Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction

TL;DR

This paper introduces a multimodal transformer framework that leverages images, GPS, radar, and LiDAR data to improve beam prediction accuracy in high-frequency wireless communications, demonstrating superior performance over other modalities.

Contribution

The paper presents a novel multimodal transformer deep learning approach for sensing-assisted beam prediction, integrating multiple data sources and advanced training techniques.

Findings

Achieved 78.44% accuracy in beam prediction using image and GPS data.

Demonstrated effective generalization to unseen day and night scenarios.

Outperformed other modalities and data processing techniques in accuracy.

Abstract

Wireless communications at high-frequency bands with large antenna arrays face challenges in beam management, which can potentially be improved by multimodality sensing information from cameras, LiDAR, radar, and GPS. In this paper, we present a multimodal transformer deep learning framework for sensing-assisted beam prediction. We employ a convolutional neural network to extract the features from a sequence of images, point clouds, and radar raw data sampled over time. At each convolutional layer, we use transformer encoders to learn the hidden relations between feature tokens from different modalities and time instances over abstraction space and produce encoded vectors for the next-level feature extraction. We train the model on a combination of different modalities with supervised learning. We try to enhance the model over imbalanced data by utilizing focal loss and exponential moving average. We also evaluate data processing and augmentation techniques such as image enhancement, segmentation, background filtering, multimodal data flipping, radar signal transformation, and GPS angle calibration. Experimental results show that our solution trained on image and GPS data produces the best distance-based accuracy of predicted beams at 78.44%, with effective generalization to unseen day scenarios near 73% and night scenarios over 84%. This outperforms using other modalities and arbitrary data processing techniques, which demonstrates the effectiveness of transformers with feature fusion in performing radio beam prediction from images and GPS. Furthermore, our solution could be pretrained from large sequences of multimodality wireless data, on fine-tuning for multiple downstream radio network tasks.