Software-Defined Networks and 5G

Latest papers

• Each Prompt Matters: Scaling Reinforcement Learning Without Wasting Rollouts on Hundred-Billion-Scale MoE

  Anxiang Zeng, Haibo Zhang, Hailing Zhang, Kaixiang Mo, Liang Yao, Ling Hu, Long Zhang, Shuman Liu, Shuyi Xie, Yanshi Li, Yizhang Chen, Yuepeng Sheng, Yuwei Huang, Zhaochen Xu, Zhiqiang Zhou, Ziqin Liew · 9 December 2025

  We present CompassMax-V3-Thinking, a hundred-billion-scale MoE reasoning model trained with a new RL framework built on one principle: each prompt must matter. Scaling RL to this size exposes critical inefficiencies-zero-variance prompts that waste rollouts, unstable importance sampling over long ho…

• Hierarchical Reinforcement Learning for the Dynamic VNE with Alternatives Problem

  Ali Al Housseini, Cristina Rottondi, Omran Ayoub · 8 December 2025

  Virtual Network Embedding (VNE) is a key enabler of network slicing, yet most formulations assume that each Virtual Network Request (VNR) has a fixed topology. Recently, VNE with Alternative topologies (VNEAP) was introduced to capture malleable VNRs, where each request can be instantiated using one…

• Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G

  Michele Polese, Niloofar Mohamadi, Salvatore D'Oro, Leonardo Bonati, Tommaso Melodia · 3 December 2025

  Data-intensive Artificial Intelligence (AI) applications at the network edge demand a fundamental shift in Radio Access Network (RAN) design, from merely consuming AI for network optimization, to actively enabling distributed AI workloads. This presents a significant opportunity for network operator…

• Goal-Oriented Multi-Agent Semantic Networking: Unifying Intents, Semantics, and Intelligence

  Shutong Chen, Qi Liao, Adnan Aijaz, Yansha Deng · 2 December 2025

  6G services are evolving toward goal-oriented and AI-native communication, which are expected to deliver transformative societal benefits across various industries and promote energy sustainability. Yet today's networking architectures, built on complete decoupling of the applications and the networ…

• Constrained Network Slice Assignment via Large Language Models

  Sagar Sudhakara, Pankaj Rajak · 2 December 2025

  Modern networks support network slicing, which partitions physical infrastructure into virtual slices tailored to different service requirements (for example, high bandwidth or low latency). Optimally allocating users to slices is a constrained optimization problem that traditionally requires comple…

• Subjective Depth and Timescale Transformers: Learning Where and When to Compute

  Frederico Wieser, Martin Benfeghoul, Haitham Bou Ammar, Jun Wang, Zafeirios Fountas · 27 November 2025

  The rigid, uniform allocation of computation in standard Transformer (TF) architectures can limit their efficiency and scalability, particularly for large-scale models and long sequences. Addressing this, we introduce Subjective Depth Transformers (SDT) and Subjective Timescale Transformers (STT), t…

• Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation

  Youngjoon Lee, Taehyun Park, Yunho Lee, Jinu Gong, Joonhyuk Kang · 25 November 2025

  Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may undermine operational security, disrupt dec…

• LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk

  Hatim Chergui, Farhad Rezazadeh, Mehdi Bennis, Merouane Debbah · 25 November 2025

  A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive tendency for large language model (LLM)-powered agents to make high-stakes decisions based on simple averages while ignoring the tail risk of extreme events. Th…

• AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networks

  Narjes Nourzad, Mingyu Zong, Bhaskar Krishnamachari · 25 November 2025

  Next-generation (NextG) cellular networks are expected to manage dynamic traffic while sustaining high performance. Large language models (LLMs) provide strategic reasoning for 6G planning, but their computational cost and latency limit real-time use. Multi-agent reinforcement learning (MARL) suppor…

• Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning

  Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah · 20 November 2025

  Next-generation networks utilize the Open Radio Access Network (O-RAN) architecture to enable dynamic resource management, facilitated by the RAN Intelligent Controller (RIC). While deep reinforcement learning (DRL) models show promise in optimizing network resources, they often struggle with robust…

• PLATONT: Learning a Platonic Representation for Unified Network Tomography

  Chengze Du, Heng Xu, Zhiwei Yu, Bo Liu, Jialong Li · 20 November 2025

  Network tomography aims to infer hidden network states, such as link performance, traffic load, and topology, from external observations. Most existing methods solve these problems separately and depend on limited task-specific signals, which limits generalization and interpretability. We present PL…

• SMART: A Surrogate Model for Predicting Application Runtime in Dragonfly Systems

  Xin Wang, Pietro Lodi Rizzini, Sourav Medya, Zhiling Lan · 17 November 2025

  The Dragonfly network, with its high-radix and low-diameter structure, is a leading interconnect in high-performance computing. A major challenge is workload interference on shared network links. Parallel discrete event simulation (PDES) is commonly used to analyze workload interference. However, hi…

• Towards a Generalisable Cyber Defence Agent for Real-World Computer Networks

  Tim Dudman, Martyn Bull · 13 November 2025

  Recent advances in deep reinforcement learning for autonomous cyber defence have resulted in agents that can successfully defend simulated computer networks against cyber-attacks. However, many of these agents would need retraining to defend networks with differing topology or size, making them poor…

• SmartSecChain-SDN: A Blockchain-Integrated Intelligent Framework for Secure and Efficient Software-Defined Networks

  Azhar Hussain Mozumder, M. John Basha, Chayapathi A. R · 10 November 2025

  With more and more existing networks being transformed to Software-Defined Networking (SDN), they need to be more secure and demand smarter ways of traffic control. This work, SmartSecChain-SDN, is a platform that combines machine learning based intrusion detection, blockchain-based storage of logs,…

• Agentic AI for Mobile Network RAN Management and Optimization

  Jorge Pellejero, Luis A. Hern\'andez G\'omez, Luis Mendo Tom\'as, Zoraida Frias Barroso · 5 November 2025

  Agentic AI represents a new paradigm for automating complex systems by using Large AI Models (LAMs) to provide human-level cognitive abilities with multimodal perception, planning, memory, and reasoning capabilities. This will lead to a new generation of AI systems that autonomously decompose goals,…

• SliceVision-F2I: A Synthetic Feature-to-Image Dataset for Visual Pattern Representation on Network Slices

  Md. Abid Hasan Rafi, Mst. Fatematuj Johora, Pankaj Bhowmik · 4 November 2025

  The emergence of 5G and 6G networks has established network slicing as a significant part of future service-oriented architectures, demanding refined identification methods supported by robust datasets. The article presents SliceVision-F2I, a dataset of synthetic samples for studying feature visuali…

• Diffusion-Based Solver for CNF Placement on the Cloud-Continuum

  \'Alvaro V\'azquez Rodr\'iguez, Manuel Fern\'andez-Veiga, Carlos Giraldo-Rodr\'iguez · 4 November 2025

  The placement of Cloud-Native Network Functions (CNFs) across the Cloud-Continuum represents a core challenge in the orchestration of current 5G and future 6G networks. The process involves the placement of interdependent computing tasks, structured as Service Function Chains, over distributed cloud…

• Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks

  Lingyi Wang, Rashed Shelim, Walid Saad, Naren Ramakrishnan · 29 October 2025

  Despite the popularity of reinforcement learning (RL) in wireless networks, existing approaches that rely on model-free RL (MFRL) and model-based RL (MBRL) are data inefficient and short-sighted. Such RL-based solutions cannot generalize to novel network states since they capture only statistical pa…

• Understanding Network Behaviors through Natural Language Question-Answering

  Mingzhe Xing, Chang Tian, Jianan Zhang, Lichen Pan, Peipei Liu, Zhaoteng Yan, Yinliang Yue · 28 October 2025

  Modern large-scale networks introduce significant complexity in understanding network behaviors, increasing the risk of misconfiguration. Prior work proposed to understand network behaviors by mining network configurations, typically relying on domain-specific languages interfaced with formal models…