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Maximizing UAV Data Efficiency in NextG Networks: A Transformer-Based mmWave Beamforming Approach
Avi Deb Raha, Apurba Adhikary, Mrityunjoy Gain, Yu Qiao, Hyeonsu Kim, Jisu Yoon, Choong Seon Hong
http://doi.org/10.5626/JOK.2025.52.2.170
Beamforming is essential in the rapidly evolving field of next generation (NextG) wireless communication, particularly when leveraging terahertz and millimeter-wave (mmWave) frequency bands to achieve ultra-high data speeds. However, these frequency bands present challenges, particularly concerning the costs associated with beam training, which can hinder Ultra-Reliable Low-Latency Communication (URLLC) in high-mobility applications, such as drone and Unmanned Aerial Vehicle (UAV) communications. This paper proposes a contextual information-based mmWave beamforming approach for UAVs and formulates an optimization problem aimed at maximizing data rates in high-mobility UAV scenarios. To predict optimal beams while ensuring URLLC, we have developed a lightweight transformerThe self-attention mechanism of the transformer allows the model to focus selectively on the most important features of the contextual information. This lightweight transformer model effectively predicts the best beams, thereby enhancing the data rates of UAVs. Simulation results demonstrate the design's effectiveness, as the lightweight transformer model significantly outperforms baseline methods, achieving up to 17.8% higher Top-1 beam accuracies and reducing average power loss by as much as 96.79%. Improvements range from 12.49% to 96.79% relative to baseline methods.
Location-Dependent and Task-Oriented Power Allocation in Holographic MIMO: A Transformer-based Approach
Apurba Adhikary, Avi Deb Raha, Monishanker Halder, Mrityunjoy Gain, Ji Su Yoon, Seong Bae Park, Choong Seon Hong
http://doi.org/10.5626/JOK.2024.51.1.93
Future communication networks are expected to provide improved throughput data services with minimal power for beamforming. The location-dependent and task-oriented resource allocation approach for holographic beamforming ensures the improvement of the channel capacity for the users by activating the required number of grids from the holographic grid array. An optimization problem is obtained for maximizing the channel capacity considering the location and task priority of the users. In this study, a Transformer-based approach that allocates the required power for serving the users to generate holographic beamforming is proposed as the solution for the optimization problem. The simulation results demonstrate that the proposed location-dependent and task-oriented Transformer-based approach effectively allocate power for holographic beamforming to serve the users.
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