Jamshid Tursunboev, Vikas Palakonda, Il-Min Kim, Sunghwan Moon, and Jae-Mo Kang, “Multi-Objective Optimisation Framework for Heterogeneous Federated Learning,” CAAI Transactions on Intelligence Technology, vol. 11, no. 1, pp. 1–14, Feb. 2026.
Hyeon-Uk Lee, Suhwan Im, Seungjae Ham, Il-Min Kim, and Jae-Mo Kang, “RTF-Skip-GANomaly: A Novel Deep Learning Framework for Anomaly Detection in Run-to-Failure Data Using Skip-GANomaly Backbone,” IEEE Signal Processing Letters, early access, Feb. 2026, doi: 10.1109/LSP.2026.3664739.
Rashid Juraev, Il-Min Kim, Sangseok Yun, and Jae-Mo Kang, “Efficient Medical Image Segmentation Using RepSegNet Lightweight Reparameterized Neural Network,” Scientific Reports, vol. 16, no. 4682, pp. 1–15, Feb. 2026.
Jae-Mo Kang, Sangseok Yun, and Il-Min Kim, “DeepQ-MIMO: A Deep-Learned Quantum MIMO System With Rydberg Atomic Receiver in IoT,” IEEE Internet of Things Journal, early access, Dec. 2025.
Mingyu Sung, Il-Min Kim, Sangseok Yun, and Jae-Mo Kang, “H2-Cache: A Novel Hierarchical Dual-Stage Cache for High-Performance Acceleration of Generative Diffusion Models,” IEEE Open Journal of the Computer Society, vol. 7, pp. 69–79, Dec. 2025.
Dong-Woo Lim and Jae-Mo Kang, “Joint Antenna Selection and Transmit Power Optimization for UAV-Assisted Relaying in Cognitive Radio IoT Networks,” Mathematics, vol. 13, no. 21, pp. 3540, Nov. 2025.
Jae-Mo Kang, Sangseok Yun, and Il-Min Kim, “CaMPASS-Net: A Deep Learning Framework on Capacity Maximization for MIMO Pinching Antenna Systems in IoT,” IEEE Internet of Things Journal, vol. 12, no. 21, pp. 45917–45920, Nov. 2025.
Mingyu Sung, Mu-Gyeong Gong, Seung-Jae Ham, Il-Min Kim, Sangseok Yun, and Jae-Mo Kang, “A Novel VLM-Guided Diffusion Model for Remote Sensing Image Super-Resolution,” IEEE Geoscience and Remote Sensing Letters, vol. 22, no. 7509505, pp. 1–5, Sep. 2025.
Jae-Mo Kang and Il-Min Kim, “How Much Training is Required for Channel Estimation in Fluid Antenna System?,” IEEE Journal on Selected Areas in Communications, early access, Sep. 2025, doi: 10.1109/JSAC.2025.3614195.
Mingyu Sung, Vikas Palakonda, Il-Min Kim, Sangseok Yun, and Jae-Mo Kang, “DeCo-MeSC: Deep Compression-Based Memory-Constrained Split Computing Framework for Cooperative Inference of Neural Network,” IEEE Transactions on Vehicular Technology, vol. 74, no. 8, pp. 13319–13324, Aug. 2025.
Jae-Mo Kang and Sangseok Yun, “Worst-Case Robust Training Design for Correlated MIMO Channels in the Presence of Colored Interference,” Mathematics, vol. 13, no. 13, pp. 2168, Jul. 2025.
Furkat Sultonov, Sangseok Yun, and Jae-Mo Kang, “Corrections to “DASK-Net: A Lightweight Dual-Attention Selective Kernel Network for Efficient Dense Prediction in Remote Sensing Imagery”,” IEEE Transactions on Geoscience and Remote Sensing, vol. 63, no. 9100902, pp. 1–2, Jun. 2025.
Sangjun Ha, Mingyu Sung, Faisal Saeed, Sangseok Yun, Il-Min Kim, and Jae-Mo Kang, “Generative-Diffusion-Model-Based Deep-Learning Framework for Remaining Useful Life Prediction,” IEEE Internet of Things Journal, vol. 12, no. 11, pp. 18431–18434, Jun. 2025.
Jae-Mo Kang, “NMAP-Net: Deep-Learning-Aided Near-Field Multibeamforming Design and Antenna Position Optimization for XL-MIMO Communications,” IEEE Internet of Things Journal, vol. 12, no. 11, pp. 18397–18413, Jun. 2025.
Furkat Sultonov, Sangseok Yun, and Jae-Mo Kang, “DASK-Net: A Lightweight Dual-Attention Selective Kernel Network for Efficient Dense Prediction in Remote Sensing Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 63, no. 5623416, pp. 1–16, May 2025.
Vikas Palakonda, Jamshid Tursunboev, Jae-Mo Kang, and Sunghwan Moon, “Metaheuristics for Pruning Convolutional Neural Networks: A Comparative Study,” Expert Systems With Applications, vol. 268, no. 126326, pp. 1–23, Apr. 2025.
Dong-Woo Lim and Jae-Mo Kang, “Joint Transmit Power and Power-Splitting Optimization for SWIPT in D2D-Enabled Cellular Networks with Energy Cooperation,” Mathematics, vol. 13, no. 3, pp. 389, Jan. 2025.
Faisal Saeed, Abdul Rehman, Hasnain Ali Shah, Muhammad Diyan, Chen Jie, and Jae-Mo Kang, “SmartFormer: Graph-Based Transformer Model for Energy Load Forecasting,” Sustainable Energy Technologies and Assessments, vol. 73, no. 104133, pp. 1–11, Jan. 2025.
Vikas Palakonda, Samira Ghorbanpour, Jae-Mo Kang, and Heechul Jung, “External Archive Guided Radial-Grid Multi Objective Differential Evolution,” Scientific Reports, vol. 14, no. 29006, pp. 1–19, Nov. 2024.
Jae-Mo Kang, “Corrections to “Deep Learning Enabled Multicast Beamforming With Movable Antenna Array”,” IEEE Wireless Communications Letters, vol. 13, no. 11, pp. 3257, Nov. 2024.
Abdul Rehman, Faisal Saeed, Muhammad Mazhar Ullah Rathore, Anand Paul, and Jae-Mo Kang, “Smart City Fire Surveillance: A Deep State-Space Model with Intelligent Agents,” IET Smart Cities, vol. 6, no. 3, pp. 199–210, Sep. 2024.
Jae-Mo Kang, “A Novel Training Design Algorithm for Kronecker-Inseparable MIMO Systems With General Power Constraints,” IEEE Transactions on Vehicular Technology, vol. 73, no. 9, pp. 14027–14032, Sep. 2024.
Vikas Palakonda, Jae-Mo Kang, and Heechul Jung, “Clustering-aided Grid-Based One-to-One Selection-Driven Evolutionary Algorithm for Multi/Many-Objective Optimization,” IEEE Access, vol. 12, 120612–120623, Sep. 2024.
Jae-Mo Kang, “On the LoRa Modulation for IoT: Preamble Designs for Channel Estimation With Single-and Multi-Chirp Transmission Strategies,” IEEE Internet of Things Journal, vol. 11, no. 17, pp. 27981–27993, Sep. 2024.
Arif Ullah, Ziaul Haq Abbas, Ghulam Abbas, Fazal Muhammad, and Jae-Mo Kang, “Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment,” Digital Communications and Networks, vol. 10, no. 4, pp. 904–917, Aug. 2024.
Mingyu Sung, Chaewon Park, Sangjun Ha, Minse Ha, Hyeonuk Lee, Jonggeun Kim, and Jae-Mo Kang, “Entropy-based Sampling for Efficient Training of Deep Learning on CNC Machining Dataset,” Electronics Letters, vol. 60, no. 15, pp. e13308, Aug. 2024.
Chae-Won Park, Vikas Palakonda, Sangseok Yun, Il-Min Kim, and Jae-Mo Kang, “OCR-Diff: A Two-Stage Deep Learning Framework for Optical Character Recognition Using Diffusion Model in Industrial Internet-of-Things,” IEEE Internet of Things Journal, vol. 11, no. 15, pp. 25997–26000, Aug. 2024.
Jae-Mo Kang, “Deep Learning Enabled Multicast Beamforming With Movable Antenna Array,” IEEE Wireless Communications Letters, vol. 13, no. 7, pp. 1848–1852, Jul. 2024.
Jamshid Tursunboev, Vikas Palakonda, and Jae-Mo Kang, “Multi-Objective Evolutionary Hybrid Deep Learning for Energy Theft Detection,” Applied Energy, vol. 363, pp. 122847, Jun. 2024.
Jae-Mo Kang, “RobuT-Net: Dual-CNN-Based Robust Training Sequence Design for IoT Systems,” IEEE Internet of Things Journal, vol. 11, no. 10, pp. 18930–18931, May 2024.
Chae Yoon Jung, Jae Mo Kang, and Dong In Kim, “A Channel Estimation Method Using Denoising Autoencoder for Large-Scale Asymmetric Backscatter Systems,” ICT Express, vol. 10, no. 2, pp. 400–405, Apr. 2024.
Vikas Palakonda, Jae-Mo Kang, and Heechul Jung, “Benchmarking Real-World Many-Objective Problems: A Problem Suite With Baseline Results,” IEEE Access, vol. 12, pp. 49275–49290, Apr. 2024.
Jae-Mo Kang and Dong-Woo Lim, “Joint Channel Training and Passive Beamforming Design for Intelligent Reflecting Surface-Aided LoRa Systems,” AIMS Mathematics, vol. 9, no. 5, pp. 11423–11431, Mar. 2024.
Jae-Mo Kang, “Deep-Learning-Based Robust Channel Estimation for MIMO IoT Systems,” IEEE Internet of Things Journal, vol. 11, no. 6, pp. 9882–9895, Mar. 2024.
Jae-Mo Kang and Dong-Woo Lim, “Robust Transceiver Design for Correlated MIMO Interference Channels in the Presence of CSI Errors under General Power Constraints,” Mathematics, vol. 12, no. 6, pp. 801, Mar. 2024.
Jae-Mo Kang and Dong-Woo Lim, “Corrections to “On the Quasi-Orthogonality of LoRa Modulation”,” IEEE Internet of Things Journal, vol. 11, no. 5, pp. 9229, Mar. 2024.
Jae-Mo Kang, “Corrections to “LoRa Preamble Detection with Optimized Thresholds”,” IEEE Internet of Things Journal, vol. 11, no. 5, pp. 9228, Mar. 2024.
Jae-Mo Kang, Dong-Woo Lim, and Kyu-Min Kang, “Corrections to “On the LoRa Modulation for IoT: Optimal Preamble Detection and Its Performance Analysis”,” IEEE Internet of Things Journal, vol. 11, no. 5, pp. 9227, Mar. 2024.
Jae-Mo Kang, “Corrections to “MIMO-LoRa for High-Data-Rate IoT: Concept and Precoding Design”,” IEEE Internet of Things Journal, vol. 11, no. 5, pp. 9226, Mar. 2024.
Yosoeb Shin, Vikas Palakonda, Sangseok Yun, Il-Min Kim, Seon-Gon Kim, Sang-Mi Park, and Jae-Mo Kang, “RandMixAugment: A Novel Unified Technique for Region- and Image-Level Data Augmentations,” IEEE Access, vol. 12, pp. 8187–8197, Jan. 2024.
Jae-Mo Kang and Kae Won Choi, “Efficient Demodulation Algorithms for MIMO-LoRa,” IEEE Internet of Things Journal, vol. 10, no. 23, pp. 21129–21130, Dec. 2023.
Vikas Palakonda and Jae-Mo Kang, “Erratum to “Pre-DEMO: Preference-Inspired Differential Evolution for Multi/Many-Objective Optimization”,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 53, no. 12, pp. 7902, Dec. 2023.
Vikas Palakonda and Jae-Mo Kang, “Pre-DEMO: Preference-Inspired Differential Evolution for Multi/Many-Objective Optimization,” IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. 53, no. 12, pp. 7618–7630, Dec. 2023.
Azadeh Motamedi, Sangseok Yun, Jae-Mo Kang, Yiqun Ge, and Il-Min Kim, “Redundancy Management in Federated Learning for Fast Communication,” IEEE Transactions on Communications, vol. 71, no. 11, pp. 6332–6347, Nov. 2023.
Hyeon-Uk Lee, Chang-Jae Chun, and Jae-Mo Kang, “Causality-Driven Efficient Feature Selection for Deep-Learning-Based Surface Roughness Prediction in Milling Machines,” Mathematics, vol. 11, no. 22, pp. 4682, Sep. 2023.
Jae-Mo Kang, “Corrections to “Intelligent Reflecting Surface: Joint Optimal Training Sequence and Refection Pattern”,” IEEE Communications Letters, vol. 27, no. 11, pp. 3142, Nov. 2023.
Vikas Palakonda and Jae-Mo Kang, “Many-Objective Real-World Engineering Problems: A Comparative Study of State-of-the-Art Algorithms,” IEEE Access, vol. 11, pp. 111636–111654, Oct. 2023.
Hasnain Ali Shah and Jae-Mo Kang, “An Optimized Multi-Organ Cancer Cells Segmentation for Histopathological Images Based on CBAM-Residual U-Net,” IEEE Access, vol. 11, pp. 111608–111621, Oct. 2023.
Seong-Hwan Kang, Vikas Palakonda, Il-Min Kim, Jae-Mo Kang, and Sangseok Yun, “Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects,” Mathematics, vol. 11, no. 18, pp. 3898, Sep. 2023.
Hasnain Ali Shah, Faisal Saeed, Muhammad Diyan, Nouf Abdullah Almujally, and Jae-Mo Kang, “ECG-TransCovNet: A Hybrid Transformer Model for Accurate Arrhythmia Detection using ECG Signals,” CAAI Transactions on Intelligence Technology, early access, Sep. 2023, doi: 10.1049/cit2.12293.
Vikas Palakonda, Jae-Mo Kang, and Heechul Jung, “An Effective Ensemble Framework for Many-Objective Optimization based on AdaBoost and K-means Clustering,” Expert Systems With Applications, vol. 227, pp. 120278, Oct. 2023.
Jae-Mo Kang and Dong-Woo Lim, “On the Quasi-Orthogonality of LoRa Modulation,” IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12366–12378, Jul. 2023.
Jae-Mo Kang, “A New Index Modulation for LoRa,” IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11938–11939, Jul. 2023.
Jae-Mo Kang, “LoRa Preamble Detection with Optimized Thresholds,” IEEE Internet of Things Journal, vol. 10, no. 7, pp. 6525–6526, Apr. 2023.
Mingyu Sung, Jaesoo Kim, and Jae-Mo Kang, “Probabilistic Classification Method of Spiking Neural Network Based on Multi-Labeling of Neurons,” Mathematics, vol. 11, no. 5, pp. 1224, Mar. 2023.
Byungjo Kim and Jae-Mo Kang, “User Grouping, Precoding Design, and Power Allocation for MIMO-NOMA Systems,” Mathematics, vol. 11, no. 4, pp. 995, Feb. 2023.
Jae-Mo Kang and Sunghwan Moon, “Error Bounds for ReLU Networks With Depth and Width Parameters," Japan Journal of Industrial and Applied Mathematics, vol. 40, no. 1, pp. 275–288, Jan. 2023.
Dong-Woo Lim, Chang-Jae Chun, and Jae-Mo Kang, “Transmit Power Adaptation for D2D Communications Underlaying SWIPT-based IoT Cellular Networks,” IEEE Internet of Things Journal, vol. 10, no. 2, pp. 987–1000, Jan. 2023.
Damar Novtahaning, Hasnain Ali Shah, and Jae-Mo Kang, “Deep Learning Ensemble-Based Automated and High-Performing Recognition of Coffee Leaf Disease,” Agriculture, vol. 12, no. 11, pp. 1909–1924, Nov. 2022.
Sijia Li, Furkat Sultonov, Jamshid Tursunboev, Jun-Hyun Park, Sangseok Yun, and Jae-Mo Kang, “Ghostformer: A GhostNet-based Two-stage Transformer for Small Object Detection,” Sensors, vol. 22, no. 18, pp. 6939, Sep. 2022.
Hasnain Ali Shah, Faisal Saeed, Sangseok Yun, Jun-Hyun Park, Anand Paul, and Jae-Mo Kang, “A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet,” IEEE Access, vol. 10, pp. 65426–65438, Jun. 2022.
Sijia Li, Furkat Sultonov, Qingshan Ye, Yong Bai, Jun-Hyun Park, Chilsig Yang, Minseok Song, Sungwoo Koo, and Jae-Mo Kang, “TA-Unet: Integrating Triplet Attention Module for Drivable Road Region Segmentation,” Sensors, vol. 22, no. 12, pp. 4438, Jun. 2022.
Vikas Palakonda, Jae-Mo Kang, and Heechul Jung, “An Adaptive Neighborhood based Evolutionary Algorithm with Pivot-Solution based Selection for Multi- and Many-Objective Optimization,” Information Sciences, vol. 607, pp. 126–152, Aug. 2022.
Jae-Mo Kang, “MIMO-LoRa for High-Data-Rate IoT: Concept and Precoding Design,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 10368–10369, Jun. 2022.
Yeong-Rok Kim, Jun-Hyun Park, Jae-Mo Kang, Dong-Woo Lim, and Kyu-Min Kang, “Deep Learning-Aided Downlink Beamforming Design and Uplink Power Allocation for UAV Wireless Communications with LoRa,” Applied Sciences, vol. 12, no. 10, pp. 4826, May 2022.
Jae-Mo Kang, Dong-Woo Lim, and Kyu-Min Kang, “On the LoRa Modulation for IoT: Optimal Preamble Detection and Its Performance Analysis,” IEEE Internet of Things Journal, vol. 9, no. 7, pp. 4973–4986, Apr. 2022.
Jae-Mo Kang, Sangseok Yun, Il-Min Kim, and Heechul Jung, “MSE-Based Joint Transceiver and Passive Beamforming Designs for Intelligent Reflecting Surface-Aided MIMO Systems,” IEEE Wireless Communications Letters, vol. 11, no. 3, pp. 622–626, Mar. 2022.
Jacob Mathias Nilsen, Jun-Hyun Park, Sangseok Yun, Jae-Mo Kang, and Heechul Jung, “Competing Miners: A Synergetic Solution for Combining Blockchain and Edge Computing in Unmanned Aerial Vehicle Networks,” Applied Sciences, vol. 12, no. 5, pp. 2581, Mar. 2022.
Furkat Sultonov, Jun-Hyun Park, Sangseok Yun, Dong-Woo Lim, and Jae-Mo Kang, “Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery,” Applied Sciences, vol. 12, no. 4, pp. 1953, Feb. 2022.
Sangseok Yun, Jae-Mo Kang, Jeongseok Ha, Sangho Lee, Dong-Woo Ryu, Jihoe Kwon, and Il-Min Kim, “Deep Learning-based Ground Vibration Monitoring: Reinforcement Learning and RNN-CNN Approach,” IEEE Geoscience and Remote Sensing Letters, vol. 19, no. 7502905, pp. 1–5, 2022.
Jamshid Tursunboev, Yong-Sung Kang, Sung-Bum Huh, Dong-Woo Lim, Jae-Mo Kang, Heechul Jung, “Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks,” Applied Sciences, vol. 12, no. 2, pp. 670, Jan. 2022.
Sangseok Yun, Jae-Mo Kang, Sooyong Choi, and Il-Min Kim, “Cooperative Inference of DNNs over Noisy Wireless Channels,” IEEE Transactions on Vehicular Technology, vol. 70, no. 8, pp. 8298–8303, Aug. 2021.
Byungjo Kim, Jae-Mo Kang, Hyung-Myung Kim, and Joonhyuk Kang, “Joint Channel Estimation, Training Design, Tx Power Allocation, and Rx Power Splitting for MIMO SWIPT Systems,” IEEE Communications Letters, vol. 25, no. 4, pp. 1269–1273, Apr. 2021.
Jae-Mo Kang, Chang-Jae Chun, Il-Min Kim, and Dong In Kim, “Deep RNN-Based Channel Tracking for Wireless Energy Transfer System,” IEEE Systems Journal, vol. 14, no. 3, pp. 4340–4343, Sep. 2020.
Jae-Mo Kang and Chang-Jae Chun, “Joint Trajectory Design, Tx Power Allocation, and Rx Power Splitting for UAV-Enabled Multicasting SWIPT Systems,” IEEE Systems Journal, vol. 14, no. 3, pp. 3740–3743, Sep. 2020.
Jae-Mo Kang, Il-Min Kim, and Chang-Jae Chun, “Deep Learning-Based MIMO-NOMA with Imperfect SIC Decoding,” IEEE Systems Journal, vol. 14, no. 3, pp. 3414–3417, Sep. 2020.
Jae-Mo Kang, “Intelligent Reflecting Surface: Joint Optimal Training Sequence and Refection Pattern,” IEEE Communications Letters, vol. 24, no. 8, pp. 1784–1788, Aug. 2020.
Jae-Mo Kang, “Reinforcement Learning Based Adaptive Resource Allocation for Wireless Powered Communication Systems,” IEEE Communications Letters, vol. 24, no. 8, pp. 1752–1756, Aug. 2020.
Jae-Mo Kang, Chang-Jae Chun, and Il-Min Kim, “Deep Learning Based Channel Estimation for MIMO Systems with Received SNR Feedback,” IEEE Access, vol. 8, pp. 121162–121181, Jul. 2020.
Jae-Mo Kang, Chang-Jae Chun, Il-Min Kim, and Dong In Kim, “Dynamic Power Splitting for SWIPT with Nonlinear Energy Harvesting in Ergodic Fading Channel,” IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5648–5665, Jun. 2020.
Chang-Jae Chun and Jae-Mo Kang, “Optimal Training Design for MIMO Amplify-and-Forward Two-Way Relay Systems in the Presence of Interference,” vol. 69, no. 5, pp. 5151–5163, IEEE Transactions on Vehicular Technology, May 2020.
Sangseok Yun, Jae-Mo Kang, Il-Min Kim, and Jeongseok Ha, “Deep Artificial Noise: Deep Learning-Based Precoding Optimization for Artificial Noise Scheme,” IEEE Transactions on Vehicular Technology, vol. 69, no. 3, pp. 3465–3469, Mar. 2020.
Jae-Mo Kang, Il-Min Kim, Sangho Lee, Dong-Woo Ryu, and Jihoe Kwon, “A Deep CNN Based Ground Vibration Monitoring Scheme for MEMS Sensed Data,” IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 2, pp. 347–351, Feb. 2020.
Chang-Jae Chun, Jae-Mo Kang, and Il-Min Kim, “Deep Learning-Based Joint Pilot Design and Channel Estimation for Multiuser MIMO Channels,” IEEE Communications Letters, vol. 23, no. 11, pp. 1999–2003, Nov. 2019.
Chang-Jae Chun, Jae-Mo Kang, and Il-Min Kim, “Deep Learning-Based Channel Estimation for Massive MIMO Systems,” IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1228–1231, Aug. 2019.
Jae-Mo Kang, Il-Min Kim, and Dong In Kim, “Joint Tx Power Allocation and Rx Power Splitting for SWIPT system with Multiple Nonlinear Energy Harvesting Circuits,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 53–56, Feb. 2019.
Chang-Jae Chun, Jae-Mo Kang, and Il-Min Kim, “Adaptive Rate and Energy Harvesting Interval Control Based on Reinforcement Learning for SWIPT,” IEEE Communications Letters, vol. 22, no. 12, pp. 2571–2574, Dec. 2018.
Jae-Mo Kang, Chang-Jae Chun, and Il-Min Kim, “Deep-Learning-Based Channel Estimation for Wireless Energy Transfer,” IEEE Communications Letters, vol. 22, no. 11, pp. 2310–2313, Nov. 2018.
Jae-Mo Kang and Il-Min Kim, “Optimal User Grouping for Downlink NOMA,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 724–727, Oct. 2018.
Xinghua Jia, Chaozhu Zhang, Jae-Mo Kang, and Il-Min Kim, “Joint Beamforming Design and Time Allocation for Wireless Powered Asymmetric Two-Way Multirelay Network,” IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9641–9655, Oct. 2018.
Jae-Mo Kang, Il-Min Kim, and Hyung-Myung Kim, “Estimation of Time-Varying Channels in MIMO Two-Way Multi-Relay Systems,” IEEE Transactions on Wireless Communications, vol. 17, no. 6, pp. 4002–4016, Jun. 2018.
Jae-Mo Kang, Il-Min Kim, and Dong In Kim, “Joint Optimal Mode Switching and Power Adaptation for Nonlinear Energy Harvesting SWIPT System over Fading Channel,” IEEE Transactions on Communications, vol. 66, no. 4, pp. 1817–1832, Apr. 2018.
Jae-Mo Kang, Il-Min Kim, and Dong In Kim, “Wireless Information and Power Transfer: Rate-Energy Tradeoff for Nonlinear Energy Harvesting,” IEEE Transactions on Wireless Communications, vol. 17, no. 3, pp. 1966–1981, Mar. 2018.
Il-Min Kim, Dong In Kim, and Jae-Mo Kang, “Rate-Energy Tradeoff and Decoding Error Probability-Energy Tradeoff for SWIPT in Finite Code Length,” IEEE Transactions on Wireless Communications, vol. 16, no. 12, pp. 8220–8234, Dec. 2017.
Jae-Mo Kang, Jun Yang, Jeongseok Ha, and Il-Min Kim, “Joint Design of Optimal Precoding and Cooperative Jamming for Multiuser Secure Broadcast Systems,” IEEE Transactions on Vehicular Technology, vol. 66, no. 11, pp. 10551–10556, Nov. 2017.
Jae-Mo Kang, Il-Min Kim, and Dong In Kim, “Mode Switching for SWIPT over Fading Channel with Nonlinear Energy Harvesting,” IEEE Wireless Communications Letters, vol. 6, no. 5, pp. 678–681, Oct. 2017.
Jae-Mo Kang, Il-Min Kim, and Hyung-Myung Kim, “Optimal Training Design for MIMO-OFDM Two-Way Relay Networks,” IEEE Transactions on Communications, vol. 65, no. 9, pp. 3675–3690, Sep. 2017.
Jae-Mo Kang and Hyung-Myung Kim, “Training Designs for Estimation of Spatially Correlated Fading Channels in MIMO Amplify-and-Forward Two-Way Multi-Relay Networks,” IEEE Communications Letters, vol. 20, no. 4, pp. 772–775, Apr. 2016.
Jae-Mo Kang and Hyung-Myung Kim, “Both Minimum MSE and Maximum SNR Channel Training Designs for MIMO AF Multi-Relay Networks with Spatially Correlated Fading,” IEEE Signal Processing Letters, vol. 18, no. 3, pp. 1888–1892, Nov. 2015.