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Publications

Selected ML/AI-Related Papers

  1. J.-H. Lee, J.-Y. Park, H.-S. Sim, and H.-S. Lee, “Multi-residential energy scheduling under time-of-use and demand charge tariffs with federated reinforcement learning,” to appear in IEEE Transactions on Smart Grid (10.1109/TSG.2023.3251956).
  2. S.-H. Hong and H.-S. Lee, “Robust energy management system with safe reinforcement learning using short-horizon forecasts,” to appear in IEEE Transactions on Smart Grid (10.1109/TSG.2023.3240588).
  3. 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).
  4. H.-S. Lee, “System-agnostic meta-learning for MDP-based dynamic scheduling via descriptive policy,” in Proc. AISTATS 2022, Mar. 2022. [ArXiv]
  5. 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]
  6. 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]
  7. 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.
  8. 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.
  9. 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.
  10. 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.

Preprints

  1. H.-S. Lee, “Automated policy design for energy supply and demand matching based on Bayesian optimization,” in preparation.
  2. K. Min and H.-S. Lee, “Universal dynamic pilot allocation for beam alignment based on multi-armed bandits,” in preparation.
  3. D.-Y. Kim, D.-E. Lee, J.-W. Kim, and H.-S. Lee, “Federated reinforcement learning for dynamic scheduling tasks in cloud-edge-end IoT networks,” in preparation.
  4. D.-Y. Kim, C.-B. Sohn, and H.-S. Lee, “IoT sensor and modulation scheduling for SWIPT using dual amplitude shift keying with double half-wave rectifier,” submitted.
  5. K. Min, H.-S. Park, and H.-S. Lee, “Adaptive beam alignment in non-stationary environments via structured bandits,” submitted.
  6. K. Min, Y. Kim, and H.-S. Lee, “Meta-scheduling framework with cooperative learning towards beyond 5G,” in revision.

International Journals

  1. J.-H. Lee, J.-Y. Park, H.-S. Sim, and H.-S. Lee, “Multi-residential energy scheduling under time-of-use and demand charge tariffs with federated reinforcement learning,” to appear in IEEE Transactions on Smart Grid (10.1109/TSG.2023.3251956).
  2. S.-H. Hong and H.-S. Lee, “Robust energy management system with safe reinforcement learning using short-horizon forecasts,” to appear in IEEE Transactions on Smart Grid (10.1109/TSG.2023.3240588).
  3. H.-S. Lee, S. Moon, D.-Y. Kim, and J.-W. Lee, “Packet-based fronthauling in 5G networks: Network slicing-aware packetization,” to appear in IEEE Communications Standards Magazine.
  4. 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).
  5. 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).
  6. 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).
  7. 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).
  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]
  9. 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]
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. S. Moon, H.-S. Lee, and J.-W. Lee, “SARA: Sparse code multiple access-applied random access for IoT devices,” IEEE Internet of Things Jorunal, Vol. 5, No. 4, pp. 3160-3174, Aug. 2018.
  22. 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.
  23. 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.
  24. 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.

International Conferences and Workshops

  1. H.-S. Lee, “System-agnostic meta-learning for MDP-based dynamic scheduling via descriptive policy,” in Proc. AISTATS 2022, Mar. 2022. [ArXiv]
  2. 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.
  3. 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.
  4. 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.
  5. D.-H.Bae, H.-S. Lee, and J.-W. Lee, “Low Latency Uplink Transmission,” in Proc. ICEIC 2017, Jan. 2017, Phuket, Thailand.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.