AI-Driven 6G Intelligent Air Interface Transmission Technology
Funding：Ministry of Science and Technology Date： 2022 . 10 -2025 . 10
To address the increasingly complex network environment of 6G, this project focuses on researching an efficient and structured wireless access architecture based on network-native intelligence.
Intrinsic Green Communication Networks based on Communication, Sensing and Computing
Funding：Ministry of Science and Technology Date： 2022 . 10 -2026 . 10
This project reveals the energy consumption scaling law of network architecture and method, investigates the intrinsic green network architecture and intelligent resource scheduling technology to realize the low energy consumption of future networks.
Theory of Low Energy Cost Personalized Federated Learning
Funding：Anhui Provincial Natural Science Foundation Date： 2022 . 01 - 2024 . 12
This project alleviates the problem of user data heterogeneity by designing a low power federated learning scheme, reducing the power consumption of system communication and designing a personalized federated learning scheme.
Novel Federative Learning Theory for Human-Machine-Thing Fusion
Funding：University of Science and Technology of China Date： 2022 . 01 - 2024 . 12
This project studies the decentralized federated learning theory based on DAC and the low network overhead user clustering federated learning theory.
OPC Intelligent Optimization
Funding：CXMT Date： 2022 . 06 - 2024. 06
This project uses machine learning to analyze the SEM images during the IC manufacturing, obtain key process information such as contour and semantics, and improve the yield rate.
Huawei Wireless Enabling
Funding：HUAWEI Technology Date： 2022 . 07 - 2023. 07
This project involves proposing enabling coefficients based on the energy-saving and emission reduction capabilities of various industries and completing their verification and analysis. The coefficient assessments for several key industries will be conducted using big data statistical analysis methods, while ensuring the protection of data privacy for all parties in a distributed carbon reduction assessment.