arXiv subjects

Tuğçe Bilen

Publications and source records attributed to Tuğçe Bilen.

11 recordsLinked to original sources

arXiv2026

Fluid Antenna Networks Beyond Beamforming: An AI-Native Control Paradigm for 6G

Fluid Antenna Systems (FAS) introduce a new degree of freedom for wireless networks by enabling the physical antenna position to adapt dynamically to changing radio conditions. While existing studies primarily emphasize physical-layer gains, their broader implications for network operation remain largely unexplored. Once antennas become reconfigurable entities, antenna positioning naturally becomes part of the network control problem rather than a standalone optimization task. This article presents an AI-native perspective on fluid antenna networks for future 6G systems. Instead of treating antenna repositioning as an isolated operation, we consider a closed-loop control architecture in which antenna adaptation is jointly managed with conventional radio resource management (RRM) functions. Within this framework, real-time network observations are translated into coordinated antenna and resource configuration decisions that respond to user mobility, traffic demand, and evolving interference conditions. To address the complexity of multi-cell environments, we explore a multi-agent reinforcement learning (MARL) approach that enables distributed and adaptive control across base stations. Illustrative results show that intelligent antenna adaptation yields consistent performance gains, particularly at the cell edge, while also reducing inter-cell interference. These findings suggest that the true potential of fluid antenna systems lies not only in reconfigurable hardware, but in intelligent network control architectures that can effectively exploit this additional spatial degree of freedom.

cs.NI↗

arXiv2026

Semantic-Aware 6G Network Management through Knowledge-Defined Networking

Semantic communication is emerging as a key paradigm for 6G networks, where the goal is not to perfectly reconstruct bits but to preserve the meaning that matters for a given task. This shift can improve bandwidth efficiency, robustness, and application-level performance. However, most existing studies focus solely on encoder-decoder design and ignore network-wide decision-making. As data traverses multiple hops, semantic relevance may decrease, routing may overlook meaningful information, and semantic distortion can increase under dynamic network conditions. To address these challenges, this paper proposes a management-oriented semantic communication framework built upon Knowledge-Defined Networking (KDN). The framework comprises three core modules: a semantic-reasoning module that computes relevance scores by mapping semantic embeddings onto a knowledge graph that encodes task concepts and contextual relationships; a semantic-aware routing mechanism that forwards data along paths that preserve meaning; and a semantic-distortion controller that adaptively adjusts encoding and routing to preserve semantic fidelity. Our ns-3 results show clear benefits: semantic delivery success improves by 12%, semantic distortion decreases by 22%, re-routing events drop by 44%, and throughput efficiency rises by 14% compared to baseline methods (shortest-path, load-based, and distortion-only routing). These results indicate that meaning-aware and feedback-driven control is essential for reliable and scalable semantic communication in future 6G networks.

cs.NI↗

arXiv2026

The Network That Thinks: Kraken* and the Dawn of Cognitive 6G

Future sixth-generation (6G) networks must evolve beyond high-speed data delivery to support intelligent, context-aware services. Emerging applications such as autonomous transportation, immersive extended reality, and large-scale sensing require networks capable of interpreting context, anticipating system dynamics, and coordinating resources according to application objectives rather than relying solely on packet-level metrics. This article introduces Kraken, a knowledge-centric architectural vision for enabling collective intelligence in 6G networks. Kraken integrates three complementary capabilities: semantic communication, which prioritizes the transmission of task-relevant information; generative reasoning, which enables predictive modeling of network and application dynamics; and goal-oriented optimization, which aligns resource allocation with application-level outcomes. These capabilities are organized within a three-plane architecture consisting of an Infrastructure Plane, an Agent Plane, and a Knowledge Plane. Together, these planes enable distributed network entities to perceive context, reason about future states, and coordinate actions through shared semantic representations. The architecture leverages emerging technologies such as O-RAN, network digital twins, and scalable MLOps pipelines, providing a practical evolutionary path from current 5G systems toward knowledge-centric 6G infrastructures. Three representative scenarios illustrate how Kraken improves efficiency and responsiveness in autonomous mobility, immersive XR services, and infrastructure monitoring. The article also outlines key research challenges and discusses the transition from today's data-centric networks toward knowledge-centric collective intelligence in future 6G systems.

cs.NI↗

arXiv2026

Kraken*: Architecting Generative, Semantic, and Goal-Oriented Network Management for 6G Wireless Systems

Sixth-generation (6G) wireless networks are expected to support autonomous, immersive, and mission-critical services that require not only extreme data rates and ultra-low latency but also adaptive reasoning, cross-domain coordination, and objective-driven control across distributed edge-cloud infrastructures. Current AI-enabled network management remains largely data-centric, relying on discriminative models that optimize intermediate quality-of-service metrics without explicitly reasoning about long-term service objectives. This article advocates a transition from bit-centric communication toward knowledge-centric coordination in 6G systems. Semantic communication prioritizes task-relevant information and contextual meaning over raw data delivery, while generative artificial intelligence enables predictive reasoning and adaptive policy synthesis aligned with dynamic service intents. Network optimization is therefore reframed around goal-oriented performance metrics capturing application-level outcomes rather than solely protocol-level indicators. To operationalize this vision, we introduce Kraken, a multi-agent architecture composed of a Knowledge Plane, a distributed Agent Plane, and a semantic-aware Infrastructure Plane. By integrating semantic communication, generative reasoning, and goal-oriented optimization over a shared knowledge substrate, Kraken enables scalable collective intelligence and outlines an evolutionary path from current 5G infrastructures toward knowledge-native 6G systems.

cs.NI↗

arXiv2026

Intelligent 6G Edge Connectivity: A Knowledge Driven Optimization Framework for Small Cell Selection

Sixth-generation (6G) wireless networks are expected to support immersive and mission-critical applications requiring ultra-reliable communication, sub-second responsiveness, and multi-Gbps data rates. Dense small-cell deployments are a key enabler of these capabilities; however, the large number of candidate cells available to mobile users makes efficient user-cell association increasingly complex. Conventional signal-strength-based or heuristic approaches often lead to load imbalance, increased latency, packet loss, and inefficient utilization of radio resources. To address these challenges, this paper proposes a Knowledge-Defined Networking (KDN) framework for intelligent user association in dense 6G small-cell environments. The proposed architecture integrates the knowledge, control, and data planes to enable adaptive, data-driven decision-making. Small-cell conditions are modeled using queueing-theoretic indicators that capture traffic load and waiting-time dynamics. Based on these indicators, a joint optimization objective reflecting latency and packet loss is formulated and solved via Lagrangian relaxation to obtain globally guided association policies. These optimization outcomes are then used to supervise a lightweight Learning Vector Quantization (LVQ) model, enabling fast and scalable inference at the network edge. Extensive NS-3 simulations under varying mobility, traffic load, packet size, and network density demonstrate that the proposed approach consistently outperforms conventional baselines. The framework reduces average latency by 30-45% in high-mobility and heavy-traffic scenarios and decreases packet loss by more than 35% under congestion. The results confirm that combining optimization-driven knowledge with lightweight learning enables scalable, QoS-aware user association for future dense 6G networks.

cs.NI↗

arXiv2026

A Modular DTaaS Architecture for Predictive Slice Management in 6G Systems

The sixth generation (6G) of wireless networks will require fundamentally new orchestration paradigms to meet stringent requirements for ultra-low latency, high reliability, and pervasive intelligence. Network slicing emerges as a key enabler to support diverse services with customized quality-of-service (QoS) guarantees. However, dynamic and fine-grained slice management poses significant challenges in terms of real-time provisioning, SLA assurance, and cross-layer observability. In this paper, we propose a novel Digital Twin as a Service (DTaaS) framework that embeds per-slice digital twins (SDTs) into the orchestration loop. Each SDT maintains a synchronized, real-time representation of its slice, leveraging multi-domain telemetry and deep sequential models to predict traffic evolution and SLA risks. The framework introduces modular intelligence layers, programmable interfaces, and edge-embedded decision-making to enable proactive provisioning, adaptive scaling, and closed-loop SLA assurance. Mathematical formulations for fidelity measurement, predictive control, and optimization objectives are provided to ensure rigor and transparency. Evaluation results demonstrate that DTaaS significantly improves SLA compliance ratio, reduces resource over-provisioning, and lowers average SLA violation probability, offering a scalable and reliable orchestration approach for 6G networks.

cs.NI↗

arXiv2025

Knowledge Defined Networking for 6G: A Reinforcement Learning Example for Resource Management

6G networks are expected to revolutionize connectivity, offering significant improvements in speed, capacity, and smart automation. However, existing network designs will struggle to handle the demands of 6G, which include much faster speeds, a huge increase in connected devices, lower energy consumption, extremely quick response times, and better mobile broadband. To solve this problem, incorporating the artificial intelligence (AI) technologies has been proposed. This idea led to the concept of Knowledge-Defined Networking (KDN). KDN promises many improvements, such as resource management, routing, scheduling, clustering, and mobility prediction. The main goal of this study is to optimize resource management using Reinforcement Learning.

cs.NI↗

arXiv2025

A Modular KDN-Based Framework for IT/OT Autonomy in Industrial Systems

The convergence of Information Technology (IT) and Operational Technology (OT) is a critical enabler for achieving autonomous and intelligent industrial systems. However, the increasing complexity, heterogeneity, and real-time demands of industrial environments render traditional rule-based or static management approaches insufficient. In this paper, we present a modular framework based on the Knowledge-Defined Networking (KDN) paradigm, enabling adaptive and autonomous control across IT-OT infrastructures. The proposed architecture is composed of four core modules: Telemetry Collector, Knowledge Builder, Decision Engine, and Control Enforcer. These modules operate in a closed control loop to continuously observe system behavior, extract contextual knowledge, evaluate control actions, and apply policy decisions across programmable industrial endpoints. A graph-based abstraction is used to represent system state, and a utility-optimization mechanism guides control decisions under dynamic conditions. The framework's performance is evaluated using three key metrics: decision latency, control effectiveness, and system stability, demonstrating its capability to enhance resilience, responsiveness, and operational efficiency in smart industrial networks.

cs.NI↗

arXiv2025

Knowledge-Defined and Twin-Assisted Network Management for 6G

The increasing complexity, dynamism, and heterogeneity of 6G networks demand management systems that can reason proactively and generalize beyond pre-defined cases. In this paper, we propose a modular, knowledge-defined architecture that integrates Digital Twin models with semantic reasoning and zero-shot learning to enable autonomous decision-making for previously unseen network scenarios. Real-time data streams are used to maintain synchronized virtual replicas of the physical network, which also forecast short-term state transitions. These predictions feed into a knowledge plane that constructs and updates a graph-based abstraction of the network, enabling context-aware intent generation via graph neural reasoning. To ensure adaptability without retraining, the management plane performs zero-shot policy matching by semantically embedding candidate intents and selecting suitable pre-learned actions. The selected decisions are translated and enforced through the control plane, while a closed-loop feedback mechanism continuously refines predictions, knowledge, and policies over time. Simulation results confirm that the proposed framework observes notable improvements in policy response time, SLA compliance rate, and intent matching accuracy.

cs.NI↗

arXiv2025

AUV-Assisted Underwater 6G: Environmental Modeling and Multi-Stage Optimization

This study presents a simulation model for underwater 6G networks, focusing on the optimized placement of sensors, AUVs, and hubs. The network architecture consists of fixed hub stations, mobile autonomous underwater vehicles (AUVs), and numerous sensor nodes. Environmental parameters such as temperature, salinity, and conductivity are considered in the transmission of electromagnetic signals; signal attenuation and transmission delays are calculated based on physical models. The optimization process begins with K-Means clustering, followed by sequential application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to refine the cluster configurations. The simulation includes key network dynamics such as multi-hop data transmission, cluster leader selection, queue management, and traffic load balancing. To compare performance, two distinct scenarios -- one with cluster leaders and one without -- are modeled and visualized through a PyQt5-based real-time graphical interface. The results demonstrate that 6G network architectures in underwater environments can be effectively modeled and optimized by incorporating environmental conditions.

cs.NI↗

arXiv2025

Federated Edge Learning for Predictive Maintenance in 6G Small Cell Networks

The rollout of 6G networks introduces unprecedented demands for autonomy, reliability, and scalability. However, the transmission of sensitive telemetry data to central servers raises concerns about privacy and bandwidth. To address this, we propose a federated edge learning framework for predictive maintenance in 6G small cell networks. The system adopts a Knowledge Defined Networking (KDN) architecture in Data, Knowledge, and Control Planes to support decentralized intelligence, telemetry-driven training, and coordinated policy enforcement. In the proposed model, each base station independently trains a failure prediction model using local telemetry metrics, including SINR, jitter, delay, and transport block size, without sharing raw data. A threshold-based multi-label encoding scheme enables the detection of concurrent fault conditions. We then conduct a comparative analysis of centralized and federated training strategies to evaluate their performance in this context. A realistic simulation environment is implemented using the ns-3 mmWave module, incorporating hybrid user placement and base station fault injection across various deployment scenarios. The learning pipeline is orchestrated via the Flower framework, and model aggregation is performed using the Federated Averaging (FedAvg) algorithm. Experimental results demonstrate that the federated model achieves performance comparable to centralized training in terms of accuracy and per-label precision, while preserving privacy and reducing communication overhead.

cs.NI↗