Software-Defined Networks and 5G

Neueste Paper

• Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks

  Hatim Chergui, Claudia Carballo Gonz\'alez, Farhad Rezazadeh, Merouane Debbah · 19. Juni 2026

  This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents. While LLMs offer powerful reasoning capabilities, we demonstrate that such agents inherently suffer from anchoring bias,…

• OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization

  Longlong Zhu, Jiashuo Yu, Zedi Chen, Yuhan Wu, Zhifan Jiang, Yuchen Xian, Yimeng Liu, Jiajie Su, Shaopeng Zhou, Xingyuan Li, Hongyan Liu, Xuan Liu, Dong Zhang, Chunming Wu, Xiang Chen · 17. Juni 2026

  Network planning optimization is a fundamental problem across diverse domains, including transportation systems, communication networks, and power grids. It requires simultaneous optimization of multiple competing objectives under complex constraints. Existing network planning optimization framework…

• A T-API-Compliant ReAct Agentic Loop for Optical Networks: Generic vs. Domain-Specific Tool Abstractions

  Seyed Morteza Ahmadian, Paolo Monti, Carlos Natalino · 17. Juni 2026

  Optical networks need intent-driven, closed-loop agentic management, a key enabler for higher autonomy levels. We present the first T-API-compliant reasoning and act (ReAct) loop. We show that domain-specific composite tools achieve 90% oracle-validated correctness with threefold token savings compa…

• Temporally Consistent Graph Q-Networks for Intelligent Network Control

  Zacharias Veiksaar, Maxime Bouton · 15. Juni 2026

  Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services. As network complexity rises, optimizing antenna parameters under dynamic or changing objectives becomes increasingly challenging. We propose a …

• Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response

  Jose Luis Lima de Jesus Silva · 15. Juni 2026

  Autonomous network-security response systems promise to reduce Security Operations Centre (SOC) reaction latency, but reward-only multi-agent reinforcement learning (MARL) can improve security reward while remaining non-deployable. We present a safety-contract graph MARL framework and instantiate it…

• Redesign Mixture-of-Experts Routers with Manifold Power Iteration

  Songhao Wu, Ang Lv, Ruobing Xie, Yankai Lin · 11. Juni 2026

  Router is the cornerstone component to the Mixture-of-Experts models. Serving as expert proxies, the rows of the router matrix compute their similarity to the MoE inputs to determine which subset of experts is activated. Ideally, each router row is designed to encode the expert matrix into this repr…

• The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers

  Yifan Lu, Qiyue Zhang, Shenrun Zhang, Zhibo Yu, Zhuang Wang, Hanjie Chen, Jiarong Xing · 9. Juni 2026

  LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by dynamically selecting a model for each query. Recent work has explored a broad range of routing methods, including clustering-based routers, learned classifiers, pairwise ranking, and confidence-based …

• BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks

  Osman Tugay Basaran, Martin Maier, Falko Dressler · 9. Juni 2026

  Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capable of real-time adaptation in dynamic environments and transparent in their decisionmaking. However, prevailing agentic AI approaches in networking, …

• AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation

  Bilal Hussain, Muhammad Bilal, Tan Li, Haris Pervaiz, Xiao Tang, Qinghe Du, Fawad Ahmad, Muhammad Azhar, Jun Zhang · 9. Juni 2026

  In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collapsing the gap between a remote breach and physical harm to milliseconds, a budget …

• From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing

  Guannan Lai, Haoran Hu, Long Chen, Zhenguo Li, Han-Jia Ye · 8. Juni 2026

  Existing LLM routing methods typically treat a model's single response to a query as its capability label for training routers. However, because LLM generation is inherently stochastic, such single-shot supervision provides only a noisy observation of a query-model pair's behavior rather than a reli…

• When Model Merging Breaks Routing: Training-Free Calibration for MoE

  Canbin Huang, Tianyuan Shi, Xiaojun Quan, Jingang Wang, Jianfei Zhang, Qifan Wang · 3. Juni 2026

  Model merging has emerged as a cost-effective approach for consolidating the capabilities of multiple LLMs without retraining. However, existing merging techniques, largely based on linear parameter arithmetic or optimization, struggle when applied to Mixture-of-Experts (MoE) architectures. We ident…

• On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

  Raj Patel, David Amebley, Taye Akinrele, Shaswata Mitra, Sayanton Dibbo, Shahram Rahimi · 2. Juni 2026

  Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks (SNNs) are t…

• AgentxGCore: Agentic AI for Next-Generation Mobile Core Network

  Maria Katarine Santana Barbosa, Kelvin L. Dias · 2. Juni 2026

  To meet the stringent requirements of emerging applications and the increasingly complex network management and operation, the Next Generation Mobile Networks (NextG), or 6G, will adopt an AI-native architecture on the Core Network (CN). In this movement, the Third Generation Partnership Project (3G…

• Jamming-Resilient PRB Reservation for Latency-Critical O-RAN Network Slicing

  Elahe Delavari, Junaid Farooq · 1. Juni 2026

  Open radio access network (O-RAN) architectures enable near real-time, software-driven control of network slicing through programmable xApps deployed on the near-real-time RAN Intelligent Controller (near-RT RIC). In industrial 5G downlink systems, adversarial jamming can abruptly reduce the effecti…

• TraceCodec: A Compiler-Backed Neural Codec for Stateful Multi-Flow Network Traffic Traces

  Junhui Ding, Xinchen Zhang, Xiaohui Xie, Shinan Liu · 29. Mai 2026

  Critical networking workflows require high-fidelity packet captures (PCAPs) for testing, security analysis, and protocol validation, not just statistical flow-level summaries. Recent packet generators have demonstrated protocol-constrained PCAP synthesis, but they universally decode directly to raw …

• Temporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction

  Yi-Ling Kuo, Hao-Yu Tien, Shih-Yu Tsai · 28. Mai 2026

  Predicting Internet round-trip time (RTT) is critical for routing optimization, quality-of-service (QoS) provisioning, and traffic engineering, yet remains challenging due to long-term temporal dependencies, evolving routing dynamics, and heavy-tailed latency distributions. While Temporal Graph Neur…

• GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing

  Tamerlan Aghayev, Maxime Elkael, Michele Polese, Minh Dat Nguyen, Gabriele Gemmi, Andrea Lacava, Ali Saeizadeh, Reshma Prasad, Paolo Testolina, Angelo Feraudo, Soumendra Nanda, Pedram Johari, Salvatore D'Oro, Tommaso Melodia · 27. Mai 2026

  Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features from standards or research papers into production code; (ii) conformance and interoperability testing; (iii) hardening aga…

• Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models

  Mohammed N. Swileh, Shengli Zhang, Kai Lei · 27. Mai 2026

  Software-Defined Networking (SDN) provides flexible and programmable network management; however, its centralized control architecture remains highly vulnerable to Distributed Denial-of-Service (DDoS) attacks, particularly Carpet-Bombing DDoS attacks that distribute malicious traffic across multiple…

• Certified Causal Attribution for Real-Time Attack Forensics in 6G Network Slicing

  Minh K. Quan, Pubudu N. Pathirana · 27. Mai 2026

  Cross-slice attack attribution in 6G networks requires identifying causal propagation chains through shared infrastructure in under 100 ms. Existing methods struggle to satisfy this strict SLA without sacrificing accuracy, because shared resource contention creates spurious correlations that are ind…

• When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control

  Guilin Zhang, Chuanyi Sun, Kai Zhao, Shahryar Sarkani, John Fossaceca · 27. Mai 2026

  A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ever, does DRL actually help? We study this in RLScale-Bench, a reproducible benchmark and evaluation protocol for DRL on …

• A Token/KV-Cache Communication Media Selection and Resource Allocation Strategy for Multi-Agent Collaboration

  Lipeng Dai, Luping Xiang, Kun Yang · 26. Mai 2026

  The convergence of large language models (LLMs) with 6G networks is fostering a paradigm of autonomous multi-agent cooperation, which in turn is expected to substantially increase east-west traffic. Although latent-space interaction mechanisms can enable more efficient collaboration than symbolic na…

• MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM

  Dongwei Wang, Jinhee Kim, Seokho Han, Denis Gudovskiy, Yohei Nakata, Tomoyuki Okuno, KhayTze Peong, Kang Eun Jeon, Jong Hwan Ko, Yiran Chen, Huanrui Yang · 26. Mai 2026

  Dynamic runtime latency and memory constraints necessitate flexible large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based on available computational resources. Recent work on such any-precision quantization either relies on hardware-inefficien…

• BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems

  Joss Armstrong · 25. Mai 2026

  Compound AI systems route tasks through hierarchies of specialised components. Attribution is dominated by Shapley-based methods (SHAP), which decompose a coalition value function into per-component marginal contributions and require evaluation of the system on arbitrary component subsets. That requ…

• DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks

  Khaled M. Naguib, Soumaya Cherkaoui, Mahmoud M. Elmessalawy, Ahmed M. Abd El-Haleem, Ibrahim I. Ibrahim · 25. Mai 2026

  Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This paper presents an intelligent resource allocation and edge caching framework for 6G O-RAN networks, leveraging Deep Q-Network (DQN) learning for optimizing e…

• Advanced AI Service Provisioning in O-RAN through LLM Engine Integration

  Seyed Bagher Hashemi Natanzi, Pranshav Gajja, Bo Tang, Vijay K. Shah · 25. Mai 2026

  The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual. Large Language Models (LLMs) o…