## 1 Introduction
### Related Works  
There are several statistical tests analyzed in nCSS and CSS in different circumstances and scenarios, and the literature usually classifies them into three categories: non-blind, semi-blind, or blind statistical tests [8; 9; 10]. The most known non-blinds are the matched-filter detection and the cyclostationary feature detection. They are classified as non-blind because they need some a priori information regarding the sensing channels formed between the PU transmitter and SUs receivers and/or the PU signal to be detected to compute their statistics. On the other hand, semi-blind statistical tests as the well-known energy detection (ED) and the eigenvalue-based maximum eigenvalue detection [6], also known as [PERSON]'s largest root test, are those statistical tests that do not require any a priori information on the sensing channel or PU signal, but use the additive white Gaussian noise (AWGN) power information in their computations. Although simpler than non-blind tests, semi-blind tests are vulnerable to inaccuracies in the noise power estimation, which is usually called noise uncertainty (NU). Therefore, NU is a significant drawback of semi-blind tests that can limit their application range. Blind tests, however, do not require any a priori information regarding sensing channel, PU signal, or noise power information in their computations. Usually, they do not have a better performance than non-blind or semi-blind tests but have lower computational complexity, which is a relevant advantage of blind tests. Besides, performance analyses of blind statistical tests in spectrum sensing have been gaining increasing attention [9] since the above-mentioned a priori information required for non-blind or semi-blind tests is commonly unknown or difficult to know in practice, especially information regarding the PU signal and/or noise power in scenarios of dynamical noise (DN) (when noise power varies over time). Some examples of blind statistical tests are the eigenvalue-based: (i) maximum-minimum eigenvalue detection (MMED) [6], also known as eigenvalue ratio detection; (ii) the generalized likelihood ratio test (GLRT) [6]; (iii) the PSD-based, cooperative power spectral density split cancellation (CPSC) [7]; and (iv) circular folding cooperative power spectral density split cancellation (CFCPSC) [11]. GLRT (and GLRT-based statistical tests) are by far the most popular of these blind tests, especially in multiantenna-CR-based spectrum sensing, and when neither information on PU signal nor precise noise power-knowledge are available. However, even blind tests as GLRT and MMED may still suffer from performance degradation under unequal noise and/or received signal powers (when received signal and/or noises are assumed to have unequal and/or time-varying variances at the inputs of the receivers of the SUs), which may lead to sensors with unequal average signal-to-noise ratios (SNRs). CFCPSC is an improved version of CPSC with more statistical power than its predecessor algorithm. Both are known because their robustness in scenarios of unequal noise and DN and low computational complexity. Thus, these characteristics make them suitable for the analyses at hand. Multiantenna-transceivers have been used in CR-based SS [12; 13; 14; 15] to increase the statistical power for detecting PU signals. For example, authors in [13] adopted different assumptions on the availability of noise power value to propose two eigenvalue-based statistical tests for multiantenna SU SS using the GLRT paradigm. Proposed methods are called arithmetic to geometric mean (AGM) and signal-subspace eigenvalue (SSE), and results show that semi-blind SSE outperforms ED with no NU, and blind AGM performs better than ED under NU. GLRT and other generalized likelihood ratio-based detectors also outperform the ED under NU in [14] in multiantenna-CR-based spectrum sensing. However, Refs. [13; 14] adopt calibrated antennas, meaning that the noise power values at the receiver antennas are identical.