Software-Defined Networks and 5G

Últimos artículos

• How to Loop MoE: Flatten the Experts, Untie the Attention

  Shouren Wang, Chuang Ma, Mohsen Hariri, Debargha Ganguly, Wang Yang, Xiaoqing Tong, Qianying Liu, Xiaotian Han, Vipin Chaudhary · 30 de septiembre de 2026

  Looped Transformers reuse one block of layers several times: by spending extra computation they push a model of fixed size further, and so use its parameters more fully; while sparse mixture-of-experts (MoE) models activate only a few of many experts for each token. Looped MoE bridges these two desi…

• HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion

  Junxiang Qiu, Zhengsu Chen, Xinting Hu, Shuo Wang, Hengheng Zhang, Shaofeng Zhang, Changcheng Li, Boyu Shi, Qi Tian · 30 de septiembre de 2026

  Mixture-of-Experts (MoE) architectures have become a standard way to scale model capacity while keeping computation sparse, yet routing remains a key determinant of MoE quality and training behavior. Prior empirical studies suggest that MoE routing reflects input semantics and upstream computation a…

• Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management

  Yao Zhang, Shengnan Li, Yuchen Song, Yidi Wang, Yue Pang, Wenbin Chen, Xiaotian Jiang, Xiao Luo, Meixia Fu, Min Zhang, Yongli Zhao, Shanguo Huang, Alan Pak Tao Lau, Danshi Wang · 29 de septiembre de 2026

  As optical networks continue to expand in scale, complexity, and service diversity, the implementation of automation has become essential for ensuring agility, efficiency, and reliability in lifecycle management (LCM) of optical networks. Large language model (LLM) Agent, distinguished by its progre…

• Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models

  Andrea Masini, Sudipta Acharya, Paolo Bellavista, Luca Foschini, Burak Kantarci · 28 de septiembre de 2026

  Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable net…

• Evolving Inspectable O-RAN Slicing xApps with LLMs

  Faezeh Dehghan Tarzjani, Bhaskar Krishnamachari · 24 de septiembre de 2026

  Open RAN (O-RAN) slicing xApps must adapt resource allocations to changing channel conditions and traffic demands while meeting service-level agreements (SLAs). Deep reinforcement learning can produce adaptive policies, but their allocation rules remain encoded in neural-network parameters. Our goal…

• Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry

  Zuyuan Zhang, Zeyu Fang, Mahdi Imani, Nathaniel D. Bastian, Tian Lan · 23 de septiembre de 2026

  Learning-based control is increasingly central to automating network operations. A learned policy, however, must satisfy cross-layer constraints on interference, power-rate coupling, flow conservation, service chains, capacity, latency, and reliability. Existing methods typically account for only a …

• TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks

  Bohao Wang, Chenwei Wu, Hang Zou, Yu Tian, Lina Bariah, Li Wei, Chongwen Huang, Yongliang Shen, Zhaoyang Zhang, Merouane Debbah · 23 de septiembre de 2026

  Large language models (LLMs) offer great potential to automate a broad range of telecom engineering tasks by reasoning over standards, network configurations, mathematical models, source code, and operational logs. However, existing telecom LLMs struggle to reliably reason across these diverse tasks…

• Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study

  Shenjia Ding, David Flynn, Paul Harvey, Takamichi Miyata, Sumiko Miyata · 21 de septiembre de 2026

  Modern networks must support changing topologies, configurations, and performance objectives, motivating fast and reliable performance estimation. Network digital twins (NDTs) enable what-if analysis for performance estimation in such network scenarios, however, existing machine learning-based NDT a…

• Learning-to-Optimize as the Missing Architectural Layer of AI-Native Networks

  Giambattista Amati, Federica Mangiatordi, Pierpaolo Salvo, Emiliano Pallotti, Simone Angelini · 21 de septiembre de 2026

  Artificial Intelligence (AI) is becoming a fundamental design principle of future AI-native communication networks, enabling autonomous resource management, adaptive control, and zero-touch network operation. While current AI-native architectures increasingly embed intelligence across network functi…

• Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks

  Giovanni Perin, Michele Rossi, Enrique Tom\'as Mart\'inez Beltr\'an, Fernando Torres-Vega, Jos\'e Mar\'ia Jorquera Valero, Manuel Gil P\'erez, Eunjeong Jeong, Nikolaos Pappas, Farah Abed Zadeh, Chamara Sandeepa, Bartlomiej Siniarski, Madhusanka Liyanage, Bet\"ul G\"uven\c{c} Paltun, Leyli Kara\c{c}ay, Ioannis Pitsiorlas, Marios Kountouris · 15 de septiembre de 2026

  As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platfo…

• Safety Signals to Verify NetOps Agents with Action-Level Granularity

  Tobias Labarta, Frederik Pahde, Novak Boskov, Maximilian Dreyer, David Birkenberger, Manzoor Ahmed Khan, Sebastian Lapuschkin, Wojciech Samek · 15 de septiembre de 2026

  Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. While agents have proven their value in incident summarization and telemetry signal extraction, their effectiveness as autonomous control-loop engines h…

• NDT Factory: Synthesizing Verified Network Digital Twins from Semantic Models via Multi-Agent LLM

  Sudipta Acharya, Petar Djukic, Burak Kantarci · 14 de septiembre de 2026

  Autonomous network management requires systems that can evaluate Network Service Intents (NSIs) under varying conditions without manual implementation of analysis logic, as envisioned in TM Forum Level~4 (L4) autonomy. Behavioral Network Digital Twins (NDTs) enable such evaluation, but existing NDTs…

• HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning

  Osama Abu Hamdan, Rabin Pandey, Hao Che, Engin Arslan, Md Arifuzzaman · 10 de septiembre de 2026

  Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often dominate round completion time and exacerbate the straggler effect. Hybrid FL ad…

• Can AI Agents Deliver Verifiable Network-Wide Outcomes Across Authority Boundaries?

  Tianzhu Zhang, Chih-Kai Huang, Meikang Qiu · 10 de septiembre de 2026

  AI agents are increasingly involved in network automation, where they can initiate configuration changes through mediated operational interfaces and assess the resulting state. Nonetheless, operational networks usually span many devices and administrative domains. Realizing an operator's intent requ…

• From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control

  Vincenzo Norman Vitale, Mohammad Solki, Antonia Maria Tulino, Andreas F. Molisch, Jaime Llorca · 4 de septiembre de 2026

  Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is essential for NextG interactive applications, yet providing strict End-to-End (E2E) peak latency guarantees remains an open challenge. Two obstacles limit the adoption of learning-based network control in this set…

• Agents That Model Agents: Five Principles Toward a Theory of Mind for 6G Networks

  Hatim Chergui, Carolina Fern\'{a}ndez-Mart\'{i}nez, Mehdi Bennis, Merouane Debbah · 3 de septiembre de 2026

  Future 6G networks will rely on Large Language Model (LLM) agents to manage the Radio Access Network (RAN). However, current architectures assume inter-agent messages convey objective facts. A message is instead a \emph{trace} of the sender's reasoning: it carries a subjective conclusion, so a synta…

• CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN

  Pranshav Gajjar, Vijay K Shah · 2 de septiembre de 2026

  The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform reasoning over real-time telemetry. The state-of-the-art training paradigms for telecom LLMs, exemplified by RANSTRUCT-style supervised fine-tuning (SF…

• TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack

  Bohao Wang, Chenwei Wu, Haoyu Li, Hang Zou, Yu Tian, Lina Bariah, Li Wei, Chongwen Huang, Yongliang Shen, Zhaoyang Zhang, Merouane Debbah · 28 de agosto de 2026

  Telecommunications is a high-leverage domain for large language model (LLM)-based reasoning because routine engineering workflows require joint grounding in normative specifications, operational telemetry, vendor-specific fault evidence, and exact RF/network calculations. However, current LLM integr…

• NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configuration

  Chang Liu, Xiaohui Xie, Xinyi Chen, Yong Cui · 25 de agosto de 2026

  Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poorly understood. An essential prerequisite is to assess such agents in a realistic but risk-free environment. Existing benchmarks, however, fall short:…

• From Reactive to Autonomous: Evolution of AI Operations in Cloud Network Infrastructure

  Arun Malik · 18 de agosto de 2026

  The operational model for cloud network infrastructure has undergone a fundamental transformation over the past decade. What began as manual, human-driven troubleshooting has evolved through scripted automation, rule-based systems, and AI-assisted operations into fully autonomous incident resolution…

• WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents

  Yuan Guo, Yilong Chen, Chao Hu, Xianghao Yu, Liang Hong, Jie Xu · 18 de agosto de 2026

  Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific and engineering research. To the best of our knowledge, this paper presents the first end-to-end autoresearch fram…

• OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks

  Farbod Tavakkoli, Roderic Paulk, Jorden Terrazas, Kenneth Church, Mark Austin, Louis Powell, Gregory Diamos, Lina Bariah, Syed Ali Raza Zaidi, Maryam Hafeez, Ali Maatouk, Imtiaz Karim · 18 de agosto de 2026

  Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open telecom AI resource with derived datasets for retrieval, reranking, instruction tuning, and safety/abstention, plus 30 full-parameter post-trained baselines across emb…

• ReForge: Keeping ABR Algorithms Never Finished with Verified Large Language Model Edits

  Zhiqiang He, Zhi Liu · 18 de agosto de 2026

  Designing an ABR algorithm for one network scenario takes an engineer months, and large language models now do this work in hours, matching or beating hand-built designs. But either way, the design fits only the world visible at its birth, and fails on the world that arrives after. We ask whether an…

• SCOPE-Router: Cost-Aware Open-Set VLM Routing for Execution-Oriented Tasks

  Tao Yu, Yifei Qu, Zhiqing Cui, Pengfei Zhou, Zhongtian Luo, Yujia Yang, Shenghua Chai, Haopeng Jin, Zhenghao Zhang, Xinming Wang, Hongzhu Yi, Wangbo Zhao, Zhenglin Wan, Yan Huang, Yeshani, Jinwen Luo, Yang You · 13 de agosto de 2026

  Model routing aims to select the most suitable model from a candidate pool for each query, balancing quality and cost. Existing VLM routing research is limited to traditional VQA evaluation, lacks systematic calibration optimization for open-set scenarios, and employs training objectives that dilute…

• CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations

  Xingyu Yan, Tingting Dai, Antonio De Domenico, Mohamed Sana, Nicola Piovesan, Changchang Li, Bowen Liu, Kun Jiang, Mengjie Zhang, Dingcheng Shan, Jing-Cheng Pang, Chenwei Wu, Sijie Wu, Lianying Chao, Haoran Cai, Jiantao Ye, Xubin Li, Simon Mark Lucas, Xin Chen · 13 de agosto de 2026

  Agents are increasingly considered for automating network operations and maintenance, where engineers must diagnose network faults, optimize configurations to enhance services, and reduce operational costs while acting under strict constraints. However, existing evaluations fail to accurately model …