## 1 Introduction  
MCRN have been developed to improve spectrum management for PU given that frequency spectrum holes are used in the transmission of SU [1]. The first task of this technology is to identify said holes within a certain frequency range through one of two alternatives. While one considers that the cognitive system has a priori knowledge of the PU signal, the other alternative consists of a blind process where the signal is unknown [2]. Mobile technologies from 1G to 5G involve different modulation schemes according to the service, distance and SNR of each user. In order to identify the PU signal, a technology must be chosen to determine the frequency range in the spectrum sensing process [3]. Once the frequency range is found, a spectrum analyzer is used to determine the power of the signal. However, since modulation schemes can change over time in certain technologies, this makes it difficult for cognitive radio network systems to distinguish a PU signal from noise [2]. Spectrum sensing mechanisms are used to detect the presence of a PU within the network. This process must be performed in real time so that there is no interference with the services of PU. In GSM systems, Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) are used along with a GMSK modulation scheme to allow phone calls and SMS services [3]. In 5G systems, Orthogonal Frequency Division Multiple Access (OFDMA) is a modulation scheme [4; 5] based on OFDM. Although these strategies can contribute to the detection of a PU with a priori knowledge of its signal, this signal is a random variable over time and can be considered as a stochastic process. Thus, systems based on a priori information will increase the probability of false alarm (\(\mathrm{P_{FA}}\)) during detection [2]. Many techniques are used in spectrum sensing such as energy detection, cyclostationary detection, matched filters [2; 6; 7; 8; 9; 10] among others. Matched filters or cyclostationary detection methods need a priori information of the PU signal while others are noise-sensitive as in the case of energy detection [11]. Matched filters and coherent detection are often used to detect a signal by comparing it to a previously known signal (called a template) with the input signal which implies having a priori knowledge of the signal. Matched filter detection is suitable when the transmission of a licensed user includes a pilot, preambles, a synchronization word or spreading codes, which can be used to build a template for spectrum sensing [12]. The energy detection method is susceptible to the uncertainty of noise power and it can only detect the presence of the signal but it cannot differentiate its type thus making it prone to attacks [11; 12]. In cyclostationary detection, the signal transmitted by a licensed user is marked by a periodic pattern. This pattern is referred to as cyclostationary and is used to detect the presence of a licensed user. However, the detection process is complex and requires long observation periods to deliver a result [12]. In contrast, cooperative detection is theoretically more accurate because the uncertainty for a single user's detection process can be minimized through collaboration. However, cooperative approaches cause adverse effects on resource-constrained networks due to the overhead traffic involved in the cooperation and require the use of a Common Control Channel (CCC). This requires creating a frequency channel for the transmission of the CCC [13]. Another approach involves the study of game theory and the impact of resource pricing on the spectrum's fragility. A NOMA wireless network is implemented, supporting both licensed and unlicensed band access, and used in 5G systems by adopting the principles of Prospect Theory. This research works with licensed and unlicensed users and can be analyzed in MCRN [14].