Wireless Signature-Based Blockage Prediction
Leaderboard
| Date | Name | scenario 17 | scenario 18 | scenario 19 | scenario 20 | scenario 21 | scenario 22 |
|---|---|---|---|---|---|---|---|
| 1/15/2021 | Wireless Intelligence Lab ASU | Future-1: 86.36% Future-5: 56.82% Future-10: 48.86% | Future-1: 93.48% Future-5: 72.17% Future-10: 58.70% | Future-1: 93.86% Future-5: 74.74% Future-10: 54.56% | Future-1: 98.15% Future-5: 66.30% Future-10: 53.52% | Future-1: 92.68% Future-5: 55.71% Future-10: 45.71% | Future-1: 83.30% Future-5: 46.67% Future-10: 45.00% |
- This table documents the different proposed wireless signature-based blockage prediction solutions. This provides a way to benchmark the performance of the proposed solutions.
- For the individual DeepSense scenarios (development datasets), we use the “Future-N” blockage 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}}
S. Wu, M. Alrabeiah, C. Chakrabarti, and A. Alkhateeb, “Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration,” in IEEE Open Journal of the Communications Society, vol. 3, pp. 776-796, 2022.
@Article{Wu2022,
author={Wu, Shunyao and Alrabeiah, Muhammad and Chakrabarti, Chaitali and Alkhateeb, Ahmed},
journal={IEEE Open Journal of the Communications Society},
title={Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration},
year={2022},
volume={3},
number={},
pages={776-796},
doi={10.1109/OJCOMS.2022.3162591},}
Introduction
LOS link blockage is a challenge: Millimeter-wave (mmWave) and sub-terahertz communications are becoming the dominant directions for modern and future wireless networks. With their large bandwidth, they have the ability to satisfy the high data rate demands of several applications such as wireless Virtual/Augmented Reality (VR/AR) and autonomous driving. Communication in these bands, however, faces several challenges at both the physical and network layers. One of the key challenges stems from the sensitivity of mmWave and terahertz signal propagation to blockages. For this, these systems need to rely heavily on maintaining line-of-sight (LOS) connections between the base stations and users. The possibility of blocking these LOS links by stationary or dynamic blockages can highly affect the reliability and latency of mmWave/terahertz systems, which makes it hard for these systems to support highly-mobile and latency-sensitive applications.
Sensing aided blockage prediction is a promising solution: The key to overcoming the link blockage challenges lies in developing a critical sense of the surrounding. 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 in predicting future blockages. For example, the sensory data collected from RGB cameras, LiDARs, Radars, GPS data, and received mmWave signal power can help in identifying probable transmitters and blockages in the wireless environment and understanding their mobility patterns. This information can be utilized by the wireless network to proactively predict incoming blockages and in initiating hand-off beforehand. We call this approach sensing-aided blockage prediction and hand-off. Wireless signature-based blockage prediction is a special case when the basestation attempts to leverage the received mmWave signal power information to predict the future LOS link blockages proactively.
What is mmWave pre-blockage signature? Consider a fixed transmitter and receiver in a certain environment with a LOS path. If an object moves in this environment till it blocks this LOS path, then, during the movement, the object acts like a scatterer for the signal propagating from the transmitter to the receiver. The received signal during this interval will experience a constructive and destructive interference from the LOS ray and the ray scattered on the moving object. Further, the contribution of the moving blockage/scatterer will change as the scatterer approaches the LOS link and before it blocks the link. We call this receive signal pattern that precedes the occurrence of a blockage and reflects the behavior of the blocking object the pre-blockage wireless signature. Below we show an example of an indoor pre-blockage signature. The upper sub-figure shows the received signal power versus time. The bottom panel shows images captured by the camera.
Wireless Signature-based blockage prediction: Specific Task Description
Wireless signature-based blockage prediction at the infrastructure is the task of predicting the future LOS link blockages proactively by utilizing a machine learning model and the pre-blockage wireless signatures.
Objective of the ML Task: At any time t, given a sequence of ‘r’ {t – r + 1, … , t} previous and current received mmWave power vectors, the primary objective of this task is to design a machine learning model that predicts the future link blockages. In general, the machine learning model is expected to return the blockage status in the future ‘k’ time-slots, i.e., {t + 1, … , t + k}. If there is any blockage during these slots, the blockage status for the ‘k’ time-slots (future-k) is considered as blocked. For ML model development, we provide a labeled dataset consisting of an ordered sequence of mmWave receive power vector (input to the ML model) and the ground-truth future blockage status. More details regarding the dataset are provided in the Dataset section below.
For further information regarding how pre-blockage signature can aid the beam prediction task, please review the paper here.
Task-Specific Dataset
DeepSense 6G: Developing efficient solutions for sensing-aided blockage 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 pre-blockage signature-based blockage prediction task, we build development/challenge datasets based on the DeepSense data from scenarios [17 – 22].
For each scenario, we provide the following datasets:
- Development Dataset: It consists of the sequence of 8 mmWave receive power vectors and the corresponding ground-truth blockage status for k-future instances. The DeepSense 6G testbed employs a phased array with 16 elements and applies a receive codebook of 64 beams at each data sample. Therefore, the received signal at any time instance t is a 64-element vector. This task involves predicting for three different future instances, i.e., future-1, future-5, and future-10.
- 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 mmWave receive power vector. 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 the following files:
- unit1: Includes the mmWave power vectors and corresponding blockage labels
- train.csv
- val.csv
- test.csv
What does each CSV file contain?
We provide the sequence of visual data and the corresponding future link blockage status. An example of 5 data samples in shown below.
Individual Download Links
To download the individual scenarios, follow the scenario-wise links provided below.
Please login to download the DeepSense datasets
ML Challenge: Wireless Signature-Based Blockage Prediction
To advance the state-of-the-art in the wireless signature-based blockage 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 sequence of mmWave receive power vector as the input and predicts the future link blockage status.
This challenge adopts the labeled development dataset described above with mmWave receive power vector and the corresponding link status.
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 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:
- Each scenario has three sub-dataset, each corresponding to one of the three future prediction window length, i.e., future-1, future-5 and future-10. The users must submit the predicted link blockage status for each sub-dataset in 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.
| sample_index | future-1 |
|---|---|
| 1 | 1 |
| 2 | 0 |
| 3 | 0 |
| 4 | 1 |
| 5 | 0 |
Evaluation
- The evaluation metric adopted in this challenge is the prediction accuracy.
- The evaluation is done based on the ML challenge (hidden) test set, which is used for benchmarking purposes.
Leaderboard Rules
- In order to participate in this challenge, 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 6 scenarios and for all three future prediction windows.
