Software-Defined Networks and 5G

Latest papers

• Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?

  Andreas Boltres, Niklas Freymuth, Gerhard Neumann · 16 February 2026

  Telemetry-Aware routing promises to increase efficacy and responsiveness to traffic surges in computer networks. Recent research leverages Machine Learning to deal with the complex dependency between network state and routing, but sacrifices explainability of routing decisions due to the black-box n…

• Resource-Aware Deployment Optimization for Collaborative Intrusion Detection in Layered Networks

  Andr\'e Garc\'ia G\'omez, Ines Rieger, Wolfgang Hotwagner, Max Landauer, Markus Wurzenberger, Florian Skopik, Edgar Weippl · 13 February 2026

  Collaborative Intrusion Detection Systems (CIDS) are increasingly adopted to counter cyberattacks, as their collaborative nature enables them to adapt to diverse scenarios across heterogeneous environments. As distributed critical infrastructure operates in rapidly evolving environments, such as dro…

• Divide, Harmonize, Then Conquer It: Shooting Multi-Commodity Flow Problems with Multimodal Language Models

  Xinyu Yuan, Yan Qiao, Zonghui Wang, Wenzhi Chen · 12 February 2026

  The multi-commodity flow (MCF) problem is a fundamental topic in network flow and combinatorial optimization, with broad applications in transportation, communication, and logistics, etc. Nowadays, the rapid expansion of allocation systems has posed challenges for existing optimization engines in ba…

• Automated Model Design using Gated Neuron Selection in Telecom

  Adam Orucu, Marcus Medhage, Farnaz Moradi, Andreas Johnsson, Sarunas Girdzijauskas · 12 February 2026

  The telecommunications industry is experiencing rapid growth in adopting deep learning for critical tasks such as traffic prediction, signal strength prediction, and quality of service optimisation. However, designing neural network architectures for these applications remains challenging and time-c…

• Interpretable Attention-Based Multi-Agent PPO for Latency Spike Resolution in 6G RAN Slicing

  Kavan Fatehi, Mostafa Rahmani Ghourtani, Amir Sonee, Poonam Yadav, Alessandra M Russo, Hamed Ahmadi, Radu Calinescu · 12 February 2026

  Sixth-generation (6G) radio access networks (RANs) must enforce strict service-level agreements (SLAs) for heterogeneous slices, yet sudden latency spikes remain difficult to diagnose and resolve with conventional deep reinforcement learning (DRL) or explainable RL (XRL). We propose \emph{Attention-…

• Hybrid Responsible AI-Stochastic Approach for SLA Compliance in Multivendor 6G Networks

  Emanuel Figetakis, Ahmed Refaey Hussein · 11 February 2026

  The convergence of AI and 6G network automation introduces new challenges in maintaining transparency, fairness, and accountability across multivendor management systems. Although closed-loop AI orchestration improves adaptability and self-optimization, it also creates a responsibility gap, where vi…

• When RL Meets Adaptive Speculative Training: A Unified Training-Serving System

  Junxiong Wang, Fengxiang Bie, Jisen Li, Zhongzhu Zhou, Zelei Shao, Yubo Wang, Yinghui Liu, Qingyang Wu, Avner May, Sri Yanamandra, Yineng Zhang, Ce Zhang, Tri Dao, Percy Liang, Ben Athiwaratkun, Shuaiwen Leon Song, Chenfeng Xu, Xiaoxia Wu · 9 February 2026

  Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating speculator training as a standalone offline modeling problem. We show that this decoupled formulation introduces substantial deployment and adaptation lag:…

• Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer's Optimization Tasks

  Ahsan Mehmood, Naveed Ul Hassan, Ghassan M. Kraidy · 9 February 2026

  This paper investigates the role of large language models (LLMs) in sixth-generation (6G) Internet of Things (IoT) networks and proposes a prompt-engineering-based real-time feedback and verification (PE-RTFV) framework that perform physical-layer's optimization tasks through an iteratively process.…

• Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model

  Md Arafat Habib, Medhat Elsayed, Yigit Ozcan, Pedro Enrique Iturria-Rivera, Majid Bavand, Melike Erol-Kantarci · 6 February 2026

  With the emergence of 6G, mobile networks are becoming increasingly heterogeneous and dynamic, necessitating advanced automation for efficient management. Intent-Driven Networks (IDNs) address this by translating high-level intents into optimization policies. Large Language Models (LLMs) can enhance…

• Dual Mind World Model Inspired Network Digital Twin for Access Scheduling

  Hrishikesh Dutta, Roberto Minerva, Noel Crespi · 5 February 2026

  Emerging networked systems such as industrial IoT and real-time cyber-physical infrastructures demand intelligent scheduling strategies capable of adapting to dynamic traffic, deadlines, and interference constraints. In this work, we present a novel Digital Twin-enabled scheduling framework inspired…

• $V_0$: A Generalist Value Model for Any Policy at State Zero

  Yi-Kai Zhang, Zhiyuan Yao, Hongyan Hao, Yueqing Sun, Qi Gu, Hui Su, Xunliang Cai, De-Chuan Zhan, Han-Jia Ye · 4 February 2026

  Policy gradient methods rely on a baseline to measure the relative advantage of an action, ensuring the model reinforces behaviors that outperform its current average capability. In the training of Large Language Models (LLMs) using Actor-Critic methods (e.g., PPO), this baseline is typically estima…

• Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL

  Erfan Miahi, Eugene Belilovsky · 4 February 2026

  Reinforcement learning (RL) is a critical component for post-training large language models (LLMs). However, in bandwidth-constrained distributed RL, scalability is often bottlenecked by the synchronization of policy weights from trainers to inference workers, particularly over commodity networks or…

• Certain Head, Uncertain Tail: Expert-Sample for Test-Time Scaling in Fine-Grained MoE

  Yuanteng Chen, Peisong Wang, Nanxin Zeng, Yuantian Shao, Gang Li, Jing Liu, Jian Cheng · 3 February 2026

  Test-time scaling improves LLM performance by generating multiple candidate solutions, yet token-level sampling requires temperature tuning that trades off diversity against stability. Fine-grained MoE, featuring hundreds of well-trained experts per layer and multi-expert activation per token, offer…

• Asynchronous MultiAgent Reinforcement Learning for 5G Routing under Side Constraints

  Sebastian Racedo, Brigitte Jaumard, Oscar Delgado, Meysam Masoudi · 3 February 2026

  Networks in the current 5G and beyond systems increasingly carry heterogeneous traffic with diverse quality-of-service constraints, making real-time routing decisions both complex and time-critical. A common approach, such as a heuristic with human intervention or training a single centralized RL po…

• From Intents to Actions: Agentic AI in Autonomous Networks

  Burak Demirel, Pablo Soldati, Yu Wang · 3 February 2026

  Telecommunication networks are increasingly expected to operate autonomously while supporting heterogeneous services with diverse and often conflicting intents -- that is, performance objectives, constraints, and requirements specific to each service. However, transforming high-level intents -- such…

• Accurate Network Traffic Matrix Prediction via LEAD: a Large Language Model-Enhanced Adapter-Based Conditional Diffusion Model

  Yu Sun, Yaqiong Liu, Nan Cheng, Jiayuan Li, Zihan Jia, Xialin Du, Mugen Peng · 3 February 2026

  Driven by the evolution toward 6G and AI-native edge intelligence, network operations increasingly require predictive and risk-aware adaptation under stringent computation and latency constraints. Network Traffic Matrix (TM), which characterizes flow volumes between nodes, is a fundamental signal fo…

• AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks

  Maxime Elkael, Salvatore D'Oro, Leonardo Bonati, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia · 3 February 2026

  Despite the programmable architecture of Open RAN, today's deployments still rely heavily on static control and manual operations. To move beyond this limitation, we introduce AgentRAN, an AI-native, Open RAN-aligned agentic framework that generates and orchestrates a fabric of distributed AI agents…

• Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning

  Qi Cao, Shuhao Zhang, Ruizhe Zhou, Ruiyi Zhang, Peijia Qin, Pengtao Xie · 2 February 2026

  Model routing chooses which language model to use for each query. By sending easy queries to cheaper models and hard queries to stronger ones, it can significantly reduce inference cost while maintaining high accuracy. However, most existing routers treat this as a fixed choice among a small set of …

• Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies

  Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang · 2 February 2026

  The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achieve genuine cognitive capabilities--fulfilling TM Forum's vision of self-configuring, self-healing, and self-optimizing s…

• SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control

  MohammadErfan Jabbari, Abhishek Duttagupta, Claudio Fiandrino, Leonardo Bonati, Salvatore D'Oro, Michele Polese, Marco Fiore, Tommaso Melodia · 30 January 2026

  Deep reinforcement learning (DRL) promises adaptive control for future mobile networks but conventional agents remain reactive: they act on past and current measurements and cannot leverage short-term forecasts of exogenous KPIs such as bandwidth. Augmenting agents with predictions can overcome this…

• Accurate Network Traffic Matrix Prediction via LEAD: an LLM-Enhanced Adapter-Based Conditional Diffusion Model

  Yu Sun, Yaqiong Liu, Nan Cheng, Jiayuan Li, Zihan Jia, Xialin Du, Mugen Peng · 30 January 2026

  Driven by the evolution toward 6G and AI-native edge intelligence, network operations increasingly require predictive and risk-aware adaptation under stringent computation and latency constraints. Network Traffic Matrix (TM), which characterizes flow volumes between nodes, is a fundamental signal fo…

• Minimum-Cost Network Flow with Dual Predictions

  Zhiyang Chen, Hailong Yao, Xia Yin · 29 January 2026

  Recent work has shown that machine-learned predictions can provably improve the performance of classic algorithms. In this work, we propose the first minimum-cost network flow algorithm augmented with a dual prediction. Our method is based on a classic minimum-cost flow algorithm, namely $\varepsilo…

• Proactive SFC Provisioning with Forecast-Driven DRL in Data Centers

  Parisa Fard Moshiri, Poonam Lohan, Burak Kantarci, Emil Janulewicz · 29 January 2026

  Service Function Chaining (SFC) requires efficient placement of Virtual Network Functions (VNFs) to satisfy diverse service requirements while maintaining high resource utilization in Data Centers (DCs). Conventional static resource allocation often leads to overprovisioning or underprovisioning due…

• Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation

  Toshinori Kitamura, Arnob Ghosh, Tadashi Kozuno, Wataru Kumagai, Kazumi Kasaura, Kenta Hoshino, Yohei Hosoe, Yutaka Matsuo · 29 January 2026

  We study the reinforcement learning (RL) problem in a constrained Markov decision process (CMDP), where an agent explores the environment to maximize the expected cumulative reward while satisfying a single constraint on the expected total utility value in every episode. While this problem is well u…

• Generalization in Reinforcement Learning for Radio Access Networks

  Burak Demirel, Yu Wang, Cristian Tatino, Pablo Soldati · 29 January 2026

  Modern RAN operate in highly dynamic and heterogeneous environments, where hand-tuned, rule-based RRM algorithms often underperform. While RL can surpass such heuristics in constrained settings, the diversity of deployments and unpredictable radio conditions introduce major generalization challenges…