Search Results for author: Qunsong Zeng

Found 7 papers, 0 papers with code

Energy-Efficient Radio Resource Allocation for Federated Edge Learning

no code implementations13 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.

Management Scheduling

An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning

no code implementations10 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.

Active Learning BIG-bench Machine Learning +2

Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing

no code implementations14 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

Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer

no code implementations24 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).

Realizing Ultra-Fast and Energy-Efficient Baseband Processing Using Analogue Resistive Switching Memory

no code implementations7 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.

Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting

no code implementations28 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.

Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G

no code implementations19 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.

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