Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study

By Dawei Chen, Sixing Yin, Qian Zhang, M. Liu, Shufang Li. IEEE Transactions on Mobile Computing, 2009

379 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Marketing

View Paper: https://www.semanticscholar.org/paper/a4fa06a43de4d3a93122f0c58a17a993c6e35e1f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Guoliang Xue
2. Ingrid Moerman
3. Jian Tang
4. E. De Poorter
5. Xing Zhang

In Electrical and Electronic Engineering

1. Guoliang Xue
2. Ingrid Moerman
3. Jian Tang
4. E. De Poorter
5. Dejun Yang
6. Zhiyuan Xu
7. Xing Zhang

In Marketing

1. Guoliang Xue
2. Jian Tang
3. Dejun Yang

Citations in the corpus

• Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach (2017, methodology): “presented a detailed study [7] with first and second order statistics of collected data, including channel occupancy/vacancy, channel utilization and temporal, spectral and spatial correlation.”
• End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, background): “The ever-increasing radio spectrum demand combined with the currently dominant fixed spectrum policy assignment [16], have inspired the concepts of cognitive radio (CR) and dynamic spectrum access (DSA) aiming to improve radio spectrum utilization.”