Software-Defined Networks and 5G

Neueste Paper

• Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks

  Fangtong Zhou, Xiaorui Liu, Ruozhou Yu, Guoliang Xue · 28. Januar 2026

  Traffic Engineering (TE) in large-scale networks like cloud Wide Area Networks (WANs) and Low Earth Orbit (LEO) satellite constellations is a critical challenge. Although learning-based approaches have been proposed to address the scalability of traditional TE algorithms, their practical application…

• In-Network Collective Operations: Game Changer or Challenge for AI Workloads?

  Torsten Hoefler, Mikhail Khalilov, Josiah Clark, Surendra Anubolu, Mohan Kalkunte, Karen Schramm, Eric Spada, Duncan Roweth, Keith Underwood, Adrian Caulfield, Abdul Kabbani, Amirreza Rastegari · 28. Januar 2026

  This paper summarizes the opportunities of in-network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this important field accessible to non-experts in AI or networking, fostering a connection between these communities. Consider…

• PROTEUS: SLA-Aware Routing via Lagrangian RL for Multi-LLM Serving Systems

  Amit Singh Bhatti, Vishal Vaddina, Dagnachew Birru · 28. Januar 2026

  Production LLM deployments serve diverse workloads where cost and quality requirements vary by customer tier, time of day, and query criticality. Model serving systems accept latency SLOs directly. LLM routers do not. They force operators to tune parameters offline and guess what accuracy might resu…

• Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks

  Mohammad Shamim Ahsan, Peng Liu · 22. Januar 2026

  In the network security domain, due to practical issues -- including imbalanced data and heterogeneous legitimate network traffic -- adversarial attacks in machine learning-based NIDSs have been viewed as attack packets misclassified as benign. Due to this prevailing belief, the possibility of (mali…

• IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networks

  Abdelrahman Soliman, Ahmed Refaey, Aiman Erbad, Amr Mohamed · 21. Januar 2026

  Intent-based networks (IBNs) are gaining prominence as an innovative technology that automates network operations through high-level request statements, defining what the network should achieve. In this work, we introduce IntAgent, an intelligent intent LLM agent that integrates NWDAF analytics and …

• Cascaded Transformer for Robust and Scalable SLA Decomposition via Amortized Optimization

  Cyril Shih-Huan Hsu · 21. Januar 2026

  The evolution toward 6G networks increasingly relies on network slicing to provide tailored, End-to-End (E2E) logical networks over shared physical infrastructures. A critical challenge is effectively decomposing E2E Service Level Agreements (SLAs) into domain-specific SLAs, which current solutions …

• Vision Language Models for Optimization-Driven Intent Processing in Autonomous Networks

  Tasnim Ahmed, Yifan Zhu, Salimur Choudhury · 21. Januar 2026

  Intent-Based Networking (IBN) allows operators to specify high-level network goals rather than low-level configurations. While recent work demonstrates that large language models can automate configuration tasks, a distinct class of intents requires generating optimization code to compute provably o…

• Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks

  Abdikarim Mohamed Ibrahim, Rosdiadee Nordin · 14. Januar 2026

  Large AI Model (LAM) have been proposed to applications of Non-Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific trainings. In this paper, we propose a Deep Reinforcement Learning (DRL) agent that is guided by a Large Language Model (LL…

• Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning

  Peihao Yan, Jie Lu, Huacheng Zeng, Y. Thomas Hou · 13. Januar 2026

  Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs. xSlice is an online learning algorithm that adaptively adjusts MAC-lay…

• Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework

  Yixiao Peng, Hao Hu, Feiyang Li, Xinye Cao, Yingchang Jiang, Jipeng Tang, Guoshun Nan, Yuling Liu · 13. Januar 2026

  While virtualization and resource pooling empower cloud networks with structural flexibility and elastic scalability, they inevitably expand the attack surface and challenge cyber resilience. Reinforcement Learning (RL)-based defense strategies have been developed to optimize resource deployment and…

• Logic-Driven Semantic Communication for Resilient Multi-Agent Systems

  Tamara Alshammari, Mehdi Bennis · 13. Januar 2026

  The advent of 6G networks is accelerating autonomy and intelligence in large-scale, decentralized multi-agent systems (MAS). While this evolution enables adaptive behavior, it also heightens vulnerability to stressors such as environmental changes and adversarial behavior. Existing literature on res…

• Agentic AI Empowered Intent-Based Networking for 6G

  Genze Jiang, Kezhi Wang, Xiaomin Chen, Yizhou Huang · 13. Januar 2026

  The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level operational intents into executable network configurations. Existing approaches to Intent-Based Networking (IBN) rely upon either rule-based systems that …

• You Only Need Your Transformer 25% of the Time: Meaning-First Execution for Eliminating Unnecessary Inference

  Ryan Shamim · 6. Januar 2026

  Modern AI inference systems treat transformer execution as mandatory, conflating model capability with execution necessity. We reframe inference as a control-plane decision problem: determining when execution is necessary versus when correctness can be preserved through alternative pathways. We intr…

• Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation

  Tianyi Zhang, Shidong Pan, Zejun Zhang, Zhenchang Xing, Xiaoyu Sun · 6. Januar 2026

  Infrastructure-as-Code (IaC) generation holds significant promise for automating cloud infrastructure provisioning. Recent advances in Large Language Models (LLMs) present a promising opportunity to democratize IaC development by generating deployable infrastructure templates from natural language d…

• Chat-Driven Optimal Management for Virtual Network Services

  Yuya Miyaoka, Masaki Inoue, Kengo Urata, Shigeaki Harada · 1. Januar 2026

  This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend …

• VL-RouterBench: A Benchmark for Vision-Language Model Routing

  Zhehao Huang, Baijiong Lin, Jingyuan Zhang, Jingying Wang, Yuhang Liu, Ning Lu, Tao Li, Xiaolin Huang · 30. Dezember 2025

  Multi-model routing has evolved from an engineering technique into essential infrastructure, yet existing work lacks a systematic, reproducible benchmark for evaluating vision-language models (VLMs). We present VL-RouterBench to assess the overall capability of VLM routing systems systematically. Th…

• SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G

  Yong Xiao, Xubo Li, Haoran Zhou, Yingyu Li, Yayu Gao, Guangming Shi, Ping Zhang, Marwan Krunz · 30. Dezember 2025

  Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decision-making, dynamic environmental adaptation, and complex missions. It has the potential to facilitate real-time network management and op…

• From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction

  Sami Marouani, Kamal Singh, Baptiste Jeudy, Amaury Habrard · 25. Dezember 2025

  Accurate prediction of flow delay is essential for optimizing and managing modern communication networks. We investigate three levels of modeling for this task. First, we implement a heterogeneous GNN with attention-based message passing, establishing a strong neural baseline. Second, we propose Flo…

• Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things

  Shaowen Qin, Jianfeng Zeng, Haodong Guo, Xiaohuan Li, Jiawen Kang, Qian Chen, Dusit Niyato · 25. Dezember 2025

  Intent-Based Networking (IBN) offers a promising paradigm for intelligent and automated network control in Industrial Internet of Things (IIoT) environments by translating high-level user intents into executable network strategies. However, frequent strategy deployment and rollback are impractical i…

• QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning

  Sebastian Racedo, Brigitte Jaumard, Oscar Delgado, Meysam Masoudi · 24. Dezember 2025

  Open Radio Access Network (O RAN) disaggregates conventional RAN into interoperable components, enabling flexible resource allocation, energy savings, and agile architectural design. In legacy deployments, the binding between logical functions and physical locations is static, which leads to ineffic…

• Graph-Symbolic Policy Enforcement and Control (G-SPEC): A Neuro-Symbolic Framework for Safe Agentic AI in 5G Autonomous Networks

  Divya Vijay, Vignesh Ethiraj · 24. Dezember 2025

  As networks evolve toward 5G Standalone and 6G, operators face orchestration challenges that exceed the limits of static automation and Deep Reinforcement Learning. Although Large Language Model (LLM) agents offer a path toward intent-based networking, they introduce stochastic risks, including topo…

• Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN

  Fatemeh Lotfi, Fatemeh Afghah · 17. Dezember 2025

  The increasing complexity of modern applications demands wireless networks capable of real time adaptability and efficient resource management. The Open Radio Access Network (O-RAN) architecture, with its RAN Intelligent Controller (RIC) modules, has emerged as a pivotal solution for dynamic resourc…

• Large Language Models as Generalist Policies for Network Optimization

  Duo Wu, Linjia Kang, Zhimin Wang, Fangxin Wang, Wei Zhang, Xuefeng Tao, Wei Yang, Le Zhang, Peng Cui, Zhi Wang · 16. Dezember 2025

  Designing control policies to ensure robust network services is essential to modern digital infrastructure. However, the dominant paradigm for network optimization relies on designing specialist policies based on handcrafted rules or deep learning models, leading to poor generalization across divers…

• A Differentiable Digital Twin of Distributed Link Scheduling for Contention-Aware Networking

  Zhongyuan Zhao, Yujun Ming, Kevin Chan, Ananthram Swami, Santiago Segarra · 12. Dezember 2025

  Many routing and flow optimization problems in wired networks can be solved efficiently using minimum cost flow formulations. However, this approach does not extend to wireless multi-hop networks, where the assumptions of fixed link capacity and linear cost structure collapse due to contention for s…

• M3Net: A Multi-Metric Mixture of Experts Network Digital Twin with Graph Neural Networks

  Blessed Guda, Carlee Joe-Wong · 11. Dezember 2025

  The rise of 5G/6G network technologies promises to enable applications like autonomous vehicles and virtual reality, resulting in a significant increase in connected devices and necessarily complicating network management. Even worse, these applications often have strict, yet heterogeneous, performa…