Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios

Leveraging sensory information to aid the millimeter-wave (mmWave) and sub-terahertz (sub-THz) beam selection process is attracting increasing interest. This sensory data, captured for example by cameras at the basestations, has the potential of significantly reducing the beam sweeping overhead and enabling highly-mobile applications. The solutions developed so far, however, have mainly considered single-candidate scenarios, i.e., scenarios with a single candidate user in the visual scene, and were evaluated using synthetic datasets. To address these limitations, this paper extensively investigates the sensing-aided beam prediction problem in a real-world multi-object vehicle-to-infrastructure (V2I) scenario and presents a comprehensive machine learning based framework. In particular, this paper proposes to utilize visual and positional data to predict the optimal beam indices as an alternative to the conventional beam sweeping approaches. For this, a novel user (transmitter) identification solution has been developed, a key step in realizing sensing-aided multi-candidate and multi-user beam prediction solutions. The proposed solutions are evaluated on the large-scale real-world DeepSense 6G dataset. Experimental results in realistic V2I communication scenarios indicate that the proposed solutions achieve between $67-84\%$ top-1 and close to 100% top-5 beam prediction accuracy for the scenarios with single-user, and between $65-80\%$ top-1 and close to 95% top-5 beam prediction accuracy for multi-candidate scenarios. Furthermore, the proposed approach can identify the probable transmitting candidate with more than 93% accuracy across the different scenarios. This highlights a promising approach for significantly reducing the beam training overhead in mmWave/THz communication systems.