A Machine Learning Approach for Mobile Collaborative Spectrum Sensing

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

A Machine Learning Approach for Mobile Collaborative Spectrum Sensing

Yang Li

Spectrum sensing in a large-scale heterogeneous network is very challenging as it usually requires a large number of static secondary users (SUs) to obtain the global spectrum states. To tackle this problem, the paper, entitled Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach published in IEEE Transactions on Signal Processing in Nov. 2018, proposes a new framework based on Bayesian machine learning. The authors exploit the mobility of multiple SUs to simultaneously collect spectrum sensing data and cooperatively derive the global spectrum states. They first develop a novel non-parametric Bayesian learning model, referred to as beta process (BP) sticky hidden Markov model (SHMM), to capture the spatial–temporal correlation in the collected spectrum data, where the SHMM models the latent statistical correlation within each mobile SU’s time series data, while the BP crealizes the cooperation among multiple SUs. Bayesian inference is then carried out to automatically infer the heterogeneous spectrum states. Based on the inference results, the authors also develop a new algorithm with a refinement mechanism to predict the spectrum availability, which enables a newly joining SU to immediately access the unoccupied frequency band without sensing. Simulation results show that the proposed framework can significantly improve spectrum sensing performance compared with the existing spectrum sensing techniques.

SPS on Twitter

  • The Brain Space Initiative Talk Series continues on Friday, 28 January when Dr. Russell A. Poldrack presents "Towar… https://t.co/r8ykdh9Vgh
  • Attention students! The 2022 5-Minute Video Clip Contest begins soon! This year's topic, "Graph Signal Processing a… https://t.co/QTMqxDaudy
  • Students, it's time to form your teams! The 2022 Signal Processing Cup competition is underway. This year's topic,… https://t.co/fVw7tA7zTG
  • The DEGAS Webinar Series continues this Thursday, 13 January when Peter Battaglia presents "Modeling Physical Struc… https://t.co/Kndvzl8BpE
  • The SPS Webinar Series continues on Wednesday, 26 January when Dr. Ba-Ngu Vo presents "Bayesian Multi-object Tracki… https://t.co/sKejcUeyys

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar