Spectrum Allocation in Wireless Networks for Crowd Labelling

Li，Xiaoyang1,2; Zhu，Guangxu3; Shen，Kaiming4; Gong，Yi2; Huang，Kaibin1

10.1109/ICASSP40776.2020.9053492

2020-05-01

1520-6149

978-1-5090-6632-2

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

2020-May

8991-8995

4-8 May 2020

Barcelona, Spain

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.

Combinatorial Optimization Multicast Spectrum Allocation Wireless Crowd Labelling

其他

英语

EI

20203309040443

Wireless networks ; Electric distortion ; Multicasting ; Signal distortion

Electricity: Basic Concepts and Phenomena:701.1 ; Telecommunication; Radar, Radio and Television:716 ; Information Theory and Signal Processing:716.1 ; Radio Systems and Equipment:716.3 ; Optical Communication:717 ; Telephone Systems and Related Technologies; Line Communications:718 ; Data Communication, Equipment and Techniques:722.3 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Optimization Techniques:921.5

2-s2.0-85089219844

Scopus

1.University of Hong Kong (HKU),Dept. of EEE,Hong Kong
2.Southern University of Science and Technology (SUSTech),Dept. of EEE,China
3.Shenzhen Research Institute of Big Data (SRIBD),China
4.University of Toronto (UToronto),Dept. of ECE,Canada

Li，Xiaoyang,Zhu，Guangxu,Shen，Kaiming,et al. Spectrum Allocation in Wireless Networks for Crowd Labelling[C],2020:8991-8995.