CORTEXA
← Browse
arxivcs.NIeess.SY2026-07-24

Location-Aware NAS Timer Optimization in NTN-TN Integrated Networks

Cheng Liu, Peng Hu

Efficient Non-Access Stratum (NAS) timer configuration is critical for reliable and energy-efficient Fifth Generation (5G) registration in Non-Terrestrial Network (NTN)-Terrestrial Network (TN) integrated systems, where Low Earth Orbit (LEO) satellite access introduces large registration bursts, heterogeneous propagation paths, and multi-hop satellite routing. Existing 3GPP NAS timers use fixed values, while prior closed-form timer models compute a global timer under network-level assumptions; both fail to capture user equipment (UE)-level differences in propagation delay, Access and Mobility Management Function (AMF) arrival position, and path reliability. In this paper, we propose a location-aware, UE-specific NAS timer optimization method for LEO NTN-TN integrated networks. The proposed method models the path-delay component using service-link geometry, ground-station distance, and Inter-Satellite Link (ISL) hop count, and adapts the endpoint-delay component according to each UE's expected AMF queue exposure and path reliability. Simulation results show that our method reduces registration latency, UE energy consumption, and avoidable registration attempts compared with fixed and global timer configurations, especially when timer over-provisioning causes unnecessary waiting.

View free PDFSource page

Related papers

arxivcs.NIcs.AI2026-07-24

A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation

Fin Gentzen, Marla Grunewald, Iulisloi Zacarias, Mounir Bensalem, Admela Jukan

Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, tool execution, and decision-making. However, the operational reliability of these agentic AI systems…

View free PDFSource page
arxiveess.SY2026-07-24

Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

Hussein Jaffal, Arianna Fois, Sarra Bouchkati, Amirali Mahjoob, Andreas Ulbig

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…

View free PDFSource page
arxivcs.NIcs.MAeess.SY2026-07-24

Predictive Lightweight MARL for Resilient Coverage in Sparse-Signaling Aerial Networks

Chuan-Chi Lai, Ang-Hsun Tsai

This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…

View free PDFSource page
arxivcs.ROeess.SY2026-07-24

Conformal Constraint Tightening for Chance-Constrained Motion Planning with Unknown Dynamics

Shubham Natraj, Bruno Sinopoli, Yiannis Kantaros

Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nomin…

View free PDFSource page
arxiveess.SY2026-07-24

StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting

Zhe Bai, Stephen Harris

This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…

View free PDFSource page
arxiveess.SY2026-07-24

Fast Frequency Services from HVDC-Connected Offshore Wind Power Plants: A Review in the European Context

Zhenghua Xu, George Alin Raducu, Behnam Nouri, Oscar Saborío-Romano, Nicolaos A. Cutululis

The rapid expansion of offshore wind energy is central to the European Union's climate-neutrality targets, with High Voltage Direct Current-connected offshore wind power plants (HVDC-OWPPs) becoming increasingly important for integrating gigawatt-scale renewable generation over l…

View free PDFSource page