Vision-Aided Beam Prediction

Leaderboard

Date	Name	scenario5	scenario6	scenario7	scenario8	scenario9
1/15/2021	Wireless Intelligence Lab ASU	Top-1: 58.27% Top-2: 79.47% Top-3: 92.72%	Top-1: 41.67% Top-2: 68.75% Top-3: 87.50%	Top-1: 43.90% Top-2: 70.73% Top-3: 82.93%	Top-1: 71.92% Top-2: 93.46% Top-3: 98.84%	Top-1: 58.59% Top-2: 80.00% Top-3: 91.55%

This table documents the different proposed vision-aided beam prediction solutions and provides a way for benchmarking.
For the individual DeepSense scenarios (development datasets), we use the “Top-k” beam prediction accuracy as the evaluation metric.
For further details and information regarding the ML challenge and how to participate, please check the “ML Challenge” section below.

License

If you want to use the dataset or scripts in this page, please cite the following two papers:

A. Alkhateeb, G. Charan, T. Osman, A. Hredzak, J. Morais, U. Demirhan, and N. Srinivas, “DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Datasets,” IEEE Communications Magazine, 2023.

@Article{DeepSense,
author={Alkhateeb, Ahmed and Charan, Gouranga and Osman, Tawfik and Hredzak, Andrew and Morais, Joao and Demirhan, Umut and Srinivas, Nikhil},
title={DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset},
journal={IEEE Communications Magazine},
year={2023},
publisher={IEEE}}

G. Charan, T. Osman, A. Hredzak, N. Thawdar and A. Alkhateeb, “Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets,” In Proc. of IEEE Wireless Communications and Networking Conference (WCNC), 2022.

@INPROCEEDINGS{Charan2022,
title={Vision-position multi-modal beam prediction using real millimeter wave datasets},
author={Charan, Gouranga and Osman, Tawfik and Hredzak, Andrew and Thawdar, Ngwe and Alkhateeb, Ahmed},
booktitle={2022 IEEE Wireless Communications and Networking Conference (WCNC)},
pages={2727–2731},
year={2022},
organization={IEEE}}

Introduction

Beam selection is a challenge: Millimeter-wave (mmWave) and sub-terahertz (sub-THz) communications are key pillars for current and future wireless communication networks. The large available bandwidth at the high frequency bands enables these systems to satisfy the increasing data rate demands of the emerging applications in autonomous driving, edge computing, mixed-reality, etc. These systems, however, require deploying large antenna arrays at the transmitters and/or receivers and using narrow beams to guarantee sufficient receiver power. Finding the best beams (out of a codebook) at the transmitter and receiver is associated with high beam training overhead (search time to find/align the best beams), which makes it hard for these systems to support highly-mobile and latency-sensitive applications.

Sensing aided beam prediction is a promising solution: The dependence of mmWave/sub-THz communication systems on the line-of-sight links between the transmitter/receiver means that the awareness about their locations and the surrounding environment (geometry of the buildings, moving scatterers, etc.) could potentially help the beam selection process. For example, the sensory data collected by RGB cameras, LiDARs, Radars, GPS receivers, etc., can enable the transmitter/receive decide on where to point their beams (or at least narrow down the candidate beam steering directions). We call this approach sensing-aided beam prediction/selection. Vision-aided beam prediction is a special case when the transmitter/receiver attempt to leverage visual data of the wireless environment, captured by RGB images, to predict their optimal beam indices.

Vision-aided beam prediction: Specific Task Description

Vision-aided beam prediction at the infrastructure is the task of predicting the optimal beam indices from a pre-defined codebook by utilizing a machine learning model and the images of the wireless environment captured by the camera installed at the basestation.

Objective of the ML Task: Given an image of the wireless environment captured (by the basestation) at any time step t, the primary objective of this task is to design a machine learning model that predicts the optimal beam index. From the wireless communication perspective, the machine learning model aims to return the index of the beam (from a pre-defined codebook) that maximizes the received signal-to-noise ratio (SNR). In general, the machine learning model is expected to return the ordered set of ‘K’ most likely beams , i.e., the top-K beams. For ML model development, we provide a labeled dataset consisting of RGB images (input to the ML model) and the ground-truth beam indices (labels). More details regarding the dataset is provided in the Dataset section below.

For further information regarding how vision can aid the beam prediction task, please refer to our paper below.

Task-Specific Dataset

DeepSense 6G: Developing efficient solutions for sensing-aided beam prediction and accurately evaluating them requires the availability of a large-scale real-world dataset. With this motivation, we built the DeepSense 6G dataset, the first large-scale real-world multi-modal dataset with co-existing communication and sensing data.

In this vision-aided beam prediction task, we build development/challenge datasets based on the DeepSense data from scenarios [5 – 9].

For each scenario, we provide the following datasets:

Development Dataset: It comprises the visual data and the corresponding 64×1 power vectors obtained by performing beam training with a 64-beam codebook at the receiver (with omni-transmission at the transmitter). The visual data comprises of the RGB images of the wireless environment captured by the camera installed at the basestation. The dataset also provides the optimal beam indices as the ground-truth labels.
Challenge Dataset: To motivate the development of efficient ML models, we propose a benchmark challenge. For this, we provide a Challenge dataset, consisting of only the input RGB images. The ground-truth labels are hidden from the users by design to promote a fair benchmarking process. To participate in this Challenge, check the ML Challenge section below for further details.

Below we explain how to access the development dataset.

Please login to download the DeepSense datasets

How to Access Task Data?

Step 1. Download All Scenarios Dataset

Step 2. Extract the VABT.zip file. Contains the scenario dataset folders

Each scenario folder consists of following files:

unit1: Includes the visual (RGB images) and corresponding beams
train.csv
val.csv
test.csv

What does each CSV file contain?

For each data collection sampling time (~100ms), we provide the corresponding visual and wireless data. Further, we compute the ground-truth beam indices from the power vectors and provide them under the “beam_index” column. An example of 5 data samples in shown below.

File could not be opened. Check the file's permissions to make sure it's readable by your server.

Individual Download Links

To download the individual scenarios, follow the scenario-wise links provided below.

Please login to download the DeepSense datasets

ML Challenge: Vision-Aided Beam Prediction

To advance the state-of-the-art in the vision-aided beam prediction task, we propose a benchmark challenge based on the DeepSense real-world dataset. The objective of the task is to develop a machine learning-based model that takes the images captured at the basestation as the input and predicts the optimal beam indices for the served mobile user.

This challenge adopts the labeled development dataset described above with RGB images and the corresponding optimal beams.

Participation Steps

Step 1. Getting started: First, we recommend the following:

Get familiarized with the data collection testbed and the different sensor modalities presented here
Next, in the Tutorials page, we have provided Python-based codes to load and visualize the different data modalities

Step 2. Task Definition: Read the vision-aided beam prediction task definition

Step 3. Development/challenge datasets: You can download the dataset using the link provided above.

Step 4. Submission: After you develop your ML model, you are invited to submit your results at submission@DeepSense6G.net. Please find the submission process and evaluation criteria below.

Submission Process

We define a standardized beam prediction result format that serves as an input to our evaluation code. There are 5 different real-world DeepSense 6G scenarios in this challenge. For each scenario, please submit the following:

The users must submit the Top-3 predicted beams for the Challenge set. An sample submission csv file is shown below.
Every submission should provide their pre-trained models, evaluation code and ReadMe file documenting the requirements to run the code.ple

sample_index	top-1 beam	top-2 beams	top-3 beams
1	5	[5,7]	[3,5,7]
2	21	[21,23]	[21,23,24]
3	62	[62,59]	[62,59,61]
4	49	[49,50]	[49,50,37]
5	12	[12, 14]	[12, 14, 15]

Evaluation

The evaluation metric adopted in this challenge is the top-k beam prediction accuracy.
The top-k accuracy is defined as the percentage of test samples whose ground-truth beam index lies in the predicted beam of top-k scores.
The evaluation is done based on the ML challenge (hidden) test set, which is used for benchmarking purposes.

Leaderboard Rules

In order to be ranked in the Leaderboard above, please submit your results following the submission process above
Further, to be ranked in the Leaderboard table, contestants need to submit the challenge set results for all the 5 scenarios.