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Publications

Selected ML/AI-Related Papers

  1. D.-Y. Kim, D.-E. Lee, J.-W. Kim, and H.-S. Lee, “Collaborative policy learning for dynamic scheduling tasks in cloud-edge-terminal IoT networks using federated reinforcement learning,” IEEE Internet of Things Journal, Vol. 11, No. 6, pp. 10133-10149, Mar. 2024 (10.1109/JIOT.2023.3327495). (JCR 2022, SCIE, Top 2.2%, IF 10.6)

  2. J.-H. LeeJ.-Y. ParkH.-S. Sim, and H.-S. Lee, “Multi-residential energy scheduling under time-of-use and demand charge tariffs with federated reinforcement learning,” IEEE Transactions on Smart Grid, Vol. 14, No. 6, pp. 4360-4372, Nov. 2023 (10.1109/TSG.2023.3251956). (JCR 2022, SCIE, Top 6.4%, IF 9.6)

  3. K. Min, Y. Kim, and H.-S. Lee, “Meta-scheduling framework with cooperative learning towards beyond 5G,” IEEE Journal on Selected Areas in Communications, Vol. 41, No. 6, pp. 1810-1824, June 2023 (10.1109/JSAC.2023.3273698). (JCR 2022, SCIE, Top 1.3%, IF 16.4)

  4. S.-H. Hong and H.-S. Lee, “Robust energy management system with safe reinforcement learning using short-horizon forecasts,” IEEE Transactions on Smart Grid, Vol. 14, No. 3, pp. 2485-2488, May 2023 (10.1109/TSG.2023.3240588). (JCR 2022, SCIE, Top 6.4%, IF 9.6)

  5. H.-S. Lee, D.-Y. Kim, and J.-W. Lee, “Radio and energy resource management in renewable energy-powered wireless networks with deep reinforcement learning,” IEEE Transactions on Wireless Communications, Vol. 21, No. 7, pp. 5435-5449, Jul. 2022 (10.1109/TWC.2022.3140731). (JCR 2022, SCIE, Top 5.6%, IF 10.4)

  6. H.-S. Lee, “System-agnostic meta-learning for MDP-based dynamic scheduling via descriptive policy,” in Proc. AISTATS 2022, Mar. 2022. [ArXiv] (Major ML/AI Conference)

  7. H.-S. Lee and J.-W. Lee, “Adaptive transmission scheduling in wireless networks for asynchronous federated learning,” IEEE Journal on Selected Areas in Communications, Vol. 39, No. 12, pp. 3673-3687, Dec. 2021 (10.1109/JSAC.2021.3118353). [ArXiv] (JCR 2022, SCIE, Top 1.3%, IF 16.4)

  8. H.-S. Lee, “Channel metamodeling for explainable data-driven channel model,” IEEE Wireless Communications Letters, Vol. 10, No. 12, pp. 2678-2682, Dec. 2021 (10.1109/LWC.2021.3111874). [Paper] (JCR 2022, SCIE, Top 17.6%, IF 6.3)

  9. H.-S. Lee, C. Shen, W. Zame, J.-W. Lee, and M. van der Schaar, “SDF-Bayes: Cautious optimism in safe dose-finding clinical trials with drug combinations and heterogeneous patient groups,” in Proc. AISTATS 2021, Apr. 2021. (Major ML/AI Conference)

  10. H.-S. Lee, Y. Zhang, W. Zame, C. Shen, J.-W. Lee, and M. van der Schaar, “Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification,” in Proc. NeurIPS 2020, Dec. 2020. (Major ML/AI Conference)

  11. H.-S. Lee, C. Shen, J. Jordon, and M. van der Schaar, “Contextual constrained learning for dose-finding clinical trials,” in Proc. AISTATS 2020, June 2020, Palermo, Italy. (Major ML/AI Conference)

  12. H.-S. Lee and J.-W. Lee, “Contextual learning based wireless power transfer beam scheduling for IoT devices,” IEEE Internet of Things Journal, Vol. 5, No. 5, pp. 9606–9620, Dec. 2019. (JCR 2022, SCIE, Top 2.2%, IF 10.6)

Preprints

  1. J. Won, H.-S. Lee, and J.-W. Lee, “A review on multi-fidelity hyperparameter optimization in machine learning,” in preparation.

  2. J. Shin, J. Won, H.-S. Lee, and J.-W. Lee, “A survey on deep learning techniques for cleaning dataset with label noise,” in preparation.

  3. K.-S. Kim and H.-S. Lee, “To exit or not to exit: Cost-effective early exit architecture based on Markov decision process,” submitted.

  4. H.-S. Lee, D.-Y. Kim and K. Min, “Self-improving interference management based on deep learning with uncertainty quantification,” submitted.

  5. J.-W. Kim and H.-S. Lee, “Early exiting-aware joint resource allocation and DNN splitting for multi-sensor digital twin in edge-cloud collaborative systems,” in revision.

  6. K. Min, H.-S. Park, and H.-S. Lee, “Beam alignment in non-stationary environments using a novel time-varying structured bandits,” in revision.

International Journals

  1. H.-S. Lee, “Automated tariff design for energy supply-demand matching based on Bayesian optimization: Technical framework and policy implications,” Energy Policy, Vol. 188, 114102, May 2024 (10.1016/j.enpol.2024.114102). (JCR 2022, SSCI, Top 2.0%/SCIE, Top 9.6%, IF 9.0)

  2. D.-Y. Kim, D.-E. Lee, J.-W. Kim, and H.-S. Lee, “Collaborative policy learning for dynamic scheduling tasks in cloud-edge-terminal IoT networks using federated reinforcement learning,” IEEE Internet of Things Journal, Vol. 11, No. 6, pp. 10133-10149, Mar. 2024 (10.1109/JIOT.2023.3327495). (JCR 2022, SCIE, Top 2.2%, IF 10.6)

  3. H.-S. Lee, D.-Y. Kim, and K. Min, “Universal dynamic pilot allocation for beam alignment based on multi-armed bandits,” IEEE Wireless Communications Letters, Vol. 13, No. 3, pp 756-760, Mar. 2024 (10.1109/LWC.2023.3342738). (JCR 2022, SCIE, Top 17.6%, IF 6.3)

  4. D.-Y. Kim, C.-B. Sohn, and H.-S. Lee, “Dynamic joint scheduling of anycast transmission and modulation in hybrid unicast-multicast SWIPT-based IoT sensor networks,” IEEE Sensors Journal, Vol. 23, No. 24, pp. 31345-31358, Dec. 2023 (10.1109/JSEN.2023.3329499). (JCR 2022, SCIE, IF 4.3, Q1)

  5. J.-H. LeeJ.-Y. ParkH.-S. Sim, and H.-S. Lee, “Multi-residential energy scheduling under time-of-use and demand charge tariffs with federated reinforcement learning,” IEEE Transactions on Smart Grid, Vol. 14, No. 6, pp. 4360-4372, Nov. 2023 (10.1109/TSG.2023.3251956). (JCR 2022, SCIE, Top 6.4%, IF 9.6)

  6. H. Je-GalS.-J. Lee, J.-H. Yoon, H.-S. Lee, J.-H. Yang, and S. Kim, “A novel time-frequency feature fusion approach for robust fault detection of marine main engine,” Journal of Marine Science and Engineering, Vol. 11, No. 8, 1577, Aug. 2023 (10.3390/JMSE11081577). (JCR 2022, SCIE, IF 2.9, Q1)

  7. K. Min, Y. Kim, and H.-S. Lee, “Meta-scheduling framework with cooperative learning towards beyond 5G,” IEEE Journal on Selected Areas in Communications, Vol. 41, No. 6, pp. 1810-1824, June 2023 (10.1109/JSAC.2023.3273698). (JCR 2022, SCIE, Top 1.3%, IF 16.4)

  8. H.-S. Lee, S. Moon, D.-Y. Kim, and J.-W. Lee, “Packet-based fronthauling in 5G networks: Network slicing-aware packetization,” IEEE Communications Standards Magazine, Vol. 7, No. 2, pp. 56-63, June 2023 (10.1109/MCOMSTD.0007.200062).

  9. S.-H. Hong and H.-S. Lee, “Robust energy management system with safe reinforcement learning using short-horizon forecasts,” IEEE Transactions on Smart Grid, Vol. 14, No. 3, pp. 2485-2488, May 2023 (10.1109/TSG.2023.3240588). (JCR 2022, SCIE, Top 6.4%, IF 9.6)

  10. H.-S. Lee, “Device selection and resource allocation for layerwise federated learning in wireless networks,” IEEE Systems Journal, Vol. 16, No. 4, pp. 6441-6444, Dec. 2022 (10.1109/JSYST.2022.3169461). (JCR 2022, SCIE, IF 4.4)

  11. K.-W. Kim, H.-S. Lee, R. Zhang, and J.-W. Lee, “Contextual learning-based waveform scheduling for wireless power transfer with limited feedback,” IEEE Internet of Things Journal, Vol. 9, No. 17, pp. 15578-15592, Sep. 2022 (10.1109/JIOT.2022.3150798). (JCR 2022, SCIE, Top 2.2%, IF 10.6)

  12. H.-S. Lee, D.-Y. Kim, and J.-W. Lee, “Radio and energy resource management in renewable energy-powered wireless networks with deep reinforcement learning,” IEEE Transactions on Wireless Communications, Vol. 21, No. 7, pp. 5435-5449, Jul. 2022 (10.1109/TWC.2022.3140731). (JCR 2022, SCIE, Top 5.6%, IF 10.4)

  13. H.-S. Lee and D.-E. Lee, “Resource allocation in wireless networks with federated learning: Network adaptability and learning acceleration,” ICT Express, Vol. 8, No. 1, pp. 31-36, Mar. 2022 (10.1016/J.ICTE.2022.01.019). (JCR 2022, SCIE, IF 5.4)

  14. H.-S. Lee, “Channel metamodeling for explainable data-driven channel model,” IEEE Wireless Communications Letters, Vol. 10, No. 12, pp. 2678-2682, Dec. 2021 (10.1109/LWC.2021.3111874). [Paper] (JCR 2022, SCIE, Top 17.6%, IF 6.3)

  15. H.-S. Lee and J.-W. Lee, “Adaptive transmission scheduling in wireless networks for asynchronous federated learning,” IEEE Journal on Selected Areas in Communications, Vol. 39, No. 12, pp. 3673-3687, Dec. 2021 (10.1109/JSAC.2021.3118353). [ArXiv] (JCR 2022, SCIE, Top 1.3%, IF 16.4)

  16. B.-H. Lee, H.-S. Lee, S. Moon, and J.-W. Lee, “Enhanced random access for massive machine type communications,” IEEE Internet of Things Journal, Vol. 8, No. 8, pp. 7046-7064, Apr. 2021. (JCR 2022, SCIE, Top 2.2%, IF 10.6)

  17. H.-S. Lee and J.-W. Lee, “Adaptive wireless power transfer beam scheduling for non-static IoT devices using deep reinforcement learning,” IEEE Access, Vol. 8, pp. 206659-206673 Nov. 2020. (JCR 2022, SCIE, IF 3.9)

  18. W. R. Zame, I. Bica, C. Shen, A. Curth, H.-S. Lee, S. Bailey, J. Weatherall, D. Wright, F. Bretz, M. van der Schaar, “Machine learning for clinical trials in the era of COVID-19,” Statistics in Biopharmaceutical Research – Special Issue on Covid-19, Vol. 12, No. 4, pp. 506-517, 2020.

  19. K.-W. Kim, H.-S. Lee, and J.-W. Lee, “Opportunistic waveform scheduling for wireless power transfer with multiple devices,” IEEE Transactions on Wireless Communications, Vol. 19, No. 9, pp. 5651-5665, Sept. 2020. (JCR 2022, SCIE, Top 5.6%, IF 10.4)

  20. H.-S. Lee, J.-Y. Kim, and J.-W. Lee, “Resource allocation in wireless networks with deep reinforcement learning: A circumstance-independent approach,” IEEE Systems Journal, Vol. 14, No. 2, pp. 2589-2592, June 2020. (JCR 2022, SCIE, IF 4.4)

  21. H.-S. Lee and J.-W. Lee, “EHLinQ: Distributed scheduler for D2D communication with RF energy harvesting,” IEEE Systems Journal, Vol. 14, No. 2, pp. 2281-2292, June 2020. (JCR 2022, SCIE, IF 4.4)

  22. H.-S. Lee and J.-W. Lee, “Adaptive traffic management and energy cooperation in renewable energy powered cellular networks,” IEEE Systems Journal, Vol. 14, No. 1, pp. 132-143, Mar. 2020. (JCR 2022, SCIE, IF 4.4)

  23. H.-S. Lee and J.-W. Lee, “Contextual learning based wireless power transfer beam scheduling for IoT devices,” IEEE Internet of Things Journal, Vol. 5, No. 5, pp. 9606–9620, Dec. 2019. (JCR 2022, SCIE, Top 2.2%, IF 10.6)

  24. D.-Y. Kim, H.-S. Lee, K.-W. Kim, and J.-W. Lee, “Dual amplitude shift keying with double half-wave rectifier for SWIPT,” IEEE Wireless Communications Letters, Vol. 8, No. 4, pp. 1020-1023, Aug. 2019. (JCR 2022, SCIE, Top 17.6%, IF 6.3)

  25. H.-S. Lee and J.-W. Lee, “Resource and task scheduling for SWIPT IoT systems with renewable energy sources,” IEEE Internet of Things Journal, Vol. 6, No. 2, pp. 2729-2748, Apr. 2019. (JCR 2022, SCIE, Top 2.2%, IF 10.6)

  26. K.-W. Kim, H.-S. Lee, and J.-W. Lee, “Waveform design for fair wireless power transfer with multiple energy harvesting devices,” IEEE Journal on Selected Areas in Communications, Vol. 37, No. 1, pp. 34-47, Jan. 2019. (JCR 2022, SCIE, Top 1.3%, IF 16.4)

  27. S. Moon, H.-S. Lee, and J.-W. Lee, “SARA: Sparse code multiple access-applied random access for IoT devices,” IEEE Internet of Things Journal, Vol. 5, No. 4, pp. 3160-3174, Aug. 2018. (JCR 2022, SCIE, Top 2.2%, IF 10.6)

  28. H.-S. Lee, C. Tekin, M. van der Schaar, and J.-W. Lee, “Adaptive contextual learning for unit commitment in microgrids with renewable energy sources,” IEEE Journal of Selected Topics in Signal Processing, Vol. 12, No. 4, pp. 688-702, Aug. 2018. (JCR 2022, SCIE, Top 12.2%, IF 7.5)

  29. H.-S. Lee and J.-W. Lee, “Task offloading in heterogeneous mobile cloud computing: Modeling, analysis, and cloudlet deployment,” IEEE Access, Vol. 6, pp. 14908-14925, Mar. 2018. (JCR 2022, SCIE, IF 3.9)

  30. H.-S. Lee and J.-W. Lee, “QC2LinQ: QoS and channel-aware distributed link scheduler for D2D communication,” IEEE Transactions on Wireless Communications, Vol. 15, No. 12, pp. 8565-8579, Dec. 2016. (JCR 2022, SCIE, Top 5.6%, IF 10.4)

International Conferences and Workshops

  1. J. Shin, J. Won, H.-S. Lee, and J.-W. Lee, “Utilizing unclean samples with label correction in a neural network,” in Proc. ICEIC 2024, Taipei, Taiwan, Jan. 2024, pp. 796-797.

  2. J.-W. KimH. Je-Gal, and H.-S. Lee, “Efficient federated digital twin synchronization in edge-cloud-collaborative system,” in Proc. IEEE MetaCom 2023 (Meta-XP Workshop), June 2023, Kyoto, Japan.

  3. H.-S. Lee, “System-agnostic meta-learning for MDP-based dynamic scheduling via descriptive policy,” in Proc. AISTATS 2022, Mar. 2022. [ArXiv] (Major ML/AI Conference)

  4. H.-S. Lee, C. Shen, W. Zame, J.-W. Lee, and M. van der Schaar, “SDF-Bayes: Cautious optimism in safe dose-finding clinical trials with drug combinations and heterogeneous patient groups,” in Proc. AISTATS 2021, Apr. 2021. (Major ML/AI Conference)

  5. H.-S. Lee, Y. Zhang, W. Zame, C. Shen, J.-W. Lee, and M. van der Schaar, “Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification,” in Proc. NeurIPS 2020, Dec. 2020. (Major ML/AI Conference)

  6. H.-S. Lee, C. Shen, J. Jordon, and M. van der Schaar, “Contextual constrained learning for dose-finding clinical trials,” in Proc. AISTATS 2020, June 2020, Palermo, Italy. (Major ML/AI Conference)

  7. D.-H.Bae, H.-S. Lee, and J.-W. Lee, “Low Latency Uplink Transmission,” in Proc. ICEIC 2017, Jan. 2017, Phuket, Thailand.

  8. H.-S. Lee, C. Tekin, M. van der Schaar, and J.-W. Lee, “Contextual learning for unit commitment with renewable energy sources,” in Proc. IEEE GlobalSIP 2016, Dec. 2016, Washington DC, USA.

  9. H.-S. Lee and J.-W. Lee, “Energy cooperation and traffic management in cellular networks with renewable energy,” in Proc. IEEE Globecom 2016, Dec. 2016, Washington DC, USA.

  10. H.-S. Lee and J.-W. Lee, “QoS and channel-aware distributed link scheduling for D2D communication,” in Proc. WiOpt 2016, May 2016, Arizona, USA.

  11. H.-S. Lee, D.-H. Bae, and J.-W. Lee, “Energy or traffic: which one to transfer,” in Proc. IEEE VTC 2015-Fall, Sept. 2015, Boston, USA.

  12. H.-S. Lee and J.-W. Lee, “QoS and channel-aware subchannel scheduling for D2D multicast communication,” in Proc. ITC-CSCC 2015, June 2015, Seoul, Korea.

Domestic Journals

  1. 김지완, 제갈홍, 이승진, 이현석, “강화학습 모델을 활용한 디지털 롤모델 트윈 연구,” 한국통신학회논문지, 48권, 2호, Feb., 2023.
  2. 배덕현, 이현석, 이장원, “이동통신망에서 저지연 상향링크 전송 기법,” 한국통신학회논문지, 42권, 1호, Jan., 2017.

Domestic Conferences And Workshops

  1. 이다은, 이현석, “메타버스에서의 지각적 어포던스 개선을 위한 강화학습 기반 실시간 추천 시스템 설계”, 한국통신학회 하계종합학술대회, June 2022.
  2. 김지완, 제갈홍, 이승진, 이현석, “강화학습 모델을 활용한 디지털 롤모델 트윈 연구”, 한국통신학회 하계종합학술대회, June 2022.
  3. 이현석, “자율트윈을 위한 연합학습 활용 방안 연구”, 한국인터넷정보학회 추계학술발표대회, Oct. 2021.
  4. 이현석, “Meijer G-함수를 활용한 딥러닝 기반 설명 가능한 패스로스 모델”, 한국 인공지능 학술대회, Dec. 2020. (최우수논문상)
  5. 김진영, 이현석, 이장원, “Q-learning을 이용한 무선 네트워크에서 패킷 스케줄링 기법”, 한국통신학회 추계종합학술대회, Nov. 2017.
  6. 이현석, 이장원, “5G 네트워크를 위한 C-RAN에서의 Function Split,” 통신정보 합동학술대회 (JCCI 2016), Apr. 2016.
  7. 송지훈, 이현석, 이장원, “Time-correlated Rayleigh fading 채널에서 멀티셀 OFDMA 네트워크의 opportunistic 스케쥴링에 관한 연구,” 한국통신학회 동계종합학술대회, Feb. 2015.

Patents

  1. J. Lee and H. Lee, “Apparatus and method for transmitting and receiving information and power in wireless communication system ,” U.S. Patent 11,533,093, Dec. 22, 2022
  2. E. Lee, H. Lee, D. Bae, J. Lee, and I. Byun, “Method for transmitting and receiving data in wireless communication system, and device therefor,” U.S. Patent 10,609,696, Mar. 31, 2020
  3. H. Noh, J. Lee, Y. Kim, H. Lee, Y. Kwak, and S. Moon, “Interface device and method in wireless communication network,” U.S. Patent 10,560,958, Feb. 11, 2020
  4. J.-W. Lee, D.-Y. Kim, H.-S. Lee, and K.-Y. Kim, “Apparatus and method for transmitting and receiving information and power in wireless communication system,” U.S. Patent 10,411,764, Sep. 10, 2019
  5. K. Park, H. Lee, J. Lee, and H. Ko, “Method of performing scheduling for D2D multicast communication in wireless communication system and apparatus therefor,” U.S. Patent 9,706,524, Jul. 11, 2017.