Software-Defined Networks and 5G

Últimos artículos

• ML-for-ML

  Yutong Zhao, Noga H. Rotman, Gianni Antichi, Ran Ben Basat · 7 de agosto de 2026

  AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important. In shared cloud clusters, training and fine-tuning jobs compete with co-running workloads for network resources, while network mechanisms and ML training choices are typically optim…

• Multi-Agent Transformer for Queue-Level XR Traffic Scheduling in TSN Networks

  Marcos Carvalho, Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci, Daniel F. Macedo · 7 de agosto de 2026

  Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as eXtended Reality (XR). However, the widespread adoption of XR introduces significant challenges due to co-located servi…

• Learning Not to Optimize: Physics-Informed Action-Space Reshaping for Intent-Based Network Control

  Zuyuan Zhang, Vaneet Aggarwal, Tian Lan · 4 de agosto de 2026

  Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to optimize, while constraints are commonly handled through penalty, barrier, or Lagrangian mechanisms. We observe that before a value function can certi…

• CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives

  Zifan Zhang, Zhichao Hou, Tingxiang Ji, Yuchen Liu · 4 de agosto de 2026

  Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon. One generative model, pretrained once over an operator's own event streams, could replace this fleet, an approach that already…

• Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research

  Jaber Daneshamooz, Eugene Vuong, Alagappan Ramanathan, Manni Moghimi, Haarika Manda, Satyam Kumar, Snithik Thode, Satyandra Guthula, Sylee Beltiukov, Dongsu Han, Tarun Mangla, Sangeetha Abdu Jyothi, Walter Willinger, Arpit Gupta · 30 de julio de 2026

  Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating the data that tests it. Consider a concrete case: does a bulk BBR download fairly share its bottleneck with competing real…

• ML-based Predictive Models for Power Consumption in Virtualised O-RANs

  Rishu Raj, Genevieve Akude, Urooj Tariq, Daniel Kilper · 28 de julio de 2026

  As communication networks adopt virtualized and disaggregated architectures, achieving energy efficiency has become increasingly important for both economic and environmental reasons. Traditional methods for power modeling are inadequate in these dynamic software-defined environments due to their in…

• MoA-Structured Decode Attention DNF Derivation, KV-Cache Accumulation, GQA/MQA, and OpenACC Kernel

  Lenore Mulin, Gaetan Hains · 23 de julio de 2026

  We derive four memory-optimal inference artifacts for transformer attention using the Mathematics of Arrays (MoA), each following directly from the forward-pass Denotational Normal Form (DNF) of with the query-row index fixed to the current decode step. The artifacts are: (1)~a single-query decode D…

• Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning

  Junyao Yang, Yucheng Shi, Zongxia Li, Zhongzhi Li, Ruhan Wang, Xiangxin Zhou, Kishan Panaganti, Haitao Mi, Leowei Liang · 22 de julio de 2026

  Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byproduct compounded by policy lag, engine delays, and mixture-of-experts routing. From a trust-region perspective, this mismatch is critical: training-inference…

• CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents

  Qijia He, Jiayi Cheng, Chenqian Le, Rui Wang, Xunmei Liu, Yixian Chen, Jie Mei, Zhihao Wang, Xupeng Chen, Yuhuan Chen, Tao Wang · 22 de julio de 2026

  Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer. Existing cost-aware systems typically treat such failures as cascade decisions: try a cheap model first, then escalate hard cases to a stronger an…

• Mobile Network Control with a World Model

  Maxime Bouton, Ioanna Mitsioni, Simon Lindst{\aa}hl, Jaeseong Jeong · 21 de julio de 2026

  The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based approach for network control that enables adaptive configuration of crucial parameters. The world model is trained from his…

• LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization

  Mazene Ameur, Abdelkader Mekrache, Bouziane Brik, Adlen Ksentini · 20 de julio de 2026

  Agentic Artificial Intelligence (AI), enabled by Large Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs). Existing surveys treat the two domains in isolation, leaving protocol integration, evaluation, and standardizati…

• Cost-Optimal Foundation Model Deployment Portfolio for Transportation Management

  Xi Cheng, Ke Liu, Siyuan Feng, Jane Lin, H. Oliver Gao · 16 de julio de 2026

  Foundation models, including large language models (LLMs) and vision-language models (VLMs), are increasingly used for transportation management center (TMC) tasks such as anomaly detection, incident reporting, and traveler information. Deploying multiple such models across TMC functions raises a po…

• Resample or Reroute? Budget-Aware Test-Time Model Selection for Large Language Models

  Teng-Ruei Chen · 10 de julio de 2026

  Routing among large language models (LLMs) trades response quality against serving cost, motivated by the reported gap between deployed routers and a per-instance oracle. Recent analysis shows that test-time resampling can recover per-instance selection headroom that no single-commit router captures…

• From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective

  Petar Djukic, Sudipta Acharya, Takai Eddine Kennouche, Burak Kantarci · 9 de julio de 2026

  Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime. However, achieving thi…

• Agentic AI for IPoDWDM Network Lifecycle Automation: An MCP-Enabled Architecture

  Chunmin Xia, Jakub Harbaczewski, Nikhil Dsilva, Julie Raulin, Dominic Schneider, Achim Autenrieth · 8 de julio de 2026

  We present a distributed, vendor-agnostic multi-MCP architecture for SDN-based automation and autonomous control of multi-vendor, multi-layer IPoDWDM networks. The framework enables E2E service lifecycle automation, closed-loop cross-layer control using GNPy model and optical telemetry, and is exper…

• Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks

  Ravi Kant Sharma · 3 de julio de 2026

  The evolution toward fully autonomous telecommunications networks (Autonomous Network Levels 4-5) requires AI/ML agents to make real-time network decisions without human intervention. However, no standardized runtime mechanism exists to intercept and validate individual inference outputs before they…

• EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning

  Ahin Lee, Sehyun Yun, Taesik Gong · 3 de julio de 2026

  Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PEFT) methods such as LoRA ignore MoE routing dynamics, leading to suboptimal resource use. We propose EPnG, an adaptive p…

• LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach

  Renxuan Tan, Rongpeng Li, Fei Wang, Chenghui Peng, Shaoyun Wu, Zhifeng Zhao, Honggang Zhang · 1 de julio de 2026

  Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured. While deep reinforcement learning (DRL)-based protocols enhance task-specified network performance, they suffer from poor generalizability and resilience, demanding costly retraining to adapt t…

• Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting

  Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Tai-Yue Li, Nan-Yow Chen, Samuel Yen-Chi Chen · 29 de junio de 2026

  Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when prediction must be performed under the memory, update, and training-budget constraints of online network control. This paper in…

• EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

  Boyun Zhang, Chao Wang, Kai Wu · 26 de junio de 2026

  In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each candidate must be trained before evaluation, and the design space is open-ended. To address these challenges, we introduce EVOM, an agentic meta-evolution…

• Geometric Fairness-Aware Routing for Federated Edge Networks

  Ratun Rahman · 26 de junio de 2026

  Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices. Existing federated routing systems often prioritize aggregate latency or throughput above fairness and the underlying geometric structure of network topologies.…

• Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN

  Khaled M. Naguib, Soumaya Cherkaoui, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem, Ibrahim I. Ibrahim · 26 de junio de 2026

  Ultra-low latency and high throughput are required for Virtual Reality (VR) services in 6G networks, which presents critical challenges for Software-Defined Radio Access Networks (SD-RANs) dynamic resource management. This work propose a mobility-driven, privacy-aware Multi-Agent Reinforcement Learn…

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

  Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah · 25 de junio de 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…

• From RAN Control to Agentic Intelligence: Architecture and Vision for Energy Efficient AI-RAN

  Sabrine Aroua, Alexis I. Aravanis, Ilias Chatzistefanidis, Hamza Abbar, Anh-Khoa Dang, Anastasios Giovanidis, Salah-Eddine El Ayoubi, Stephane Senecal, Martha Vlachou Konchylaki, Navid Nikaein · 23 de junio de 2026

  Future 6G networks will rely on highly distributed, AI-native Radio Access Networks (RANs), where communication and AI workloads share a common infrastructure. This evolution, combined with increasing deployment density and continuous AI processing, is expected to significantly increase RAN energy c…

• O-RAN Xapps Conflict Prediction Using Graph Convolutional Networks

  Maryam Al Shami, Jun Yan, Emmanuel Thepie Fapi · 23 de junio de 2026

  O-RAN hosts many intelligent applications known as eXtended Applications (xApps). xApps are applications that leverage advanced algorithms to make dynamic decisions for network optimization. Each application operates with distinct optimization objectives and is managed by independent operators while…