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Enabling Efficient RF Sensing With Small Language Models via Functional Data Analysis and Parameter Efficient Tuning
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DOI：10.1109/JIOT.2026.3672036.png)
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Abstract
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This article proposes FDALLM-Small, a unified and lightweight radio frequency (RF) sensing framework that integrates functional data analysis (FDA) with parameters efficiently tuned small language models. By transforming raw RF measurements into smooth and structured functional embeddings and encoding them into standardized functional prompts, the framework enables compact large language models (LLMs) to perform classification and localization tasks with strong accuracy and robustness. Through low-rank adaptation (LoRA)-based fine-tuning, small LLMs effectively learn discriminative RF patterns while updating only a tiny fraction of model parameters, making the approach highly efficient and suitable for on-device deployment. Experiments on the XRF55 and AdaRF datasets demonstrate that the FDA-prompting pipeline substantially boosts model performance, allowing small LLMs to surpass conventional deep learning baselines and approach the accuracy of large API-based LLMs without relying on cloud computation. A scaling study further shows that smaller models consistently offer the best performance–efficiency tradeoffs, highlighting the intrinsic compatibility between FDA representations and compact architectures. These results confirm the practicality of FDALLM-Small as an edge-friendly and computationally efficient solution for real-world RF sensing applications.
Keywords:
Functional data analysis (FDA)
large language models (LLMs)
parameter-efficient fine-tuning (PEFT)
radio frequency (RF) sensing
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