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Eigenvalue Based Signal Detection Algorithm for Spectrum Sensing in CRNWs

Authors:

Muhammad Karam Shehzad,

Abbirah AhmedAuthors Info & Claims

ICSPS 2017: Proceedings of the 9th International Conference on Signal Processing Systems

Pages 134 - 139

https://doi.org/10.1145/3163080.3163096

Published: 27 November 2017 Publication History

Abstract

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 maximum-minimum eigenvalue based signal detection algorithm to detect the signals having low SNR. The proposed algorithm is based on the sample covariance matrix of received signal. The ratio of the maximum and minimum eigenvalue of the covariance matrix is used as a test statistics to detect the presence or absence of a signal and thresholds are based on random matrix theory (RMT). The proposed algorithm does not need any prior knowledge of signal for detection. Simulations are based on digitally modulated signals to analyze the results. The proposed algorithm increases detection probability at an expense of computational complexity.

References

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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

Index Terms

Eigenvalue Based Signal Detection Algorithm for Spectrum Sensing in CRNWs
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems
      1. Digital signal processing
2. Networks
  1. Network components
    1. Wireless access points, base stations and infrastructure
      1. Cognitive radios

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cover image ACM Other conferences

ICSPS 2017: Proceedings of the 9th International Conference on Signal Processing Systems

November 2017

237 pages

ISBN:9781450353847

DOI:10.1145/3163080

Copyright © 2017 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]

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New York, NY, United States

Publication History

Published: 27 November 2017

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ICSPS 2017

ICSPS 2017: The 9th International Conference on Signal Processing Systems

November 27 - 30, 2017

Auckland, New Zealand

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Overall Acceptance Rate 46 of 83 submissions, 55%

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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

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