The Open Radio Access Network (O-RAN) NearReal-Time RAN Intelligent Controller (Near-RT RIC) hosts xApps that perform per-User-Equipment (UE) closed-loop control over the E2 interface, and proactive control of handover, scheduling, and anomaly response depends on the freshness of the underlying counters. Surveyed per-UE xApps that explicitly describe their E2 binding rely on Periodic Report subscriptions through E2 Service Model for Key Performance Measurement (E2SM-KPM), which forces every measurement to wait for a configured reporting period before reaching the Near-RT RIC. This paper surveys seven per-UE Near-RT RIC xApp works published between 2023 and 2026 and aligns them with the Measurement Event trigger style introduced in E2SM-KPM v7.0 (2025). The new trigger style enables cell-condition-gated per-UE reporting through REPORT Service Styles 3 and 5, while perUE-bound event conditions remain explicitly out of scope and constitute a clean specification-evolution opportunity. Closing the gap between Periodic-only xApp practice and the Measurement Event primitive opens a class of event-driven per-UE xApps that the current control loop cannot support.
Thanh Thien-An Dang, Dongwook Won, Juyoung Kim et al.· International Conference on...· 1 citation
Unmanned aerial vehicles (UAVs) are emerging as mobile edge nodes for temporary coverage, aerial sensing, disaster response, public-safety monitoring, intelligent transportation, and smart-agriculture services. Although federated learning (FL) enables distributed model training without transferring raw data, conventional flat FL is not well aligned with UAV-enabled edge systems because ground clients may experience mobility-dependent availability, unstable wireless links, and costly longrange synchronization with a remote coordinator. Hierarchical federated learning (HFL) mitigates these limitations by introducing UAV edge aggregators between ground clients and the global coordinator. This article provides a simulation-based assessment of a secure HFL-UAV architecture implemented in Python. The simulator captures non-independent and identically distributed (non-IID) client data, mobility-aware client-UAV association, two-level model aggregation, malicious client behavior, trust-weighted robust aggregation, optional differential privacy, secure-aggregation overhead, communication cost, latency, and UAV energy consumption. Under a model-replacement attack, the secure HFL-UAV scheme maintains the learning performance of the considered synthetic classification task while substantially reducing the aggregation trust assigned to malicious clients. The results also expose important design tradeoffs: security mechanisms increase local communication overhead, UAV-assisted aggregation introduces energy cost, and trust filtering must be calibrated carefully under non-IID data. The proposed simulation framework therefore provides a reproducible basis for evaluating secure aerial edge learning systems.
Ton That Tam Dinh, Manh Cuong Ho, Ayalneh Bitew Wondmagegn et al.· International Conference on...· 0 citations
Intent-Based Networking (IBN) has emerged as a promising paradigm for simplifying network management by allowing operators and applications to specify high-level service objectives rather than low-level device configurations. Early IBN research was mainly developed in Software-Defined Networking (SDN), Network Function Virtualization (NFV), transport networks, core networks, and data-center environments, where programmability, virtualization, and relatively stable infrastructure models enabled intent translation, orchestration, and assurance. However, realizing IBN in end-to-end mobile networks is more challenging because the Radio Access Network (RAN) is highly dynamic, wireless-channeldependent, mobility-sensitive, and governed by multiple control timescales. The emergence of Open RAN (O-RAN) changes this landscape by making the RAN programmable, disaggregated, data-driven, and control-lable through non-real-time and near-real-time intelligent control loops. This survey reviews the evolution of IBN from SDN/NFV-enabled automation toward O-RAN-driven end-to-end intent-based networking for 5G-Advanced and 6G. We discuss architectural mechanisms, key challenges, recent advances in AI-driven and agentic IBN, and future research directions including Large Language Model (LLM)-based intent translation, contractbased O-RAN slicing, digital twin-assisted validation, and trustworthy closed-loop orchestration.
Dongwook Won, Thanh Thien-An Dang, Ton That Tam Dinh et al.· International Conference on...· 0 citations
