ProtoAoA: Few-Shot Angle-of-Arrival Estimation using Prototypical Networks

TL;DR

This paper introduces ProtoAoA, a few-shot learning approach using Prototypical Networks for angle-of-arrival estimation in wireless communications, achieving high accuracy with minimal training data.

Contribution

The paper applies Prototypical Networks to AoA estimation, demonstrating effective classification with limited data and real-world validation on an SDR testbed.

Findings

Achieves 3° MAE with 4-shots on unseen angles

Attains 2° MAE with 32-shots

Requires only 23% of dataset classes for training

Abstract

Angle-of-arrival (AoA) estimation is a crucial function in wireless communications used for localization, beam-forming, interference management, and other applications. Deep learning (DL) solutions have been proposed for AoA to mitigate limitations of traditional AoA estimation techniques such as sensitivity to noise and the inability to generalize across different array characteristics. A challenge, however, of DL-based approaches is their reliance on large data collection campaigns and model training. This paper proposes the application of Prototypical Networks (PN) to address this challenge and utilizes a real-world dataset collected on a software defined radio (SDR) testbed to validate the effectiveness of the proposed solution. Prototypical Networks excel in extracting representative embeddings from unstructured input data, establishing class prototypes during training that can be few-shot trained on unseen classes. We demonstrate the efficacy of PNs for AoA classification using complex IQ samples, focusing on its ability to correctly classify new, unseen angles that the model was not trained on previously. Our results show that training our proposed ProtoAoA on only 23% of the AoA dataset classes can attain a mean absolute error (MAE) of 3 degrees with only 4-shots of training on the unseen angles - and an MAE of 2 degrees with 32-shots of training data. These results demonstrate that the developed prototypical network architecture requires remarkably few data samples to achieve reliable AoA estimation - and highlights its potential for other wireless applications where data availability is limited.