Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems (1804.10334v3)

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. Further, 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. Simulation results 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.

• The paper introduces a deep learning framework that leverages distributed base stations to predict optimal beamforming vectors for mobile mmWave systems.
• Empirical results demonstrate near-theoretical coverage and data rates while significantly reducing the training overhead compared to conventional methods.
• The study paves the way for robust implementations in vehicular communications and wireless VR/AR applications, enhancing system adaptability under mobility constraints.

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

The paper explores the challenges and solutions associated with enabling high mobility in millimeter wave (mmWave) systems. These challenges are primarily characterized by the sensitivity of mmWave signals to blockage and the consequent reliability issues, as well as the excessive training overhead required for optimal beamforming vector identification.

The proposed solution introduces an integrated framework combining coordinated beamforming and deep learning to effectively address these issues in highly-mobile mmWave applications. The strategy involves using a network of distributed base stations (BSs) that collaboratively serve a mobile user, leveraging omni or quasi-omni beam patterns for uplink transmission. The signals received jointly at these BSs provide unique signatures indicative of both user location and environmental interactions. The deep learning model utilizes these signatures to predict and configure the beamforming vectors at the BSs.

Empirical results reveal that the deep-learning coordinated beamforming method achieves coverage and data rates close to the theoretical upper bound, which assumes prior knowledge of optimal beamforming vectors. Notably, it surpasses conventional mmWave beamforming techniques, particularly in scenarios where users exhibit high mobility and the BSs employ extensive antenna arrays.

Implications and Future Directions

The proposed integrated solution holds significant promise for mmWave communications, marked by negligible training overhead and effective beamforming. It represents a pivotal advancement in supporting highly-mobile applications, such as vehicular communications and wireless virtual/augmented reality, characterized by stringent latency and reliability demands.

From a theoretical standpoint, this research underscores the potential of combining machine learning tools with advanced signal processing techniques to enhance communication system adaptability and robustness. On the practical front, it signifies a step towards more efficient network planning and resource allocation in dense mmWave deployments.

Future research may explore several extensions, including multi-user environments, dynamic network scenarios with temporal variations, and refined machine learning models potentially leveraging advanced architectures such as convolutional neural networks. Additionally, investigating the interoperability and performance of these systems in more complex, real-world scenarios will be crucial to advancing their practical implementation. Overall, this paper provides a foundational exploration into the collaborative dynamic between deeply integrated learning methodologies and modern communication structures.