Ever since the introduction of fifth generation (5G) mobile communications, the mobile telecommunications industry has been debating whether 5G is an “evolution” or “revolution” from the previous legacy mobile networks, but now that 5G has been commercially available for the past few years, the research direction has recently shifted towards the upcoming generation of mobile communication system, known as the sixth generation (6G), which is expected to drastically provide significant and evolutionary, if not revolutionary, improvements in mobile networks. The promise of extremely high data rates (in terabits), artificial intelligence (AI), ultra-low latency, near-zero/low energy, and immense connected devices is expected to enhance the connectivity, sustainability, and trustworthiness and provide some new services, such as truly immersive “extended reality” (XR), high-fidelity mobile hologram, and a new generation of entertainment. Sixth generation and its vision are still under research and open for developers and researchers to establish and develop their directions to realize future 6G technology, which is expected to be ready as early as 2028. This paper reviews 6G mobile technology, including its vision, requirements, enabling technologies, and challenges. Meanwhile, a total of 11 communication technologies, including terahertz (THz) communication, visible light communication (VLC), multiple access, coding, cell-free massive multiple-input multiple-output (CF-mMIMO) zero-energy interface, intelligent reflecting surface (IRS), and infusion of AI/machine learning (ML) in wireless transmission techniques, are presented. Moreover, this paper compares 5G and 6G in terms of services, key technologies, and enabling communications techniques. Finally, it discusses the crucial future directions and technology developments in 6G.

Ultra-dense networks represent the trend for future wireless 5G networks, which can provide high transmission rates in dense urban environments. However, a massive number of small cells are required to be deployed in such networks, and this requirement increases interference and number of handovers (HOs) in heterogeneous networks (HetNets). In such scenario, mobility management becomes an important issue to guarantee seamless communication while the user moves among cells. In this paper, we propose an auto-tuning optimization (ATO) algorithm that utilizes user speed and received signal reference power to adapt HO margin and time to trigger. The proposed algorithm aims to reduce the number of frequent HOs and HO failure (HOF) ratio. The performance of the proposed algorithm is evaluated through simulation with a two-tier model that consists of 4G and 5G networks. Simulation results show that the average rates of ping-pong HOs and HOF are significantly reduced by the proposed algorithm compared with other algorithms from the literature. In addition, the ATO algorithm achieves a low call drop rate and reduces HO delay and interruption time during user mobility in HetNets.

Handover (HO) management is essential in any mobile cellular network. It ensures seamless connectivity to the User Equipment (UE) while moving from a Base Station (BS) to another within the coverage area. HO optimization refers to adopting intelligent and automatic HO techniques in mobile networks. HO optimization is taking more importance in the Fifth-Generation (5G) and Beyond (B5G) systems due to the requirements and specifications that B5G targets. The requirements of the B5G, such as global connectivity, ultra-low latency, big data analytics, extreme data rate transmissions, a massive number of devices in a small area, etc., and the new technologies that will support the B5G network, such as Millimeter Wave (mmWave), Terahertz (THz) communication, Ultra-Dense Networks (UDNs), etc. All these cause new HO optimization challenges and require new solutions for HO optimization techniques. This paper comprehensively provides the HO optimization challenges and solutions in B5G. Firstly, it provides a research background and explanation for the HO in legacy. Then, it investigates the HO optimization challenges in B5G, including future research directions. After that, the paper discusses the most prominent and recent techniques and technologies solutions for HO optimization management in B5G. Finally, it highlights the potential techniques for HO optimization in B5G.

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics.

Mobility management is essential in mobile communication networks to provide a smooth connection during users’ mobility. Handover control parameters (HCPs), such as handover margin (HOM) and time‐to‐trigger (TTT), are major and essential factors in mobility management that must be defined carefully to make efficient handover (HO) procedures. Their impact becomes more critical with the deployment of fifth‐generation (5G) mobile networks and beyond (B5G). This is due to the different characterizations of next mobile networks, such as the use of millimeter‐wave (mmWave) bands, the ultradense deployment of small base stations (BSs), large mobile connection traffic growth, and other more critical factors. The case becomes more sensitive with the high mobility speed scenarios. This study proposes different HCP system settings to be investigated and analyzed over B5G networks. They will be investigated with various mobile speed scenarios to illustrate their impact on the network performance. Various key performance indicators (KPIs) are considered to evaluate and validate system performance, such as reference signal received power (RSRP), HO probability (HOP), HO ping‐pong (HOPP), radio link failure (RLF), HO interruption time (HIT), and HO failure (HOF). Results show that the various system settings provide different and significant impacts on the performance of B5G networks. Furthermore, the setting of HCP1 obtained the best performance in RSRP and RLF with ‐69.7 dBm and 4.8%, respectively, while the optimum performance of HOPP, HIT, HOP, and HOF is achieved in the HCP6 setting with 0%, 0.02 ms, 0.05%, and 0%, respectively. Moreover, the overall outcome of all HCP settings is 54.94%. These results indicate a tradeoff between RLF and HOPP with various HCP settings in B5G mobile networks. Thus, the HCP system settings must be adjusted carefully considering other factors, such as mobile environment and use case.

The Fifth Generation (5G) and Beyond (B5G) mobile system utilizes advanced technologies, such as millimeter Wave (mmWave) and Ultra-Dense Networks (UDNs), to meet future networks’ requirements. However, implementing these technologies may pose several challenges to the B5G network. One key challenge is the need for efficient Mobility Robustness Optimization (MRO) processes, particularly for Handover (HO). MRO aims to ensure seamless connectivity and uninterrupted user experience while moving from one cell to another within the coverage area. Thus, this study has presented a new, intelligent, and robust self-optimization HO technique. It is designed to work efficiently with the B5G networks. The technique is based on Machine Learning (ML), particularly the Regression Tree (RT) model. Additionally, it is known as ML-based Self-Optimization Handover Technique (ML-SOHOT). The technique is evaluated and validated using different major HO metrics, including Handover Probability (HOP), Handover Failure (HOF), and Handover Ping-Pong (HOPP) across various mobility patterns in B5G, specifically considering an urban environment. The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. Furthermore, ML-SOHOT achieves an average HO performance improvement of up to 96% compared to competitive algorithms from the literature. Consequently, the technique could enhance the overall B5G system performance and user experience.