Software-Defined Networks and 5G

Latest papers

• RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models

  Sai Hao, Hao Zeng, Hongxin Wei, Bingyi Jing · 10 March 2026

  Efficiently routing queries to the optimal large language model (LLM) is crucial for optimizing the cost-performance trade-off in multi-model systems. However, most existing routers rely on single-model selection, making them susceptible to misrouting. In this work, we formulate LLM routing as the $…

• CNFP: Optimizing Cloud-Native Network Function Placement with Diffusion Models on the Cloud Continuum

  \'Alvaro V\'azquez Rodr\'iguez, Manuel Fern\'andez-Veiga, Carlos Giraldo-Rodr\'iguez · 5 March 2026

  The placement of Cloud-Native Network Functions across the Cloud-Continuum represents a core challenge in the orchestration of current 5G and future 6G networks. The process entails the implementation of interdependent computing tasks, which are structured as Service Function Chains, over distribute…

• A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications

  Ozan Ayg\"un, Vincenzo Norman Vitale, Antonia M. Tulino, Hao Feng, Elza Erkip, Jaime Llorca · 5 March 2026

  Next-generation networks aim to provide performance guarantees to real-time interactive services that require timely and cost-efficient packet delivery. In this context, the goal is to reliably deliver packets with strict deadlines imposed by the application while minimizing overall resource allocat…

• Network Topology Optimization via Deep Reinforcement Learning

  Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang · 4 March 2026

  Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely difficult to obtain an optimal solution, especially since topol…

• High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach

  Shibing Mo, Jiarui Zhang, Jiayu Xie, Xiangyi Teng, Jing Liu · 4 March 2026

  In order to evaluate the invulnerability of networks against various types of attacks and provide guidance for potential performance enhancement as well as controllability maintenance, network controllability robustness (NCR) has attracted increasing attention in recent years. Traditionally, control…

• How Small Can 6G Reason? Scaling Tiny Language Models for AI-Native Networks

  Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah · 3 March 2026

  Emerging 6G visions, reflected in ongoing standardization efforts within 3GPP, IETF, ETSI, ITU-T, and the O-RAN Alliance, increasingly characterize networks as AI-native systems in which high-level semantic reasoning layers operate above standardized control and data-plane functions. Although fronti…

• TopoEdge: Topology-Grounded Agentic Framework for Edge Networking Code Generation and Repair

  Haomin Qi, Bohan Liu, Zihan Dai, Yunkai Gao · 3 March 2026

  TopoEdge is a topology-grounded, edge-deployable framework for end-to-end software-defined networking (SDN) configuration generation and repair, motivated by the brittleness of configuration artefacts under topology variation and by strict operational constraints on latency, privacy, and on-site exe…

• WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks

  Jingwen Tong, Zijian Li, Fang Liu, Wei Guo, Jun Zhang · 3 March 2026

  The integration of large language models (LLMs) into wireless networks has sparked growing interest in building autonomous AI agents for wireless tasks. However, existing approaches rely heavily on manually crafted prompts and static agentic workflows, a process that is labor-intensive, unscalable, …

• SLA-Aware Distributed LLM Inference Across Device-RAN-Cloud

  Hariz Yet, Nguyen Thanh Tam, Mao V. Ngo, Lim Yi Shen, Lin Wei, Jihong Park, Binbin Chen, Tony Q. S. Quek · 2 March 2026

  Embodied AI requires sub-second inference near the Radio Access Network (RAN), but deployments span heterogeneous tiers (on-device, RAN-edge, cloud) and must not disrupt real-time baseband processing. We report measurements from a 5G Standalone (SA) AI-RAN testbed using a fixed baseline policy for r…

• Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence

  Zhizhou He, Yang Luo, Xinkai Liu, Mahdi Boloursaz Mashhadi, Mohammad Shojafar, Merouane Debbah, Rahim Tafazolli · 2 March 2026

  Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, a…

• MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs

  Dongwei Wang, Jinhee Kim, Seokho Han, Denis Gudovskiy, Yohei Nakata, Tomoyuki Okuno, KhayTze Peong, Kang Eun Jeon, Jong Hwan Ko, Yiran Chen, Huanrui Yang · 25 February 2026

  Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based on available computational resources. However, it has been observed that the calibration parameters for quantizati…

• SkillOrchestra: Learning to Route Agents via Skill Transfer

  Jiayu Wang, Yifei Ming, Zixuan Ke, Shafiq Joty, Aws Albarghouthi, Frederic Sala · 24 February 2026

  Compound AI systems promise capabilities beyond those of individual models, yet their success depends critically on effective orchestration. Existing routing approaches face two limitations: (1) input-level routers make coarse query-level decisions that ignore evolving task requirements; (2) RL-trai…

• SeedFlood: A Step Toward Scalable Decentralized Training of LLMs

  Jihun Kim, Namhoon Lee · 23 February 2026

  This work presents a new approach to decentralized training-SeedFlood-designed to scale for large models across complex network topologies and achieve global consensus with minimal communication overhead. Traditional gossip-based methods suffer from message communication costs that grow with model s…

• Agentic Wireless Communication for 6G: Intent-Aware and Continuously Evolving Physical-Layer Intelligence

  Zhaoyang Li, Xingzhi Jin, Junyu Pan, Qianqian Yang, Zhiguo Shi · 20 February 2026

  As 6G wireless systems evolve, growing functional complexity and diverse service demands are driving a shift from rule-based control to intent-driven autonomous intelligence. User requirements are no longer captured by a single metric (e.g., throughput or reliability), but by multi-dimensional objec…

• Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning

  Zifan Wang, Riccardo De Santi, Xiaoyu Mo, Michael M. Zavlanos, Andreas Krause, Karl H. Johansson · 20 February 2026

  Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment. Existing entropy-regularized methods primarily maximize expected reward, providing no mechanism to shape tail behavior. However, tail control is often essential: the lower tail det…

• A Scalable Approach to Solving Simulation-Based Network Security Games

  Michael Lanier, Yevgeniy Vorobeychik · 19 February 2026

  We introduce MetaDOAR, a lightweight meta-controller that augments the Double Oracle / PSRO paradigm with a learned, partition-aware filtering layer and Q-value caching to enable scalable multi-agent reinforcement learning on very large cyber-network environments. MetaDOAR learns a compact state pro…

• ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networks

  Feiran You, Hongyang Du · 19 February 2026

  Heterogeneous Networks (HetNets) pose critical challenges for intelligent management due to the diverse user requirements and time-varying wireless conditions. These factors introduce significant decision complexity, which limits the adaptability of existing Deep Reinforcement Learning (DRL) methods…

• High-Fidelity Network Management for Federated AI-as-a-Service: Cross-Domain Orchestration

  Merve Saimler, Mohaned Chraiti, Ozgur Ercetin · 18 February 2026

  To support the emergence of AI-as-a-Service (AIaaS), communication service providers (CSPs) are on the verge of a radical transformation-from pure connectivity providers to AIaaS a managed network service (control-and-orchestration plane that exposes AI models). In this model, the CSP is responsible…

• Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation

  Vignesh Sriram, Yuqiao Meng, Luoxi Tang, Zhaohan Xi · 17 February 2026

  Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and digital twin systems are largely reactive, detecting failures only after service degradation occurs. We propose Adversarial Network Imagination, a closed-loop …

• Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization

  Jie Zheng, Ruichen Zhang, Dusit Niyato, Haijun Zhang, Jiacheng Wang, Hongyang Du, Jiawen Kang, Zehui Xiong · 17 February 2026

  Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learning (RL) is a powerful framework, it often encounters difficulties with high-dimensional state spaces and complex enviro…

• An Overlay Multicast Routing Method Based on Network Situational Aware-ness and Hierarchical Multi-Agent Reinforcement Learning

  Miao Ye, Yanye Chen, Yong Wang, Cheng Zhu, Qiuxiang Jiang, Gai Huang, Feng Ding · 17 February 2026

  Compared with IP multicast, Overlay Multicast (OM) offers better compatibility and flexible deployment in heterogeneous, cross-domain networks. However, traditional OM struggles to adapt to dynamic traffic due to unawareness of physical resource states, and existing reinforcement learning methods fa…

• An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading

  Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Tharaka Hewa, Abdul Rahman, Xueping Liang, Safdar H. Bouk, Amin Hass, Peter Foytik, Ng Wee Keong, Kasun De Zoysa · 17 February 2026

  6G networks are expected to be AI-native, intent-driven, and economically programmable, requiring fundamentally new approaches to network slice orchestration. Existing slicing frameworks, largely designed for 5G, rely on static policies and manual workflows and are ill-suited for the dynamic, multi-…

• AGORA: Agentic Green Orchestration Architecture for Beyond 5G Networks

  Rodrigo Moreira, Larissa Ferreira Rodrigues Moreira, Maycon Peixoto, Flavio De Oliveira Silva · 17 February 2026

  Effective management and operational decision-making for complex mobile network systems present significant challenges, particularly when addressing conflicting requirements such as efficiency, user satisfaction, and energy-efficient traffic steering. The literature presents various approaches aimed…

• Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management

  Hojjat Navidan, Mohammad Cheraghinia, Jaron Fontaine, Mohamed Seif, Eli De Poorter, H. Vincent Poor, Ingrid Moerman, Adnan Shahid · 17 February 2026

  Open Radio Access Networks (O-RAN) promise flexible 6G network access through disaggregated, software-driven components and open interfaces, but this programmability also increases operational complexity. Multiple control loops coexist across the service management layer and RAN Intelligent Controll…

• Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV

  Tianfu Wang, Liwei Deng, Xi Chen, Junyang Wang, Huiguo He, Zhengyu Hu, Wei Wu, Leilei Ding, Qilin Fan, Hui Xiong · 17 February 2026

  Resource allocation (RA) is critical to efficient service deployment in Network Function Virtualization (NFV), a transformative networking paradigm. Recently, deep Reinforcement Learning (RL)-based methods have been showing promising potential to address this complexity. However, the lack of a syste…