Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

• Published in: IEEE access Vol. 6; pp. 37328 - 37348
• Main Authors: Alkhateeb, Ahmed, Alex, Sam, Varkey, Paul, Li, Ying, Qu, Qi, Tujkovic, Djordje
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antenna arrays; Array signal processing; Augmented reality; Beamforming; Channel estimation; Deep learning; Machine learning; Millimeter wave; Millimeter waves; Mobile communication systems; Network latency; Radio equipment; Radio frequency; Ray tracing; Sensors; Training; Vectors (mathematics); vehicular communications; Virtual reality; Wireless communication; Wireless communications; wireless virtual/augmented reality
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2850226

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.