Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 9 references
Abstract
Next-generation networks $(5 \mathrm{G} / 6 \mathrm{G})$ provide capabilities such as network slicing, enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and edge computing. However, configuring the related radio, core, slicing, and edge resources requires significant operational expertise. This work proposes an Intent-Based Networking (IBN) framework that combines Natural Language Processing, Large Language Models (LLMs), TMF921-compliant intent representation, and CAMARA APIs to simplify the definition and activation of network-sensitive vehicular services. In the proposed model, a Service Provider establishes a business agreement with a Network Operator, while the operator administrator defines service requirements through a conversational interface. These requirements are translated into machine-readable intents and mapped into network and edge orchestration actions. The framework is evaluated through a Teleoperated Driving (ToD) use case for autonomous vehicle repositioning. Results show that the intent translation and management pipeline introduces limited and repeatable overhead, while orchestration time is mainly affected by the underlying MANO/IaaS platform. The results indicate that combining IBN and CAMARA APIs can support flexible service preparation by operators and dynamic service consumption by applications.
Intent-Based Networking (IBN) has emerged as a promising paradigm for simplifying network management by allowing operators and applications to specify high-level service objectives rather than low-level device configurations. Early IBN research was mainly developed in Software-Defined Networking (SDN), Network Function Virtualization (NFV), transport networks, core networks, and data-center environments, where programmability, virtualization, and relatively stable infrastructure models enabled intent translation, orchestration, and assurance. However, realizing IBN in end-to-end mobile networks is more challenging because the Radio Access Network (RAN) is highly dynamic, wireless-channeldependent, mobility-sensitive, and governed by multiple control timescales. The emergence of Open RAN (O-RAN) changes this landscape by making the RAN programmable, disaggregated, data-driven, and control-lable through non-real-time and near-real-time intelligent control loops. This survey reviews the evolution of IBN from SDN/NFV-enabled automation toward O-RAN-driven end-to-end intent-based networking for 5G-Advanced and 6G. We discuss architectural mechanisms, key challenges, recent advances in AI-driven and agentic IBN, and future research directions including Large Language Model (LLM)-based intent translation, contractbased O-RAN slicing, digital twin-assisted validation, and trustworthy closed-loop orchestration.
Dongwook Won, Thanh Thien-An Dang, Ton That Tam Dinh et al.· International Conference on...· 0 citations
Efficient long-term network evolution is becoming increasingly critical in dense 5G-Advanced and beyond cellular systems, where persistent traffic imbalances and localized congestion pose significant challenges that conventional short-term radio resource management alone cannot fully mitigate. This paper proposes a digital twin (DT)-enabled non-real-time (NRT) network evolution framework integrated with a large language model (LLM). Within this architecture, the digital twin provides a high-fidelity, controllable environment for evaluating infrastructure actions, while the LLM serves as a strategic orchestration engine that recommends cost-efficient network upgrades based on observed network states. Unlike traditional optimization methods that require exhaustive mathematical reformulations for each specific scenario, the proposed framework leverages the reasoning capabilities of LLMs to interpret operator objectives and constraints in natural language, generating structured evolution plans. The considered NRT action space encompasses antenna upgrades, bandwidth expansion, and new base station (BS) deployment. A techno-economic formulation is introduced to jointly evaluate load reduction performance and overall economic expenditure. Numerical results in a dense cellular scenario demonstrate that the framework effectively reduces peak resource utilization and provides diverse, coordinated evolution strategies tailored to varying network conditions.
Yukai Wang, Janghee Woo, G. Hahm et al.· International Conference on...· 0 citations
Sixth-generation mobile networks are approaching a structural inflection point. Five generations of vendor-led architecture have left operators dependent on platforms they cannot fully modify and artificial-intelligence inference layers they cannot audit. This article argues that 6G should reverse that trajectory by reordering five priorities: control first; customer outcomes before peak rates; business guarantees before megabytes; software-driven operations with governed agentic artificial intelligence; and technology in service of those priorities. Four contributions operationalize the thesis. The Control Compact is an own-federate-consume taxonomy that allocates architectural sovereignty by strategic value. The Guarantee Economy is a six-tier outcome-priced model aligned with IMT-2030 usage scenarios and converts operator control into enforceable service-level objectives. An operator-grade Network MCP Platform shows how autonomous agents could enter the service-based architecture through a governed tool plane with auditable hooks for identity, charging, lawful intercept, enforcement, and digital-twin validation. A standardization section states Rakuten Mobile's public position on AI-agent scope, radio access, migration, non-terrestrial networks, physical layer, core, and spectrum. The framework distinguishes operational evidence from national-scale cloud-native Open RAN and core network deployments, standards-grounded extrapolation, and forward-looking architecture and commercial proposals. A three-phase roadmap separates standards milestones from operator deployment targets and identifies validation gates and stakeholder implications.
David Soldani, P. Nahi, Awn Muhammad et al.· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
As Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enabled networks scale in size and complexity, monitoring and managing Service Function Chains (SFCs) under stringent latency and resource constraints becomes increasingly challenging. Although Deep Reinforcement Learning (DRL) is widely applied to SFC provisioning and Virtual Network Function (VNF) placement, enhanced network state monitoring is crucial to capture unexpected network conditions and guide DRL agents toward more adaptive decisions. In this context, Language Models (LMs) enable flexible, natural-language (NL)–based, query-driven network monitoring; however, directly processing complex multi-metric NL queries is computationally expensive and error-prone. This paper proposes an end-to-end (E2E) edge-based query translation pipeline that decomposes multi-metric NL queries into simpler single-metric sub-queries. Query decomposition is performed using a retrieval-augmented language model (RAG-LLM) and compared with a lightweight rule-based decomposition baseline. The resulting sub-queries are translated into Structured Query Language (SQL) using FLAN-T5. A cloud-only baseline, which directly translates NL queries to SQL without decomposition, is also evaluated. The results show that the rule-based edge pipeline achieves the lowest latency, reducing E2E latency by up to 78% compared to RAG-LLM and 18% compared to cloud execution under high workloads. Under increasing arrival rates for the largest workload, the rule-based edge pipeline maintains superior performance over cloud, reducing total E2E latency by 57% at $\lambda = 0.8$ . While RAG-LLM provides greater flexibility for unseen query patterns, both edge-based approaches achieve 100% NL2SQL accuracy with zero decomposition failures, outperforming the cloud-only baseline (95% accuracy).
Parisa Fard Moshiri, Xinyu Zhu, Poonam Lohan et al.· IEEE Transactions on Network...· 0 citations
The transition to the Sixth Generation (6G) of mobile networks requires proactive and deterministic orchestration to satisfy the stringent key performance indicators of future services, including ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications. Digital Twin Networks (DTN) have recently emerged as a foundational technology to meet these demands, offering real-time and high-fidelity virtual replicas of the physical network. Although the current literature explores DTNs conceptually, a gap exists in the coverage of technical classification and computational feasibility evaluations. This survey addresses this gap by formally categorizing state-of-the-art DTN architectures into passive monitoring twins and active control twins. We provide an in-depth evaluation of their underlying enabling technologies, specifically ray-tracing, reconfigurable intelligent surfaces, artificial intelligence, and mobile edge computing. Importantly, this paper conducts a detailed mathematical and computational complexity analysis of state-of-the-art solutions to assess hardware scalability and inference bottlenecks. These architectures are then linked to various forthcoming 6G use cases, including smart cities, Industry 5.0, healthcare, and smart grids. Finally, we synthesize crucial unresolved challenges, highlighting graphics processing unit hardware limitations, cyber-physical actuation latency, and the need for a zero-trust security paradigm, offering strategic research directions to realize the unified internet of everything.
Charalampos Oikonomidis, E. T. Michailidis, N. Miridakis· 0 citations