Low Earth orbit (LEO) satellite networks are characterized by dynamic network topologies and on-demand service requirements from Internet of Things (IoT) applications, which make efficient and intelligent flow scheduling challenging. Conventional schemes rely on static configurations or manual rules, thus making it difficult to capture and respond to diverse service demands. Moreover, they often fail to model task-resource relationships effectively, hindering the generation of real time, executable scheduling policies. To address these challenges, we propose a knowledge-enhanced intent-driven flow scheduling (KIFS) framework. In particular, we design a unified pipeline that first translates user intents into precise quality of service (QoS) requirements. It incorporates a network state awareness module to estimate per-link bandwidth and utilization, and constructs a task-resource knowledge graph (KG) to enhance the deep Q-network (DQN) agent via state augmentation, action pruning, and reward shaping. Finally, the framework translates the resulting policies into standards-compliant SRv6 configurations for real-time deployment. In simulations, the proposed KIFS framework demonstrates superior performance compared to standard baselines in terms of flow success rate (FSR) and QoS satisfaction.

As differentiated services emerge, intent-driven management and orchestration in the 6G terrestrial networks will face two key challenges. On the one hand, the intent mapping gap, limited orchestration flexibility, and lack of policy abstraction constrain intent refinement across the Business Support System, Operation Support System, and Network Operation Provider layers. On the other hand, multi-domain orchestration among the radio access, transport, and core networks remains hard due to limited global awareness, resource conflicts, and inconsistent policy models. In this paper, we present LAMIO-6G, a large AI model-empowered framework for cross-layer intent management and multi-domain policy orchestration, which autonomously generates policies at different levels of abstraction from intents. The LAMIO-6G incorporates two key models: (i) a unified network policy model and (ii) a monitor-analyze-plan-execute-knowledge feedback closed-loop model. To address limited intent generality and scenario generalization in the Business Support System layer, we then introduce intent decomposition techniques via generic large AI models using low-rank adaptation-based fine-tuning, design of intent decomposition prompts, and few-shot learning-assisted intent decomposition. Within the Operation Support System layer, we design an intent reasoning and optimization scheme through collaboration between domain-specific large AI models guided by long-short chain-of-thought techniques and lightweight proximal policy optimization model. Finally, we present a proof-of-concept implementation of a wireless energy-saving intent. Simulation results demonstrate that the DeepSeek-R1-14B model achieves 17% to 30% gains over all baseline schemes in fine-tuned metrics, intent decomposition and reasoning accuracy, and intent optimization performance.

The x ad hoc network (xANET) termed as a family of ad hoc networks, including mobile ad hoc network (MANET), vehicular ad hoc network (VANET), flying ad hoc network (FANET) and satellite ad hoc network (SANET), has found a wide range of applications in providing ubiquitous wireless services. Despite its broad utility, the dynamic nature and lack of a centralized controller pose significant challenges to effective and flexible network management for xANET. Conventional network management protocols face challenges such as scalability, security vulnerabilities, configuration complexity, robustness, and performance resilience. Recent efforts have presented different approaches, focusing on policy-based network management (PBNM) and intent-driven network management (IDNM). However, there is no comprehensive survey to clarify their concepts and classifications. This paper presents a survey of the network management evolution for xANET, covering configuration-based, policy-based and the latest intent-driven approaches. We first introduce the characteristics and applications of xANET. Meanwhile, we investigate the network management concepts and challenges. Moreover, we survey the evolution of management protocols for xANET, including simple network management protocol (SNMP), PBNM, and IDNM. Then, we follow the detailed network management of xANET from configuration-based to policy-based and intent-driven approaches. Through comparative analysis, it is found that IDNM employs a more intelligent management protocol, demonstrating higher efficiency and flexibility in handling complex tasks and dynamic network management. This makes it better suited to addressing the challenges of xANET management. Finally, we summarize the remaining challenges and possible future research directions.

Task-oriented heterogeneous Flying Ad Hoc Networks (FANETs) are emerging as network paradigms to meet increasingly complex task requirements. However, conventional cross-domain routing protocols face challenges such as heavy address matching, high routing overhead, and a lack of task-adaptive capabilities. We propose an intent-driven cross-domain routing framework, termed ICRT. The ICRT framework explores and exploits typical capabilities of intent-driven networks and named data networks to decouple the task control plane from the network forwarding plane to achieve routing flexibility. Furthermore, we propose a novel ICRT protocol to assign appropriate cross-domain forwarding routes to different services, ensuring that the transmission links of the services are adapted to the business requirements. Finally, we construct an emulation platform to validate the feasibility and efficiency of the presented ICRT framework and protocol. Simulation results show that the ICRT protocol outperforms the conventional FANET routing protocol, achieving a 350 ms reduction in end-to-end delay, an 11.8% increase in packet delivery ratio, a 1.5 Mbps improvement in network throughput, and a 10% reduction in routing overhead. It also ensures that the differences between all service requirements and link metrics remain within the order of $10^{-4}$.

To support on-demand network services towards 6G networks, we present a large language model-empowered intent-driven network management and orchestration framework named LIMO. Although large language models have already been applied to intent-driven network management, there still lacks an intent full lifecycle design encompassing intent translation, policy generation, code generation, and continuous assurance. To bridge this gap, we present the LIMO framework with intent-enabled, policy generation, and code implementation layers, each integrating distinct large language model functionalities. Meanwhile, we deploy a proof-of-concept implementation of the presented LIMO framework in 6G radio access networks. In terms of the intent translation, fine-tuned large language models achieve 48%-50% higher accuracy compared to their foundation models without domain-specific adaptation. For the policy optimization, the presented framework outperforms five baseline schemes, achieving a maximum service-level agreement satisfaction rate of 96.0%. Finally, the presented LIMO successfully generates executable code for managing and orchestrating the underlying infrastructure. Overall, experimental results validate the feasibility and effectiveness of the presented LIMO framework.

With the virtualization, intelligence, and autonomous features driving the development of end-to-end (E2E) 6th-generation (6G) mobile communication systems, on-demand resilient network orchestration and configuration are becoming increasingly complicated. Meanwhile, due to human involvement, network complexity grows exponentially while its scalability remains constrained. There is an urgent need for novel networking paradigms to facilitate network management and control, particularly with service multiplicity and network dynamics. Intent-Driven Network (IDN) is essential for addressing these challenges and enhancing network scalability. Although the IDN has attracted wider research attention, there is a lack of a systematic review and comprehensive survey to clarify the basics and summarize the state of the art of IDN research status. In this survey, we investigate and provide an overview of the applications of the IDN. First, we discuss the intent-driven E2E 6G mobile communication system framework, and then introduce the key components of this framework. Second, IDN design and applications from an E2E 6G mobile communication system perspective are investigated and surveyed. Moreover, we discuss the full-life cycle management of generic IDN techniques, contributing to reducing network complexity. This survey will continue to review the research advances in other network paradigms related to IDN design and applications. Finally, we conclude this survey with open issues, challenges, and future research directions.

With recent advancements in the sixth generation (6G) communication technologies, more vertical industries have encountered diverse network services. How to reduce energy consumption is critical to meet the expectation of the quality of diverse network services. In particular, the number of base stations in 6G is huge with coupled adjustable network parameters. However, the problem is complex with multiple network objectives and parameters. Network intents are difficult to map to individual network elements and require enhanced automation capabilities. In this paper, we present a network intent decomposition and optimization mechanism in an energy-aware radio access network scenario. By characterizing the intent ontology with a standard template, we present a generic network intent representation framework. Then we propose a novel intent modeling method using Knowledge Acquisition in automated Specification language, which can model the network ontology. To clarify the number and types of network objectives and energy-saving operations, we develop a Softgoal Interdependency Graph-based network intent decomposition model, and thus, a network intent decomposition algorithm is presented. Simulation results demonstrate that the proposed algorithm outperforms without conflict analysis in intent decomposition time. Moreover, we design a deep Q-network-assisted intent optimization scheme to validate the performance gain.

With the rapid development of telecommunication networks, various service requirements and on-demand network configurations have been becoming increasingly complicated. Meanwhile, the number of network elements and configurable network parameters will be significantly huge. Network operators cannot effectively handle various coupling problems among these adjustable parameters. Therefore, intent-based network management is essential for addressing these couplings and dependencies. However, it is a significant challenge for network operators to analyze the supply relationships between network-level intents and these adjustable parameters. Network-level intent decomposition (NID) can be introduced to reduce manual operations to enhance the degree of automation; however, there is a lack of a survey and clear methodology for complex NID. In this article, in order to overcome long manual configuration cycles and inflexible policy scheduling challenges in network intelligent control, we clarify a novel concept of the NID and provide a precise classification of the NID framework. This article presents a comprehensive survey of the NID from both the design time and run time perspectives. The design time contains intent modeling, intent decomposition, and intent validation, and the run time is mainly oriented to intent optimization. We then present an implementation framework of the NID. Finally, we design an intelligent NID system for an energy- aware open radio access network and demonstrate its feasibility and effectiveness.

Intent-Driven Networking (IDN) enables users to express high-level intents in natural language, which are then automatically refined into executable network configurations. Intent refinement plays a critical role in accurately refining human-declarative intents into network-level intents, and further refining into machine-readable policies. To address the limitations of existing intent refinement methods of lacking generalization and weak context awareness, this paper proposes a knowledge-enhanced Large Language Model (LLM) for intent refinement mechanism. The proposed approach achieves accurate and controllable refinement from natural language to structured network intents. Simulation results demonstrate the effectiveness and robustness of the proposed mechanism, particularly in complex and layered intent expression in flying ad hoc networking.

With emerging differentiated network services, intent-driven management and orchestration in the sixth-generation networks face challenges in on-demand and timely network configuration. Conventional intent-driven network approaches rely on structured templates for specific scenarios. This paper develops a large language model (LLM)-enhanced intent-driven management and orchestration framework that automatically refines user intent into abstract network policies. To improve the generality and accuracy of intent refinement, we design a novel intent decomposition mechanism based on a fine-tuned generic LLM, along with intent decomposition prompts. Moreover, we introduce an intent optimization method, leveraging a lightweight proximal policy optimization framework to model distributed energy-saving policies and select the optimal policy. Simulation results show that the proposed framework outperforms baseline schemes by 16% to 31% in terms of intent decomposition accuracy and intent optimization performance.

As the number of satellites and satellite tasks continues to increase, multi-satellite collaboration task planning faces several challenges, including high complexity, high timeliness and limited scalability. This paper presents a method for task planning based on behavior tree, which leverages modularity and hierarchical structure of behavior tree to simplify planning process and enhance timeliness. We also propose an intelligent planning algorithm combining variable neighborhood search (VNS) algorithm with backtracking search algorithm (BSA) to reduce solution space and improve convergence speed. Experimental results show that this method improves satellite task planning timeliness by 16.7%, overcomes the flexibility and timeliness disadvantages of traditional methods in complex environments, and highlights the significant advantages and potential applications of behavior tree in multi-satellite collaboration field.

With the advent of the sixth-generation (6G) era, the scale and complexity of communication networks have expanded dramatically, making conventional manual network management methods inefficient and error-prone. Intent-driven network (IDN) enables network operators to express high-level intents using natural language, which are then automatically translated into executable network configurations. However, the unstructured and ambiguous nature of intents poses challenges to achieving accurate intent-to-configuration translation. The emergence of a large language model (LLM) offers a promising solution to this problem. This paper proposes an LLM-empowered framework for generating IDN configurations, which integrates fine-tuning, retrieval-augmented generation, prompt engineering, and knowledge distillation techniques. We validate the effectiveness of the proposed framework through a network slicing use case implemented using open-source tools. Experimental results demonstrate that the proposed framework improves configuration generation time and accuracy by 24% and 25%, respectively, compared to the baseline schemes.

With the growing adoption of metaverse access devices, traditional terrestrial communication networks have become a performance bottleneck. To address this challenge, we integrate the Digital Twin Satellite Network (DTSN) into metaverse infrastructure and propose a cloud-edge collaborative architecture that enables efficient processing of edge-collected user data while enhancing system resilience and scalability. However, the open nature of satellite networks exposes DTSN relay nodes to sophisticated security threats. To mitigate these risks, we design a lightweight blockchain-based authentication protocol that leverages dynamic key negotiation to optimize the distribution of computational load between satellites and Digital Twins (DTs), reducing the burden on satellites while improving overall efficiency. The security of the proposed protocol is formally verified using Burrows-Abadi-Needham (BAN) logic, and performance evaluations based on computational and communication costs demonstrate its superiority over existing metaverse authentication schemes.

As voice assistants (VAs) become increasingly popular, concerns about their privacy and security have garnered significant attention. VAs nowadays rely on voiceprint authentication to enhance their security. However, this method is susceptible to spoofing attacks, where attackers may use recording or synthesis techniques to mimic the user's voice, thereby bypassing the authentication mechanism. To address this, we introduce "BoneAuth," a novel liveness detection system in this article. It offers continuous voice authentication for users, enhancing the security of VAs. BoneAuth is designed to be used in wearable devices with built-in microphones, such as Bluetooth earphones. Our basic idea is continuously matching the user's voice signals with the vibration signals produced by their vocal cords during speech. Specifically, our system uses the device's built-in microphone to concurrently capture vibrations from bone conduction (BC) and voices from air conduction (AC). We introduce a signal separation algorithm that, by measuring the unique threshold range of the user, can separate the AC and BC signals from the mixed microphone signals. By continuously comparing the consistency of the two signals, our system can determine whether the user's voice is a real live voice or artificially generated voice. Our system does not require user-specific passphrases for authentication, making it easy to deploy and use without the need for additional user actions or hardware. We demonstrate the feasibility of our method using commercial off-the-shelf Bluetooth earphones. Extensive experiments show an accuracy rate close to 98.15%, proving the effectiveness of our approach.

The continuous emergence of novel services and massive connections involve huge energy consumption towards ultra-dense radio access networks. Moreover, there exist much more number of controllable parameters that can be adjusted to reduce the energy consumption from a network-wide perspective. However, a network-level energy-saving intent usually contains multiple network objectives and constraints. Therefore, it is critical to decompose a network-level energy-saving intent into multiple levels of configurated operations from a top-down refinement perspective. In this work, we utilize a softgoal interdependency graph decomposition model to assist energy-saving scheme design. Meanwhile, we propose an energy-saving approach based on deep Q-network, which achieve a better trade-off among the energy consumption, the throughput, and the first packet delay. In addition, we illustrate how the decomposition model can assist in making energy-saving decisions. Evaluation results demonstrate the performance gain of the proposed scheme in accelerating the model training process.

The data traffic volume of the 6th generation (6G) mobile communication networks is huge, and there are novel challenges in various communications services and scenarios. This calls for ultra-dense and heterogeneous deployments of network nodes both on the ground and in space, resulting in ultra-dense space-air-ground network. However, conventional models are not available to analyze and design the interactions among heterogeneous network nodes. Game theory can provide an effective mathematical modeling framework for analysis and design. For the 6G space-air-ground networks, the characteristics of stochastic, ultra-dense, and distributed control will cause conventional game theoretical approaches to confront the challenge of the curse of dimensionality. Mean-field game (MFG) can be introduced to decouple dynamic management and control among agents, to decouple their interactions in a high-dimensional regime. Although the MFG finds wide application, there lacks a comprehensive survey to clarify the basics and summarize the state of the art of MFG research status. In this survey, we investigate and provide an overview of the applications of the MFG. First, we discuss diverse 6G space-air-ground networking paradigms, and then introduce the basic concepts of the MFG. Second, various MFG-based optimal control policies together with mean-field equilibrium (MFE) solutions are investigated and surveyed. Moreover, we discuss the effectiveness of combining the MFG with other game-theoretic approaches and machine learning methods, which leads to the improvement of multi-agent system performances. Finally, we outline some open issues, technical challenges, and future research directions based on the current state of the art.

As Software Defined Networking (SDN) continues to evolve, the demand for enhanced reliability and security within network infrastructures has surged. To address these pressing needs, we put forth a novel, blockchain-based SDN multi-domain network security architecture designed to bolster the safety measures of distributed systems. Furthermore, we’ve developed an innovative primary backup path optimization algorithm, which capitalizes on maximum disjoint in in-band mode to elevate the resilience of communication services. When a network encounters a failure, seamlessly transitioning the control path is crucial for maintaining uninterrupted, dependable operations and fortifying network resilience. In order to validate our approach, we conducted a series of simulation experiments, leveraging Pica8 and ONOS controllers to design the system architecture. Remarkably, the numerical outcomes revealed that our pioneering primary and backup control path algorithms significantly outperform the conventional shortest path algorithm in the face of network failure. By enhancing throughput and reducing packet loss rates, our proposal ultimately augments communication reliability.

In this paper, a laser-powered aerial mobile edge computing (MEC) architecture is proposed, where a high-altitude platform (HAP) integrated with an MEC server transfers laser energy to charge aerial user equipments (AUEs) for offloading their computation tasks to the HAP. Particularly, we identify a new privacy vulnerability caused by the transmission of wireless power transfer (WPT) signaling in the presence of a malicious smart attacker (SA). To address this vulnerability, the interaction between the HAP and the SA in their allocation of tile grids as charging points to the AUEs in laser-enabled WPT is formulated as a Colonel Blotto game (CBG), which models the competition of two players for limited resources over multiple battlefields for a finite time horizon. Moreover, the utility function that each player receives over a battlefield is developed by identifying the tradeoff between privacy protection level and energy consumption of each AUE. We further obtain the mixed-strategy Nash equilibrium for the modified CBG with asymmetric players. Simulation results are presented to show the effectiveness of this game framework.

This letter considers an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system in the presence of an aerial eavesdropper (ARE), which is able to launch the attacks by allocating the false signaling transfer channels to the ground devices (GDs). To defend against such attacks, we design an asymmetric Colonel Blotto (CB) game framework to formulate the competitive channel allocation problem for the UAV as a defender and the ARE as an attacker. The competitive interaction between the defender and the attacker in their channel allocations to the GDs is modeled as the competition of two players in the CB game for limited resources over a finite set of battlefields. In addition, we derive the mixed-strategy Nash equilibrium solution to the game for the asymmetric resources between two players. As results, we show that the proposed game framework achieves considerable performance gains for the expected total payoff that the defender receives across the battlefield set compared to the baseline schemes.

With the rapid development of unmanned aerial vehicles (UAVs), it has been considered as an effective solution for emergency communications. In order to solve the contradiction between the rapid growth of user equipments and the shortage of spectrum resources as well as the problem that a single UAV cannot meet the multimission requirements, we integrate the cognitive radio spectrum access and UAVs to form a cognitive UAV swarm. Since most of the base stations cannot work properly in disaster scenarios, cognitive UAV swarm transmits the collected information through multihop routing in the form of a decentralized distributed ad hoc network. In order to solve the problems of low robustness of single paths and unstable links, we explore a multipath stability optimization problem and then propose a multipath stable routing algorithm based on the multidimensional hypergraph matching method. The results show that compared with the greedy algorithm, the proposed algorithm can obtain higher path stability while obtaining multiple node disjoint paths. Finally, this paper provides a reference value for establishing multipath routing of UAV ad hoc networks in emergency communication scenarios.