Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks.

Over the past few years, wireless communication has grown dramatically, and the consumer demand for wireless services has seen a significant jump. One of the main challenges for beyond fifth generation (B5G) networks is the increased capacity of the network. The continuously increasing number of network users and the limited radio spectrum in wireless technologies have led to severe congestion in communication channels. This issue leads to traffic congestion at base stations and introduces interference in the network, thereby degrading system capability and quality of service. Interference reduction has thus become a major design challenge in wireless communication systems. This review paper comprehensively explores interference management (IM) strategies in B5G networks. We critically analyze and summarize existing research on interference issues related to device-to-device communication, heterogeneous networks, inter-cell interference, and artificial intelligence (AI)-based frameworks. The paper reviews a wide range of methodologies, highlights the strengths and limitations of state-of-the-art approaches, and discusses standardized techniques such as power control, resource allocation, spectrum separation and mode selection, carrier aggregation, load balancing and cell range expansion, enhanced inter-cell interference coordination, coordinated scheduling and beamforming, coordinated multipoint, and AI-based interference prediction methods. A structured taxonomy and comparative summary are introduced to help categorize these techniques. Several related works based on their methodologies, shortcomings, and future directions have been critically reviewed. In addition, the paper identifies open research challenges and outlines key trends that are shaping future B5G IM systems. A comparative visualization is also provided to highlight dominant and underexplored optimization objectives across IM domains. This review serves as a valuable reference for researchers aiming to understand and evaluate current and emerging solutions for interference mitigation in B5G wireless systems.

The next generation of wireless networks is poised to deliver dependable, extensive, and extremely low-latency mobile broadband communications across various industries. In this regard, reconfigurable intelligent surface (RIS)-aided non-terrestrial base station (NTBS) is crucial for developing three-dimensional (3D) networks that aim to integrate the terrestrial and non-terrestrial infrastructures. The integration of RISs with NTBSs has resulted in additional capabilities to attain faster and more flexible mobile coverage. In this paper, we employ a novel RIS-aided NTBSs with non-orthogonal multiple access in downlink heterogeneous networks (HetNets), in which the RIS is utilized to improve the transmission of signals from multiple NTBSs to ground users (GUs). We aim to maximize the system sum rate (SSR) and energy efficiency (EE) by mitigating inter-cluster and intra-cluster interference. The formulated optimization problem is non-convex due to the joint optimization of NTBS 3D positions, RIS reflection angles, RIS reflection coefficients, and successive interference cancellation among GUs. To address this problem, a modified gray wolf optimization -based meta-heuristic algorithm is proposed. In particular, the original optimization problem is divided into three sub-problems (i.e., RIS reflection angles, RIS reflection coefficients, and NTBS 3D position), which are then addressed alternately using the proposed optimization technique. The novelty of our proposed work is presented by reformulating the distance equations to minimize the distance between the wolves and their prey and enhance the search process’s precision. The simulation results stated that the suggested algorithm outperforms the traditional schemes in terms of both SSR and EE. Furthermore, the results show that incorporating RIS into multi-NTBS HetNets effectively boosts overall performance by improving the channel quality between NTBSs and their respective GUs while reducing inter-NTBS interference.

Intelligent reflection surface (IRS) is an advanced technology that effectively transforms the wireless propagation environment to next-generation wireless networks. In this paper, we employ the IRS-supported multi-aerial base station (ABS) in a downlink non-orthogonal multiple access (NOMA) heterogeneous network (HetNets), where the transmission signals from multiple ABSs to ground users (GUs) are enhanced using the IRS. Our goal is to maximize the system sum rate (SSR) by mitigating inter-cluster and intra-cluster interference and to maximize system energy efficiency (SEE) by minimizing ABS power transmission while preserving the required minimum data rate. The formulated optimization problem is non-convex due to the joint optimization of ABS power transmission, ABS three-dimensional (3D) positions, IRS reflection angles (RAs), IRS reflection coefficients (RCs), and decoding order (DEO) among GUs. To tackle this problem, a meta-heuristic algorithm based on the modified gray wolf optimization (MGWO) and an alternation max/min technique based on the developed Block Coordinate Descent (DBCD) are proposed. In particular, the original optimization problem is divided into four sub-problems; the first three sub-problems (RS RAs, IRS RCs, and ABS 3D positions) are alternately addressed using the MGWO technique; and the fourth sub-problem (ABS power transmission) is addressed using the DBCD technique. Finally, a dynamic DEO strategy is utilized to address the NOMA DEO among GUs. The numerical results demonstrated that the proposed schemes outperformed the conventional schemes, achieving up to a 43.289 % improvement in SSR and a 71.536% enhancement in SEE, while minimizing the total power consumption. Moreover, the results demonstrate that integrating IRS into multi-ABS HetNets significantly improves the total performance by enhancing the quality of the channel between ABSs and their corresponding GUs while minimizing inter-ABS interference. Finally, by strategically optimizing ABS 3D positions and utilizing DEO among GUs, the SSR and SEE gain achieved by NOMA is shown to be more significant compared to conventional methods.

The rapid growth of devices in the Internet of Everything (IoE) poses significant challenges in achieving high-capacity and energy-efficient connectivity in 6G wireless networks. Hovered base stations (HBSs) provide a promising solution for enhancing physical-layer performance; however, their mobility and inefficient transmit power allocation (PA) may increase interference and energy consumption. In this paper, a multi-HBS-based NOMA transmission framework is proposed for downlink 6G networks, where each HBS serves multiple IoE devices. The proposed framework jointly optimizes HBS three-dimensional (3D) trajectory, transmit PA, and dynamic decoding order execution to maximize total sum rate (TSR) and total energy efficiency (TEE) under minimum data-rate constraints. The resulting optimization problem is non-convex due to the coupling among optimization variables and constraints. To efficiently solve this problem, a low-complexity and fast-converging hybrid optimization framework integrating a developed genetic algorithm and modified gray wolf optimization is adopted. Simulation results demonstrate that the proposed framework significantly outperforms existing benchmark schemes, achieving up to 23.6% improvement in TSR and 35.8% improvement in TEE. These results confirm the effectiveness of the proposed joint optimization framework for improving overall network performance.