The integration of cognitive radio (CR) technology and non-orthogonal multiple access (NOMA) techniques, referred to as CR-based NOMA system, has been recently configured as a promising solution to meet the requirements of beyond fifth generation (B5G) networks, especially those related to Internet-of-Things (IoT) applications. With such integration, power domain NOMA allows multiple users to share the same orthogonal resource blocks, while CR technology enables opportunistic bandwidth utilization by permitting secondary users (SUs) to access the licensed spectrum frequency without interrupting the primary users’ (PUs) activities. To support the massive connectivity requirements of IoT-based networks, several multiple-access techniques have been recently combined with CR-based NOMA systems, including orthogonal multiple access (OMA) and multiple-antenna techniques. For example, in CR-based OMA-NOMA systems, the licensed frequency band is split into several channels, and a set of SUs is served on each channel using the NOMA technique. This paper provides an overview and analysis of the state-of-the-art CR-based NOMA network architecture. It summarizes the main design challenges related to the practical implementation of such systems. Furthermore, this paper presents the advances of combining CR-based NOMA with recent multiple-access techniques, where the potential capabilities and the design challenges of such integrated systems are also investigated and discussed. On the other hand, this paper demonstrates the potential capabilities of deploying recent technologies in CR-based NOMA networks. The technologies include intelligent reflecting surfaces, terahertz communications, machine learning, unmanned aerial vehicles, and hybrid NOMA systems. Finally, future research directions and open issues are provided and discussed.

In unmanned aerial vehicle (UAV)-assisted wireless networks, the potential of the high probability of line-of-sight links could be a disadvantage for other nearby wireless networks because of interference caused by a UAV. For this reason, using interference mitigation techniques (IMTs) for UAV-assisted wireless networks is necessary to minimize interference effects. For instance, the mobility of UAVs gives a chance to manage interference better to avoid being a source of interference or being susceptible to interference. After the first-generation mobile network was introduced in the early 1980s, mobile communication systems were developed through several stages of evolution over the following few decades to satisfy rising demand and more stringent specifications. Different IMTs are needed due to these generations’ differences. Unlike previous research, this research presents a systematic review of IMTs in the current and future UAV-assisted wireless networks and discusses their challenges. We also review the current research trends of UAV IMTs and their future insights. Our motivation for conducting this research is the need for a systematic review addressing these issues. In this systematic review, we use a more precise classification of the IMTs for UAV-assisted wireless networks. We classify them into four different techniques, namely, adaptive modulation and coding schemes, dynamic antenna pattern adjustment, dynamic transmit power control, and sub-channel scheduling. Moreover, we discuss the IMTs for UAVs using B5G wireless technologies such as intelligent reflecting surfaces, rate-splitting, super modular massive multiple-input multiple-output, Terahertz communications, holographic beamforming, blockchain-based spectrum sharing, and artificial intelligence and machine learning schemes. Furthermore, we identify the challenges of UAV IMTs and their open research directions such as wireless radio frequency spectrum scarcity, lack of networking scalability, beam distortion, resource-constrained management, and new sources of interference. We believe this research will help researchers choose the best technique to mitigate the interference effects in UAV-assisted wireless networks.

Recently, unmanned aerial vehicles (UAVs) have been used as flying base stations (BSs) to take advantage of line‐of‐sight (LOS) connectivity and efficiently enable fifth‐generation (5G) and cellular network coverage and data rates. On the other hand, nonorthogonal multiple access (NOMA) is a promising technique to help achieve unprecedented requirements by simultaneously allowing multiple users to send data over the same resource block. In this paper, we study a UAV‐enabled uplink NOMA network, where the UAV collects data from ground users while flying at a certain altitude. Unlike all existing work on this topic, this study consists of two stages. In the first stage, we use the well‐known Particle Swarm Optimization (PSO) algorithm, which is a metaheuristic algorithm, to deploy the UAV in 3D space, so that the users’ sum pathlosses are minimized. In the second stage, we investigate the user pairing problem and propose a dynamic power allocation technique for determining the user’s power allocation coefficients, as well as a closed‐form equation for the ergodic sum‐rate. Results show our PSO‐based algorithm prevailing over the Genetic Algorithm (GA) and random deployment methods. The proposed dynamic power allocation strategy maximizes the network’s ergodic sum‐rate and outperforms the fixed power allocation strategy. Additionally, the results reveal that the best pairing scheme is the one that keeps uniform channel gain difference in the same pair.

Non‐orthogonal multiple access (NOMA) has recently attracted a lot of attention as a promising multiple access technology to be used in the fifth generation (5G) cellular networks. In this study, the authors investigate uplink NOMA transmissions in a cooperative cellular network in the presence of frequency selective multipath fading channels. Specifically, two users communicate with the base station simultaneously with existence of a half‐duplex relay employing the decode‐and‐forward scheme to assist the far user. At the transmitting nodes, the power allocation factors are decided based on the statistical channel state information (CSI) rather than perfect CSI, while at the receiving nodes, the decoding order between the users is pre‐specified and the effect of imperfect successive interference cancellation implementation is taken into account. Under Rayleigh fading channels, the authors derive the exact ergodic rate for the near user and the upper bound of the ergodic rate for the far user, and present an ad‐hoc approach to determine the power allocation factors when the target data rates are given. Simulation results show that NOMA consistently outperforms orthogonal multiple access, however, the performance of uplink NOMA depends heavily on the proper choices of the power allocation factors and the relay location.

The deployment of intelligent reflecting surfaces (IRS) in non-orthogonal multiple access (NOMA), known as IRS-assisted NOMA-based systems, has recently been considered a potential solution to address the complicated demands of beyond-fifth-generation communication networks. This paper investigates a multi-objective allocation resource allocation technique for an IR-assisted hybrid time division multiple access (TDMA)-NOMA network. To reflect the requirements of such a system, two conflicting performance metrics, namely energy efficiency (EE) and spectral efficiency (SE), are simultaneously optimized under a set of quality-of-service constraints. The proposed SE-EE trade-off design is formulated as a multi-objective optimization (MOO) framework. However, such an MOO problem cannot be solved by conventional approaches. Therefore, the weighted product method (WPM) is proposed to transform the MOO problem into a conventional single-objective optimization (SOO) problem. Meanwhile, the SOO problem through the WPM approach is non-convex in nature, where the optimization parameters, namely the power allocation and the reflecting coefficients of the IRS elements, are jointly designed. As a result, an iterative technique is designed to address this problem and assess the optimization variables. The simulation results demonstrate that the proposed WPM for the SE-EE trade-off resource allocation technique can balance competing optimization variables alongside meeting the system’s demands.