Sunwoo Kim - IEEE Xplore Author Profile

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In this paper, we propose a two-dimensional signal path classification (2D-SPC) for reconfigurable intelligent surface (RIS)-assisted near-field (NF) localization. In the NF regime, multiple RIS-driven signal paths (SPs) can contribute to precise localization if these are decomposable and the reflected locations on the RIS are known, referred to as SP decomposition (SPD) and SP labeling (SPL), res...Show More
A wide-spatial coverage, low-profile, pattern reconfigurable, millimeter-wave antenna array designed for Vehicle-to-Everything (V2X) communication systems, crucial for fifth-generation wireless networks. The array features a hybrid structure and beam-scanning capabilities, ensuring extensive spatial coverage in the n257 band. Utilizing PIN diode-controlled strips, the antenna enables vertical beam...Show More
In this paper, we propose a digital twin data refinement algorithm leveraging autoencoder for real environment data augmentation. Data augmentation by digital twin for machine learning-based indoor positioning is already a well-known method. However, as accurate replication of real-world indoor environments is still challenging, the differences between real-world data and digital twin data are ine...Show More
In this paper, we propose an extended Kalman filter(EKF)-based Doppler positioning in a single low-earth orbit (LEO) satellite system. Due to the high mobility of LEO satellites, the usability of velocity-based measurements is essential. The proposed algorithm estimates the receiver's position using EKF based on Doppler measurements obtained from the position and velocity information of a single s...Show More
In this paper, we propose a near-field localization algorithm for an unmanned aerial vehicle (UAV)-mounted hybrid reconfigurable intelligent surface (HRIS) system. Since the position of HRIS is unknown, joint localization of HRIS and UE should be considered. To solve this problem, the HRIS is equipped with a single radio frequency (RF) chain receiver, enabling tunable reflection and sensing throug...Show More
Integrated sensing and communication (ISAC) has been highlighted as a solution to the current spectrum congestion issues. However, sensing and communication from the base station (BS) may not be feasible in a blockage scenario. Hence, we propose a UAV relaying network with ISAC capabilities. In particular, we propose unmanned aerial vehicles (UAVs) to sense the users (UEs) and relay the sensed inf...Show More
This paper analyzes the performance of 5G positioning using positioning reference signal (PRS) measurements in urban macro (UMa) scenarios, as proposed for positioning scenarios in the 3rd-generation partnership project (3GPP). To construct a positioning environment similar to real-world conditions, antennas used in small cells are configured to implement a beam pattern. Moreover, the antenna conf...Show More
In this paper, we present an automatic modulation classification (AMC) algorithm for identifying overlapped signals. The proposed algorithm leverages dual-type images as deep learning input data, which is composed of spectrogram and signal images. The dual-type images have the features of both individual images, such as frequency change over time and information on amplitude and phase. We improve ...Show More
In this paper, we propose a deep learning-driven landmark mapping algorithm using channel impulse responses (CIRs). Existing radio simultaneous localization and mapping (SLAM) utilize less accurate signal channel information and has a high computational complexity in processing. To address these challenges, we leverage raw data, CIRs, instead of angle and distance information. Furthermore, we repl...Show More
In this paper, we propose a graph convolutional network (GCN)-based cooperative localization method for 5G mobile networks using round-trip time (RTT) and angle of arrival (AoA) measurements. Implemented in an Urban Macrocell (UMa) scenario with non-line-of-sight (NLOS) conditions and high measurement noise, our approach enhances localization accuracy and reduces computational complexity. Compared...Show More
Reconfigurable Intelligent Surfaces (RIS) and Un-manned Aerial Vehicles (UAVs) have emerged as promising technologies for the 6th-Generation (6G) network. The integration of RIS with the UAV (RIS-UAV) can enhance ground communication by providing a 360° panoramic reflection. Existing RIS-UAV mainly considers passive elements which suffer from double path loss problems. This motivates the use of th...Show More
This paper presents a robust near-field (NF) beam tracking algorithm for terahertz communications based on deep Q-network (DQN). Traditional NF beam tracking methods relying on mobility models are fatal in ultra-massive MIMO systems, where even the slightest error could result in beam tracking failures. Thus, the proposed algorithm aims to maintain a stable beamforming gain by tracking the mobile ...Show More
Reconfigurable intelligent surface (RIS) is an emerging technique for robust millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we study the channel estimation problem for extremely large-scale RIS (XL-RIS) assisted multi-user XL-MIMO systems with hybrid beamforming structures. In this system, we propose an unified channel estimation method that yields a notable...Show More
Reconfigurable Intelligent Surfaces (RIS) and Un-manned Aerial Vehicles (UAVs) have emerged as promising tech-nologies for the 6th-Generation (6G) network. The integration of RIS with the UAV (RIS-UAV) can enhance ground communication by providing a 360°panoramic reflection. Existing RIS-UAV mainly considers passive elements which suffer from double path loss problems. This motivates the use of th...Show More
This paper presents an efficient channel estimation algorithm for multi-user reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) systems. In this paper, the concept of low rank matrix completion (LRMC) is exploited to reduce beam training overhead for channel estimation. The proposed beam training samples part of each channel matrix in a special pattern that is suitable for LRM...Show More
In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces (RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation. Therefore, simultaneous radio localization and mapping (SLAM) with RISs appears as an emerging research direction within the 6G ecosystem. In this paper, we propose a novel...Show More
In this paper, we propose location-aware beam training and multi-dimensional atomic norm minimization (ANM)-based channel estimation for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. The use of both location information and RIS beamwidth adaptation allows a significant reduction of beam training overhead. However, considering a trade-off between accuracy and beam training...Show More
This work proposes an efficient controller placement algorithm in SDN to address scalability challenges in dense sensor networks. An important question must be answered: given network topology, how many controllers are needed, and where should they be placed to satisfy network-specific requirements? This requires the decision-maker to define a set of objectives that must be considered during place...Show More
In indoor THz communication, access points (APs) transmit and receive broadband THz signals using multiple-input multiple-output (MIMO) array antennas, and indoor mobile robots can receive the transmitted signals from these APs and use them for positioning. However, due to various obstacles in the communication environment, not only line-of-sight (LOS) but also non-LOS (NLOS) signals are received,...Show More
This paper introduces mmWave channel state information (CSI) acquisition testbed for the demonstration of several algorithms in mmWave communications research fields. The testbed comprises multiple field-programmable gate arrays (FPGA), a radio frequency (RF) converter, and a phased array antenna for mmWave signal generation and high-speed data processing. We also propose system design and impleme...Show More
In a dynamic environment with varying degrees of mobile ground users, optimizing the placement of UAVs are important in improving network throughput. Moreover, for integrated sensing and communication (ISAC) enabled UAV-BSs, the resource allocation for sensing and communication relies on the dynamic nature of the network environment. To keep track of the changes in the environment UAVs are require...Show More
This paper presents the penetration loss of different materials for indoor millimeter-wave (mmWave) communication. The measurements of penetration loss were conducted in indoor environments via the mmWave multiple-input multiple-output (MIMO) testbed. The mmWave MIMO testbed consists of the NI PXI platforms, the TMYTEK mixer, and the TMYTEK uniform planar array antenna. The experiment was conducte...Show More
In this paper, we propose a fingerprinting-based localization method in the 5G millimeter-wave (mmWave) smallcell channel. The proposed method uses measurements that do not require additional processes to collect, such as synchronization signal-reference signal received power (SS-RSRP) used for synchronization between the user and base station in 5G communication and transmitter (TX) beam ID data ...Show More
This paper presents an approach for enhancing indoor localization accuracy using a hybrid quantum deep neural network model (H-QDNN). To improve the accuracy of indoor localization based on contemporary techniques, we employ the combined strengths of quantum computing (QC) and deep neural networks (DNN). The strengths of QC, which accelerates the training process and enables efficient handling of ...Show More
This paper proposes a direction-of-arrival (DoA) estimation algorithm based on semi-supervised learning in the presence of hardware impairments. The proposed algorithm estimates DoA through the following two steps. In the first step, the array response vectors with hardware impairments are estimated by the network version of dictionary learning with un-labeled data. The second step estimates the D...Show More