Argus: Predictable Millimeter-Wave Picocells with Vision and Learning Augmentation

Hem Regmi, Sanjib Sur
Computer Science and Engineering
University of South Carolina

Argus is the first system to enable millimeter-wave deployers to quickly complete site-surveys without sacrificing the accuracy and effectiveness of thorough network deployment surveys.

Awards

Overview

We propose Argus, a system to enable millimeter-wave (mmWave) deployers to quickly complete site-surveys without sacrificing the accuracy and effectiveness of thorough network deployment surveys. Argus first models the mmWave reflection profile of an environment, considering dominant reflectors, and then use this model to find locations that maximize the usability of the reflectors. The key component in Argus is an efficient machine learning model that can map the visual data to the mmWave signal reflections of an environment and can accurately predict mmWave signal profile at any unobserved locations. It allows Argus to find the best picocell locations to provide maximum coverage and also lets users self-localize accurately anywhere in the environment. Furthermore, Argus allows mmWave picocells to predict device’s orientation accurately and enables object tagging and retrieval for VR/AR applications. Currently, we implement and test Argus on two different buildings consisting of multiple different indoor environments. However, the generalization capability of Argus can easily update the model for unseen environments, and thus, Argus can be deployed to any indoor environment with little or no model fine-tuning.

Publications

  • Argus: Predictable Millimeter-Wave Picocells with Vision and Learning Augmentation
    Hem Regmi, Sanjib Sur
    POMACS/SIGMETRICS’22 Proceedings of the ACM on Measurement and Analysis of Computing Systems, Vol. 6, No. 1, Article 2, March 2022.  [Paper] [Slides] [Talk]
  • Towards Deep Learning Augmented Robust D-Band Millimeter-Wave Picocell Deployment
    Hem Regmi, Sanjib Sur
    SIGMETRICS/PERFORMANCE’22 Proceedings of the ACM Joint International Conference on Measurement and Modeling of Computer Systems, Mumbai, India, June 2022. [Paper] [Poster]
  • 🏆 MilliDrone: A Drone Platform to Facilitate Scalable Survey of Outdoor Millimeter-Wave Signal Propagation
    Ian C. McDowell, Rahul Bulusu, Sanjib Sur
    HotMobile’22 ACM International Workshop on Mobile Computing Systems and Applications, Tempe, Arizona, March 2022.  [Paper] [Poster] (Best Poster Runner-up Award)
  • 🏆 VisualMM: Visual Data & Learning Aided 5G Picocell Placement
    Timothy Dayne Hooks, Hem Regmi, Sanjib Sur
    HotMobile’21 ACM International Workshop on Mobile Computing Systems and Applications, Virtual, February 2021.  [Paper] [Poster] (Best Poster Award)

Presentations

Code and Dataset

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