Software-Defined Networks and 5G

Últimos artículos

• NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration

  Ahmed Twabi, Yepeng Ding, Tohru Kondo · 14 de abril de 2026

  As agentic network management gains popularity, there is a critical need for evaluation frameworks that transcend static, one-shot testing. To address this, we introduce NetAgentBench, a dynamic benchmark that evaluates agent interactions through a Finite State Machine (FSM) formalization guaranteei…

• Event-Driven Temporal Graph Networks for Asynchronous Multi-Agent Cyber Defense in NetForge_RL

  Igor Jankowski · 13 de abril de 2026

  The transition of Multi-Agent Reinforcement Learning (MARL) policies from simulated cyber wargames to operational Security Operations Centers (SOCs) is fundamentally bottlenecked by the Sim2Real gap. Legacy simulators abstract away network protocol physics, rely on synchronous ticks, and provide cle…

• Building Better Environments for Autonomous Cyber Defence

  Chris Hicks, Elizabeth Bates, Shae McFadden, Isaac Symes Thompson, Myles Foley, Ed Chapman, Nickolas Espinosa Dice, Ankita Samaddar, Joshua Sylvester, Himanshu Neema, Nicholas Butts, Nate Foster, Ahmad Ridley, Zoe M, Paul Jones · 13 de abril de 2026

  In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (ACD). This paper details the knowledge shared by participants both during the workshop and shortly afterwards by contributing herein. The workshop part…

• GAN-Enhanced Deep Reinforcement Learning for Semantic-Aware Resource Allocation in 6G Network Slicing

  Daniel Benniah John · 13 de abril de 2026

  Sixth-generation (6G) wireless networks must support heterogeneous services: enhanced Mobile Broadband (eMBB) requiring 1 Tbps data rates, massive Machine-Type Communications (mMTC) supporting 10 million devices per km, and Ultra-Reliable Low-Latency Communications (URLLC) with 0.1-1 ms latency. Cur…

• TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

  Chenhao Ye, Huaizheng Zhang, Mingcong Han, Baoquan Zhong, Xiang Li, Qixiang Chen, Xinyi Zhang, Weidong Zhang, Kaihua Jiang, Wang Zhang, He Sun, Wencong Xiao, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau · 13 de abril de 2026

  Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computational resources. However, existing weight transfer approaches either fail to provide flexibility for dynamically scaling clusters or incur fundamental dat…

• Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models

  Umesh Biswas, Shafqat Hasan, Syed Mohammed Farhan, Nisha Pillai, Charan Gudla · 10 de abril de 2026

  Software-Defined Networking (SDN) improves network flexibility but also increases the need for reliable and interpretable intrusion detection. Large Language Models (LLMs) have recently been explored for cybersecurity tasks due to their strong representation learning capabilities; however, their lac…

• Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations

  Yuanhang Li · 10 de abril de 2026

  Operating LEO mega-constellations requires translating high-level operator intents ("reroute financial traffic away from polar links under 80 ms") into low-level routing constraints -- a task that demands both natural language understanding and network-domain expertise. We present an end-to-end syst…

• A Family of Open Time-Series Foundation Models for the Radio Access Network

  Ioannis Panitsas, Leandros Tassiulas · 7 de abril de 2026

  The Radio Access Network (RAN) is evolving into a programmable and disaggregated infrastructure that increasingly relies on AI-native algorithms for optimization and closed-loop control. However, current RAN intelligence is still largely built from task-specific models tailored to individual functio…

• Analyzing Symbolic Properties for DRL Agents in Systems and Networking

  Mohammad Zangooei, Jannis Weil, Amr Rizk, Mina Tahmasbi Arashloo, Raouf Boutaba · 7 de abril de 2026

  Deep reinforcement learning (DRL) has shown remarkable performance on complex control problems in systems and networking, including adaptive video streaming, wireless resource management, and congestion control. For safe deployment, however, it is critical to reason about how agents behave across th…

• Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms

  Andreas Boltres, Niklas Freymuth, Benjamin Schichtholz, Michael K\"onig, Gerhard Neumann · 6 de abril de 2026

  Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within milliseconds. Live telemetry data can provide informative signals to routing algorithms, and recent work has trained neural networks to exploit such sign…

• ParetoBandit: Budget-Paced Adaptive Routing for Non-Stationary LLM Serving

  Annette Taberner-Miller · 2 de abril de 2026

  Production LLM serving often relies on multi-model portfolios spanning a ~530x cost range, where routing decisions trade off quality against cost. This trade-off is non-stationary: providers revise pricing, model quality can regress silently, and new models must be integrated without downtime. We pr…

• Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery

  Deemah H. Tashman, Soumaya Cherkaoui · 2 de abril de 2026

  Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and large-scale Internet of Things deployments. A key enabling mechanism is radio access network (RAN) slicing, which dynamic…

• 6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management

  Jiao Chen, Jianhua Tang, Xiaotong Yang, Zuohong Lv · 1 de abril de 2026

  Autonomous 6G network management requires agents that can execute tools, observe the resulting state changes, and adapt their decisions accordingly. Existing benchmarks based on static questions or scripted episode replay, however, do not support such closed-loop interaction, limiting agents to pass…

• From Simulation to Deep Learning: Survey on Network Performance Modeling Approaches

  Carlos G\"uemes-Palau, Miquel Ferriol-Galm\'es, Jordi Paillisse-Vilanova, Pere Barlet-Ros, Albert Cabellos-Aparicio · 31 de marzo de 2026

  Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a given network. Its applications range from network planning and troubleshooting to feeding information to network controlle…

• UNIFERENCE: A Discrete Event Simulation Framework for Developing Distributed AI Models

  Do\u{g}a\c{c} Eldenk, Stephen Xia · 30 de marzo de 2026

  Developing and evaluating distributed inference algorithms remains difficult due to the lack of standardized tools for modeling heterogeneous devices and networks. Existing studies often rely on ad-hoc testbeds or proprietary infrastructure, making results hard to reproduce and limiting exploration …

• Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

  Aymen Bouferroum (FUN), Valeria Loscri (FUN), Abderrahim Benslimane (LIA) · 26 de marzo de 2026

  The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integrated into critical industrial processes. Existing security approaches typically address threats at a single network layer, often relying on expensive hardware…

• Expected Reward Prediction, with Applications to Model Routing

  Kenan Hasanaliyev, Silas Alberti, Jenny Hamer, Dheeraj Rajagopal, Kevin Robinson, Jasper Snoek, Victor Veitch, Alexander Nicholas D'Amour · 24 de marzo de 2026

  Reward models are a standard tool to score responses from LLMs. Reward models are built to rank responses to a fixed prompt sampled from a single model, for example to choose the best of n sampled responses. In this paper, we study whether scores from response-level reward models lifted to score a m…

• Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence

  Alex Popa, Adrian Taylor, Ranwa Al Mallah · 24 de marzo de 2026

  Reinforcement learning techniques are being explored as solutions to the threat of cyber attacks on enterprise networks. Recent research in the field of AI in cyber security has investigated the ability of homogeneous multi-agent reinforcement learning agents, capable of inter-agent communication, t…

• Cascade-Aware Multi-Agent Routing: Spatio-Temporal Sidecars and Geometry-Switching

  Davide Di Gioia · 19 de marzo de 2026

  A common architectural pattern in advanced AI reasoning systems is the symbolic graph network: specialized agents or modules connected by delegation edges, routing tasks through a dynamic execution graph. Current schedulers optimize load and fitness but are geometry-blind: they do not model how fail…

• Toward Experimentation-as-a-Service in 5G/6G: The Plaza6G Prototype for AI-Assisted Trials

  Sergio Barrachina-Mu\~noz, Marc Carrascosa-Zamacois, Horacio Bleda, Umair Riaz, Yasir Maqsood, Xavier Calle, Selva V\'ia, Miquel Payar\'o, Josep Mangues-Bafalluy · 18 de marzo de 2026

  This paper presents Plaza6G, the first operational Experiment-as-a-Service (ExaS) platform unifying cloud resources with next-generation wireless infrastructure. Developed at CTTC in Barcelona, Plaza6G integrates GPU-accelerated compute clusters, multiple 5G cores, both open-source (e.g., Free5GC) a…

• Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization

  Xudong Wang, Chaoning Zhang, Jiaquan Zhang, Chenghao Li, Qigan Sun, Sung-Ho Bae, Peng Wang, Ning Xie, Jie Zou, Yang Yang, Hengtao Shen · 16 de marzo de 2026

  Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the quality--cost trade-off space. Despite these advances, real-world deployment is often constrained by high inference cos…

• A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks

  Hatim Chergui, Farhad Rezazadeh, Merouane Debbah, Christos Verikoukis · 16 de marzo de 2026

  The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs). While KPIs have enabled automation gains under TM Forum Levels 1--3, they remain numerical abstractions that act only as proxies for the real essence of communication networks: seamless …

• RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning

  Carlos G\"uemes-Palau, Miquel Ferriol-Galm\'es, Jordi Paillisse-Vilanova, Albert L\'opez-Bresc\'o, Pere Barlet-Ros, Albert Cabellos-Aparicio · 13 de marzo de 2026

  Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost and accuracy. This paper introduces RouteNet-Gauss, a novel…

• Utility Function is All You Need: LLM-based Congestion Control

  Neta Rozen-Schiff, Liron Schiff, Stefan Schmid · 12 de marzo de 2026

  Congestion is a critical and challenging problem in communication networks. Congestion control protocols allow network applications to tune their sending rate in a way that optimizes their performance and the network utilization. In the common distributed setting, the applications cannot collaborate…

• ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuning

  Ruizhong Qiu, Hanqing Zeng, Yinglong Xia, Yiwen Meng, Ren Chen, Jiarui Feng, Dongqi Fu, Qifan Wang, Jiayi Liu, Jun Xiao, Xiangjun Fan, Benyu Zhang, Hong Li, Zhining Liu, Hyunsik Yoo, Zhichen Zeng, Tianxin Wei, Hanghang Tong · 12 de marzo de 2026

  Low-rank adapters (LoRAs) are a parameter-efficient finetuning technique that injects trainable low-rank matrices into pretrained models to adapt them to new tasks. Mixture-of-LoRAs models expand neural networks efficiently by routing each layer input to a small subset of specialized LoRAs of the la…