Korea University, AIM Laboratory is selected as University ICT Research Center (ITRC), as a Center Leader
Net-Zero CAFE (Connectivity and Autonomy for Future Ecosystem) Research Center
(대학ICT연구센터(ITRC), 탄소중립 미래자율통신 연구센터)
[참여기관: 숙명여자대학교, 아주대학교, 중앙대학교, 한국외국어대학교]
Korea University, AIM Laboratory is selected as Software Star-Lab
Quantum AI Empowered Second-Life Platform Technology
(SW스타랩, 퀀텀 인공지능기반 Second-Life 플랫폼 기술)

A Searchable Encryption Technique for Secure Color Image Retrieval

Quantum federated learning for vehicular computing scenarios

Privacy-sensitive parallel split learning

Antenna Selection Optimization in Polarization Reconfigurable MIMO System

Privacy-Preserving Transfer Learning using Learnable Encryption

Chaos Theory-Based Learnable Encryption Scheme for Privacy-Preserving Medical Image Analysis

Adaptive and Stabilized Streaming for Edge-Assisted Connected Vehicles under Heterogeneous Computing Constraints

Learning-based second price auction for distributed delivery scheduling in smart and autonomous urban air mobility

Augmented State Neural-Transition Extended Kalman Filter

Overview of distributed federated learning: Research issues, challenges, and biomedical applications

Introduction to Reinforcement Learning for Torpedo Guidance with Obstacle Avoidance

Distributed and autonomous aerial data collection in smart city surveillance applications

Trends in blockchain and federated learning for data sharing in distributed platforms

A Research on Diffusion-based Offline Reinforcement Learning for Data-Driven Control

Stable marriage matching for traffic-aware space-air-ground integrated networks: A gale-shapley algorithmic approach

A Similarity-Based Multi-Knowledge Transfer Algorithm for Enhancing Learning Efficiency of Reinforcement Learning-Based Autonomous Agent

Trends in LEO satellite handover algorithms

Quantum scheduling for millimeter-wave observation satellite constellation

Scalable quantum convolutional neural networks

Joint behavioral cloning and reinforcement learning method for propofol and remifentanil infusion in anesthesia

Fast and reliable offloading via deep reinforcement learning for mobile edge video computing

제약 구역을 고려한 동적계획법 기반 최적 중계소 배치 기법

Hallucination-aware generative pretrained transformer for cooperative aerial mobility control

LLM-based Interactive Coding Education via Predictive Query Management and Student-Centered Fine-Tuning: Design and Implementation with 1500-Student Class …

Introduction to Heterogeneous Knowledge Transfer for Quantum Convolutional Neural Networks

신뢰성 있는 지구 저궤도 위성 통신망 최적화를 위한 강화학습 기반 동적 라우팅 알고리즘

Performance comparison of SRCNN, VDSR, and SRDenseNet deep learning models in embedded autonomous driving platforms

Diffusion-based quantum error mitigation using stochastic differential equation

저궤도 인공위성에 적용되는 심층강화학습 기술 동향

AoI-aware Markov decision policies for caching

Trends in Transfer Learning on Reinforcement Learning for Mobility Control

A tutorial on quantum approximate optimization algorithm (QAOA): Fundamentals and applications

The useful quantum computing techniques for artificial intelligence engineers

Quantum convolutional neural network for resource-efficient image classification: A quantum random access memory (QRAM) approach

Quantum Heuristic Solver using QAOA for the Maximum Independent Set Problem

Hybrid mac for military uav networks

Trends in Reinforcement Learning Methods for Stock Prediction

Self-configurable stabilized real-time detection learning for autonomous driving applications

Sionna: Introduction to embedded open-source semantic communication platforms

Quantum reinforcement learning for coordinated satellite systems

Fully-Distributed Fairness-Aware Federated Learning

Introduction to quantum reinforcement learning: Theory and pennylane-based implementation

Quantum reinforcement learning for large-scale multi-agent decision-making in autonomous aerial networks

Spatio-temporal split learning

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications

A tutorial on quantum convolutional neural networks (QCNN)

S2i-bird: Sound-to-image generation of bird species using generative adversarial networks

Multiagent DDPG-based deep learning for smart ocean federated learning IoT networks

Quantum multi-agent reinforcement learning for autonomous mobility cooperation

Multi-agent deep reinforcement learning using attentive graph neural architectures for real-time strategy games

Quantum Teleportation: A Premier Quantum Communication Protocol

Proper Cost Hamiltonian Design for Combinatorial Optimization Problems: A Boolean Function Approach

Understanding the Qubit Trajectory in Parameterized Quantum Circuits

Quantum neural networks: Concepts, applications, and challenges

Introduction to Quantum Multi-Agent Reinforcement Learning: Concepts and Applications

Learnable Encryption with a Diffusion Property

Infrastructure-assisted on-driving experience sharing for millimeter-wave connected vehicles

Bitcoin price forecasting via ensemble-based LSTM deep learning networks

Visualization of deep reinforcement autonomous aerial mobility learning simulations

Spatio-Temporal Multi-Metaverse Dynamic Streaming for Hybrid Quantum-Classical Systems

EQuaTE: Efficient quantum train engine design and demonstration for dynamic software analysis

Markov decision policies for distributed angular routing in LEO mobile satellite constellation networks

Quantum-Amplitude Embedded Adaptation for Parameter-Efficient Fine-Tuning in LLMs

Advanced taxiing path guidance using multi-agent reinforcement learning for air traffic management

Stabilized classification control using multi-stage quantum convolutional neural networks for autonomous driving

Auction-based Truthful Distributed Resource Allocation for Smart Grid Systems

Ddpg-based deep reinforcement learning for loitering munition mobility control: Algorithm design and visualization

Quantum Multi-Agent Reinforcement Learning Software Design and Visual Simulations for Multi-Drone Mobility Control

Infrastructure-assisted cooperative multi-UAV deep reinforcement energy trading learning for big-data processing

Behind the Curtains of VLM: Analyzing the Influence of Background Knowledge

Entropy-aware similarity for balanced clustering: A case study with melanoma detection

Quantum Multi-Agent Reinforcement Learning for Joint Cube-Satellites and High-Altitude Long-Endurance Aerial Vehicles in SAGIN

Adaptive Excavation Automation in Complex Soil Environments Using Reinforcement Learning

Securing heterogeneous IoT with intelligent DDoS attack behavior learning

Reinforcement Learning for Semiconductor Wafer Scheduling

A tutorial on quantum graph recurrent neural network (QGRNN)

Hybrid quantum-classical style transfer (student abstract)

How Can Quantum Deep Learning Improve Large Language Models?

Spatio-temporal split learning for privacy-preserving medical platforms: Case studies with covid-19 ct, x-ray, and cholesterol data

Trends in 3D Point Cloud Contents Sampling in Mobile AR/VR Platforms

Guest editorial: Recent and future evolution of Wi-Fi

Dynamic Decision-Making for Stabilized Deep Learning Software

Joint Interference Approximation and Guard-Band Management for Spectrum-Efficient Integrated NTN-TN Networks

Multi-Agent Cooperation via Graph Convolutional Reinforcement Learning

Entanglement-aware Quantum Error Mitigation for Complex Quantum Circuits

Quantum split learning for privacy-preserving information management

Power demand forecasting using long short-term memory neural network based smart grid

Economic theoretic LEO satellite coverage control: An auction-based framework

Trends in neural architecture search: Towards the acceleration of search

Stabilized Robust Control for Lightweight Autonomous Aircraft Mobility: A Quantum Reinforcement Learning Approach

Logarithmic dimension reduction for quantum neural networks

Carbon-Aware Energy-Efficient Aerial Mobility Control via Large Language Models

Integrated Control, Communication, and Computing for Mission-Critical Embedded Unmanned Aerial Vehicles

Stabilized detection accuracy maximization using adaptive SAR image processing in LEO networks

Attention-based reinforcement learning for real-time UAV semantic communication

3D modeling and webvr implementation using azure kinect, open3D, and three. js

Learning-based dot-grid alignment for projection distortion correction

Slimmable quantum federated learning

Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching

Fast quantum convolutional neural networks for low-complexity object detection in autonomous driving applications

Energy-efficient rate-adaptive GPS-based positioning for smartphones

Communication-efficient and distributed learning over wireless networks: Principles and applications

Cooperative multi-agent deep reinforcement learning for reliable surveillance via autonomous multi-UAV control

Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions

Cooperative management for PV/ESS-enabled electric vehicle charging stations: A multiagent deep reinforcement learning approach

Quality-aware streaming and scheduling for device-to-device video delivery

Towards 5G: Applications, requirements and candidate technologies

XOR mixup: Privacy-preserving data augmentation for one-shot federated learning

Opportunities in 5G networks: A research and development perspective

Auction-based charging scheduling with deep learning framework for multi-drone networks

Residential demand response for renewable energy resources in smart grid systems

Energy-efficient mobile charging for wireless power transfer in Internet of Things networks

Fast millimeter-wave beam training with receive beamforming

Mempool optimization for defending against DDoS attacks in PoW-based blockchain systems

Orchestrated scheduling and multi-agent deep reinforcement learning for cloud-assisted multi-UAV charging systems

Movement-aware vertical handoff of WLAN and mobile WiMAX for seamless ubiquitous access

Soyi Jung

Soohyun Park

Minseok Choi

MyungJae Shin

Wonjun Lee

Andreas F. Molisch

Hankyul Baek

Gyu Seon, Kim

Dohyun Kwon

Jaeho Choi

Anna Yoo Jeong Ha

Seok Bin Son

Emily Jimin Roh

Kyeongseon Kim

Byungju Lim

Jaehyun Chung

Ju-Hyung Lee

Hyojun Ahn

Seungcheol Oh

Minjoo Kim

Joongheon Kim

Many emerging smartphone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy. A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy. In this paper, we present RAPS, rate-adaptive positioning system for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques to cleverly determine …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML …

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments.This article creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimizing overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This article develops a multiagent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this article employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user …

Recently, the Blockchain-based cryptocurrency market witnessed enormous growth. Bitcoin, the leading cryptocurrency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this article, we study Bitcoin and Ethereum and explore features in their network that explain their price hikes. We gather data and analyze user and network activity that highly impact the price of these cryptocurrencies. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that help us to determine the demand and supply dynamics in a cryptocurrency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Based on our experimental results using two large datasets for validation, we confirm that our approach provides an accuracy of up to 99% for Bitcoin and Ethereum price prediction in both …

This article proposes a novel multiagent deep reinforcement learning method for the energy management of distributed electric vehicle charging stations with a solar photovoltaic system and energy storage system. In the literature, the conventional method is to calculate the optimal electric vehicle charging schedule in a centralized manner. However, in general, the centralized approach is not realistic under certain environments where the system operators for multiple electric vehicle charging stations handle dynamically varying data, such as the status of the energy storage system and electric vehicle-related information. Therefore, this article proposes a method that can compute the scheduling solutions of multiple electric vehicle charging stations in a distributed manner while handling run-time time-varying dynamic data. As shown in the data-intensive performance evaluation, it can be observed that the proposed …

On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users' devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a subset of video files from a library, and users requesting a file that is not already in their library obtain it from neighboring devices through D2D communication. We develop centralized and distributed algorithms for the delivery phase, encompassing a link scheduling and a streaming component. The centralized scheduling is based on the max-weighted independent set (MWIS) principle and uses message-passing …

Convolutional Neural Network (CNN) is a popular model in computer vision and has the advantage of making good use of the correlation information of data. However, CNN is challenging to learn efficiently if the given dimension of data or model becomes too large. Quantum Convolutional Neural Network (QCNN) provides a new solution to a problem to solve with CNN using a quantum computing environment, or a direction to improve the performance of an existing learning model. The first study to be introduced proposes a model to effectively solve the classification problem in quantum physics and chemistry by applying the structure of CNN to the quantum computing environment. The research also proposes the model that can be calculated with O(log(n)) depth using Multi-scale Entanglement Renormalization Ansatz (MERA). The second study introduces a method to improve the model’s performance by adding …

This book brings together a group of visionaries and technical experts from academia to industry to discuss the applications and technologies that will comprise the next set of cellular advancements (5G). In particular, the authors explore usages for future 5G communications, key metrics for these usages with their target requirements, and network architectures and enabling technologies to meet 5G requirements. The objective is to provide a comprehensive guide on the emerging trends in mobile applications, and the challenges of supporting such applications with 4G technologies.

This article proposes a novel multiagent deep reinforcement learning-based algorithm which can realize federated learning (FL) computation with Internet-of-Underwater-Things (IoUT) devices in the ocean environment. According to the fact that underwater networks are relatively not easy to set up reliable links by huge fading compared to wireless free-space air medium, gathering all training data for conducting centralized deep learning training is not easy. Therefore, FL-based distributed deep learning can be a suitable solution for this application. In this IoUT network (IoUT-Net) scenario, the FL system needs to construct a global learning model by aggregating the local model parameters that are obtained from individual IoUT devices. In order to reliably deliver the parameters from IoUT devices to a centralized FL machine, base station like devices are needed. Therefore, a joint cell association and resource …

Over the past few years, many researchers around the world have been keen to know the potential and efficiency of quantum computers. The researchers have focused on specific issues that classical computers cannot solve or issues that quantum computers can handle in a better way. Among these various attractive research topics in quantum computers, this paper introduces the Quantum Approximate Optimization Algorithm (QAOA) which guarantees relatively considerable performances in many combinatorial optimization problems. For the comprehensive understanding of QAOA, this paper also describes the approximate optimization, the Quantum Alternating Operator Ansatz, and applications. Besides the theories of QAOA and Quantum Alternating Operator Ansatz, this paper explains the applications of QAOA to major combinatorial optimization problems such as maximum cut (MaxCut) problem and the max …

User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and identically distributed (non-IID) data problem, in this work we develop a privacy-preserving XOR based mixup data augmentation technique, coined XorMixup, and thereby propose a novel one-shot FL framework, termed XorMixFL. The core idea is to collect other devices' encoded data samples that are decoded only using each device's own data samples. The decoding provides synthetic-but-realistic samples until inducing an IID dataset, used for model training. Both encoding and decoding procedures follow the bit-wise XOR operations that intentionally distort raw samples, thereby preserving data privacy. Simulation results corroborate that XorMixFL achieves up to 17.6% higher accuracy than Vanilla FL under a non-IID MNIST dataset.

5G is a dramatic paradigm shift from 4G that requires a high flexibility and intelligence in many disruptive technologies. 5G means a new transmission scheme, virtualized SDN-like control, extreme energy efficiency, new regulatory and standardization issues, and support of a 1000x higher data rate than 4G. This book focuses on the R&D of 5G networks, especially its core components such as millimeter wave communications, Massive MIMO, cloud-based networking, SDN support, Big Data, energy-efficient protocols, and cognitive spectrum management.

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of …

Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks. The research topics and directions of deep learning and quantum computing have been separated for long time, however by discovering that quantum circuits can act like artificial neural networks, quantum deep learning research is widely adopted. This paper explains the backgrounds and basic principles of quantum deep learning and also introduces major achievements. After that, this paper discusses the challenges of quantum deep learning research in multiple perspectives. Lastly, this paper presents various future research directions and application fields of quantum deep learning.

With the current state of development in demand response (DR) programs in smart grid systems, there have been great demands for automated energy scheduling for residential customers. Recently, energy scheduling in smart grids have focused on the minimization of electricity bills, the reduction of the peak demand, and the maximization of user convenience. Thus, a user convenience model is proposed under the consideration of user waiting times, which is a nonconvex problem. Therefore, the nonconvex is reformulated as convex to guarantee optimal solutions. Moreover, mathematical formulations for DR optimization are derived based on the reformulated convex problem. In addition, two types of pricing policies for electricity bills are designed in the mathematical formulations, i.e., real-time pricing policy and progressive policy. With real-time pricing policy, convexity is guaranteed whereas progressive policy …

The Internet of Things (IoT) is expected to play an important role in the construction of next generation mobile communication services, and is currently used in various services. However, the power-hungry battery significantly limits the lifetime of IoT devices. Among the various lifetime extension techniques, this paper discusses mobile charging, which enables wireless power transfer based on radio frequency with mobile chargers (MCs). MCs function as traveling target IoT networks that provide energy to battery-operated IoT devices. However, MCs with an energy-constrained battery result in limitation of travel-time. This paper formulates a problem to minimize energy consumption for charging IoT devices by determining the path of motion of an MC and efficient charging points, and proves that the problem is NP-hard. An efficient algorithm, named best charging efficiency (BCE), is proposed to solve the problem and …

This paper proposes fast millimeter-wave (mm-wave) beam training protocols with receive beamforming. Both IEEE standards and the academic literature have generally considered beam training protocols involving exhaustive search over all possible beam directions for both the beamforming initiator and responded However, this operation requires a long time (and thus overhead) when the beamwidth is quite narrow such as for mm-wave beams (1° in the worst case). To alleviate this problem, we propose two types of adaptive beam training protocols for fixed and adaptive modulation, respectively, which take into account the unique propagation characteristics of millimeter waves. For fixed modulation, the proposed protocol allows for interactive beam training, stopping the search when a local maximum of the power angular spectrum is found that is sufficient to support the chosen modulation/coding scheme. We …

In this paper, we present a new form of attack that can be carried out on the memory pools (mempools) of blockchain-based cryptocurrencies. Towards that end, we study such an attack on Bitcoin mempool and explore its effects on transactions fee paid by legitimate users. We also propose countermeasures to contain such an attack. Our countermeasures include fee-based and age-based designs, which optimize the mempool size and help in countering the effects of DDoS attacks. We further evaluate our designs by simulations and analyze their usefulness in varying attack conditions. Our analyses can be extended to other blockchain-based applications which use memory pools to cache network activities.

This paper proposes a cloud-assisted joint charging scheduling and energy management framework for unmanned aerial vehicle (UAV) networks. For charging the UAVs those are extremely power hungry, charging towers are considered for plug-and-play charging during run-time operations. The charging towers should be cost-effective, thus it is equipped with photovoltaic power generation and energy storage systems functionalities. Furthermore, the towers should be cooperative for more cost-effectiveness by intelligent energy sharing. Based on the needs and setting, this paper proposes 1) charging scheduling between UAVs and towers and 2) cooperative energy managements among towers. For charging scheduling, the UAVs and towers should be scheduled for maximizing charging energy amounts and the scheduled pairs should determine charging energy allocation amounts. Here, two decisions are …

This paper addresses a movement-aware vertical (MAV) handover algorithm between WLAN and Mobile WiMAX for seamless ubiquitous access. An MAV handover algorithm is proposed in this paper to exploit movement pattern for avoiding unnecessary handovers in the integrated WLAN and Mobile WiMAX networks. If a mobile station (MS)'s velocity is high and its movement pattern is irregular, unnecessary handovers likely occur more frequently. Therefore, the MS velocity and moving pattern are important factors for the handover decision procedure. To avoid unnecessary handovers, the MAV handover algorithm adjusts the dwell time adaptively and predicts the residual time in the cell of target base station (BS). Consequently, the adaptive dwell timer of MAV handover algorithm allows an MS a better connection as long as possible. Our simulation results show that the reduction of unnecessary handovers by …

Professor

Korea Univ., AI and Mobility (AIM) Lab

교수님 소개

김중헌

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Antenna Selection Optimization in Polarization Reconfigurable MIMO System

Privacy-Preserving Transfer Learning using Learnable Encryption

Augmented State Neural-Transition Extended Kalman Filter

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

김중헌

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Antenna Selection Optimization in Polarization Reconfigurable MIMO System

Privacy-Preserving Transfer Learning using Learnable Encryption

Augmented State Neural-Transition Extended Kalman Filter

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집