no code implementations • 19 Aug 2023 • Qunsong Zeng, Jiawei Liu, Mingrui Jiang, Jun Lan, Yi Gong, Zhongrui Wang, Yida Li, Can Li, Jim Ignowski, Kaibin Huang
To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient baseband processors.
no code implementations • 28 Jul 2023 • Hai Wu, Qunsong Zeng, Kaibin Huang
To overcome the bottleneck, we propose the framework of model broadcasting and assembling (MBA), which represents the first attempt on leveraging reusable knowledge, referring to shared parameters among tasks, to enable parameter broadcasting to reduce communication overhead.
no code implementations • 7 May 2022 • Qunsong Zeng, Jiawei Liu, Jun Lan, Yi Gong, Zhongrui Wang, Yida Li, Kaibin Huang
To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-efficient (UFEE) baseband processors.
no code implementations • 24 Feb 2021 • Qunsong Zeng, Yuqing Du, Kaibin Huang
To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point).
no code implementations • 14 Jul 2020 • Qunsong Zeng, Yuqing Du, Kaibin Huang, Kin K. Leung
Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation.
Information Theory Signal Processing Information Theory
no code implementations • 10 Nov 2019 • Dingzhu Wen, Xiaoyang Li, Qunsong Zeng, Jinke Ren, Kaibin Huang
Specifically, the metrics that measure data importance in active learning (e. g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.
no code implementations • 13 Jul 2019 • Qunsong Zeng, Yuqing Du, Kin K. Leung, Kaibin Huang
To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling.