Work

Deep learning‐based multimodal beam tracking for unmanned aerial vehicle communication

Year: 2026

Type: article

Abstract: Millimeter wave (mmWave) communication systems use beamforming and large antenna arrays to achieve high data rates by directing signals through narrow beams, reducing interference and enhancing transmission efficiency. Efficient beamforming requires real‐time beam adjustments to adapt to user positions and environmental changes, but traditional methods relying on frequent measurements can lead to significant overhead in dynamic environments. AI/ML approaches leveraging sensor data and historical information can improve beam prediction and tracking efficiency. Building on this, we propose a multimodal beam tracking model for UAV communication, integrating image and GPS data to predict UAV movement and optimize beam tracking. The model employs ResNet‐SE blocks for feature extraction, CAformer blocks for multimodal data fusion, and LSTM for capturing sequential historical features. Experimental results show that the proposed model outperforms single‐modal methods, achieving a 24.4% improvement in Top‐1 beam accuracy.

Source: ETRI Journal

DOI: 10.4218/etrij.2025-0272

Authors: Yerin Yeo, Junghyun Kim, Dukhyun You, Junhwan Lee

Institutions: Sejong University, Electronics and Telecommunications Research Institute

Language: English

Topic: Millimeter-Wave Propagation and Modeling

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion