# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 1 Introduction  
The rapid development of wireless communication and the resulting unprecedented growth rate of interconnected wireless devices bring challenges for 5G and Beyond 5G (B5G) mobile systems, such as shortage of spectrum resources, spectrum utilization efficiency, mobility, user fairness and so on [1]. In order to cope with these challenges, several candidate technologies have been suggested in recent years. However, a single technology may not be able to satisfy the performance goals of next-generation wireless networks, thus demanding the integration of multiple technologies [2]. Among various potential technologies of the future, cognitive radio (CR) and non-orthogonal multiple access (NOMA) are two primary techniques for enhancing spectrum utilization efficiency [3]. The principal idea behind cognitive radio networks (CRNs) is to offer higher spectral efficiency by enabling the coexistence of primary users (PUs) and secondary users (SUs) using dynamic spectrum access techniques [4]. The fundamental spectrum-sharing modes in CRNs are underlay, overlay and interweave [5, 6]. In underlay mode, SUs transmit simultaneously with the PU on the same time slot and frequency as long as the interference caused for the PU stays within tolerable limits. Overlay is another spectrum-sharing paradigm in CRNs in which SUs acquire a priori knowledge of the PU's signal and relay the PU's message while simultaneously transmitting their own message. Such cooperative spectrum sharing via the secondary relay offers an increased data rate for the SU and better reliability of data detection for the PU. In a coexistence scenario of multiple potential SUs,the primary network tends to select the best secondary relay for cooperation to achieve the maximum possible advantage. In interweave mode, also known as opportunistic spectrum access, SUs employ spectrum-sensing techniques for the detection of spectrum holes and transmit only if the PU is absent. Recently, NOMA has emerged as an attractive technology for serving a massive number of users by multiplexing in the power domain over the same code/time/frequency [7]. For this purpose, NOMA employs a combination of superposition coding (SC) and successive interference cancellation (SIC) to retrieve the multiplexed signals [8]. Unlike conventional multiple access techniques, NOMA improves user fairness and meets quality-of-service (QoS) requirements through dynamic resource allocation, i.e., by allocating more power to users operating at long distances or having poor channel conditions to achieve the desired total throughput [9]. Integrating cooperative diversity with these two competent technologies, i.e., CR and NOMA, can further enhance communication reliability and system capacity [10]. In the domain of numerical optimization, computational intelligence (CI), commonly defined as soft computing, is a part of computer and engineering sciences that consists of a set of nature-inspired computational methods to address real-world data-driven problems. The motivation behind CI-based techniques is to apply a heuristic approach of perception and control for processing complex data sources under uncertain environmental conditions. The CI-based methods are regarded as \"intelligent\" methods that offer better results than conventional methods such as regression methods [11]. During the last two decades, various CI techniques have been extensively applied to diverse optimization problems ranging from industrial applications to complex scientific research problems [12]. The three core components that exist under the umbrella of CI are fuzzy logic (FL), artificial neural networks (ANN), and evolutionary algorithms (EA). Among these, EAs are inspired by the collective intelligence of natural systems and the biological principle of selection to solve numerical optimization problems using different approaches such as genetic programming and swarm intelligence.