Multipath networks have been extensively studied to improve throughput and alleviate network congestion. However, multipath networks, with the exception of equal-cost multi-path networks, are difficult to implement because they schedule network resources at cross layers and require complex signaling mechanisms to monitor network status (e.g., round trip time, bandwidths, and link state). Virtualization, in contrast, provides network isolation with protocols and resources and simplifies the process of coupling the interaction between signaling mechanisms, which makes multipath implementation practical. This paper proposes a multipath network virtualization scheme that implements software-defined networking (SDN) and network function virtualization (NFV). Multipath networks can be deployed in the virtualized environment. The proposed scheme attains a summary of network resources, such as network topology and link bandwidth, and can schedule these resources for selecting and spreading flow over multiple paths. In addition, virtualization provides computing and storage resources for flow splitting, tag adding, and packet reordering, among other applications. Specifically, the tags are designed for forwarding and packet reordering. Our experiment builds the platform based on open platform for NFV and SDN on the practice testbed. To verify its functionalities, we first evaluate the overhead of each component and then implement and compare five state-of-the-art multipath models on the proposed platform. Our scheme is compatible with these multipath models. Moreover, the experimental results show that the model with flow splitting has superior performance with respect to load balancing.

The Internet of Things (IoT) system provides sensing and computing services via terrestrial networks. However, the restricted coverage of terrestrial networks, such as base stations, limits the ubiquitous IoT services. Low-Earth orbit (LEO) satellites are able to provide network coverage for terrestrial IoT devices in unconnected scenarios, e.g., maritime. IoT devices in such scenarios usually have restricted onboard computation and power resources. In this article, we present an LEO-assisted IoT network (L-IoT) architecture where a device splits its task and offloads a portion of its task to the LEO to process within the coverage time. We formulate a task split problem with communication and computation resource allocation (SCC) to minimize the L-IoT energy consumption. We proposed an alternating optimization for split ratio and resource allocation (AOSR) algorithm. In particular, we use the outputs of Karush-Kuhn–Tucker (KKT) for resource allocation as part of the reward that feeds twin-delayed deep deterministic policy gradient. Lastly, the results of numerical simulations show that the proposed AOSR approach reduces 12.7% energy consumption compared to soft actor-critic (SAC) and 15% to deep deterministic policy gradient (DDPG).

Low-altitude economy network has been enabled to serve many civil scenarios, such as Uncrewed Aerial Vehicles (UAVs)-based parcel delivery. Nonetheless, existing UAV technologies cannot fully guarantee accurate and real-time parcel delivery. The potential Digital Twins (DT) technology has ability to enhance the performance of low-altitude UAV delivery by accurate scenario imitation and derivation. Unfortunately, existing DT technologies might not perform suitable path planning for real-time UAV delivery in highly dynamic scenarios. To address this issue, we propose a Long Short-Term DT (LST-DT)-based UAV delivery framework, by analyzing different kinds of physical data for real-time and accurate delivery. First, we implement a long-term DT based on static data including delivery destinations and latency as well as delivery environment. It can assist UAVs in exploring cooperative delivery decisions with high-efficiency resource scheduling for a high successful delivery ratio. Then, we design a short-term DT with a imitation learning mechanism to explore feasible delivery paths under the imitation of UAV status. Simulation results demonstrate that our LST-DT framework reduces system latency by an average of 45.0% compared to state-of-the-art benchmarks while improving the successful delivery ratio by an average of 19.5%.

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.