A Frequency Domain Approach to Eigenvalue-Based Detection with Diversity Reception and Spectrum Estimation

Ebtihal H G Yousif, Tharmalingam Ratnarajah, Mathini Sellathurai

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
88 Downloads (Pure)


In this paper, we investigate a frequency domain approach for eigenvalue-based detection of a primary user, based on equal gain combining (EGC) and spectrum estimation with Bartlett's method. This paper considers two techniques for eigenvalue detection which are Maximum Eigenvalue Detection (MED) and the Maximum-Minimum Eigenvalue (MME) detector. We exploit the eigenvalues that are associated with the Hermitian form representation of Bartlett's estimate to assess the performance of the aforementioned eigenvalue techniques in the frequency domain. For each case, we quantify the performance based on the probabilities of false alarm and missed detection over Rayleigh and Rician fading. A bivariate Mellin transform approach is employed to obtain the probability distribution function for the ratio of the extreme eigenvalues under each hypothesis. All obtained formulas are validated via Monte-Carlo simulations, and the results give a clear insight into the performance of the investigated methods. In frequency domain, MED outperforms both the MME detector and Periodogram-based energy detection even in a worst case scenario of noise uncertainty, while the MME detector exhibits heavy-tailed statistical characteristics and thus its receiver operating characteristics tend to stay on the line of no-discrimination. The performance of MED is further enhanced by careful choice of combinations of the total length of the sensing frame and number of sub-slots.
Original languageEnglish
Pages (from-to)35-47
Number of pages13
JournalIEEE Transactions on Signal Processing
Issue number1
Early online date28 Aug 2015
Publication statusPublished - 1 Jan 2016


Dive into the research topics of 'A Frequency Domain Approach to Eigenvalue-Based Detection with Diversity Reception and Spectrum Estimation'. Together they form a unique fingerprint.

Cite this