slice stores the context in a separate shared database. The proposed architecture leverages new SDN extended federation modules in compliance with the ETSI requirements for inter-MEC system coordination. The federation modules support a more flexible and scalable creation of network slices at MEC servers, efficient resource utilization, and mobility of network slices across MEC servers. The simulation results show that our proposed architecture outperforms the existing SDN-based approaches for network slicing in MEC by achieving high slice acceptance rates and reduced slice migration delay.

doi:10.1093/comjnl/bxy010 Comp J 2018;61(7): 1064–1074 The affiliations of the following authors have been corrected since the original publication of this article: Yonghong Hou Lin Xue Shuo Li Jiaming Xing

The next-generation mobile cellular networks are dedicated to providing a valued and unique service experience by supporting ultrareliable and low-latency communication (URLLC), high throughput, and high availability. Multiaccess edge computing (MEC) is an emerging network solution that provides services and computing functions on edge nodes to provide users with a reliable and high-quality service experience. However, achieving satisfactory Quality of Service (QoS) for diverse service requests in a mobile environment is challenging because of the densely deployed yet resource-constrained MEC servers. A solution to ensure continued service quality is to migrate the services according to the mobility of users. However, in a highly mobile environment such as vehicular communications, this may result in a repeated relocation of services, incurring high operational costs and poor utilization of network resources. Moreover, each service has its own set of communication requirements, such as delay and bandwidth. Meeting these requirements in a highly dynamic and complex vehicular environment is an exacting challenge. Software-defined networking (SDN) concepts are leveraged in MEC to provide a unified control plane interface that performs effective network and service mobility management, to manage the heterogeneity of service requests within the resource-constrained MEC servers. We conducted various Proof-of-Concept (PoC) experiments in an overlapped vehicle-to-everything (V2X) networking environment to demonstrate the feasibility of our proposed system that ensures interconnection and federation among distributed MEC servers and mobile networks.

HyperText Transfer Protocol (HTTP) streaming has been widely used for multimedia delivery nowadays. To adapt to the heterogeneous networks and different terminals, the rate adaptation controller is developed as the core part of the video transmission system. In this paper, a three-input fuzzy controller is designed to enhance the users’ quality-of-experience (QoE) on watching online video. The introduction of fuzzy logic is able to solve the problem that there is no rule to set the reasonable buffer thresholds in the buffer-based adaptation controller. The normalized throughput, the buffer level and the buffer variation are used as the inputs of the controller to minimize the inevitable mismatch caused by limited bitrate levels and to help the system converge to a stable state. The proposed controller is compared with three other controllers under three simulation network conditions and an actual vehicle condition. A QoE model is simultaneously used to get intuitive and comprehensive results. The results show that among all the four conditions, the proposed controller can provide better QoE than other algorithms.