While reconfigurable intelligent surfaces (RISs) are among the key enablers for next-generation (NextG) wireless networks, efficient feedback reporting for joint base station (BS) precoding and passive RIS configuration remains a major challenge due to the associated signaling overhead. By extending the standard-compliant channel state information reference signal framework, this paper introduces a novel channel information indicator (CII) that jointly represents the active BS precoding matrix and passive RIS configuration within a single feedback metric for multi-user multiple-input single-output systems. Simulation results demonstrate that the proposed unified feedback framework significantly reduces uplink signaling overhead compared with conventional disjoint reporting schemes. Furthermore, despite only a modest increase in the feedback payload, the proposed CII-based scheme outperforms conventional precoding matrix indicator approaches in terms of system performance, offering a practical and standards-compatible solution for RIS integration in NextG wireless networks.
Ali Fuat Sahin, Sefa Kayraklik, Ali Gorcin et al.· 0 citations
The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strategic reasoning required to quickly adapt to dynamic Integrated Terrestrial and Non-Terrestrial Networks (ITNTNs). Conversely, Large Language Models (LLMs) excel at semantic reasoning but suffer from high inference latency, rendering them unsuitable for real-time aerodynamic control. To bridge this gap, we propose a novel Hierarchical LLM-driven control framework. A massive cloud-based LLM deployed on a High-Altitude Platform Station (HAPS) manages slow-timescale global load balancing, while lightweight edge-LLMs on individual UAVs translate local observations into tactical sub-goals. These sub-goals guide a fast-timescale physical DRL controller to execute collision-free, handover-aware trajectories. Simulation results demonstrate that our agentic architecture significantly reduces collision rates and improves aggregate system throughput compared to existing baselines.
Zijiang Yan, Hao Zhou, W. Jaafar et al.· arXiv.org· 0 citations
Simulation results show that relaying information via FD-HAPS significantly improves the capacity of cell-edge UEs compared with a terrestrial-only network.
A hybrid LEO-terrestrial architecture that integrates Software-Defined Networking ground station clusters with repeater-assisted reception and a structured fallback mechanism is proposed and results highlight the effectiveness of the proposed framework in enabling adaptive and delay-efficient control in next-generation LEO satellite systems.
Wafa Hasanain, Pablo G. Madoery, H. Yanikomeroglu et al.· IEEE Open Journal of the Com...· 0 citations
Simulation results reveal that both setup penalties and controller activation thresholds are critical determinants of system behavior across both optimization-based and heuristic schemes, highlighting the necessity of jointly optimizing reassignment policies and controller activation strategies to support robust, low-latency, and resource-aware SDN architectures for large-scale LEO satellite constellations.
Wafa Hasanain, Pablo G. Madoery, Halim Yanikomeroglu et al.· IEEE Open Journal of the Com...· 0 citations
Simulation results demonstrate that the proposed GNN-RSMA interference management algorithm outperforms conventional multiple access schemes while achieving fairness and worst-user performance comparable to successive convex approximation (SCA)-based optimization at only a fraction of its computational cost.
Afsoon Alidadi Shamsabadi, Animesh Yadav, H. Yanikomeroglu· 0 citations
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