Tuğçe Bilen

Ian F. Akyildiz, Tuğçe Bilen

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.

Tuğçe Bilen, Ian F. Akyildiz

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.