One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed.

The development of beyond 5G (B5G) future wireless communication networks (FWCN) needs novel solutions to support high-speed, reliable, and low-latency communication. Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are promising techniques that can enhance wireless connectivity in urban environments where tall buildings block line-of-sight (LoS) links. However, existing UAV-assisted communication strategies do not fully address key challenges like mobility management, handover failures (HOFs), and path disorders in dense urban environments. This paper introduces a deep deterministic policy gradient (DDPG)-based UAV-RIS framework to overcome these limitations. The proposed framework jointly optimizes UAV trajectories and RIS phase shifts to improve throughput, energy efficiency (EE), and LoS probability while reducing outage probability (OP) and HOF. A modified K-means clustering algorithm is used to efficiently partition the ground users (GUs) considering the newly added GUs as well. The DDPG algorithm, based on reinforcement learning (RL), adapts UAV positioning and RIS configurations in a continuous action space. Simulation results show that the proposed approach significantly reduces HOF and OP, increases EE, enhances network throughput, and improves LoS probability compared to UAV-only, RIS-only, and without UAV-RIS deployments. Additionally, by dynamically adjusting UAV locations and RIS phase shifts based on GU mobility patterns, the framework further enhances connectivity and reliability. The findings highlight its potential to transform urban wireless communication by mitigating LoS blockages and ensuring uninterrupted connectivity in dense environments.

Non-orthogonal multiple access (NOMA) is a technology that leverages user channel gains, offers higher spectral efficiency, improves user fairness, better cell-edge throughput, increased reliability, and low latency, making it a potential technology for the next generation of cellular networks. The application of NOMA in the power domain (NOMA-PD) with multiple-input multiple-output (MIMO) and other emerging technologies allows to achieve the demand for higher data rates in next-generation networks. This survey aims to funnel down NOMA MIMO resource allocation issues and different optimization problems that exist in the literature to enhance the data rate. We examine the most recent NOMA-MIMO clustering, power allocation, and joint allocation schemes and analyze various parameters used in optimization methods to design 5G systems. We finally identify a promising research problem based on the signal-to-interference-plus-noise ratio (SINR) parameter in the context of NOMA-PD with MIMO configuration.

Few troubleshooting steps have been taken during the non-operation period of UiTM ground station (UiTMSAT) where some of the main equipment and systems were malfunction. After few resolutions performed, the root cause of the defect was detected. The issue occurred due to the lighting strike that has transferred abundant amount of electricity to the devices in the monitor room. This paper explains the resolution steps that were taken to resolve the issue in the ground station. The maintenance is performed on radio, rotator controller, rotator interface, terminal node controller (TNC) and the software used for the satellite operation. The issues on the radio and rotator devices made some difficulties in using the operation software which are the SatPC and BIRDS operation software. Several attempts have been done and through this, many lessons were learned by the ground station operator. These lessons were shared to other BIRDS members during the ground station workshop at Kyushu Institute of Technology (KYUTECH), Japan.

An essential requirement for the design of a wireless communication system is the determination of the path loss. This study compares and estimates path loss using urban-micro environment path loss models. Path loss model optimization is taken into consideration to represent the real propagation path and to find the optimized path loss model using a genetic algorithm and particle swarm optimization. The analytically measured path loss is contrasted with the optimized path loss values of each model and error statics are used to assess each model's performance. From the simulation results, it can be deduced that the 5GCM open square model generates the mean square error and standard deviation with the lowest values using both genetic algorithm and particle swarm optimization. The error statistics such as MSE, RMSE and SE of 5GCM-OS LOS scenario using GA are 5.43, 2.33 and 2.33 and in NLOS scenario are 8.86, 2.97 and 3.21, using PSO algorithm in LOS scenario are 5.15, 2.67 and 1.56, in NLOS scenario are 7.84, 2.80 and 1.9 respectively. The 3GPP-SC model in LOS scenario using PSO algorithm is the best path loss model which provides minimized error statistics of 3.85, 1.96 and 1.47 respectively. The PSO algorithm works absolutely better than the GA and produces lowest error statistics compared to the GA optimization algorithm. Therefore, the 5G small cell network operators can improve the service quality at millimeter wave frequencies by employing PSO optimization strategy.

Terahertz (THz) (0.1–10 THz) communication is a promising technology for sixth-generation wireless networks due to its ultrahigh data rates and low latency. This study investigates the impact of atmospheric gases on signal attenuation and path loss in the frequency range of 300–400 GHz and a path distance of 1 Km in the outdoor environment. The large-scale path loss models: Close-In (CI) free space, Alpha–Beta–Gamma (ABG), and Close-In Frequency dependence (CIF), are summarized and are used to estimate the path loss and optimized path loss model in an atmospheric absorption environment. Additionally, the impact of rain intensity on THz channel performance is examined with raindrop size distribution (DSD) models. Simulation results reveal that rainfall intensity, water vapor and dry air significantly affect THz signal propagation. At 380 GHz and 800 m path difference, the CI path model yields the highest path loss of 632 dB, primarily due to significant attenuation from rain, water vapor, and dry air. In contrast, the ABG model exhibits the lowest path loss of 196 dB at 300 GHz over a 100 m distance under the same environmental conditions. These results shows that the dynamic variations in rain intensity, rain drop size distribution, water vapor, and dry air absorption leads to substantial fluctuations in path loss, even when the rain rate remains constant. THz frequency band of 300–400 GHz range is the usable band for 6G wireless communications except 380 GHz band.Therefore, the CI and CIF models are identified as the most suitable models for optimized path loss estimation in 6G wireless networks.