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ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications

Muhammad Alrabeiah, Andrew Hredzak, Zhenhao Liu, Ahmed Alkhateeb

arXiv:1911.06257v2cs.LGcs.ITeess.SPmath.ITstat.ML

TL;DR

Vision-aided wireless communication needs suitable development datasets, yet publicly available data combining visual and wireless information are absent. ViWi uses 3D modeling and ray tracing to generate paired synthetic data, and its framework offers datasets and a shared basis for benchmarking solutions.

  • Problem

    Vision-aided wireless research needs suitable datasets for algorithm development and evaluation, but no publicly available datasets are known to provide co-existing visual and wireless data.

    Source: §I · p. 1

  • Method

    ViWi uses 3D modeling and ray-tracing simulators to generate parameterized visual and wireless data from the same scenes.

    Source: §I · p. 1

  • Results

    ViWi offers four outdoor datasets, each with an RGB image, depth map, wireless channel, and user location, for benchmarking different solutions.

    Source: §I · p. 2, §VII · p. 5

  • Takeaways & Limitations

    The framework provides a unified, modular way to generate development datasets and benchmark vision-aided wireless solutions.

    Source: §II · p. 3, §VII · p. 5

Abstract

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The growing role that artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of \textit{vision-aided wireless communications}, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first and most important challenge to vision-aided wireless communications. This paper addresses this issue by introducing the Vision-Wireless (ViWi) dataset framework. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed.

I. INTRODUCTION

Vision-aided wireless communication could use visual data to address challenges such as beam training and blockage prediction, but research in this direction needs suitable development datasets. ViWi provides a framework for generating paired visual and wireless data and releases four datasets.

I. INTRODUCTION

  • Large antenna arrays face channel and beam training overhead and mmWave blockage sensitivity, motivating investigation of visual data for wireless tasks.The passage names beam and blockage prediction, channel subspace prediction, hand-over prediction, and proactive network management as possible tasks.Source: §I · p. 1
  • To the authors’ knowledge, no publicly available dataset provides co-existing visual and wireless data, despite the need to evaluate algorithms and compare solutions on common data.Useful datasets should also be accurate, scalable across scenarios and sizes, and parameterized for system, channel, and visual-data settings.Source: §I · p. 1
  • ViWi uses 3D modeling and ray-tracing simulators to generate high-fidelity synthetic visual and wireless data from the same scenes.Its goal is to encourage research that uses computer vision, machine learning, and point-cloud analysis for wireless challenges.Source: §I · pp. 1–2
  • The first release makes four ViWi datasets publicly available, each comprising an image, depth map, wireless channel, and user location.The framework is intended to support algorithm development and evaluation, reproducibility, benchmarks, and comparison on common data.Source: §I · pp. 1–2

II. FRAMEWORK OVERVIEW

ViWi generates synthetic visual and wireless data through a three-stage pipeline. Its scenario-and-parameter description makes datasets reproducible and supports benchmarking.

II. FRAMEWORK OVERVIEW

  • Synthetic data generation is motivated by the cost and scalability challenges of acquiring real-world visual and wireless data with separate equipment and lengthy collection processes.The framework uses game engines and ray-tracing software as its backbone.Source: §II · p. 2
  • The pipeline defines a scenario, generates visual and wireless raw data, then processes both using user-defined parameters.Scenario definition builds visual elements in a game engine and models their electromagnetic properties in ray-tracing software; raw data include images, depth maps, point clouds, and ray properties.Source: §II · p. 2
  • Parameterized processing can transform visual data and produce wireless channels and user locations from raw data.Visual options include changing color space or resolution, adding artifacts, and distorting point clouds.Source: §II · p. 2
  • A dataset is fully described by its scenario name and parameter sets, enabling its regeneration and supporting benchmarking.The framework states that these inputs suffice to describe or reproduce a dataset.Source: §II · pp. 2–3

III. VIWI: A DETAILED DESCRIPTION

The section explains ViWi’s stages through two example datasets generated from the same scenario.

III. VIWI: A DETAILED DESCRIPTION

  • Two example datasets—distributed-camera and co-located-camera—illustrate ViWi’s stages using the same example scenario.The examples are used to examine the inner workings of each stage.Source: §III · p. 3

A. Scenarios Definition

The example scenario pairs a detailed visual city-street scene with a ray-tracing version whose complexity can be reduced for simulation.

A. Scenarios Definition

  • The visual instance depicts a car driving through a city street, with five possible car trajectories, each containing 1,000 points spaced 0.089 meters apart and trajectories separated by 0.5 meters.The scene is built in Blender from metropolitan elements and is used for both example datasets with minor visual changes.Source: A. Scenarios Definition · p. 3
  • For ray tracing, fine visual details that slow simulation can be removed or replaced with simpler geometry without major impact on simulation results.The wireless instance is completed by assigning dielectric properties to its objects.Source: A. Scenarios Definition · p. 3
  • Although the first ViWi release can generate four datasets from four scenario raw-data sets, the discussion uses two examples for clarity.Source: A. Scenarios Definition · p. 3

B. Raw-Data Generation

ViWi generates visual and wireless raw data from separate instances of the same scenario. The visual setup uses distributed or co-located cameras, while the wireless setup uses transmitters and receivers along the car trajectories.

B. Raw-Data Generation

  • Both scenario instances generate visual and wireless raw data, including RGB images, depth maps, angles of departure, path gains, and channel impulse responses.Each simulated ray from transmitter to receiver contributes wireless raw data.Source: B. Raw-Data Generation · p. 3
  • The distributed-camera dataset uses three cameras 30 meters apart; the co-located-camera dataset places three differently oriented cameras midway along the street.The distributed cameras are 5 meters high with 100-degree fields of view; the co-located cameras use 75-, 110-, and 75-degree fields of view.Source: B. Raw-Data Generation · p. 3
  • Wireless generation uses 60 GHz half-wave dipoles, with three base stations in the distributed setup and one in the co-located setup.Receivers are placed along the same five predefined car trajectories in both datasets.Source: B. Raw-Data Generation · pp. 3–4
  • The wireless scenario instance simplifies some visual geometry, including omitting traffic lights and using simpler buildings than the visual instance.Source: Figure 2 · p. 4

C. Parametrized Processing

ViWi's optional, parameterized processing stage lets users control how raw visual and wireless data are turned into task-specific datasets. Visual and wireless processing are handled independently.

C. Parametrized Processing

  • An optional processing stage converts primitive raw data into a final dataset when users need control over acquisition quality or environmental settings.Source: C. Parametrized Processing · p. 4
  • Visual-data scripts select a scenario and can apply blurring, noise corruption, resolution control, and color-space transformations to images or depth maps.Source: C. Parametrized Processing · p. 4
  • The example visual setups contrast distributed cameras with cameras co-located midway along the street.Source: Figure 3 · p. 4
  • Wireless scripts parameterize active base stations, antenna counts across three axes, and antenna spacing to produce task-specific samples such as channels and user locations.The wireless parameter definitions follow those of the DeepMIMO dataset.Source: C. Parametrized Processing · p. 4

IV. HOW TO USE VIWI?

The first ViWi release includes four sets of raw data and a package for generating datasets. Visual data are directly included, while wireless data require script-based processing.

IV. HOW TO USE VIWI?

  • The release provides four raw-data sets and a dataset-generating package; it includes unprocessed visual data directly in the final datasets.Visual files use JPEG for RGB images and MAT format for depth maps.Source: §IV · p. 4
  • RGB images and corresponding depth maps are stored separately at 720p resolution, or 720 × 1280 pixels.Depth maps use MATLAB-native MAT data structures readable with Python or R.Source: §IV · p. 4
  • Generating visual samples requires unpacking the compressed directory containing the image and depth-map subdirectories.Source: §IV · p. 5
  • Wireless raw data are unpacked and processed with the ViWi script; each base station contributes files for angles of departure, complex impulse response, and path gains.The distributed-camera dataset has nine wireless MAT files, while the co-located-camera dataset has three.Source: §IV · p. 5

V. POSSIBLE APPLICATIONS

Vision-aided wireless communications is presented as a relatively new research direction with potential. ViWi can support investigations of more problems and benchmarking of more computer-vision-powered solutions.

V. POSSIBLE APPLICATIONS

  • Vision-aided wireless communications is a relatively new research direction with potential.Source: §V · p. 5
  • ViWi makes it possible to investigate more problems in vision-aided wireless communications.Source: §V · p. 5
  • ViWi can be used to benchmark more computer-vision-powered solutions.Source: §V · p. 5

A. Camera-Aided Beam Prediction

Camera-aided beam prediction is proposed as an extension to approaches that rely exclusively on wireless data, using visual information about the transmitter and receiver surroundings. ViWi’s two datasets pair RGB images and depth maps with channels for every user position.

A. Camera-Aided Beam Prediction

B. Blockage Prediction

Blockage prediction remains an elusive problem requiring awareness of surroundings and their dynamics, together with intelligent analysis and prediction. ViWi’s colocated-camera dataset offers data for studying more advanced approaches.

B. Blockage Prediction

  • Blockage prediction is described as elusive in wireless communications and requires sensing surroundings and their dynamics alongside intelligent analysis and prediction.Source: B. Blockage Prediction · p. 5
  • Machine-learning approaches to blockage prediction have been investigated, with overall promising results.Source: B. Blockage Prediction · p. 5
  • ViWi’s colocated-camera dataset supports study of advanced blockage-prediction solutions with wireless and depth data, RGB images, and user locations.Source: B. Blockage Prediction · p. 5

VII. CONCLUSION

ViWi is a framework for unified, modular dataset generation and benchmarking in vision-aided wireless communications. Its current version provides four outdoor datasets, with future work aimed at expanding scenarios and data-processing features.

VII. CONCLUSION

  • ViWi offers a unified, modular framework for generating development datasets and benchmarking different vision-aided wireless solutions.Source: §VII · p. 5
  • The current ViWi version provides four datasets for outdoor scenarios, each containing sequences of RGB images, depth maps, wireless channels, and user locations.Source: §VII · p. 5
  • Future work includes expanding ViWi’s scenario database and incorporating more data-processing features.Source: §VII · p. 5
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