MAchine Intelligence and Networking (MAIN) lab에서는 지능형 의사결정, 설명 가능한 인공지능 등의 기계학습/인공지능 관련 분야에 관한 연구와 함께, 인공지능 기술을 지능형 사물인터넷과 같은 미래 무선 통신 네트워크에 응용하는 연구를 수행하고 있습니다.
MAchine Intelligence and Networking (MAIN) lab develops machine learning (ML) methods for intelligent decision-making and explainable AI and applies AI/ML for wireless networks.
Recent News
[2023 Feb.] The article “Multi-residential energy scheduling under time-of-use and demand charge tariffs with federated reinforcement learning” has been accepted in IEEE Transactions on Smart Grid.
[2023 Jan.] The article “Robust energy management system with safe reinforcement learning using short-horizon forecasts” has been accepted in IEEE PES letters (to be published in IEEE Transactions on Smart Grid).
[2022 Dec.] The paper “Device selection and resource allocation for layerwise federated learning in wireless networks” has been published in IEEE Systems Journal.
[2022 Aug.] The paper “Contextual learning-based waveform scheduling for wireless power transfer with limited feedback” has been published in IEEE Internet of Things Journal. [Link]
[2022 Jul.] The article “Packet-based fronthauling in 5G networks: Network slicing-aware packetization” has been accepted in IEEE Communications Standards Magazine.
[2022 Jul.] The paper “Radio and Energy Resource Management in Renewable Energy-Powered Wireless Networks With Deep Reinforcement Learning” has been published in IEEE Transactions on Wireless Communications. [Link]
[2022 Apr.] The paper “Device selection and resource allocation for layerwise federated learning in wireless networks” has been accepted in IEEE Systems Journal.
[2022 Mar.] The paper “Resource allocation in wireless networks with federated learning: Network adaptability and learning acceleration” has been published in ICT Express. [Link]