Deep learning-based radar-assisted beam prediction

Beam selection in millimeter wave (mmWave) communication systems rely on information about the environment surrounding the communication target, and the use of deep learning methods to analyze sensing data acquired by low-cost radar sensors can effectively reduce communication overhead. In this paper, we further investigate the radar-based beam selection problem using deep learning methods. The beam selection performance of the Feature Pyramid Network (FPN) network and an optimized version of the Residual Networks (Resnet) network is evaluated for a large-scale real-world dataset, DeepSense 6G, and a targeted network is proposed for beam selection. The experimental results show that the accuracy of beam selection is improved by 18.5% compared to the original Lenet network.

[1]  A. Alkhateeb,et al.  DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset , 2022, IEEE Communications Magazine.

[2]  Quan Zhou,et al.  AMCRN: Few-Shot Learning for Automatic Modulation Classification , 2022, IEEE Communications Letters.

[3]  Ahmed Alkhateeb,et al.  Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration , 2021, 2022 IEEE Wireless Communications and Networking Conference (WCNC).

[4]  Ngwe Thawdar,et al.  Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets , 2021, 2022 IEEE Wireless Communications and Networking Conference (WCNC).

[5]  Derrick Wing Kwan Ng,et al.  Bayesian Predictive Beamforming for Vehicular Networks: A Low-Overhead Joint Radar-Communication Approach , 2020, IEEE Transactions on Wireless Communications.

[6]  Muhammad Alrabeiah,et al.  Millimeter Wave Base Stations with Cameras: Vision-Aided Beam and Blockage Prediction , 2019, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[7]  Muhammad Alrabeiah,et al.  Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels , 2019, IEEE Transactions on Communications.

[8]  Amitava Ghosh,et al.  Millimeter wave V2I beam-training using base-station mounted radar , 2019, 2019 IEEE Radar Conference (RadarConf).

[9]  Ying Li,et al.  Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems , 2018, IEEE Access.

[10]  Robert W. Heath,et al.  Position-aided millimeter wave V2I beam alignment: A learning-to-rank approach , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[11]  Robert W. Heath,et al.  Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information , 2017, IEEE Transactions on Wireless Communications.