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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.
Reinforcement Learning-Based Trajectory Optimization of Solar Panel-Equipped UAV BS for Energy Efficiency
Dong Uk Kim, Choong Seon Hong, Seong Bae Park, Jong Won Choi
http://doi.org/10.5626/JOK.2023.50.10.899
5G and B5G wireless communication systems use new bands, such as millimeter-wave, to meet user requirements. However, these new bands have limitations such as lower diffraction, lower transmittance, and stronger straightness than traditional frequency bands. To address these limitations, a cellular communication paradigm supported by Unmanned Aerial Vehicle (UAV), makes communication services more flexible than existing ground base stations. However, UAVs have limited battery capacity, which affects the life of telecommunications services. To address this problem, this paper considers UAVs equipped with solar panels. Movement toward energy generation and altitude for user data rate maximization due to solar power of UAVs can consume a lot of energy. Energy generation, data rate maximization, and energy consumption have a trade-off relationship. Therefore, in this study, we proposed a system to locate UAVs that could optimize the above trade-off relationship using agents learned using a reinforcement learning algorithm called "Proximal Policy Optimization (PPO)" and compare the system proposed in this paper.
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