Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

By Ahmed Alkhateeb, Sam P. Alex, Paul Varkey, Ying Li, Qi Qu, Djordje Tujkovic. IEEE Access, 2018

482 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cc779ba71eabdf68944051eea852a24eaf79092c

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In Electrical and Electronic Engineering

Citations in the corpus

Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning (2019, methodology): “Deep learning solutions have been proposed in the literature for addressing design challenges in mmWave and massive MIMO systems [18]–[20].”
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels (2019, background): “This is mainly due to (i) the large training overhead associated with adjusting the beamforming vectors of the large mmWave arrays and (ii) the high sensitivity of mmWave signal propagation to blockages [1]–[3].”
A Deep Learning Framework for Optimization of MISO Downlink Beamforming (2019, methodology): “The sum rate performance in [40] was restricted by the quantized codebook constraint.”
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication (2018, background): “To this end, it is assumed that the BS perfectly knows all communication channels h 2 ,k ∀ k = 1 , 2 , . . . , K and H 1 , which can be acquired by the methods described in e.g. [47], [48].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[446] propose a millimeter wave communication system that utilizes MLPs to predict beamforming vectors from signals received from distributed base stations.”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, methodology): “An additional trend that has proven to perform well is the enhancement of the current state of spatial multiplexing angle with massive MIMO technology, allowing BSs to accurately direct the beams to individual users.”
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems (2018, methodology): “More specifically, following [1], the developed solution requires the mobile user Basestation 1”
Energy Efficient Multi-User MISO Communication Using Low Resolution Large Intelligent Surfaces (2018, background): “, [26], [27]); this is left as future work.”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “[65] further considered that the training overhead should be reasonable, and a novel deep-learning coordinated”