Spectrum sensing based on unit-covariance for cognitive radios

Spectrum scarcity has emerged as a serious problem encountered when trying to launch new wireless technology such as WI-FI and Long-Term Evolution. Cognitive Radio technologies are the biggest solution to the increasing spectrum scarcity problem. The most significant problem in Cognitive Radio is to find spectrum gaps both quickly and successfully. In this study, a new spectrum sensing model is proposed that applies a unit test to the covariance matrix of the received signal by the Secondary Users. Thus, the test statistic and threshold for the proposed method were obtained theoretically, and the performance of this method was tested in a MIMO-OFDM based detection model. In addition, the detection performance of the method as compared with different spectrum detection methods in terms of performance and time. According to the obtained simulation results, the proposed method showed better detection performance than the conventional Energy Detection (ED), Maximum-Minimum Eigenvalue (MME), Covariance Frobenius Norm (CFN)-based detector, and others. It has been observed that the proposed detector has advantages in terms of processing cost and detection time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.