research-article

Unified Analysis of Semi-Blind Spectrum Sensing Techniques under Low-SNR for CRNWs

Authors:

Muhammad Karam Shehzad,

Abbirah AhmedAuthors Info & Claims

ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

Pages 208 - 211

https://doi.org/10.1145/3015166.3015205

Published: 21 November 2016 Publication History

Abstract

Spectrum sensing (signal detection) under low signal to noise ratio is a fundamental problem in cognitive radio networks. In this paper, we have analyzed maximum eigenvalue detection (MED) and energy detection (ED) techniques known as semi-blind spectrum sensing techniques. Simulations are performed by using independent and identically distributed (iid) signals to verify the results. Maximum eigenvalue detection algorithm exploits correlation in received signal samples and hence, performs same as energy detection algorithm under high signal to noise ratio. Energy detection performs well under low signal to noise ratio for iid signals and its performance reaches maximum eigenvalue detection under high signal to noise ratio. Both algorithms don't need any prior knowledge of primary user signal for detection and hence can be used in various applications.

References

[1]

A. Preet and A. Kaur, "Review paper on Cognitive Radio Networking and Communications," vol. 5, no. 4, pp. 5508--5511, 2014.

[2]

J. Mitola and G. Q. Maguire, "Cognitive radio: making software radios more personal," IEEE Pers. Commun., vol. 6, no. 4, pp. 13--18, 1999.

[3]

Fcc, "FCC 05-57: Report and Order In the Matter of Facilitating Opportunities for Flexible, Efficient and Reliable Spectrum Use Employing Cognitive Radio Technologies," pp. 1--42, 2005.

[4]

J. O. D. Neel, "Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms," Design, p. 385, 2006.

[5]

D. Cabric, S. M. Mishra, and R. W. Brodersen, "Implementation Issues in Spectrum Sensing for Cognitive Radios," pp. 772--776, 2004.

[6]

K. D. Singh, P. Rawat, and J. Bonnin, "Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges," pp. 1--22, 2014.

[7]

S. Parsons, "Literature Review of Cognitive Radio Spectrum Sensing EE 359 Project," 2014.

[8]

M. F. Sohail and M. A. Ashraf, "SPECTRUM SENSING FOR COGNITIVE RADIO NETWORKS," vol. 2, no. 1, pp. 24--27, 2013.

[9]

R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty," 2005 Int. Conf. Wirel. Networks, Commun. Mob. Comput., vol. 1, pp. 464--469, 2005.

[10]

M. Ghozzi, F. Marx, M. Dohler, and J. Palicot, "Cyclostatilonarilty-Based Test for Detection of Vacant Frequency Bands," 2006 1st Int. Conf. Cogn. Radio Oriented Wirel. Networks Commun., no. 1, pp. 1--5, 2006.

[11]

P. Qihang, Z. Kun, W. Jun, L. Shaoqian, and S. Province, "A Distributed Spectrum Sensing Scheme Based on Credibility and Evidence Theory in Cognitive Radio Context," 17th Annu. IEEE Int. Symp. Pers. Indoor Mob. Radio Commun., 2006.

[12]

N. Sai Shankar, C. Cordeiro, and K. Challapali, "Spectrum agile radios: Utilization and sensing architectures," 2005 1st IEEE Int. Symp. New Front. Dyn. Spectr. Access Networks, DySPAN 2005, pp. 160--169, 2005.

[13]

W. Yue and B. Zheng, "A Two-Stage Spectrum Sensing Technique in Cognitive Radio Systems Based on Combining Energy Detection and One-Order Cyclostationary Feature Detection," vol. 8, pp. 327--330, 2009.

[14]

Hao Hu "Cyclostationary approach to signal detection and classification in cognitive radio systems," 2009.

[15]

Y. Zeng and Y. Liang, "Covariance Based Signal Detections For Cognitive Radio," pp. 202--207, 2007.

Digital Library

[16]

Y. Zeng and Y. Liang, "Eigenvalue-Based Spectrum Sensing Algorithms for Cognitive Radio," vol. 57, no. 6, pp. 1784--1793, 2009.

Digital Library

[17]

A. Ghasemi and E. S. Sousa, "Optimization of Spectrum Sensing for opportunistic Spectrum Access in cognitive Radio Networks, "2007 4th IEEE Consum, commun New. Conf, pp. 1022--1026, 2007.

Digital Library

[18]

F.F. Digham, M. S. Alouini, and M.K. Sinon, " On the Energy Detection of Unknown Signals Over Fading Channels", Commun IEEE Trans, Vol. 55, no. 1, pp. 21--24, 2007.

[19]

G. Ganesan and Y. Li, "Agility improvement through cooperative diversity in cognitive radio," GLOBECOM -- IEEE Glob. Telecommun. Conf., vol. 5, pp. 2505--2509, 2005.

[20]

S. Zhang and Z. Bao, "An Adaptive Spectrum Sensing Algorithm under Noise Uncertainty," pp. 0--4, 2011.

[21]

J. Xie and J. Chen, "An adaptive double-threshold\spectrum sensing algorithm under noise uncertainty," Proc. -- 2012 IEEE 12th Int. Conf. Comput. Inf. Technol. CIT 2012, pp. 824--827, 2012.

Digital Library

[22]

Y. Zeng, C. L. Koh, and Y. Liang, "Maximum Eigenvalue Detection: Theory and Application," pp. 4160--4164, 2008.

[23]

S. Atapattu, C. Tellambura, and H. Jiang, "Spectrum sensing via energy detector in low SNR," IEEE Int. Conf. Commun., 2011.

[24]

C. Cordeiro, K. Challapali, D. Birru, and N. Sai Shankar, "IEEE 802.22: An introduction to the first wireless standard based on cognitive radios," J. Commun., vol. 1, no. 1, pp. 38--47, 2006.

Cited By

K. Shehzad MAhmad AHassan SJung H(2021)Backhaul-Aware Intelligent Positioning of UAVs and Association of Terrestrial Base Stations for Fronthaul ConnectivityIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30773148:4(2742-2755)Online publication date: 1-Oct-2021
https://doi.org/10.1109/TNSE.2021.3077314
Shehzad MHassan SLuque-Nieto MPoncela JJung H(2020)Energy efficient placement of UAVs in wireless backhaul networksProceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond10.1145/3414045.3415936(1-6)Online publication date: 25-Sep-2020
https://dl.acm.org/doi/10.1145/3414045.3415936

Unified Analysis of Semi-Blind Spectrum Sensing Techniques under Low-SNR for CRNWs

Recommendations

Eigenvalue Based Signal Detection Algorithm for Spectrum Sensing in CRNWs
ICSPS 2017: Proceedings of the 9th International Conference on Signal Processing Systems

Spectrum sensing (signal detection) is a fundamental problem in cognitive radio networks. Signals, having low signal to noise ratio, are more difficult to detect due to rapid fluctuation of noise. In this paper, we propose a double threshold based ...
Queuing-Theoretical Multi-user Spectrum Sensing and Access in Multi-channel Cognitive Radio Networks
CYBERC '15: Proceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

The rapid growth of wireless communications has made the spectrum resource becoming more and more scarcity. The concept of cognitive radio was proposed to address the issue of spectrum efficiency and had been receiving an increasing attention in recent ...
An Adaptive Sensing Time Based on SNR for Spectrum Utilization Enhancement in Cognitive Radio

Cognitive radio is considered as the most intelligent solution to enable flexible use of the frequency spectrum. It allows the secondary user to use the band in which the primary user is idle. Spectrum sensing is a major task in cognitive radio systems. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

November 2016

235 pages

ISBN:9781450347907

DOI:10.1145/3015166

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICSPS 2016

ICSPS 2016: 8th International Conference on Signal Processing Systems

November 21 - 24, 2016

Auckland, New Zealand

Acceptance Rates

ICSPS 2016 Paper Acceptance Rate 46 of 83 submissions, 55%;

Overall Acceptance Rate 46 of 83 submissions, 55%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
68
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

K. Shehzad MAhmad AHassan SJung H(2021)Backhaul-Aware Intelligent Positioning of UAVs and Association of Terrestrial Base Stations for Fronthaul ConnectivityIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.30773148:4(2742-2755)Online publication date: 1-Oct-2021
https://doi.org/10.1109/TNSE.2021.3077314
Shehzad MHassan SLuque-Nieto MPoncela JJung H(2020)Energy efficient placement of UAVs in wireless backhaul networksProceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond10.1145/3414045.3415936(1-6)Online publication date: 25-Sep-2020
https://dl.acm.org/doi/10.1145/3414045.3415936

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents