Lightweight and Generalizable AoA Estimation for IoT: A Novel Few-Shot Learning Approach

The Internet of Things (IoT) integrates deep learning (DL) to enhance real-time data processing across diverse applications. However, deploying DL models on resourceconstrained IoT devices remains challenging, especially for tasks such as Angle-of-Arrival (AoA) estimation in dynamic environments. Variations in deployment conditions, such as changing modulation schemes, lead to domain shifts that degrade traditional models' performance, underscoring the need for adaptive, low-complexity DL frameworks. This paper introduces a novel compact phase and amplitude representation within a Prototypical Network-based approach, optimized for domain-adaptive AoA prediction in IoT and validated using real data from a softwaredefined radio (SDR) testbed. Compared to covariance and raw IQ data, our proposed representation reduces Mean Absolute Error (MAE) by approximately 32 % and 55 %, respectively, in unseen modulation scenarios. Further, evaluations on an SDR dataset collected using a$2 \times 2$Uniform Rectangular Array (URA) configuration with seven modulation schemes demonstrate that Prototypical Networks with few-shot learning enable accurate and robust adaptation with minimal data, maintaining high accuracy across both seen and unseen modulations