Jihong Park

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Lecturer

Deakin University

Australia

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About

Jihong Park is a Lecturer at Deakin University in Australia. His research areas include over-the-air federated learning, visual scene recovery using Wi-Fi channel state information, and advanced digital communications. He has published recent articles on topics such as channel-adaptive communications, multi-agent reinforcement learning, and quantum federated learning.

Recent Grants

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Intelligent and Trustworthy Data-aided Sensing for Satellite IoT

Open Date: 2020-10-01

Close Date:

Grant: Close

URLLC of Massive Connectivity

Open Date: 2020-08-01

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Articles (20)

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2020

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Collaborators (7)

Seong-Lyun Kim

Yonsei University

SOUTH KOREA

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Bassel Al Homssi

Lecturer of Intelligent Space Systems

University of New South Wales

AUSTRALIA

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Soohyun Park

SOUTH KOREA

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Seng Wai Loke

Professor in Computer Science

Deakin University

AUSTRALIA

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Jinho Choi

Professor

Deakin University

AUSTRALIA

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Seung-Woo Ko

Assistant Professor

Inha University

SOUTH KOREA

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MASAHIRO MORIKURA

Kyoto University

JAPAN

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Jihong Park

Research Interests

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About

Recent Grants

Articles (20)

Entanglement-Controlled Quantum Federated Learning

Zeroth-Order Over-the-Air Federated Large Model Tuning Over Edge Networks

Enabling Visual Scene Recovery From Wi-Fi CSI for Occlusion-Free Surveillance

Blind Training for Channel-Adaptive Digital Semantic Communications

Deep Learning-Based Modulation and Power Control: A BER Perspective

Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity

Distributed Multi-Agent Reinforcement Learning for Scalable Cell-Free MIMO Networks

Predictive Covert Communication Against Multi-UAV Surveillance Using Graph Koopman Autoencoder

Combinatorial Data Augmentation: A Key Enabler to Bridge Geometry- and Data-Driven WiFi Positioning

Semantics Alignment Via Split Learning for Resilient Multi-User Semantic Communication

Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks

Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory

Communication and Consensus Co-Design for Distributed, Low-Latency and Reliable Wireless Systems

Blockchained on-device federated learning

Communication-efficient multimodal split learning for mmWave received power prediction

Hiding in the Crowd: Federated Data Augmentation for On-device Learning

Mean-field game theoretic edge caching in ultra-dense networks

Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning

Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning

RIS-Assisted Coverage Enhancement in Millimeter-Wave Cellular Networks

Collaborators (7)

Seong-Lyun Kim

Bassel Al Homssi

Soohyun Park

Seng Wai Loke

Jinho Choi

Seung-Woo Ko

MASAHIRO MORIKURA

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