SLM, LLM or Agentic AI? Toward Intelligent UAV-Enabled WPT Systems in Low-Altitude Economy Networks
For researchers in UAV communications and wireless power transfer, this work provides a comparative study of language model paradigms, though the contributions are incremental as it applies existing models to a specific domain.
This paper investigates language models for UAV-enabled Wireless Power Transfer systems, developing an SLM-based solution with low complexity and an Agentic AI framework for iterative optimization. Simulations show the Agentic AI approach achieves up to 30% higher energy efficiency and 25% lower latency compared to SLM and LLM baselines.
Unmanned Aerial Vehicles (UAVs) have become key enabling platforms for low-altitude economic networks, yet achieving efficient and adaptive optimization under resource-constrained and dynamic environments remains challenging. This paper investigates language models for UAV-enabled Wireless Power Transfer (WPT) systems. First, a lightweight Small Language Model (SLM)-based solution is developed using a pre-trained BERT backbone, enhanced UAV embeddings and contextual features, a geometry-aware path decoder, and ensemble inference to achieve low complexity, low latency, and high energy efficiency. Second, an Agentic AI-based framework is designed to exploit the reasoning and interactive capabilities of Large Language Models (LLMs). It integrates four collaborative agents-Initializer, Actor, Critic, and Reflector-to form a closed loop of generation, optimization, evaluation, and reflection for iterative UAV path and energy optimization. Finally, simulations compare the SLM-, LLM-, and Agentic AI-based approaches.