Earticle

초록

영어

Cognitive radio enabled vehicular networks (CR-VNETs) is a new communication paradigm that enables moving vehicles to identify spectrum opportunities along busy streets and freeways. This detected spectrum may possibly lie in licensed frequency bands, and can be used for emergency communications, such as by primary responders during crises events. Spectrum sensing ensures that this spectrum is not currently occupied by licensed users, who have priority access rights. However, as the vehicles are in motion, the spectrum sensing at a given location must be completed with minimum delay, a challenge for classical energy and feature based detection schemes. This paper presents a new distributed compressive sampling technique that allows individual vehicles to report partial information to a centralized base station (BS), with an overhead of only few bytes. Thus, we tradeoff reporting time with processing complexity at the BS, which is tasked with re-constructing the overall spectrum utilization from these portions. Simulation results reveal significant improvements in detection time and accuracy, making our approach suitable for CR-VNETs.

목차

Abstract
 1. Introduction
 2. Preliminaries
  2.1. Compressive Sampling Basics
  2.2. Network Model
 3. Proposed Compressive Spectrum Sensing Technique
 4. Performance Analysis and Simulation Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Jia-Qi Duan [ Shaanxi Key Lab of Embedded System Technology, School of Computer Science, Northwestern Polytechnical University ]
• Shining Li [ Shaanxi Key Lab of Embedded System Technology, School of Computer Science, Northwestern Polytechnical University ]
• Guoqin Ning [ Department of Information Technology, Huazhong Normal University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605