DeepMIMO v3 is now available + 25 new scenarios!

Millimeter Wave Beam Prediction Based on Multipath Signature


Leveraging the wireless signature of the receive signals at multiple base stations to predict the mmWave beams with a few pilots

Key ideas

  • Leveraging deep neural networks and prior observations for fast beam prediction
  • Coordinated beam prediction further enhances the prediction performance 
  • Wireless signatures capture not just the user position but also the interaction with the surrounding environment


  • Fast beam prediction/alignment in mmWave/THz systems 
  • Efficient coordinated transmission and reliability enhancement in small-cell mmWave/THz networks 

More information about this research direction

Paper: A. Alkhateeb, S. Alex, P. Varkey, Y. Li, Q. Qu and D. Tujkovic, “Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems,” in IEEE Access, vol. 6, pp. 37328-37348, 2018.

Abstract: Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications, such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Furthermore, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Extensive simulation results based on accurate ray-tracing, show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead. Compared with traditional beamforming solutions, the results show that the proposed deep learning-based strategy attains higher rates, especially in high-mobility large-array regimes.

author={Alkhateeb, Ahmed and Alex, Sam and Varkey, Paul and Li, Ying and Qu, Qi and Tujkovic, Djordje},
journal={IEEE Access},
title={Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems},

To reproduce the results in this paper:

These simulations use the DeepMIMO scenarios:
Example result:

Having questions or feedback?

Send an email to 


Or post your question in the DeepMIMO forum