Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration

IEEE Open Journal of the Communications Society ( Volume: 3)

Shunyao Wu, Muhammad Alrabeiah, Chaitali Chakrabarti, and Ahmed Alkhateeb

Wireless Intelligence Lab

Arizona State University

Given a sequence of mmWave/sub-THz received signal power, can the basestation proactively predict future line-of-sight (LOS) link blockages?  Our machine learning-based solution can leverage the mmWave/sub-THz receive power signal to predict whether or not a future blockage will happen.


Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter-wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previous approaches relied mainly on either (i) multiple-connectivity, which under-utilizes the network resources, or on (ii) the use of out-of-band and non-RF sensors to predict link blockages, which is associated with increased cost and system complexity. In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line of sight (LOS) link blockages. The proposed solution utilizes deep neural networks and special patterns of received signal power, that we call pre-blockage wireless signatures to infer future blockages. Specifically, the developed machine learning models attempt to predict: (i) If a future blockage will occur? (ii) When will this blockage happen? (iii) What is the type of blockage? And (iv) what is the direction of the moving blockage? To evaluate our proposed approach, we build a large-scale real-world dataset comprising nearly 0.5 million data points (mmWave measurements) for both indoor and outdoor blockage scenarios. The results, using this dataset, show that the proposed approach can successfully predict the occurrence of future dynamic blockages with more than 85% accuracy. Further, for the outdoor scenario with highly-mobile vehicular blockages, the proposed model can predict the exact time of the future blockage with less than 100 ms error for blockages happening within the future 600 ms. These results, among others, highlight the promising gains of the proposed proactive blockage prediction solution which could potentially enhance the reliability and latency of future wireless networks.

Proposed Solution

This figure illustrates the overall system model where a mmWave/sub-THz basestation utilizes the received mmWave/sub-THz signal power to enable the proposed proactive dynamic link blockage prediction approach.

Video Presentation

DeepSense 6G Dataset

DeepSense 6G is a real-world multi-modal dataset that comprises coexisting multi-modal sensing and communication data, such as mmWave wireless communication, Camera, GPS data, LiDAR, and Radar, collected in realistic wireless environments.  Link to the DeepSense 6G website is provided below. 

Scenarios [17 -22]

In this blockage prediction task, we build development/challenge datasets based on the DeepSense data from scenarios 17 – 22. For further details regarding the scenarios, follow the links provided below. 


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,” to be available on arXiv, 2022. [Online]. Available:

author = {Alkhateeb, A. and Charan, G. and Osman, T. and Hredzak, A. and Morais, J. and Demirhan, U. and Srinivas, N.},
title = {{DeepSense 6G}: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset},
journal={to be available on arXiv},
year = {2022},
url = {},}

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.

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},