Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration

2021/11/18 by Umut Demirhan, Demirhan, Umut, Ahmed Alkhateeb +1 · 11 citations
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Microwave Engineering and Waveguides #Millimeter-Wave Propagation and Modeling #Signal Processing (eess.SP) #Terahertz technology and applications #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2111.09676

openalex publication_date 2021/11/18 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

This paper presents the first machine learning based real-world demonstration for radar-aided beam prediction in a practical vehicular communication scenario. Leveraging radar sensory data at the communication terminals provides important awareness about the transmitter/receiver locations and the surrounding environment. This awareness could be utilized to reduce or even eliminate the beam training overhead in millimeter wave (mmWave) and sub-terahertz (THz) MIMO communication systems, which enables a wide range of highly-mobile low-latency applications. In this paper, we develop deep learning based radar-aided beam prediction approaches for mmWave/sub-THz systems. The developed solutions leverage domain knowledge for radar signal processing to extract the relevant features fed to the learning models. This optimizes their performance, complexity, and inference time. The proposed radar-aided beam prediction solutions are evaluated using the large-scale real-world dataset DeepSense 6G, which comprises co-existing mmWave beam training and radar measurements. In addition to completely eliminating the radar/communication calibration overhead, the experimental results showed that the proposed algorithms are able to achieve around 90% top-5 beam prediction accuracy while saving 93% of the beam training overhead. This highlights a promising direction for addressing the beam management overhead challenges in mmWave/THz communication systems.

Cited by

• AMBER: An Adaptive Multimodal Mask Transformer for Beam Prediction with Missing Modalities
• Look Before Switch: Sensing-Assisted Handover in 5G NR V2I Networks
• Radar-Aided Near-Field Beam Prediction via Beam Map Learning for XL-MIMO V2I Communications
• Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
• Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
• CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks
• Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness
• When Vision-Language Model (VLM) Meets Beam Prediction: A Multimodal Contrastive Learning Framework
• Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
• Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems
• Cooperative Mapping, Localization, and Beam Management via Multi-Modal SLAM in ISAC Systems

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