Joint spectrum sensing method based on energy and covariance matrix theory

2012 ◽  
Author(s):  
Zhang Yi ◽  
Huang Yin ◽  
Zhang Xikai
Keyword(s):  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Matrix Theory ◽  
Joint Spectrum

scholarly journals Spectrum Sensing in Cognitive Radio by Use of Volume-Based Method

2018 ◽  
Vol 7 (2.17) ◽  
pp. 34
Author(s):  
C S. Preetham ◽  
Ch Mahesh ◽  
Ch Saranga Haripriya ◽  
Ramaraju Anirudh ◽  
M S. Sireesha
Keyword(s):  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Random Matrix Theory ◽  
Random Matrix ◽  
Matrix Theory ◽  
Sample Covariance Matrix ◽  
Closed Form Expression ◽  
Sample Covariance ◽  
Signal Sample ◽  
Deficiency Cases

Spectrum sensing is the mission of finding the licensed user signal situation, i.e. to determine the existence and deficiency of primary (licensed) user signal, the recent publications random matrix theory algorithms performs better-quality in spectrum sensing. The RMT fundamental nature is to make use of the distributed extremal eigenvalues of the arrived signal sample covariance matrix (SMC), specifically, Tracy-Widom (TW) distribution which is useful to certain extent in spectrum sensing but demanding for numerical evaluations because there is absence of closed-form expression in it. The sample covariance matrix determinant is designed for two novel volume-based detectors or signal existence and deficiency cases are differentiated by using volume. Under the Gaussian noise postulation one of the detectors theoretical decision thresholds is perfectly calculated by using Random matrix theory. The volume-based detectors efficiency is shown in simulation results. 

Enhanced Spectrum Sensing Algorithm Based on MME Detection and OAS Shrinkage Estimator

2019 ◽  
Vol 10 (1) ◽  
pp. 83-97
Author(s):  
Amoon Khalil ◽  
Mohiedin Wainakh
Keyword(s):  
Cognitive Radio ◽  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Detection Performance ◽  
Shrinkage Estimator ◽  
Detection Window ◽  
Simulation Results

Spectrum Sensing is one of the major steps in Cognitive Radio. There are many methods to conduct Spectrum Sensing. Each method has different detection performances. In this article, the authors propose a modification of one of these methods based on MME algorithm and OAS estimator. In MME&OAS method, in each detection window, OAS estimates the covariance matrix of the signal then the MME algorithm detects the signal on the estimated matrix. In the proposed algorithm, authors assumed that there is correlation between two consecutive decisions, so authors suggest the OAS estimator depending on the last detection decision, and then detect the signal using MME algorithm. Simulation results showed enhancement in detection performance (about 2dB when detection probability is 0.9. compared to MME&OAS method).

scholarly journals ELASTIC- Enabling Massive-Antenna for Joint Spectrum Sensing and Sharing: How Many Antennas Do We Need?

2019 ◽  
Vol 5 (2) ◽  
pp. 267-280 ◽  
Author(s):  
Hussein Kobeissi ◽  
Youssef Nasser ◽  
Oussama Bazzi ◽  
Amor Nafkha ◽  
Yves Louet
Keyword(s):  
Spectrum Sensing ◽  
Joint Spectrum
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