## 2 Literature Review  
The previous research works show that a combination of OFDM and MIMO is a frequently used communication technique in CRNs [11; 12; 13]. As presented in [12; 13; 14; 15; 16], the ED in the MIMO-OFDM communication system employing SLC approaches is a promising candidate for improving SS. In [12], the detection performance of the PU signal using SLC ED techniques was analysed. Compared to other ED methods, the authors in [12] showed that an ED process based on the SLC concept can guarantee a high detection probability at moderate to low SNRs when an ascending number of OFDM symbols is used. In [13], the effect of false alarm probabilities, the SNRs, different modulation schemas, and symbol length on the detection probability in OFDM systems was analysed for SISO and single-input multiple-output (SIMO) systems. The ED and cyclo-stationary feature detection performance was examined using the SLC and Equal Gain Combining (EGC) techniques. It was demonstrated that the EGC approach requires exact information about PU signals and provides efficient signal detection. Nevertheless, the SLC method does not require such information and thus achieves lower spectrum efficiency. In addition, the SLC technique demands the use of combiners and detectors to obtain a more precise ED process, which increases the cost of implementation. Despite the stated drawbacks, the SLC is less complex than other diversity combining methods in terms of its practical implementation. The authors of [11] discussed the difficulties encountered during the hardware implementation of an OFDM signal detection utilizing the ED SLC method and ED square law selection (SLS) approaches. Significant challenges such as modelling the PU signals, RF imperfections, SNR estimation, and the impact of fading channels were also analysed. Approaches employing simplifications were suggested to facilitate the hardware realizability of the antenna diversity in an actual implementation scenario. The effect of NU fluctuations on ED performance was analysed in [17; 18; 19]. Because NU fluctuation impairs the performance of the ED process, the authors in [17] developed a kernelized detecting approach that focused on the selection of DT. An estimation of the ED performance utilizing MIMO systems was offered for licensed users affected by Gaussian noise in the Cognitive Radio Internet of Things (CR-IoT) network. The results showed that the proposed kernelized detector was not extremely sensitive to the number of samples, Gaussian noise, the number of SU Rx chains, or the SNR values. In [18], we examined the effect of NU on the ED process in the SISO-OFDM systems that operate on margin-constrained, rate-constrained or combined transmission concepts. The results show that NU has a powerful impact on PU signal detection. To minimize the impact of NU fluctuation, in [19], the analysis was extended in relation to the ED performed with a DDT adjustment. The results indicated that DDT adaptation in SISO-OFDM systems can improve the efficiency of the detection of PU signals. Furthermore, the results shown in [20; 21; 22] extended the analysis related to the PU signal detection in MIMO-OFDM systems. In [20], we presented a novel algorithm for the simulation and performance analyses of the ED employing SLC approach in MIMO-OFDM systems. In [21], we examined the PU signal detection efficiency of the ED method based on SLC in environments characterized by various SNR levels and NUs. The results indicatedthat the ED process employing SLC which exploits the MIMO transmission positively affects detection probability, which is otherwise prone to the negative impact of NU. The analyses in [22] were extended in [23] with an examination of the impact of various SNR values on detection probability in the ED process influenced by NU and DDT adaptions. The results showed that DDT adjustments reduce the effect of NU and significantly improve the ED efficiency in MIMO-OFDM systems that use the SLC technique.