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This paper presents the first machine learning based real-world demonstrationfor radar-aided beam prediction in a practical vehicular communicationscenario. Leveraging radar sensory data at the communication terminals providesimportant awareness about the transmitter/receiver locations and thesurrounding environment. This awareness could be utilized to reduce or eveneliminate the beam training overhead in millimeter wave (mmWave) andsub-terahertz (THz) MIMO communication systems, which enables a wide range ofhighly-mobile low-latency applications. In this paper, we develop deep learningbased radar-aided beam prediction approaches for mmWave/sub-THz systems. Thedeveloped solutions leverage domain knowledge for radar signal processing toextract the relevant features fed to the learning models. This optimizes theirperformance, complexity, and inference time. The proposed radar-aided beamprediction solutions are evaluated using the large-scale real-world datasetDeepSense 6G, which comprises co-existing mmWave beam training and radarmeasurements. In addition to completely eliminating the radar/communicationcalibration overhead, the experimental results showed that the proposedalgorithms are able to achieve around $90\%$ top-5 beam prediction accuracywhile saving $93\%$ of the beam training overhead. This highlights a promisingdirection for addressing the beam management overhead challenges in mmWave/THzcommunication systems.

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