Search Results for author: Qunsong Zeng

Found 5 papers, 0 papers with code

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.

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).

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

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 +1

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

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