ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks

TL;DR

This paper introduces a novel application of Prototypical Networks to improve deep beam prediction generalization across unseen antennas, significantly enhancing accuracy and addressing domain shift challenges in mmWave communication.

Contribution

It pioneers the use of Prototypical Networks for domain adaptation in wireless beam prediction, enabling models to generalize to unseen antenna environments.

Findings

Achieved 74.11% beam classification accuracy across different antennas.

Improved performance by 398% over baseline models without domain adaptation.

First demonstration of Prototypical Networks in wireless domain adaptation.

Abstract

Deep learning techniques have recently emerged to efficiently manage mmWave beam transmissions without requiring time consuming beam sweeping strategies. A fundamental challenge in these methods is their dependency on hardware-specific training data and their limited ability to generalize. Large drops in performance are reported in literature when DL models trained in one antenna environment are applied in another. This paper proposes the application of Prototypical Networks to address this challenge and utilizes the DeepBeam real-world dataset to validate the developed solutions. Prototypical Networks excel in extracting features to establish class-specific prototypes during the training, resulting in precise embeddings that encapsulate the defining features of the data. We demonstrate the effectiveness of PN to enable generalization of deep beam predictors across unseen antennas. Our approach, which integrates data normalization and prototype normalization with the PN, achieves an average beam classification accuracy of 74.11 percent when trained and tested on different antenna datasets. This is an improvement of 398 percent compared to baseline performances reported in literature that do not account for such domain shifts. To the best of our knowledge, this work represents the first demonstration of the value of Prototypical Networks for domain adaptation in wireless networks, providing a foundation for future research in this area.