Enabling Efficient RF Sensing with Small Language Models via Functional Data Analysis and Parameter Efficient Tuning
Article
Sun, Y, Wang, X, Cao, G et al. (2026). Enabling Efficient RF Sensing with Small Language Models via Functional Data Analysis and Parameter Efficient Tuning
. IEEE Internet of Things Journal, 10.1109/JIOT.2026.3672036
Sun, Y, Wang, X, Cao, G et al. (2026). Enabling Efficient RF Sensing with Small Language Models via Functional Data Analysis and Parameter Efficient Tuning
. IEEE Internet of Things Journal, 10.1109/JIOT.2026.3672036
This paper proposes FDALLM-Small, a unified and lightweight RF sensing framework that integrates Functional Data Analysis (FDA) with parameter 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 LLMs to perform classification and localization tasks with strong accuracy and robustness. Through 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 trade offs, 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.
