arXiv subjects

Atefeh Hajijamali Arani

Publications and source records attributed to Atefeh Hajijamali Arani.

6 recordsLinked to original sources

arXiv2026

AoI-Driven Hierarchical Learning for Cooperative Resource Sharing in Multi-Operator UAV Networks

Uncrewed aerial vehicle (UAV)-assisted networks provide a versatile paradigm for on-demand connectivity. However, in multi-operator aerial networks (MOANs), the joint optimization of cooperative resource sharing and 3D trajectory control to maintain information freshness is a complex combinatorial problem, which can be shown to be NP-hard. To address this computational complexity, we propose an age of information (AoI)-driven hierarchical deep reinforcement learning (DRL) framework. Specifically, a Dueling Double Deep Q-Network (D3QN) architecture is deployed at both the operator and UAV decision layers to mitigate overestimation bias and enhance stability in high-dimensional state spaces. To improve system resilience, we introduce an AoI- and load-aware outage compensation mechanism that prioritizes users based on instantaneous transmission demands and temporal freshness. Furthermore, a normalized load exchange balance metric is incorporated to regulate cooperative behavior and ensure resource fairness across operators. Simulation results demonstrate that the proposed hierarchical D3QN significantly outperforms conventional DRL, non-cooperative, and cooperative benchmarks, reducing the average AoI by up to 56.1% under severe congestion while ensuring superior inter-operator fairness and outage mitigation.

eess.SY↗

arXiv2026

LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset

Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and mission requirements require intelligent adaptive decision-making. Existing optimization-based, Machine Learning (ML), and Reinforcement Learning (RL) approaches often rely on predefined models or task-specific training, limiting their generalization and adaptability in uncertain scenarios. Recent Large Language Model (LLM)-assisted approaches offer promising reasoning capabilities but remain constrained by limited agentic functionality, including insufficient memory, planning, and tool interaction mechanisms.This paper proposes an LLM-Agent-Based Path Finder (LAPF) framework for autonomous UAV navigation in town-scale outdoor environments. LAPF extends LLM-assisted navigation by integrating perception, memory, planning, and action modules into a closed-loop cognitive architecture. The proposed agent leverages prior navigation experiences, performs Chain-of-Thought (CoT) reasoning, couples each detected hazard to a bounded corrective action, and dynamically refines waypoint decisions based on environmental feedback.The three independent trials per method demonstrate that LAPF achieves mean path lengths of 512.83 m and 506.37 m, compared to the straight-line optimum of 497.33 m, corresponding to path length reductions of 17.2% and 15.6% relative to CoT prompting and absolute path efficiencies of 97.1% and 98.1% in open-field and obstacle-injected scenarios, respectively. Furthermore, LAPF is the only evaluated approach that couples every detected hazard to a bounded, metric-neutral corrective action while maintaining near-goal stability, with zero clamp events in both scenarios, whereas CoT prompting increases from 9.7 to 14.0 events.

cs.RO↗

arXiv2026

AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring

Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous inspection and sensor data collection in large-scale infrastructure monitoring applications, such as pipeline monitoring, where timely anomaly detection is critical. Jointly optimizing data-collection schedules and flight velocities is a critical challenge, as inefficiencies can increase packet loss and inspection latency. While online deep reinforcement learning (DRL) is a widely investigated approach, it suffers from low sample efficiency, substantial training requirements, and simulation-to-reality gaps in time-sensitive scenarios. Large language models (LLMs) offer a promising alternative through in-context learning (ICL); however, their substantial input requirements can introduce considerable computational and communication overhead. To address this, we propose Attention-Based In-Context Learning for Velocity Control and Data Collection Scheduling (AIC-VDS), a joint optimization framework designed to minimize packet loss under partial and potentially outdated local network-state information. AIC-VDS utilizes an attention module to process real-time network-state data, including sensor battery levels, sensor queue lengths, communication channel conditions, UAV locations, time since the previous sensor visit, and sensor urgency scores. This module extracts task-relevant features to reduce input overhead before querying the LLM. The LLM leverages these compressed natural-language prompts to generate adaptive data-collection schedules and velocity-control decisions for UAV execution. Simulation results show that the attention-based representation reduces the average prompt length by 50\%, while AIC-VDS rapidly stabilizes packet loss in the considered scenario.

cs.AI↗

arXiv2026

Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility. Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence beyond conventional optimization-based and learning-based approaches. By integrating LLMs into UAV systems, advanced environmental understanding, swarm coordination, mobility optimization, and high-level task reasoning can be achieved, thereby allowing more adaptive and context-aware aerial operations. This survey systematically explores the intersection of LLMs and UAV technologies and proposes a unified framework that consolidates existing architectures, methodologies, and applications for UAVs. We first present a structured taxonomy of LLM adaptation techniques for UAVs, including pretraining, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering, along with key reasoning capabilities such as Chain-of-Thought (CoT) and In-Context Learning (ICL). We then examine LLM-assisted UAV communications and operations, covering navigation, mission planning, swarm control, safety, autonomy, and network management. After that, the survey further discusses Multimodal LLMs (MLLMs) for human-swarm interaction, perception-driven navigation, and collaborative control. Finally, we address ethical considerations, including bias, transparency, accountability, and Human-in-the-Loop (HITL) strategies, and outline future research directions. Overall, this work positions LLM-assisted UAVs as a foundation for intelligent and adaptive aerial systems.

cs.RO↗

arXiv2020

Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits

A cellular-connected unmanned aerial vehicle (UAV)faces several key challenges concerning connectivity and energy efficiency. Through a learning-based strategy, we propose a general novel multi-armed bandit (MAB) algorithm to reduce disconnectivity time, handover rate, and energy consumption of UAV by taking into account its time of task completion. By formulating the problem as a function of UAV's velocity, we show how each of these performance indicators (PIs) is improved by adopting a proper range of corresponding learning parameter, e.g. 50% reduction in HO rate as compared to a blind strategy. However, results reveal that the optimal combination of the learning parameters depends critically on any specific application and the weights of PIs on the final objective function.

cs.IT↗

arXiv2020

Learning in the Sky: An Efficient 3D Placement of UAVs

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations can deliver a fast and flexible solution for serving varying traffic demand. In order to adequately benefit of UAVs deployment, their efficient placement is of utmost importance, and requires to intelligently adapt to the environment changes. In this paper, we propose a learning-based mechanism for the three-dimensional deployment of UAVs assisting terrestrial cellular networks in the downlink. The problem is modeled as a non-cooperative game among UAVs in satisfaction form. To solve the game, we utilize a low complexity algorithm, in which unsatisfied UAVs update their locations based on a learning algorithm. Simulation results reveal that the proposed UAV placement algorithm yields significant performance gains up to about 52% and 74% in terms of throughput and the number of dropped users, respectively, compared to an optimized baseline algorithm.

eess.SP↗