Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels (1910.02900v3)

Abstract: Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. These gains attracted increasing interest in the last few years. Prior work, however, has focused on extracting spatial channel characteristics at the sub-6GHz band first and then use them to reduce the mmWave beam training overhead. This approach has a number of limitations: (i) It still requires a beam search at mmWave, (ii) its performance is sensitive to the error associated with extracting the sub-6GHz channel characteristics, and (iii) it 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 correct blockage status directly from the sub-6GHz channel, which overcome the limitations in prior work. 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 use the sub-6GHz channel to directly predict the optimal mmWave beam and the correct blockage status with success probabilities that can be made arbitrarily close to one. Then, we develop an efficient deep learning model and empirically evaluate its beam/blockage prediction performance using the publicly available dataset DeepMIMO. The results show that the proposed solution can predict the mmWave blockages with more than 90$\%$ success probability. Further, these results confirm the capability of the proposed deep learning model in predicting the optimal mmWave beams and approaching the optimal data rates, that assume perfect channel knowledge, while requiring no beam training overhead...

• The paper establishes that deep neural networks can learn mapping functions from sub-6GHz channels to accurately predict mmWave beams and blockage status.
• It demonstrates a dual-band system where a shared DNN architecture achieves over 90% blockage prediction success and near-optimal beamforming performance.
• The study highlights the potential for reducing beam training overhead and enhancing reliability in mmWave networks, setting a new benchmark for future research.

Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels

In recent developments, millimeter wave (mmWave) communication has emerged as a crucial technology for achieving high data rate wireless communication systems. However, the deployment of mmWave systems is challenged by the substantial overhead associated with beam training processes and the high sensitivity of mmWave signal propagation to blockages. Solving these challenges is vital for enhancing the reliability and scalability of mmWave systems, particularly in environments with high mobility. The paper "Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels" by Alrabeiah and Alkhateeb addresses these issues by leveraging sub-6GHz channel information to predict optimal mmWave beams and blockage status, employing deep learning models.

Summary of Contributions

The significance of this work lies in its theoretical and empirical exploration of using deep learning models to predict mmWave communication parameters. The key contributions include:

1. Theoretical Foundation: It establishes that, under certain conditions, there exist mapping functions capable of predicting mmWave beams and blockage status directly from sub-6GHz channel information. These mappings can theoretically overcome previous limitations by reducing the need for exhaustive beam training and providing accurate blockage predictions.
2. Deep Learning Application: The paper demonstrates that deep neural networks (DNNs) can learn these mapping functions, achieving high prediction accuracy for mmWave beams and blockages. It utilizes the universal approximation theory to prove that DNNs can approximate these complex mappings to a high degree of accuracy.
3. Performance Metrics and Results: Leveraging the DeepMIMO dataset for simulations, results showed impressive prediction accuracy, with the proposed model achieving over 90% success probability in blockage prediction and closely approximating optimal data rates for beamforming.
4. System Model and Neural Network Design: Practical deployment involves a dual-band system where a sub-6GHz transceiver aids in predicting the parameters for a mmWave transceiver. The proposed DNN model uses a shared architecture for predicting beams and blockages, facilitating transfer learning and computational efficiency.

Implications for Future Research

This work presents several implications for both theoretical and practical developments in wireless communications:

• Cost Efficiency: Demonstrates the potential of reducing the training overhead in mmWave systems by leveraging existing sub-6GHz infrastructure, leading to more cost-effective solutions for network operators.
• Reliability Enhancements: Provides methods for enhancing the reliability of mmWave networks by predictive adaptability to blockages, crucial for mission-critical applications.
• Further Exploration in Mapping Dynamics: While the paper establishes conditions for the existence of mapping functions, future work could explore more dynamic environments and the impact of additional system parameters on the prediction accuracy.
• Integration in 5G and Beyond: As 5G systems increasingly adopt mmWave bands alongside existing sub-6GHz infrastructure, integrating these predictive models could become a standard for improving system performance, latency, and reliability.

In conclusion, Alrabeiah and Alkhateeb’s paper opens a promising avenue for addressing some of the most pressing challenges in mmWave communications by incorporating deep learning techniques to reduce complexity and enhance performance. The groundwork laid by this paper sets the stage for substantial advancements in both the theoretical underpinnings and practical application of predictive modeling in next-generation communication systems.