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Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets

Gouranga Charan, Tawfik Osman, Andrew Hredzak, Ngwe Thawdar, Ahmed Alkhateeb

arXiv:2111.07574v1eess.SPcs.IT

TL;DR

Large-array mmWave/THz systems incur high beam-training overhead, motivating faster beam selection for mobile applications. The paper combines camera and position data in a multimodal machine-learning framework evaluated on real-world data, achieving over 75% top-1 and nearly 100% top-3 accuracy.

  • Problem

    Large antenna arrays and narrow beams make beam selection costly, while practical position and visual sensors have accuracy and environmental limitations.

  • Method

    A multimodal machine-learning framework predicts codebook beams from camera imagery and user position, using position-only, vision-only, and joint models.

  • Results

    The joint model achieves more than 75% top-1 beam prediction accuracy and close to 100% top-3 accuracy.

  • Takeaways & Limitations

    Multimodal sensory data can support fast beam prediction and substantially reduce beam-training overhead in realistic mmWave/THz scenarios.

Abstract

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Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenna arrays deployed at these systems. In particular, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. To address these challenges, this paper proposes a multi-modal machine learning based approach that leverages positional and visual (camera) data collected from the wireless communication environment for fast beam prediction. The developed framework has been tested on a real-world vehicular dataset comprising practical GPS, camera, and mmWave beam training data. The results show the proposed approach achieves more than $\approx$ 75\% top-1 beam prediction accuracy and close to 100\% top-3 beam prediction accuracy in realistic communication scenarios.

I. INTRODUCTION

MmWave/THz systems need large antenna arrays and narrow beams, but beam selection creates substantial training overhead for highly mobile applications. The paper therefore proposes combining positional and visual data for beam prediction.

  • Large antenna arrays and narrow beams create high beam-training overhead that hinders highly mobile mmWave/THz applications.
  • Classical adaptive codebooks, beam tracking, and compressive-sensing methods typically reduce training overhead by only one order of magnitude.
  • Practical positioning sensors may be too inaccurate for narrow-beam alignment, while visual data can be sensitive to lighting and weather.
  • The proposed framework combines position and camera data to improve robustness to positioning inaccuracies and visual-condition changes.

II. MILLIMETER WAVE BEAM PREDICTION: SYSTEM MODEL AND PROBLEM FORMULATION

The system uses a large-array mmWave base station with an RGB camera to serve a mobile user through a predefined beam codebook. The formulation frames beam prediction as selecting the codebook beam that maximizes received signal power.

  • II. MILLIMETER WAVE BEAM PREDICTION: SYSTEM MODEL AND PROBLEM FORMULATION: The beam-prediction challenge arises because finding optimal transmitter and receiver beams incurs substantial control overhead in large-array mmWave and THz systems.
  • II. MILLIMETER WAVE BEAM PREDICTION: SYSTEM MODEL AND PROBLEM FORMULATION: The task uses visual and position side information to predict the best beamforming vector from a predefined codebook.
  • II. MILLIMETER WAVE BEAM PREDICTION: SYSTEM MODEL AND PROBLEM FORMULATION: The formulation models beam prediction as a multi-modal classification problem based on information available from the wireless environment.
  • A. System Model: The system comprises a mmWave base station with an N-element antenna array and RGB camera serving a single-antenna mobile user.
  • A. System Model: A predefined codebook F contains M beamforming vectors, each represented as an N-dimensional complex vector.
  • A. System Model: The received signal uses the selected beamforming vector, transmitted complex symbol, average transmit power, and additive complex Gaussian noise.

B. Problem Formulation

The paper predicts the optimal codebook beam from camera images and real-time user position rather than explicit channel knowledge. A learned model maps each image-location pair to beam probabilities and selects the highest-probability beam.

  • B. Problem Formulation: The task selects the codebook beam that maximizes received signal power for a single user.
  • B. Problem Formulation: The model uses RGB images and real-time two-dimensional user position instead of explicit channel knowledge.
  • B. Problem Formulation: A learned prediction function maps each image-location pair to a probability distribution over the codebook beams.
  • B. Problem Formulation: The beam with the maximum predicted probability becomes the selected beamforming vector.
  • B. Problem Formulation: The model parameters are learned from labeled samples containing image-location inputs and ground-truth optimal beams.
  • B. Problem Formulation: The next stage presents a multi-modal machine-learning model for mmWave/THz beam prediction.

III. PROPOSED SOLUTION: MULTI-MODAL VISION-POSITION BASED BEAM PREDICTION

The proposed solution is a machine-learning multimodal beam-prediction framework for scenarios with one candidate user visible in the scene. It addresses data-fusion and neural-network design challenges by combining visual and positional information.

  • III. PROPOSED SOLUTION: MULTI-MODAL VISION-POSITION BASED BEAM PREDICTION: The paper proposes multimodal machine learning for beam prediction when a single candidate user appears in the visual scene.
  • III. PROPOSED SOLUTION: MULTI-MODAL VISION-POSITION BASED BEAM PREDICTION: The solution considers data-fusion strategy and neural-network design as central challenges of combining visual and positional modalities.

A. Key Idea

The paper replaces conventional narrow-beam training with beam selection based on visual and positional information, allowing either modality to contribute to prediction.

  • A. Key Idea: Narrow beams provide high received-power gain but require substantial training overhead with large antenna arrays.This overhead is a central motivation for side-information-based prediction.
  • A. Key Idea: Beam selection uses visual and/or position data instead of conventional beam training.The target is the optimal beam index from a predefined beam codebook.
  • A. Key Idea: The framework evaluates whether combining visual and position data improves on using either modality alone.The paper explicitly compares vision-only, position-only, and multimodal prediction.

B. Machine Learning Model

The proposed model compares separate vision-based and position-based classifiers with a multimodal architecture that fuses learned visual features and normalized position data.

  • B. Machine Learning Model: Three models predict the optimal beam index from vision alone, position alone, or both modalities.The multimodal model merges the two data sources before classification.
  • B. Machine Learning Model: The vision-only model fine-tunes an ImageNet2012-pretrained ResNet-50 after replacing its final layer with an M-neuron classifier.It maps RGB images to beam indices in a supervised end-to-end training procedure.
  • B. Machine Learning Model: The position-only model uses normalized latitude and longitude as inputs to a two-hidden-layer MLP with 256 units per hidden layer.Its output layer contains M units corresponding to beam classes.
  • B. Machine Learning Model: The multimodal architecture concatenates a CNN-derived visual feature vector with normalized position data before an MLP classifier.The classifier outputs a probability distribution over the codebook's M beams.

IV. TESTBED DESCRIPTION AND DEVELOPMENT DATASET

The study uses a real-world vehicular mmWave testbed and aligned wireless, visual, and position data collected across distinct wireless environments and times of day.

  • IV. TESTBED DESCRIPTION AND DEVELOPMENT DATASET: The development dataset combines vehicle-transmitter, base-station receiver, GPS, RGB-camera, and mmWave beam-training data.The testbed uses a 60 GHz transmitter and a 16-element phased-array receiver with a 64-beam codebook.
  • IV. TESTBED DESCRIPTION AND DEVELOPMENT DATASET: Fig. 3 shows two wireless environments, with daytime data in (a) and nighttime images in (b) and (c).The vehicle transmitter is highlighted in the displayed environments.
  • IV. TESTBED DESCRIPTION AND DEVELOPMENT DATASET: The collected position data are aligned with the RGB and wireless measurements.This alignment supports multimodal beam-prediction experiments.

B. Development Dataset

The development dataset draws on two DeepSense 6G scenarios collected at different locations and times, then preprocesses the beam-power data for beam prediction.

  • B. Development Dataset: Scenarios 5 and 6 provide diverse wireless, visual, and position data collected at different locations and times of day.Different base-station heights and inter-station distances further enhance dataset diversity.
  • B. Development Dataset: The raw 64-beam power vector is downsampled to 32 elements by selecting every other beam.Because the original beams are highly overlapped, this downsampling does not reduce the total covered area.

V. EXPERIMENTAL SETUP

The evaluation trains and compares vision-only, position-only, and joint vision-position models using top-k beam prediction accuracy on a validation set.

  • Compared Models: Three models are evaluated: vision-based ResNet-50, position-based two-layer MLP, and a multi-modal vision-position model.The vision and position models are trained on image and position data, respectively.
  • Evaluation Procedure: The models are trained and tested on validation data to assess the effect of different sensory modalities on beam prediction.
  • Evaluation Metrics: Top-1 accuracy is the primary metric, complemented by top-2 and top-3 accuracy for evaluating beam prediction.Top-k accuracy measures the percentage of validation samples whose optimal beam appears among the k highest-probability predictions.

VI. PERFORMANCE EVALUATION

The multi-modal model generally outperforms vision-only and position-only alternatives across day and night datasets, achieving strong top-1 and near-perfect top-3 accuracy.

  • Model Comparison: More than 75% top-1 and close to 100% top-3 accuracy are achieved by the multi-modal model across the evaluated datasets.Top-3 accuracy means that testing only the three suggested beams can match exhaustive search over 32 beams.
  • Model Comparison: The multi-modal model generally achieves higher beam prediction accuracy than both position-only and vision-only models.The comparison covers top-1, top-2, and top-3 accuracies for day and night data.
  • Model Comparison: Vision-based prediction outperforms position-based prediction for both day and night datasets.The paper attributes this difference to practical GPS positions being insufficiently accurate for narrow-beam prediction.
  • Dataset Size: The multi-modal model learns the beam prediction task using 40% of the total training samples, approximately 840 of 2100 samples.The dataset contains around 3000 points, split into 70% training and 30% validation.
  • Dataset Size: Smaller datasets may suffice for close to 100% beam prediction accuracy when evaluating the top three predicted beams.

VII. CONCLUSION

The paper proposes multimodal beam prediction from visual and positional data and evaluates it on a real-world vehicular dataset. It reports more than 75% optimal-beam prediction success without beam training overhead and close to 100% top-3 accuracy.

  • Conclusion: The approach uses visual and positional sensory data with multimodal machine learning for fast mmWave/THz beam prediction.It is evaluated using a real-world dataset containing position, camera, and mmWave beam training data.
  • Conclusion: More than 75% success probability is achieved for predicting the optimal beam without beam training overhead, with close to 100% top-3 accuracy.
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