To fulfill future diverse user requirements, 6G networks are envisioned to provide everyone-centric customized services ubiquitously and precisely. However, the diversity in user requirements and the heterogeneity in network resources challenge conventional network operators in network management and service provision. In this article, we investigate the artificial intelligence (AI) service provision in the multilayer heterogeneous network. To provide ubiquitous intelligence to users with different computing requirements, an intelligence-native network architecture is designed. Based on the proposed architecture and the AI model stitching mechanism, we formulate the joint AI provision and access selection problem as a mixed integer nonlinear programming (MINLP) problem to maximize the average user satisfaction value and user satisfaction rate. Then, a heuristic solution based on Dung Beetle algorithm is proposed to optimize the AI model selection, AI service deployment, user access, and stitching coefficient jointly. Extensive simulations are conducted to evaluate the performance of our proposed architecture and algorithm.

Presents corrections to the paper, (Correction to “Toward Native Intelligence: An Efficient and Flexible AI Services Provision Scheme in Multilayer Heterogeneous Networks”).

Federated learning (FL) presents a novel paradigm for constructing global models by leveraging distributed client data while preserving privacy. Despite clients’ readiness to contribute computational resources via WiFi networks, the concurrent model uploads often trigger the competitive backoff mechanism inherent in the carrier sense multiple access with collision avoidance (CSMA/CA) protocol, which impairs the training efficiency and performance of FL. To address this challenge, this paper proposes an innovative sliding window-based asynchronous update approach for federated learning, named as FedSW. By properly configuring the sliding window size at the wireless access point (AP) of the WiFi network, FedSW orchestrates local training, model upload, aggregation, and distribution in harmony with the sliding window progress. This synchronization significantly improves training efficiency and model performance. Furthermore, the versatility of FedSW is demonstrated through its seamless integration with state-of-the-art (SOTA) algorithms. Our methodology is rigorously evaluated against FL benchmarks, showcasing its superior effectiveness. Simulation results confirm that FedSW consistently outperforms conventional benchmarks in terms of convergence, regardless of the sliding window size, while significantly reducing latency.

As an integral component of the space-air-ground integrated network (SAGIN), the low Earth orbit (LEO) satellite network has displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites pose unprecedented challenges in network management and service delivery. In this paper, we investigate the service function chain (SFC) orchestration in dynamic LEO satellite networks to achieve flexible and efficient service provision. Considering the service requirements and the limitations of network resources, we formulate the SFC orchestration problem as the integer nonlinear programming (INLP) problem for maximizing the service acceptance and the load fairness of satellites. Then, an efficient heuristic algorithm is proposed to solve this problem. Addressing the situation with frequent service requests, a graph attention network (GAT)-based approach with low complexity is also presented. Simulation results demonstrate that our proposed approaches outperform the benchmarks by a substantial margin in terms of load fairness and service acceptance. Besides, the proposed GAT-based approach shows its advantage in computation complexity, and exhibits robustness in unstable network scenarios with intermittent link interruptions.

As an integral component of space-air-ground integrated networks (SAGINs), the low Earth orbit (LEO) satellite networks have displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites poses unprecedented challenges in network management, multi-dimensional resource scheduling, and service delivery. In this paper, we study the service function chain (SFC) orchestration in dynamic LEO satellite networks, with the aim of achieving flexible and efficient service provision. Considering the service requirements and the load fairness of LEO satellite networks, we formulate the SFC deployment problem as an integer nonlinear programming (INLP) problem. We then introduce a load-aware SFC orchestration algorithm to improve serving capacity and load fairness. Additionally, we address the issue of SFC migration in dynamic LEO satellite networks to ensure service continuity. To minimize the service interruption and network resource wastes, a Tabu search (TS)-based approach is presented to optimize the virtual network function (VNF) migration. Simulation results demonstrate that our proposed approaches outperform the benchmark by a substantial margin in terms of load fairness, without compromising service acceptance.

Integrated satellite-terrestrial network (ISTN) can provide a continuous service for vehicular users in remote areas with a seamless network coverage. However, considering the difference in the usage costs between satellite and terrestrial networks and the variability of services for latency requirements, it is of great significance to design a cost-effective data offloading decision for reducing network overhead and ensuring task delay requirements. In this paper, we design a cost-effective data offloading mechanism for vehicles in ISTN. The default transmission for remote areas is via the satellite, where the terrestrial networks can offload the data with intermittent coverage in an opportunistic manner due to the vehicle mobility. To model the diversity in service delay requirements, a virtual queue is exploited to capture the residual maximum delay tolerance of each service as time elapses. We formulate the satellite-terrestrial collaborative transmission as a non-linear programming (NLP) problem. To solve the problem, we propose a reinforcement learning (RL)-based data offloading algorithm for real-time decision making. Simulation results show that the RL-based data offloading algorithm reduces the network overhead and outperforms other baseline schemes we proposed.