mmWave Beam and Blockage Prediction Using Sub-6GHz Channels


Using Sub-6GHz channels to predict mmWave beams and blockages

Key ideas

  • Mathematically proving the feasibility of prediction mmWave beams using sub-6GHz channels
  • Using sub-6GHz channels (which can be acquired with low training effort with high SNR) to predict mmWave beams and link blockage status
  • Leveraging deep neural networks to efficiently prediction mmWave beams using sub-6GHz channels


  • Reducing or Eliminating mmWave beam training overhead in 5G/B5G systems by relying on sub-6GHz channels
  • Enhancing the reliability of mmWave systems by predicting possible link blockages using sub-6GHz channels

More information about this research direction

Paper: Muhammad Alrabeiah and Ahmed Alkhateeb, “Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels,” in IEEE Transactions on Communications, vol. 68, no. 9, pp. 5504-5518, Sept. 2020.

Abstract: Predicting the millimeter wave (mmWave) beams and blockages using sub-6 GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. Prior work has focused on extracting spatial channel characteristics at the sub-6 GHz band and then use them to reduce the mmWave beam training overhead. This approach still requires beam refinement at mmWave and does not normally account for the different dielectric properties at the different bands. In this paper, we first prove that under certain conditions, there exist mapping functions that can predict the optimal mmWave beam and blockage status directly from the sub-6 GHz channel. These mapping functions, however, are hard to characterize analytically which motivates exploiting deep neural network models to learn them. For that, we prove that a large enough neural network can predict mmWave beams and blockages with success probabilities that can be made arbitrarily close to one. Then, we develop a deep learning model and empirically evaluate its beam/blockage prediction performance using a publicly available dataset. The results show that the proposed solution can predict the mmWave blockages with more than 90% success probability and can predict the optimal mmWave beams to approach the upper bounds while requiring no beam training overhead.

author={Alrabeiah, Muhammad and Alkhateeb, Ahmed},
journal={IEEE Transactions on Communications},
title={Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels},

To reproduce the results in this paper:

Simulation codes (based on DeepMIMO v1):
These simulations use the DeepMIMO scenarios:
Example: Steps to generate the results in this figure
  1. Generate the datasets using the scenarios ‘O1_28’ and ‘O1_3p5’ from the DeepMIMO datasets. Use the parameters illustrated in Table.1 in Section VII-B of the paper. (Note that the DeepMIMO source data are available on this link).
  2. Prepare two MATLAB structures, one for the sub-6GHz data and the other for 28GHz. Please refer to the comments at the beginning of main.m for more information on the data structures.
  3. Assign the paths to the two MATLAB structures to the two parameters: options.dataFile1 and options.dataFile2 in the beginning of main.m.
  4. Run main.m to get the figure on the right.

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