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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.
Post-training Methods for Improving Korean Document Summarization Model
So-Eon Kim, Seong-Eun Hong, Gyu-Min Park, Choong Seon Hong, Seong-Bae Park
http://doi.org/10.5626/JOK.2023.50.10.882
The document summarization task generates a short summary based on a long document. Recently, a method using a pre-trained model based on a transformer model showed high performance. However, as it was proved that fine-tuning does not train the model optimally due to the learning gap between pre-training and fine-tuning, post-training, which is additional training between pre-training and fine-tuning, was proposed. This paper proposed two post-training methods for Korean document summarization. One was Korean Spacing, which is for learning Korean structure, and the other was First Sentence Masking, which is for learning about document summarization. Experiments proved that the proposed post-training methods were effective as performance improved when the proposed post-training was used compared to when it was not.
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.
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.
Response-Considered Query Token Importance Weight Calculator with Potential Response for Generating Query-Relevant Responses
So-Eon Kim, Choong Seon Hong, Seong-Bae Park
http://doi.org/10.5626/JOK.2022.49.8.601
The conversational response generator(CRG) has made great progress through the sequence-to-sequence model, but it often generates an over-general response which can be a response to all queries or an inappropriate response. Some efforts have been made to modify the traditional loss function to solve this problem and reduce the generation of irrelevant responses to the query by solving the problem of the lack of background knowledge of the CRG, but they did not solve both problems. This paper propose the use of a query token importance calculator because the cause of generating unrelated and overly general responses is that the CRG does not capture the core of the query. Also, based on the theory that the questioner induces a specific response from the listener and designs the speech, this paper proposes to use the golden response to understand the core meaning of the query. The qualitative evaluation confirmed that the response generator using the proposed model was able to generate responses related to the query compared to the model that did not use the proposed model.
A Study on the Intelligent Delivery Management System Using UAV-Edge Computing Technology
Chu Myaet Thwal, Minkyung Lee, Choong Seon Hong
http://doi.org/10.5626/JOK.2020.47.12.1208
With the recent advancement of the digital and Internet-of-Things (IoT) technologies, cities globally are rapidly transforming into smart cities. In a parallel to the IoT technology, another technology that has substantially improved in recent years is the Unmanned Aerial Vehicle (UAV) technology, resulting in cheaper, more powerful and reliable UAVs. In this paper, we integrate the aid of the IoT and propose an intelligent delivery management system in coordination with edge computing and UAV technology. The proposed system is an innovative system that facilitates in reducing the operational delay of the delivery services and provides greater and faster facilities to customers. The whole procedure of the delivery process is managed by the edge-based control stations serving as the media between the retailers and UAVs. These stations are distributed across urban areas and are responsible for assigning tasks to the UAVs by performing crucial calculations and communications between the retailers and UAVs. By applying the proposed intelligent delivery scheme in smart city applications, it can be expected to reduce delays in delivery services because of the shortage of manual labor and traffic conditions, thus providing greater and faster facilities to the customers.
Deploying UAV based on Reinforcement Learning for Throughput Maximization in UAV Environments
http://doi.org/10.5626/JOK.2020.47.7.700
Because of the commercialization of the 5G network, many base stations must enhance a reliable communication quality. Thus, many studies are being conducted to provide mobility and economic benefits to the UAVs-Base Station (UAVs-BS) on behalf of the ground base stations. In this paper, we propose a system to identify a location wherein multiple users can access optimal service throughput by considering users’ requirements and the Base Station(BS)’s position in UAVs communication. Based on the Air-To-Ground(A2G) Path Loss Model, the virtual communication environment is established and Max-Min Airtime Fairness is applied for equitable channel usage time distribution according to user requirements. Additionally, the Proximal Policy Optimization (PPO) algorithm is applied to set an optimal location with the maximum throughput. As a result, the proposed systems allow the UAVs to be in the locations with high service throughput for users with different demands.
A Transport Theoretic Approach for Computational Task Migration in Multi-Access Edge Computing
Sarder Fakhrul Abedin, Md. Shirajum Munir, SeokWon Kang, Choong Seon Hong
http://doi.org/10.5626/JOK.2019.46.10.1061
In the present work, the problem of computational task migration in the Multi-Access Edge Computing (MEC) Network has been addressed and the goal is to minimize the computational cost including the task migration cost of the MEC network. Apparently, at first, we have formulated a Hitchcock-Koopmans transportation problem, which corresponds to the task migration from the over-utilized MEC servers to the under-utilized MEC server. Second, we have solved the transportation problem using the Vogel’s Approximation Algorithm (VAM), where the optimal task migration was achieved. Finally, in the simulation, we have demonstrated that the proposed approach significantly outperforms the baseline approach in terms of the task migration cost, average response time, and average queuing delay in the MEC network.
Radio Resource Allocation in 5G New Radio: A Neural Networks Approach
Madyan Alsenwi, Kitae Kim, Choong Seon Hong
http://doi.org/10.5626/JOK.2019.46.9.961
The minimum frequency-time unit that can be allocated to User Equipments (UEs) in the fifth generation (5G) cellular networks is a Resource Block (RB). A RB is a channel composed of a set of OFDM subcarriers for a given time slot duration. 5G New Radio (NR) allows for a large number of block shapes ranging from 15 kHz to 480 kHz. In this paper, we address the problem of RBs allocation to UEs. The RBs are allocated at the beginning of each time slot based on the channel state of each UE. The problem is formulated based on the Generalized Proportional Fair (GPF) scheduling. Then, we model the problem as a 2-Dimension Hopfield Neural Networks (2D-HNN). Finally, in an attempt to solve the problem, the energy function of 2D-HNN is investigated. Simulation results show the efficiency of the proposed approach.
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