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Your search keyword '"You, Young-Hwan"' showing total 34 results
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34 results on '"You, Young-Hwan"'

17. Complexity-Efficient Sidelink Synchronization Signal Detection Scheme for Cellular Vehicle-to-Everything Communication Systems.

Author

You, Young-Hwan and Jung, Yong-An

Subjects
*SIGNAL detection, *TELECOMMUNICATION systems, *SYNCHRONIZATION, *MAXIMUM likelihood detection, *MACHINE-to-machine communications
Abstract

Synchronization is a challenging issue in vehicle-to-everything (V2X) cellular communication, especially when V2X devices need to directly communicate with each other outside the network coverage area. By adopting the maximum likelihood principle, we propose joint detection of the sidelink secondary synchronization signal (SL-SSS) and carrier frequency offset (CFO) in a V2X system using 5G new radio sidelink. We formulate an effective joint coherent synchronization scheme for cellular V2X applications by decoupling the estimation of the sidelink identity and CFO, which requires a priori knowledge of channel state information. To verify the feasibility of the proposed detection scheme, we derive a simplified implementation of the proposed SL-SSS detector and a closed-form expression for the detection probability. Simulation results demonstrate that the proposed SL-SSS detector exhibits either comparable performance in terms of detection probability or reduced complexity when compared with conventional SL-SSS detectors. Using the proposed method in the cellular V2X system enables V2X devices to achieve robust synchronization with reduced power consumption during the initial synchronization procedure, while also offering valuable insights for designing a simple, efficient sidelink synchronization receiver. [ABSTRACT FROM AUTHOR]

Published
2023
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18. Graph Convolutional Network Design for Node Classification Accuracy Improvement.

Author

Sejan, Mohammad Abrar Shakil, Rahman, Md Habibur, Aziz, Md Abdul, Baik, Jung-In, You, Young-Hwan, and Song, Hyoung-Kyu

Subjects
CLASSIFICATION
Abstract

Graph convolutional networks (GCNs) provide an advantage in node classification tasks for graph-related data structures. In this paper, we propose a GCN model for enhancing the performance of node classification tasks. We design a GCN layer by updating the aggregation function using an updated value of the weight coefficient. The adjacency matrix of the input graph and the identity matrix are used to calculate the aggregation function. To validate the proposed model, we performed extensive experimental studies with seven publicly available datasets. The proposed GCN layer achieves comparable results with the state-of-the-art methods. With one single layer, the proposed approach can achieve superior results. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Performance of a Double RIS Communication System Aided by Partially Active Elements.

Author

Yoo, Seung-Geun, Kim, Min-A, Kim, Jin-Woo, Park, Sang-Wook, You, Young-Hwan, and Song, Hyoung-Kyu

Subjects
SINGULAR value decomposition, COVARIANCE matrices, WIRELESS communications, TELECOMMUNICATION systems, ENERGY consumption
Abstract

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance the spectral efficiency of wireless communication systems. However, if there are many obstacles between the RIS and users, a single RIS may not provide sufficient performance. For this reason, a double RIS-aided communication system is proposed in this paper. However, this system also has a problem: the signal is attenuated three times due to the three channels created by the double RIS. To overcome these attenuations, an active RIS is proposed in this paper. An active RIS is almost the same as a conventional RIS, except for the included amplifier. Comprehensively, the proposed system overcomes various obstacles and attenuations. In this paper, an active RIS is applied to the second RIS. To reduce the power consumption of active elements, a partially active RIS is applied. To optimize the RIS elements, the sum of the covariance matrix is found by using channels related to each RIS, and the right singular vector is exploited using singular value decomposition for the sum of the covariance matrix. Then, the singular value of the sum of the covariance value is checked to determine which element is the active element. Simulation results show that the proposed system has better sum rate performance compared to a single RIS system. Although it has a lower sum rate performance compared to a double RIS with fully active elements, the proposed system will be more attractive in the future because it has much better energy efficiency. [ABSTRACT FROM AUTHOR]

Published
2023
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