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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.
A Deep Learning Approach for Target-oriented Communication Resource Allocation in Holographic MIMO
Apurba Adhikary, Md. Shirajum Munir, Avi Deb Raha, Min Seok Kim, Jong Won Choe, Choong Seon Hong
http://doi.org/10.5626/JOK.2023.50.5.441
In this paper, we propose a single-cell massive multiple-input multiple-output (mMIMO) system assisted with holography that performs target-oriented communication resource allocation for heterogeneous users. This paper proposes a technique that can minimize the number of active grids from holographic grid arrays (HGA) for confirming the requirement of lower power toward beamforming to serve target-oriented users. Therefore, we formulated a problem by maximizing the signal-to-interference-noise ratio (SINR), which, in turn, maximizes the efficient resource allocation for the users by generating effective beamforming and controlling the sum-power rule. Additionally, our holography-assisted mMIMO system is capable of serving heterogeneous user equipment simultaneously with a lower power budget. To devise the artificial intelligence (AI)-based solution, we developed a sequential neural network model for grid activation decisions with minimized power constraint. Finally, the simulation and performance evaluation results show that power was allocated efficiently, and effective beams were formed for serving the users with a lower RMSE score of 0.01.
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