System-Level Comparison of Multimodal and In-Band mmWave Sensing for Beam Prediction in 6G ISAC

Publication Type

Journal Article

Name of Author

• Abidemi Orimogunje, African Center of Excellence in IoT, University of Rwanda, RwandaFollow
• Hyunwoo Park, Department of Electronic Engineering, Hanyang University
• Igbafe Orikumhi, Department of Electronic Engineering Hanyang University Seoul, South Korea
• Sunwoo Kim, Department of Electronic Engineering Hanyang University Seoul, South Korea
• Dejan Vukobratovic, Department of of Power Electronics and Communication Engineering University of Novi Sad, Serbia

Publication Date (Issue Year)

2026

Journal Name

IEEE

Abstract

Integrated sensing and communication (ISAC) can reduce beam-training overhead in mmWave vehicle-toinfrastructure (V2I) links by enabling in-band sensing-based beam prediction, while exteroceptive sensors can further enhance the prediction accuracy. This work develop a system-level framework that evaluates camera, LiDAR, radar, GPS, and in-band mmWave power, both individually and in multimodal fusion using the DeepSense-6G Scenario-33 dataset. A latency-aware neural network composed of lightweight convolutional (CNN) and multilayer-perceptron (MLP) encoders predict a 64-beam index. We assess performance using Top-k accuracy alongside spectral-efficiency (SE) gap, signal-to-noise-ratio (SNR) gap, rate loss, and end-to-end latency. Results show that the mmWave power vector is a strong standalone predictor, and fusing exteroceptive sensors with it preserves high performance: mmWave alone and mmWave+LiDAR/GPS/Radar achieve 98% Top-5 accuracy, while mmWave+camera achieves 94% Top-5 accuracy. The proposed framework establishes calibrated baselines for 6G ISAC-assisted beam prediction in V2I systems.

Keywords

System-Level Comparison, Multimodal, In-Band mmWave Sensing, Beam Prediction, 6G ISAC

Rsif Scholar Name

Abidemi Matthew Orimogunje

Rsif Scholar Nationality

Nigeria

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Rwanda (UR), Rwanda

Recommended Citation

Orimogunje, A., Park, H., Orikumhi, I., Kim, S., & Vukobratovic, D. (2026). System-Level Comparison of Multimodal and In-Band mmWave Sensing for Beam Prediction in 6G ISAC. IEEE https://doi.org/10.1109/ICAIIC68212.2026.11454218