An Efficient Nocturnal Scenarios Beamforming Based on Multi-Modal Enhanced by Object Detection

J Nie, Y Cui, T Yu, J Mu, W Yuan… - 2023 IEEE Globecom …, 2023 - ieeexplore.ieee.org
J Nie, Y Cui, T Yu, J Mu, W Yuan, X Jing
2023 IEEE Globecom Workshops (GC Wkshps), 2023ieeexplore.ieee.org
The progress in integrated sensing and communication (ISAC) technologies has facilitated
the application of sensing data for beamforming, resulting in a reduction of training
overhead. Nevertheless, the diminished visibility during nocturnal scenarios poses a
significant impact on beamforming performance. In this research, we proposed a machine-
learning approach that relies on object detection and multimodal fusion to achieve efficient
beamforming prediction by leveraging visual and positional data collected from nighttime …
The progress in integrated sensing and communication(ISAC) technologies has facilitated the application of sensing data for beamforming, resulting in a reduction of training overhead. Nevertheless, the diminished visibility during nocturnal scenarios poses a significant impact on beamforming performance. In this research, we proposed a machine-learning approach that relies on object detection and multimodal fusion to achieve efficient beamforming prediction by leveraging visual and positional data collected from nighttime vehicle communication scenarios. Experimental findings reveal that our developed model achieves the top-1 accuracy exceeding 60% and top-5 accuracy approaching 100%, all the while substantially mitigating the training overhead.
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