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The Fifth Generation (5G) and Beyond (B5G) mobile systems employ 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 Handover (HO) optimization processes. HO aims to ensure seamless connectivity and uninterrupted services for users while moving from one cell to another within the coverage area. Thus, this study introduces a new, intelligent, and robust self-optimization HO technique designed to work efficiently with the B5G networks. The technique utilizes Machine Learning (ML), particularly leveraging the Regression Tree (RT) model. In this study, the proposed technique is referred to as the 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.

Significant discrepancies have been observed between the measured attenuation induced by rain over mm-wave terrestrial links at very short communication paths and the predicted measurement by ITU-R P.530-18. Recent observations indicate that the rain rate at 0.01 % occurrence used by the ITU-R prediction method does not represent effective rain intensity for less than 1 km of path length, despite its accuracy for paths longer than 1 km. These deviations can be attributed to several factors, such as spatial inhomogeneity, rain cell diameter, and environmental variations. Additionally, sudden changes in the propagation environment, such as wind direction, humidity, and wind speed, contribute to non-uniform rain distributions. Additionally, there is still a lack of comprehensive investigations due to the involved experimental difficulties. Thus, an effective rain rate concept and model are proposed to represent rain intensity variations for short paths to eliminate the need for an effective path length that more accurately predicts rain attenuation at path lengths exceeding 1 km. The proposed model is based on the measured R0.01 %, short path (less than 1 km) and frequency. Two-year measurements of both the rainfall rate and rain attenuation over two experimental links operating at 26 and 38 GHz at a 0.3 km path length are used to validate and enhance the model. The measurements and experiments are conducted in Malaysia. The result indicated that the shorter the link, the higher the expected Reff. One aspect that may partially justify this significant increase in Reff is the fact that the ITU-R P.838-3 model does not consider the impact of the raindrop size distribution (DSD)for tropical climates when predicting rain attenuation at a short-range mm-wave link. The proposed model estimations are compared with experimental attenuation results reported at 73.5GHz and 83.5GHz over a 0.3 km path length. Several experimental results reported from different regions around the globe are used to validate the proposed model. The outcomes are in good agreement. The findings emphasize the importance of developing region-specific models that consider local meteorological variations, potentially offering significant improvements to the reliability and design of mm-wave communication systems and realizing the future goal of 6G wireless mobile fronthaul.

Multi-Access Edge Computing (MEC) is a key technology in the evolution of mobile networks, especially for fifth and sixth generation (5G and 6G). MEC optimises communication and computational resources by hosting application processes close to the user equipment (UE) at the network edge, improving communication reliability and stability while reducing latency. A key feature of MEC is its ultra-low latency response, enabling real-time applications in next-generation networks. However, the mobility of UEs and the limited coverage of edge servers can lead to frequent handovers during the offloading process. As a result, advanced handover management strategies are required to jointly optimise handover and computation offloading. This paper provides a comprehensive overview of handover decision making in MEC systems, with particular emphasis on the challenges and solutions associated with the integration of communication, computation and mobility management. We examine the current state of research, provide an analysis of existing studies, and identify key gaps in the literature. The novelty of this work lies in its holistic approach to exploring the interdependencies between handover management, mobility control and computation offloading, while proposing future research directions. This work provides a forward-looking perspective on how these interrelated issues are likely to evolve, particularly in the context of future mobile networks using MEC technology.

Significant advancements have been achieved in the field of computer vision pertaining to the detection of human faces. This technological development holds great potential for a wide range of applications including but not limited to identification, surveillance and expression recognition. Unconstrained face identification has been significantly improved by the advancements in Deep Learning algorithms (DL). However, the presence of severe occlusion is an ongoing obstacle particularly when it obstructs a substantial section of the facial area, resulting in the absence of crucial facial characteristics. Furthermore, the limited availability of comprehensive datasets containing substantially obscured faces exacerbates the problem, impeding the efficacy of face detection programs. This study presents a new methodology, which incorporates an advanced occluded face detection (OFD) model, in order to enhance feature extraction and detection network. A dataset was developed specifically for training and testing the model. The new dataset includes faces with significant occlusion. The utilization of contextual-based annotation approaches improves the depiction of crucial facial characteristics. The OFD model exhibits exceptional performance and attaining a notable accuracy rate of 57.84%, a precision rate of 73.70% and a recall rate of 42.63%. These results surpass those achieved by alternative methods such as YOLO-v3 and Mobilenet-SSD. This study shows the capacity to make substantial progress in detecting occluded faces, hence offering the ability to make a positive influence on the domains of identification, surveillance and expression recognition.

Recent developments in the fields of communications, smart transportation systems and computer systems have significantly expanded the potential for intelligent solutions in the domains of traffic safety, convenience and efficiency. The utilization of Artificial Intelligence (AI) is presently prevalent across diverse sectors of application due to its significant capacity to augment conventional data-driven methodologies. In the domain of Vehicular Ad hoc NETworks (VANETs), data is regularly gathered from several sources. The collected data serves multiple goals, such as facilitating efficient routing, enhancing driver awareness, forecasting mobility patterns to prevent potential risks, and ultimately enhancing passenger comfort, safety and overall road experience. Internet of thing (IoT) can be a good solution fro many issues that of VANETs. In order to make a complete system of VANETs communications are very essential like 5G and 6G. This study provides a detailed examination of AI, IoT and 5G/6G methodologies currently being investigated by multiple research endeavors in the field of VANETs. The merits and demerits of the AI-based methodologies suggested for the VANET domain have been analyzed with the using of IoT and 5G/6G. In conclusion, forthcoming research prospects in the field of VANETs are ascertained.

Power transmission and distribution networks frequently face issues, especially in harsh environments, leading to high maintenance costs and the need for uninterrupted electricity. Current field inspections by skilled personnel are labor-intensive, costly, and slow, often lacking efficiency and posing safety risks. While automated helicopters, flying robots, and climbing robots have been explored for visual inspections, the widespread adoption of automatic vision-based inspection remains limited due to high accuracy demands and unique challenges. This highlights the need for a fully autonomous vision-based system to inspect electrical power infrastructure and predict potential future faults. This research introduces Unmanned Aerial Vehicles (UAVs) as a promising solution for infrastructure inspection, deep learning for data analysis and prediction, and a mathematical model to ensure system accuracy doesn't rely solely on the dataset. As a proof of concept, YOLO V8 was employed to predict electrical faults in insulators, achieving a box loss of 0.525, a classification loss of 0.3887, and a precision of 0.8976, demonstrating high accuracy and low loss.

In this paper, the graph segmentation (GSeg) method has been proposed. This solution is a novel graph neural network framework for network embedding that leverages the inherent characteristics of nodes and the underlying local network topology. The key innovation of GSeg lies in its encoder-decoder architecture, which is specifically designed to preserve the network’s structural properties. The key contributions of GSeg are: (1) a novel graph neural network architecture that effectively captures local and global network structures, and (2) a robust node representation learning approach that achieves superior performance in various network analysis tasks. The methodology employed in our study involves the utilization of a graph neural network framework for the acquisition of node representations. The design leverages the inherent characteristics of nodes and the underlying local network topology. To enhance the architectural framework of encoder- decoder networks, the GSeg model is specifically devised to exhibit a structural resemblance to the SegNet model. The obtained empirical results on multiple benchmark datasets demonstrate that the GSeg outperforms existing state-of-the-art methods in terms of network structure preservation and prediction accuracy for downstream tasks. The proposed technique has potential utility across a range of practical applications in the real world.

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

This research paper introduces an innovative approach to predicting failures in Local Area Network (LAN) switches, combining Double Exponential Smoothing (DES) with a suite of Machine Learning (ML) algorithms including Random Forest (RF), Logistic Regression (LR), Decision Trees (DT), and Support Vector Machines (SVM). The primary objective of this study is to enhance the accuracy and timeliness of LAN switch failure predictions, thereby facilitating more proactive and effective network management. Our methodology involves the integration of DES for trend analysis and forecasting in time-series data, with the advanced predictive capabilities of the aforementioned ML algorithms. This hybrid approach not only leverages the strengths of DES in identifying underlying patterns in failure data but also capitalizes on the diverse predictive models to handle various aspects of failure prediction more robustly. The paper details the process of data collection, preprocessing, and the specific application of DES and each ML algorithm to the dataset. A notable contribution of this research is the development of a framework that effectively combines the output of DES with ML models, leading to a significant improvement in predictive accuracy as compared to traditional methods. Through rigorous testing and validation; the proposed approach demonstrated a marked increase in the precision and reliability of failure predictions. The results indicate that the integration of DES with ML algorithms can substantially aid in preemptive maintenance and decision-making processes in LAN management. The implications of these findings are profound, suggesting that such a combined approach can greatly enhance network stability and efficiency. While the focus of this study is on LAN switches, the methodology has the potential for broader applications in various fields of network management and predictive maintenance.

The rapid growth of the massive smart Internet of Things (IoT) with mobile connections, the enhanced Mobile Broadband (eMBB) and the high demand for building a connected and intelligent world increase the probability of mobile satellite systems to be a major network in providing internet communication services in the future. Currently, the mobile satellite systems are envisioned as a significant solution for providing mobile services in different settings and for various vital objectives. These satellite systems have special qualities in each of these situations, including extensive coverage area, robustness, and ability to broadcast/multicast. The Low Earth Orbit (LEO) systems are the best promising technology that will offer internet services among the different types of satellite systems. However, the LEO systems are still experiencing certain restrictions with respect to connectivity, stability, and mobility support; because of which communication becomes unreliable. Therefore, the aim of this paper is to broadly explain the LEO systems and services in a comprehensive manner using a variety of perspectives. The paper focus is on key aspects of mobile internet based on satellite systems. This paper illustrates the integration of LEO systems with fifth and sixth generations of mobile cellular networks as well as with the IoT networks. It discusses the problems being faced as a result of the integration between cellular with IoT and satellite systems by comprehending which future research plans are outlined.

The evolution of science has given rise to many technologies that have changed the world. The upcoming Six-Generation (6G) mobile network indicates a fundamental transformation in wireless technologies, enhancing connectivity and data transmission rates. In this circumstance, Mobile Edge Computing (MEC) is a paradigm technology that emerges as a key major supporter of enhancing mobility awareness. Edge computing offers improved efficiency for service migration from the edge node to the user. However, mobility management in MEC is a complex challenge as seamless handovers between edge nodes must be efficiently executed to ensure uninterrupted service for mobile devices, demanding intricate coordination and low-latency decision-making. To the best of the author's knowledge, there has been no comprehensive work on the most recent developments in mobility awareness using mobile edge computing in 6G. However, this paper aims to present a general overview of the intersection between mobility awareness and MEC over 6G networks. The general concept of MEC in 6G mobile networks is comprehensively introduced. This will highlight the integration between MEC and 6G for bringing more efficient network and service migration to the edge, reducing latency, and enhancing the user experience. Meanwhile, this survey discusses augmented reality with MEC applications. This survey discusses the integration of mobility awareness and mobile edge computing in upcoming mobile applications and emphasizes the need for 6G networks. This integration results in providing seamless communication during handovers between the serving base station and the target base station. This study contributes to understanding the upcoming trends that will enable the operation of mobility awareness and MEC operation in the 6G mobile communication. Furthermore, we delve into a comprehensive overview of the challenges and future research directions for mobility management with MEC in 6G mobile networks, underlining the complexities and potentials of integrating mobility awareness and mobile edge computing.

This paper presents the measurement of mobile broadband (MBB) performance and specifies insightful knowledge about 3G and 4G technologies of dense urban and urban morphologies in Malaysia, Thailand, and Singapore. This study will also contribute to planning efficiently for the deployment of 5G network in these three countries. The data to characterize and analyze the MBB performances are gathered through a driving test (DT) conducted in areas of the three countries for two months starting from January to February by utilizing an unbranded smartphone named Samsung Galaxy S6. The performance data in our MBB analysis was collected based on diverse national mobile network operators (MNOs) in outdoor areas of the selected states in the mentioned countries. Four different performance metrics (coverage, speed, latency, and satisfaction) were investigated to assess the network performance for one of MBB's services that contain video streaming services. Within video services, two distinct resolutions including 720p (low) and 1080p (high) for an identical YouTube video were loaded alternately by the same smartphone. The research is meant to mimic real scenarios in the case of the unlocked smartphone to any technology during the time of loading a video, which contributes to knowing the network coverage of 2G, 3G, and 4G technologies in the chosen areas. The analysis of the 2G technology is excluded in the current paper because of the limited data amount of 2G. The MBB performance measurement results in the tested areas revealed that, on average, the 4G network outperformed the 3G network according to the investigated four performance indicators. Therefore, across the tested territories, the 4G technology offered better network coverage compared to the 3G technology. The vMOS scores for 4G technology across video services were more than 3, whereas the 3G technology has achieved scores smaller than 3. Also, the 4G network has achieved E2E RTT-Ping server latency improvements of 89.90%, 59.30%, 67.48%, and 58.04% compared to the 3G network in the urban and dense urban of Klang Valley, Malaysia, urban of Singapore, and urban of Thailand, respectively. Moreover, it is found that 4G technology can provide improvements up to the factors of 1.29, 1.61, 1.98, and 1.35 in the download speed for video streaming compared to the 3G technology for urban and dense urban of Klang Valley, Malaysia, urban of Singapore, and urban of Thailand, respectively.

The integration of cellular connectivity in drones signifies a crucial leap forward, offering the potential to revolutionize multiple industries. This comprehensive review examines the latest developments in the field of machine learning based handover (HO) decision-making for connected drones in future networks. The paper reviews a spectrum of machine learning techniques and evaluates their effectiveness in drones’ HO, exploring avenues such as hybrid AI models that combine the strengths of different ML approaches. Notably, combining deep reinforcement learning with other techniques forms promising solutions. The review finds that deep reinforcement learning models, when integrated with other techniques such as dueling double deep Q-network, have shown promising results in realizing optimized HO decisions and improving overall reliability. Additionally, the paper addresses prevalent research challenges, including issues related to high mobility, the three-dimensional nature of drone flight, small-cell deployment, and integration into cellular networks, emphasizing the importance of innovative solutions to achieve a more efficient and seamless handover process. By considering these obstacles and offering a forward-looking perspective outlining potential research directions, the review contributes to guiding future advancements in drones’ HO decision-making, ultimately facilitating the realization of more efficient and reliable drone operations and unlocking the full potential of drone connectivity and mobility within future networks.

Ensuring reliable and stable communication links between User Equipment (UE) and serving cellular networks during UE movement is one of the significant difficulties facing the deployment of the Fifth Generation (5G) and Sixth Generation (6G) of cellular networks. Therefore, the Handover Parameters Self-Optimization (HPSO) function has been introduced in modern cellular networks to address mobility management issues. Its main purpose to automatically optimize Handover Control Parameters (HCPs) settings. But the probability of estimating suboptimal HCP settings remains an issue, leading to a critical impact on the network performance. This results in an increase in Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The challenge becomes even more critical with the advent of 5G and 6G in cellular networks, owing to various factors. These factors include the extensive deployment of small base stations and the massive growth of connected devices. Despite the development of several HPSO algorithms, the existing techniques fall short of providing optimal solutions. Furthermore, the issue of suboptimal parameters remains unaddressed. This paper introduces an Artificial Intelligence Multiple Linear Regression (AI-MLR) model as an algorithm for optimizing mobility robustness in 5G cellular networks. The objective of the AI-MLR model is to automatically optimize HCP settings based on network experiences, leveraging the Instantaneous Indication Measure (IIM) Function. The AI-MLR model dynamically and instantly estimates HCP settings for UE by utilizing the IIM function. This function evaluates UE experiences through instantaneous Signal-to-Interference-plus-Noise Ratio (SINR) levels from both the target and serving base stations. Initially, the UE captures instantaneous measurements of SINR levels, serving as input parameters for the IIM function. The function then produces an output that acts as an indicator for automating the optimization process of the HCP settings. The proposed algorithm undergoes a comprehensive investigation and validation against various benchmark methods found in the literature, encompassing different mobility speed scenarios over a 5 G cellular network. This study involved the development of a simulation model using Matlab software. Performance evaluation utilized a range of Key Performance Indicators (KPIs), including HOP, HPPP, and RLF. The simulation results demonstrate that the proposed solution achieves significant improvements in terms of HOP, HPPP, and RLF under diverse movement speed scenarios, compared to the algorithms examined in the literature.

Cancer is a life-threatening ailment characterized by the uncontrolled proliferation of cells. Breast cancer (BC) represents the most highly infiltrative neoplasms and constitutes the primary cause of mortality in the female population due to cancer-related complications. Consequently, the imperative for early detection and prognosis has emerged as a means to enhance long-term survival rates and mitigate mortality. Emerging artificial intelligence (AI) technologies are being utilized to aid radiologists in the analysis of medical images, resulting in enhanced outcomes for individuals diagnosed with cancer. The purpose of this survey is to examine peer-reviewed computer-aided diagnosis (CAD) systems that have been recently developed and utilize machine learning (ML) and deep learning (DL) techniques for the diagnosis of BC. The survey aims to compare these newly developed systems with previously established methods and provide technical details, as well as the advantages and disadvantages associated with each model. In addition, this paper addresses several unresolved matters, areas of research that require further exploration, and potential avenues for future investigation in the realm of advanced computer-aided design (CAD) models utilized in the interpretation of medical images. Furthermore, the integration of Internet of Things (IoT) in BC research and treatment holds immense significance by facilitating real-time monitoring and personalized healthcare solutions. IoT devices, such as wearable sensors and smart implants, enable continuous data collection, empowering healthcare professionals to track patients' vital signs, response to treatment, and overall health trends, fostering more proactive and tailored approaches to BC management. Moreover, the advent of 5G technology in BC applications promises to revolutionize communication speeds and data transfer, enabling rapid and seamless transmission of large medical datasets. This high-speed connectivity enhances the efficiency of remote diagnostics, telemedicine, and collaborative research efforts, ultimately accelerating the pace of innovation and improving patient outcomes in BC care. The present study aims to examine various classifiers utilized in ML and DL methodologies for the purpose of diagnosing BC. Research findings have demonstrated that DL has superior performance compared to standard ML methods in the context of BC diagnosis, particularly when the dataset is extensive. The existing body of research indicates that there are significant gaps in knowledge that need to be addressed in order to enhance healthcare outcomes in the future. These gaps highlight the pressing need for both practical and scientific research in the field. Finally, IoT and 5G will be how they can be used in order to enhance BC detection, treatment and patient care.

The rapid advancement of fifth-generation (5G)-and-beyond networks coupled with unmanned aerial vehicles (UAVs) has opened up exciting possibilities for diverse applications and cutting-edge technologies, revolutionizing the way connections, communications, and innovations unfold in the digital age. This paper presents a comprehensive survey of the deployment scenarios, applications, emerging technologies, regulatory aspects, research trends, and challenges associated with the use of UAVs in 5G-and-beyond networks. It begins with a succinct background and motivation, followed by a systematic UAV classification and a review of relevant works. The survey covers UAV deployment scenarios, including single and multiple UAV configurations. The categorization of UAV applications in 5G is presented, along with investigations into emerging technologies for enhancing UAV communications. Regulatory considerations encompassing flight guidelines, spectrum allocation, privacy, and safety are discussed. Moreover, light is shed on the latest research trends and open challenges in the field, with promising directions for future investigations identified, concluding with a summary of key findings and contributions. This survey serves as a valuable resource for researchers, practitioners, and policymakers in the UAV and communication domains. Additionally, it offers a comprehensive foundation for informed decision-making, fostering collaboration, and driving advancements in UAV and communication technologies to address the evolving needs of our interconnected world.

High mobility travelling trains and drones connected via ultra-dense mobile networks may lead to frequent handovers (HOs). As a consequence, this could arise the mobility problems of the serving network such as handover ping-pong (HOPP), radio link failure (RLF), handover probability (HOP), and handover failure (HOF). Mobility robustness optimization (MRO) function can contribute for fixing such related problems. This can be performed by self-optimization process for the handover control parameters (HCPs), that including time-to-trigger (TTT) and handover margin (HOM). Although various proposed solutions available in the literature, the issues have not been addressed efficiently. Thus, this study proposes a fuzzy logic controller (FLC) along with weighted function (WF) to perform efficient HO self-optimization process for the HCPs over the heterogeneous networks (Het-Nets). The proposed algorithm is defined as velocity-aware-fuzzy logic controller-weighted function (VAW-FLC-WF) algorithm. Additionally, a trigger timer is used along with the proposed algorithm for the purpose of reducing the ratio of HOPP. The objective of the integrated algorithms is to minimize the connections issues such as HOPP, RLF, and received signal reference power (RSRP). Besides, this study highlighted the significant of categorizing the speed scenarios in reducing the mobility issues by comparing the results with non-categorized speed scenarios (proposed FLC-WF). The proposed integrated algorithms show a significant enhancements as compared to the algorithms investigated from the literature. The average RLF probability of the proposed (VAW-FLC-WF) was reduced to 0.006 which was the lowest probability compared to the other HO algorithms. Besides, RSRP, HOPP were shown noticeable improvements compared to other HO algorithms.

The exponential surge in mobile device usage has propelled a steep rise in mobile data demand, particularly with the advent of 5G networks aiming to cater to this burgeoning need. Ensuring top-notch quality of service (QoS) is pivotal in deploying such advanced wireless technologies without compromising base station throughput. Continuous enhancements in established cellular networks like 3G, 4G, and emerging 5G networks remain imperative to uphold satisfactory network performance. This paper comprehensively analyzes 5G network performance across indoor and outdoor environments in Muscat, Oman. The study involved walk tests within three indoor locations and drive tests outdoors, using devices from three mobile providers: Omantel, Ooredoo, and Vodafone. Metrics such as signal quality, data rate, ping, and serving time were assessed. Results indicated the dominance of 4G networks indoors and 5G outdoors across all providers. In the outdoor scenario, the observed data rate for the 5G network did not meet anticipated levels, potentially due to handover issues or other factors affecting signal continuity. Moreover, the measurement data collected within indoor scenarios indicates that approximately 80% of the recorded data stems from 4G networks. As a consequence, this disproportionate reliance on 4G is likely influencing network performance, possibly leading to decreased data rates. While 5G showcased incremental improvements over 4G in some instances, it is still in its nascent phase. Recommendations include continuous network monitoring by operators and augmented 5G base station deployment to heighten QoS by ensuring higher data rates and minimal latency across both indoor and outdoor settings.

Load balancing is a major challenge in heterogeneous networks (HetNets) consisting of 5G and 6G ultra-dense small cells with long-term evaluation advanced (LTE-A) networks. A key factor in achieving efficient load balancing during user mobility is creating appropriate optimisation for handover control parameters (HCP). This paper proposes a coordinated load balancing algorithm for LTE-A/fifth generation (5G) HetNets. The algorithm automatically optimises HCP settings for a given user based on three bounded functions (the signal-to-interference-plus-noise ratio (SINR) of the user equipment (UE), the number of physical resource blocks (PRBs) per UE and the UE’s speed) as well as their automatic weight levels. A two-step target cell determination strategy is implemented according to the cell load level and RSRP criteria, ensuring that users are handed over to low-loaded target cells. A new HO procedure that considers the pilot signal power is also proposed, which includes the number of PRBs per UE and the RSRP. Cells with freer PRBs are prioritised in user association to provide load balance and enhanced throughput. The proposed load balancing algorithm is compared with five other load balancing algorithms selected from the literature. The simulation results reveal that under various mobile speed scenarios, the proposed load balancing scheme enhances network performance in terms of load level, throughput, spectral efficiency and call dropping ratio (CDR).

Among the different challenges related to the improvement of wireless sensor network (WSN) performance, the optimization of energy consumption takes an important place. Basically, sensor nodes in WSN are powered by limited battery. These batteries can allow sensor nodes to be functional for a limited period of time then sensor nodes turn out to be dead. Enhancing energy consumption and finding new manners to save sensor nodes' energy is becoming essential and more challenging. Improving energy consumption for each sensor nodes leads to enhance the lifetime of WSN. Clustering is among the outstanding strategies to reach this goal. In this paper, a new scheme of routing protocol in WSN will be presented in order to minimize the energy consumption and improve WSN lifetime. The new scheme is proposed in order to find the best selection of cluster head (CH) by taking in consideration the energy at each sensor node. It has been proposed based on Firefly Algorithm. We have compared the proposed scheme with LEACH, EAMMH, SEP, E-SEP, BRE, NEAHC and WEB protocol. Experimental results show that the proposed scheme yields better performance than the compared routing protocols in terms of better energy consumption and packet delivery between sensor nodes and base station. Results obviously prove that the proposed scheme could improve the WSN lifetime.

Various speed scenarios such as high-speed travelling trains and connected drones over ultra-dense heterogeneous networks (HetNets) may result in a large number of handovers (HOs), which may cause further mobility challenges. Therefore, mobility robustness optimization (MRO) function has been proposed to contribute for detecting and correcting the mobility issues including too late HO, too early HO, and HO to the wrong cells. This function can be more effective in reducing these challenges related to mobility when proper optimization settings is performed for the handover control parameters (HCPs) (i.e., time-to-trigger (TTT) and handover margin (HOM)). In this paper, a trigger timer is proposed to reduce the unnecessary HOs. Meanwhile, this work proposes a weighted algorithm for optimizing the HCPs automatically based network experiences. The proposed algorithm rely on various factors for performing the optimization process. That includes, mobile movement speed, network traffic load, and the measurement report of the received signal reference power. Research work conducted by Matlab simulator that implement HetNets that consider Fifth Generation (5G) network and system settings based on 3GPP. Besides, 15 users were investigated using several mobile speed scenarios over Voronoi 5G network. The simulation results show that a significant achievement has been performed by the proposed algorithm as compared to the other algorithms investigated from the literature. The proposed algorithm has minimized the Radio Link Failure (RLF), Handover Ping-Pong (HOPP), Handover Probability (HOP), and handover interruption time by 8.8 %, 6.9 %, 6.7 %, and 344 %, respectively, lower than the other algorithms presented.

In the face of increasing global disruptions, the cybersecurity field is confronting rising threats posed by offensive groups and individual hackers. Traditional security measures often fall short in detecting and mitigating these sophisticated attacks, necessitating advanced intrusion detection methods. The goal of our study is to develop robust network intrusion detection methods using machine learning techniques. In addition, we evaluate the effectiveness of various machine learning models in detecting network intrusions. Model performances are optimized through hyperparameter tuning and feature selection. A range of classification and clustering models have been employed. Data from SIEM systems capturing real-time statistics from cloud-hosted Windows virtual machines has been gathered and augmented with web attack logs from CICIDS2017, each comprising approximately fifteen thousand rows. Hyperparameter tuning, data normalization, standardization and feature selection techniques for model optimization have been used in our study. The research showcases the potential of machine learning in enhancing network intrusion detection capabilities. The findings underscore the effectiveness of the Random Forest Classifier (0.97) and highlight the importance of utilizing diverse datasets and advanced optimization techniques. This study offers valuable insights and sets a foundation for future advancements in cybersecurity strategies and intrusion detection systems.

Effective mobility management in heterogeneous networks is significant in ensuring seamless handovers (HOs) between diverse cell types, especially as users move between macrocells, small cells, and femtocells. The widespread and overlapping deployment of diverse cells raises the HO probability occurrence massively, particularly when users are on the move and connected to multiple cells simultaneously to ensure uninterrupted connectivity. This phenomenon leads to an increase in HO ping pong (HOPP) and HO failure (HOF) occurrences, ultimately degrading network performance. In this context, this paper proposes a self-optimization algorithm to address the contradiction in the optimization tasks of mobility robustness optimization (MRO) and load balancing optimization (LBO) functions. The algorithm aims to facilitate seamless user communication as individuals move across various deployment scenarios. The MRO function leverages two key parameters: the reference signal received power (RSRP) levels of serving and target cells, as well as user movement speed. On the other hand, the LBO function takes into account the traffic load of serving and target cells to determine the suitable values of HO control parameters. Moreover, our research contributes to the present network optimization challenges and positions itself as an enabler for the seamless integration of emerging technologies. As the wireless ecosystem continues to evolve, with the advent of edge computing and network slicing technologies, our self-optimization algorithm offers adaptability and scalability to meet the evolving demands of next-generation wireless networks. This forward-looking solution benefits network operators and end-users by providing robust and efficient mobility management. The proposed algorithm demonstrates its effectiveness through comprehensive simulations by significantly reducing HOPP and HOF compared to investigated methods selected from the literature, showcasing its potential to enhance network performance and user experience.

One of the most crucial issues in mobile networks is ensuring reliable and stable connectivity during mobility. In recent years, numerous research has examined Fourth Generation (4G) and Fifth Generation (5G) mobile networks to address issues related to handover (HO) self-optimisation. Different approaches have been developed to identify the best Handover Control Parameters (HCPs) settings, including Time-To-Trigger (TTT) and Handover Margin (HOM), since managing HCP values is a key factor in determining the efficiency and accuracy of handover decisions. The purpose of this work is to address the challenges associated with HO management in Sixth Generation (6G) mobile networks, where a massive number of diverse devices, high mobility, and varying network conditions (e.g., Ultra Dense Network (UDN), Heterogeneous Network (HetNet) and Millimetre Waves (mmWaves)) are established. This, in turn, presents complex HO issues which require solutions that can adapt to different deployment settings. To provide a brief background of mobility management, the paper presents the basic HO concept, HO history, and HO procedure. Additionally, a comparison of HO in 4G to 6G is discussed to gain a better understanding of technology development and its effect on HO solutions. Furthermore, the basics of Mobility Robustness Optimisation (MRO) is explained, which involve HCP values. Previous MRO approaches have made significant advancements; however, they often lack adaptability and robustness to dynamic network conditions. By leveraging Artificial Intelligence (AI) algorithms with MRO techniques, this approach offers an improved solution to optimize handover decisions in a self-optimizing manner, thereby enhancing the overall network performance. To achieve this goal, it is necessary to analyze previous MRO and AI-integrated solutions and make comparisons between them. Additionally, the advantages and disadvantages of these solutions are considered. The contribution of this work lies in identifying the directions for HO self-optimization in 6G deployment. It demonstrates that deploying an AI-based solution would benefit future MRO deployments. This survey will aid in the analysis of mobility management challenges, particularly for the future mobile MRO self-optimization implementation in future technologies.

With the rapid developments of fifth generation (5G) mobile communication networks in recent years, different use cases can now significantly benefit from 5G networks. One such example is high-speed trains found in several countries across the world. Due to the dense deployment of 5G millimetre wave (mmWave) base stations (BSs) and the high speed of moving trains, frequent handovers (HOs) occur which adversely affect the Quality-of-Service (QoS) of mobile users. User association for load balancing is also a key issue in 5G networks. Therefore, HO optimisation and resource allocation are major challenges in the mobility management of high-speed train systems. Handover Margin (HOM) and Time-to-Trigger (TTT) parameters are crucial for the HO process since they affect the key performance indicators (KPIs) of high-speed train systems in 5G networks. To manage system performance from the aspect of predictive analytics, we have modelled system performance of mobility management through machine learning (ML). First, the HO management process of a high-speed train scenario is framed as a supervised ML problem. The inputs for the problem are regression task, HOM and TTT and the outputs are key performance indicators (KPIs). Second, data processing is accomplished after generating a simulation dataset. Several methods are employed for the dataset, such as Adaptive Boosting (AdaBoost), Gradient Boosting Regression (GBR), CatBoost Regression (CBR), Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Kernel Ridge Regression (KRR) and K-Nearest Neighbour Regression (KNNR). Tenfold cross validation is then applied for choosing the best hyperparameters. Finally, the deployed methods are compared in terms of the Mean Absolute Error (MAE), Mean Square Error (MSE), Maximum Error (Max E), and R2 score metrics. From the MAE results, CBR achieves the best outcomes for load level and throughput KPIs with 0.003 and 0.0144, respectively. On the other hand, GBR achieves the best results for call dropping ratio (CDR), radio link failure (RLF) and spectral efficiency KPIs with 0.354, 0.082 and 0.354, respectively. CBR also follows GBR for the three KPIs with 0.356, 0.082 and 0.357, respectively. Only a slight difference in estimations is present. MLP achieves the best results for HO ping-pong (HOPP) and HO probability (HOP) KPIs with 0.0045 and 0.011, respectively. This is followed by GBR and CBR. From the MSE outcomes, CBR and GBR exhibit the best results for load level and throughput KPIs with 2e-5 and 3e-5, respectively. GBR attains the best results for CDR, RLF and spectral efficiency KPIs with 0.25, 0.011 and 0.025, respectively. Accordingly, CBR follows GBR with slightly different errors for the three KPI estimations. MLP achieves the best results for HOPP and HOP KPIs with 5e-5 and 3.6e-5, respectively. Again, this is followed by GBR and CBR for the estimation of these results. This indicates that CBR and GBR can capture relationships between inputs and KPIs for the dataset used in this study, outperforming all other methods generally used for solving this problem.

Heterogeneous networks (HetNets) are a promising solution for managing the exponential increase in the number of mobile users while maintaining high data rates and coverage. HetNets consist of various cell types with different cell coverage and system capacities. However, the traffic load in HetNets is variable, uneven and random over time, leading to unequal cell loads. Some cells have excessive user presence where the competition for system resources is high, while other cells have low user presence where system resources are not fully utilised. This paper proposes a mobility load balancing algorithm that prioritises millimetre-wave (mmWave) cells in target cell selection and user association to provide load balancing in 5G HetNets and improve overall system performance. A two-step target cell selection method that considers the load level of cells and reference signal received power (RSRP) is proposed. This method prioritises mmWave cells that meet cell load level and RSRP conditions in target cell selection, taking full advantage of the unique properties of mmWave cells (enhanced network throughput, spectral efficiency and enormous bandwidth) to balance traffic loads. The HO triggering and decision-making process is independently performed for each user. In case the serving cell is overloaded, different HO procedures are applied for load balancing depending on the target cell type (macro base station (BS) or mmWave BS). The scope of the study is further expanded by applying different HO procedures according to the serving and target cell types to maintain mobility robustness in case the serving cell is not overloaded. This research proposes various system scenarios with fixed HO margin (HOM) values and fixed time-to-trigger (TTT) durations to examine the effects of HCP settings on the proposed algorithm’s performance in terms of average load level of the serving cell, throughput, spectral efficiency and call dropping ratio (CDR). The system that provides the highest overall performance is applied to the proposed algorithm. To further assess and validate the performance of the proposed algorithm, it is compared with other load balancing algorithms from the literature. The simulation results reveal that the proposed algorithm does provide noticeable enhancements in network performance in terms of load level, throughput, spectral efficiency and CDR for various mobile speed scenarios as compared to existing load balancing algorithms.

Mobile connections and applications are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide, which greatly increases the demands on data traffic. This led to considering millimeter-wave bands and ultra-dense deployment as part of the key enabler solutions in fifth-generation (5G) networks. However, these solutions radically increase the number of handovers (HOs), thus increasing the rate of unnecessary HO and dropping call probabilities. In this regard, optimizing HO control parameters appropriately is the main factor that can efficiently address HO issues during user mobility. This paper proposes a fuzzy-coordinated self-optimizing HO scheme to achieve a seamless HO while users move in multi-radio access networks. The proposed scheme resolves the conflict between mobility robustness and load balancing functions by utilizing a fuzzy system considering three input parameters: signal-to-interference-plus-noise ratio, cell load and UE speed. Simulation results show that the proposed scheme manages to control the mobility optimization in terms of ping-pong HO, radio link failure and HO latency over different mobile speed scenarios. Moreover, the proposed scheme reduces the outage probability compared to other schemes from literature.

Enhanced mobile broadband is an essential target in fifth-generation (5G) networks with higher demands among service consumers. It offers very high-speed Internet connections for several geographical areas: urban, suburban, and rural. Many mobile network operators (MNOs) continuously monitor the quality of service in terms of multiple services to guarantee high network performance. To the best of our knowledge, no extensive studies and analyses are conducted on mobile broadband (MBB) services that cover various implementation scenarios and several performance metrics. This study comprehensively analyzes the existing MBB performance in an urban area: Cyberjaya City, Malaysia. The measurement data were collected through drive tests from various MNOs supporting 3G and 4G technologies: Maxis, Celcom, Digi, U Mobile, and Unifi. Several performance metrics, such as signal quality, throughput (downlink and uplink), ping, and handover, were measured during the drive tests. The data measurements were conducted in two scenarios: outdoor and indoor environments. Measurement results of the outdoor drive test demonstrate that the maximum average throughput with downlink and uplink data rates is 14.3 and 7.1 Mbps, respectively, whereas the minimum average ping and loss are 36.5 ms and 0.14, respectively, for all MNOs. However, the in-building measurements achieve an acceptable overall average data rate of 2 Mbps. This paper provides several suggestions and recommendations for MBB providers to improve their performance networks and quality of experience to meet customers’ satisfaction. Several limitations not considered in this study and can point to possible future work are presented.

The sixth-generation (6G) technology of mobile networks will establish new standards to fulfill unreachable performance requirements by fifth-generation (5G) mobile networks. This is due to the high requirements for more intelligent network, ultra-lower latency, extreme network communication speed, and supporting massive number of various connected applications. In the long term, the convergence of various business developments with communication platforms, as initiated by 5G, will exaggerate and highlight areas where 5G's capabilities will fall short of performance requirements. Motivated by the development of applications in massive connections, future networks, developments, and technological advancements for mobile communications that go beyond fifth-generation (B5G) networks are being developed. In this context, highly immersive applications are demanded, such as three-dimensional (3D) communications, digital twins, or massive extended reality (XR)/virtual reality (VR) applications, which will need 6G capabilities to be realized at scale to be commercially feasible. Mainly, we anticipate that only the upcoming 6G networks will be capable of running extremely high-performance connectivity with massive numbers of connected devices, even under laborious scenarios such as extreme density, diverse mobility, and energetic environments. In this article, we look at the most recent trends and future emerging trends that are possible to operate 6G network. Paper aims to provide more inclusive and brief review about 6G mobile communication technology in one survey paper. Initially, a comprehensive overview of the 6G system is introduced in terms of visions, drivers, requirements, architecture, and usage scenarios required to enable 6G applications. After that, the opportunities and advantages of 6G mobile technology has been discussed. Further, the promising new techniques that enable 6G technology has been highlighted. This is followed by a potential discussion of challenges and research directions. This article is envisioned to serve as an informative guideline to stimulate interest and further studies for subsequent research and development of 6G networks. Paper will enable the readers to briefly figure out the key requirements, targets, that will be need and the applications, advantages, and opportunities that can be offered as well as the challenges that need to be addressed before the implementation of this new technology.

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

In the so near future, climate change caused by Carbon dioxide (CO2) emissions as a result of an increase in energy demand (ED) in the buildings sector becomes really hazard to our green environment. Both climate change and increase in ED are affected by each other. Such increase in ED would increase both climate change and global warming. Fortunately, there is, however, still plenty of time to address this issue. This increase might be significantly and effectively controlled. One of the effective solutions is to apply demand response (DR) strategies exploiting the smart grid (SG). Hence, SG contributes to help reduce both ED and CO2 emissions. Of importance is establishing SGs-adopted buildings in which DR role could act as an operating and management system aiming to reach energy sustainability. We review researches on DR strategies and technologies applied to SGs-adopted buildings. Unlike previous literature reviews considering certain types of buildings or strategies which therefore narrow the focus of review papers, this review has analyzed numerous types of buildings and a variety of technologies to expand the review’s scope. Concluded remarks, insights, challenges faced by researchers, and suggestions proposed by this review paper have been in detail discussed. Limitations, further gaps in future research on DR strategies, potential implications of DR in SG-adopted buildings have been also discussed. Towards sustainable energy in the buildings sector utilizing DR strategies, points of strength and weakness concluded from the reviewed articles have been highlighted and discussed to open trends that might potentially enhance future proposed DR strategies applied to SG-adopted buildings.

Mobile broadband (MBB) services are rapidly growing, causing a massive increase in mobile data traffic growth. This surge in data traffic is due to several factors (such as the massive increase of subscribers, mobile applications, etc.) which have led to the need for more bandwidth. Mobile service providers are constantly improving their network efficiency by upgrading current networks and investing in newer mobile network generations. However, these improvements will not be enough to accommodate the future spectrum demands. This paper proposes a time series forecasting model to analyze future spectrum demands based on the spectrum efficiency growth of MBB networks. This model depends on two key input data: the average spectrum efficiency per site and the number of sites per technology. The model is used to predict the spectrum efficiency growth of three countries (Turkey, Malaysia, and Oman) from 2015 to 2025. The proposed model is compared with various traditional statistical models such as the Moving Average (MA), Auto-Regression (AR), Autoregressive–Moving-Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA). The forecasted results indicate that the average spectrum efficiency and growth will continue to rise multiple times by 2025 compared to 2015. The data from this prediction model can be used as input data to forecast the required spectrum needed in future for any specific country. This study further contributes to the network planning of future mobile networks for Fifth Generation (5G) and Sixth Generation (6G) technology. The proposed model obtains higher accuracy (by 90%) compared to other models. The proposed model is also applicable to any country, especially when new wireless communication technologies emerge in future. It is customizable and scalable since spectrum regulators can add additional metrics that positively contribute towards accurately estimating future spectrum efficiency growth.

Different fields have been thriving with the advents in mobile communication systems in recent years. These fields reap benefits of data collected by Internet of Things (IoT) in next generation (5G and 5BG) mobile networks. The IoT concept transforms different fields by providing large amount of data to be used in their operations. This is achieved by massively utilized sensors and mobile devices that acquire data from internet connected devices to keep track of physical systems. Hence, different use cases benefit from the data generated thanks to future mobile network systems. Intelligent Transportation Systems, Smart Energy, Digital Twins, Unmanned Aerial Vehicles (UAVs), Smart Health, Cyber Security are of significant use cases that big data plays an important role for them. Large amount of data entails more intelligent systems with respect to conventional methods, and it also entails highly reduced response time for use cases. Artificial intelligence and machine learning models are adept in satisfying the requirements of this big data situations for different use cases. In this sense, this paper provides a survey of machine learning and artificial intelligence applications for different use cases enabled by future mobile communication systems. An overview of machine learning types and artificial intelligence is presented to provide insights into the intelligent method concepts. Available studies are extensively summarized, and they are also grouped to provide a complete overview of the study. Discussions on the reviewed papers based on artificial intelligence and machine learning concepts are made, and some descriptive figures about the results of the discussions are also given in the paper. Finally, research challenges for artificial intelligence and machine learning applications in the use cases are introduced, future research directions and concluding remarks are presented accordingly.

In cognitive radio networks (CRNs), the computational complexity of resource allocation algorithms is a significant problem that must be addressed. However, the high computational complexity of the optimal solution for tackling resource allocation in CRNs makes it inappropriate for use in practical applications. Therefore, this study proposes a power-based pricing algorithm (PPA) primarily to reduce the computational complexity in downlink CRN scenarios while restricting the interference to primary users to permissible levels. A twostage approach reduces the computational complexity of the proposed mathematical model. Stage 1 assigns subcarriers to the CRN's users, while the utility function in Stage 2 incorporates a pricing method to provide a power algorithm with enhanced reliability. The PPA's performance is simulated and tested for orthogonal frequency-division multiplexing-based CRNs. The results confirm that the proposed algorithm's performance is close to that of the optimal algorithm, albeit with lower computational complexity of O(Molg(M)).

The need for load balancing will be more significant in fifth generation mobile network and beyond due to the massive upsurge in the quantity of users and rising use of small cell sizes. The load balancing function must be efficiently designed to distribute loads between various cells by moving excess traffic from high-load cells to adjacent inactive cells. The load balancing self-optimization feature is an important function that optimises the handover control parameter by switching some loads in overloaded cells to adjacent cells with fewer loads. This paper has focused on analysing the performance of load balancing self-optimization within fifth generation cellular networks. Also, this paper provides a brief overview about load balancing in 5G and 6G mobile networks and highlighting the sources of issues, technical challenges, some of the suggested solutions and highlighting the research challenges that are needed to be addressed in the second phase of 5G and 6G mobile networks. At the same time highlighting the research that has been conducted in the literature to address these stranding issues. Study also, developing simulation model that can be utilized to study, investigate and analysing LBSO in 5G mobile network with a variety of mobile speed scenarios. The work developed a simulation model that is utilized to study, investigate, and analyse LBSO in 5G mobile networks with a variety of mobile speed scenarios. Optimization algorithms were selected from the literature and validated to ensure their efficiency and functionality with different mobility scenarios, based on the UE condition after each measurement report. The network evaluation and analysis have been conducted in terms of ping-pong handover probability, radio link failure and spectral efficiency. The simulation outcomes explain that the Optimization based on the Distance algorithm demonstrated a noticeable performance enhancement through significantly reduces the PPHP, RLF and SE for different mobile speed scenarios over the entire simulation as compared to the Cost Function and Fuzzy Logic algorithms. These obtained results indicate that the location of user is a significant factor that contributes effectively in optimizing handover control parameters in future mobile networks. Thus, considering the distance as a direct or not direct factor in designing handover Optimization algorithms will contribute effectively in estimating the suitable handover control parameters in mobile networks.

The impact of atmospheric attenuation on wireless communication links is much more severe and complicated in tropical regions. That is due to the extreme temperatures, intense humidity, foliage and higher precipitation rain rates with large raindrop sizes. This paper investigates the propagation of the mm-waves at the 38 GHz link based on real measurement data collected from outdoor microcellular systems in Malaysia. The rainfall rate and received signal level have been measured simultaneously in 1-minute time intervals for one year over a 300 m path length. The rain attenuation distributions at different percentages of exceedance time have been compared with the modified distance factor of the ITU-R P.530-17 model. The average link availability calculated with the measured rain rates has been analysed. Additionally, the key propagation channel parameters such as the path loss, path loss exponent, Rician K-factor, root mean square, delay spread and received power have been investigated considering the rain attenuation. These propagation channel parameters have been analysed using MATLAB software and explained with the help of the latest NYUSIM channel model software package (Version 2.0). The analysis results have been classified considering rain attenuation, antenna setup, link distances, antenna height and antenna gain. The outcomes revealed that the rain fade predicted by applying the modified distance factor provides high consistency with the measured fade in Malaysia and several available measurements from different locations. The large-scale path loss model in the NYUSIM simulation result was around 126.23 dB by considering the rain attenuation effects on the 300m path length. This work shows that the NYUSIM channel model offers more accurate rendering results of path loss for omnidirectional and directional antenna transmissions without rain fade. This study proves that the ability to provide good coverage and ultra-reliable communication for outdoor and outdoor-to-indoor applications during rain in tropical regions must be sufficiently addressed.

The massive deployment of small-sized cells for the Fifth Generation (5G) mobile network will increase the Handover Probability (HOP), potentially causing higher Handover Ping-Pong Probability (HPPP) and/or Radio Link Failure (RLF). Inappropriate usage of Handover Control Parameter (HCP) settings may further exasperate this issue. Therefore, Mobility Robustness Optimisation (MRO) has been introduced and further developed as a significant Self-Optimisation Network (SON) function in the 5G network and beyond. The main aim of MRO is to address Mobility Management (MM) issues during user mobility between cells to ensure a smooth connection. Although various algorithms were suggested in the literature, they mostly cater to 4G networks which may not be effective for the 5G network due to different network characterisations. This paper analyses the performance of various MRO algorithms with various system settings and scenarios for the 5G network. The investigated algorithms from the literature include the Distance (Dis), Cost Function (CF), Fuzzy Logic Controller (FLC) and Handover Performance Indicator (HPI). Validation has been accomplished for different mobility conditions in the 5G network. A simulation based on the MATLAB software has been conducted using various system tools. The evaluation analysis is in terms of Signal to-Interference-plus-Noise-Ratio (SINR), HPPP and RLF effects since these are major indicators in assessing system performance and selecting the handover decision during user mobility. The simulation outcomes show that the HPI algorithm performance is more reactive to mobile speed scenarios over time, significantly reducing the HPPP compared to the other algorithms which do not provide large reactions in the same conditions. Simultaneously, the HPI algorithm exhibits the highest RLF and SINR from among the other algorithms. The distance algorithm is the best in terms of RLF and SINR, achieving an acceptable level in terms of HPPP. These results point to that the MRO algorithm that operate based on distance is the most robust compared to the other investigated algorithms, confirming the potential of the Dis approach for the 5G network.

Ensuring reliable and stable communication during the movements of mobile users is one of the key issues in mobile networks. In the recent years, several studies have been conducted to address the issues related to Handover (HO) self-optimization in Heterogeneous Networks (HetNets) for Fourth Generation (4G) and Fifth Generation (5G) mobile networks. Various solutions have been developed to determine or estimating the optimum and ideal settings of Handover Control Parameters (HCPs), such as Time-To-Trigger (TTT) and Handover Margin (HOM). However, the complexity, high requirements, and the upcoming structure of ultra-dense HetNets require more advanced HO self-optimization techniques for future implementation. This paper studies HO self-optimization techniques that may implemented in the next-generation mobile HetNets by reviewing state-of-the-art algorithms. The solutions discussed in this survey are more focus on Mobility Robustness Optimization (MRO), which is a significant self-optimization function in 4G and 5G mobile networks. The applied solutions will preserve the continuous connection between the User Equipment (UE) and eNBs during UE mobility, thereby enhancing connection quality. The various algorithms and techniques applied to HO have revealed different outcomes. This paper discusses the pros and cons of these techniques, and further examines HO self-optimization challenges and solutions. New future directions for the implementation of HO self-optimization are also identified. This survey will contribute to the understanding of the issues related to mobility management, particularly in relation to the self-optimization of HO control parameters in future mobile HetNets.

The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.

Metaverse is a vision enabling to constitute an environment in which someone could see real and virtualized worlds. The Metaverse is a product (or something similar as we do not yet know its final form) of such technologies. In this circumstance, when applications that utilize the Metaverse are used, there seem to be no transportation charges, and there is no cap on amounts of individuals, users, players, learners, or trainee who can take part. Hence, and due to such a feature, the Metaverse has attracted various researchers from different fields where it has been exploited by them to contribute to those fields and research areas. As for example, it is possible to teach various target audiences by offering different events and classes from any location in the globe. In order for a participant to utilize the Metaverse, there are necessary conditions to be considered as well as other settings to be initialized. In line of this, virtual reality, augmented reality, availability of required sensors, smart glasses, headsets, and few others are considered some examples of such conditions and settings that the Metaverse requires. Despite the advantages that Metaverse offers us, there are a number of considerations that must be taken into account while developing it by interested researchers. One of these concerns is the Metaverse privacy regarding the participants (represented as avatars inside the Metaverse environment). Another issue is that since the Metaverse is still in its early stages, many attempts have to be made from interested researchers who engage to develop it to enhancing it. This review aims to survey related articles that concern the Metaverse and its development providing a review of the chronological stages throughout the history of the development of Metaverse. It aims also to list a number of recent technological advances allowing the Metaverse. Besides, Metaverse’s definitions, properties, architecture, and applications have been discussed and listed in this review. The novelty of this article is that it has suggested a framework to a number of issues that are still paid attention for potential solutions by researchers aiming to contribute to researchers and designers to consider such an issue and its corresponding solution for future research works and enhancement. Challenges faced by researchers and other relevant concerned issues related to Metaverse have been in detail discussed and highlighted. Besides, future trends have been clarified.

Ultra-dense heterogeneous networks (HetNets) are deployment scenarios in the advent of fifth generation (5G) and beyond network generations. A massive number of small base stations (SBSs) and connected devices have been exponentially increasing. This has subsequently led to a rise of several mobility management issues which require optimization techniques to avoid performance degradation. Machine learning (ML) is a promising approach for future mobile communication networks (5G and beyond). It has the ability of improving the efficiency of complicated heterogeneous and decentralized networks. ML has proven to be significant in the mobility management field since it optimizes handover control parameters (HCPs) over various dynamic environments. To the best of the authors’ knowledge, no comprehensive survey deeply discussing a state-of-the-art ML algorithms in mobility robustness optimization (MRO) functions. However, each summarized algorithm in this study includes deployment scenario, ML type, methodology used, criteria, HCPs, key performance indicators (KPIs), simulators, and achievements which can assist researchers for future investigations in MRO functions. In addition, this study serves as a guide in the selection of proper optimization algorithms according to the outcomes of each algorithm. Furthermore, this study presented the common types of ML and the techniques used from each type to optimize the HCPs of the MRO functions. Moreover, high-mobility-aware and network topologies are presented in MRO function for further system enhancements. Besides, the survey further highlights several potential problems for upcoming research and provides future directions to address the issues of next generation wireless networks.

Supporting seamless connection through user mobility is a critical challenge that must be addressed in mobile Heterogeneous Networks (HetNets). The case will become more worse in the future Ultra-Dense HetNets with the deployments of Fifth Generation (5G) and beyond mobile networks. That due to various key important factors, these factors include the use of Millimetre Waves (mmWaves), the massive growth of connected mobile equipment, overlapping network deployments, implementation of Dual Connectivity (DC), Carrier Aggregation (CA), emergence of connected drones, the massive deployment of small cells and the required for supporting high mobility speed scenarios. Consequently, this paper provides a comprehensive review of handover management in future mobile Ultra-Dense HetNets. Different mobility and handover management techniques are discussed to highlight their contribution in providing seamless connection during user mobility. Several key challenges, opportunities and prospective solutions are also examined to pinpoint key issues and determine suitable solutions that may contribute to solving mobility problems in future mobile networks. Conclusively, background studies and practical solutions are presented and discussed for future research directions.

Wireless Power Transfer (WPT) is an innovative technology employed for enhancing the energy sustainability of wireless devices with a limited life span. The idea of integrating WPT in wireless communication leads to the idea of Simultaneous Wireless Information and Power Transfer (SWIPT) that transfers information and power to wireless devices simultaneously, thereby resulting in a drastic increase in spectral efficiency of the network. SWIPT aided Cooperative Relaying (CoR) has emerged as a new trend for Fifth Generation (5G) and Beyond 5G (B5G) systems owing to the rapidly increasing challenges faced by these networks. Cooperative relaying combined with SWIPT can be helpful in overcoming the rising demands of next generation wireless networks by providing an enhanced data rate, low latency, shorter coverage, wide spread connectivity of massive number of devices along with energy-efficiency. This article provides a comprehensive review of SWIPT technology that enables the use of CoR networks for 5G and B5G mobile networks including the significance, technologies, and protocols which can be applied. This article also examines the deployment of cooperative SWIPT involving a single relay, multiple relays and optimal relay selection, multi antenna systems and optimal beamforming. SWIPT under the influence of Hardware Impairments (HI), imperfect Channel State Information (CSI), non-linear energy harvesting models, Intelligent Reconfigurable Surface (IRS), massive MIMO, massive access for the Internet of Things (IoT) has been discussed in detail. Meanwhile, this study discusses key challenges being faced in the implementation of SWIPT for future wireless networks that need to be addressed efficiently.

The 10-Gigabit Passive Optical Network (GPON), also known as (XGPON), is a key technology for the next generation of optical fibre communication systems that can improve reliability data rate. However, the increment in the transmission distance and data rate will lead to increased dispersion. This paper proposes a model based on the Sub-Carrier Multiplexing/Wavelength Division Multiplexing-Radio over Fibre-10-Gigabit Passive Optical Network system (SCM/WDM-RoF-XGPON) for Radio Frequency (RF) using a conventional Optical Network Unit (ONU) with the Square Root Module (SRM) on the side of the receiver. It is connected to a central office Optical Line Terminal (OLT) via an access network for multi-channel optical fibre transfers with a distance of 80 km and at 10 Gbps data rate. The Optisystem 15 simulation software is used to evaluate the proposed system based on the Bit Error Rate (BER), Quality factor (Q-factor) and eye diagram. The proposed system of bidirectional fibre links uses 1270 nm and 1577 nm wavelengths for uplink and downlink transmissions, respectively. The performance is further enhanced by using SRM to compensate for the square law characteristics, making it suitable for broadband services. Due to the utilisation of SRM in the architecture, the reported results reveal a double enhancement in successful transition performance at an optical fibre length of 80 km distance. The BER displayed significant improvement (less than 1.00E-09) with SRM at 80 km; however, without SRM and at 50 km, the BER is less than 1.00E-09. Investigations have presented an enhancement in the Q-factor’s effectiveness with the use of SRM, which helps to increase the XGPON length. When the length of the fibre is 80 km, the Q-factor is 6 with SRM, while it is 3.7 without SRM.

Drones will be a significant part of future mobile communication networks, serving as mobile users or acting as mobile base stations at sky. Although they will provide several solutions related to mobile communication networks and other non-communication services, drones also possess numerous challenges, especially when it comes to their handover management. Unlike terrestrial networks, drones are mobile devices that move in a three-dimension (3D) environment, which further complicates mobility issues. Therefore, this paper provides an overview on the handover management for connected drones in the future mobile networks. The study summarizes how current research efforts approach the issues that characterize drones, with special focus on the handover process. This work also provides a general concept of drone integration in heterogeneous networks and discusses specific solutions for addressing possible problems. This survey further offers a brief discussion and guidance for upcoming research directions related to connected drones in future heterogeneous networks.

The performance of Mobile Broadband (MBB) services of Fourth Generation (4G) and Third Generation (3G) mobile networks over urban morphology is studied in Malaysia based on experimental measurements of drive test data. The aim of this study is to provide a roadmap for service providers to establish a realistic plan for future Fifth Generation (5G) networks. This work is a continuation of our previous work for the scope of rural areas in Malaysia. The MBB measurement data have been gathered through drive tests conducted in the urban areas of four states throughout Malaysia (namely, Klang Valley/Selangor, Johor, Sarawak and Sabah) to characterise and analyse MBB performances. The gathered data are from the cities, highways and federal roads of the chosen states, and encompasses three main Mobile Network Operators (MNOs). Data has been collected in a time span of 2 months, from January to February, using the Samsung Galaxy S6 smartphone handsets. Four MBB Key Performance Indicators (KPIs) are considered in this study (coverage, latency, satisfaction and speed) for two MBB services (web browsing and video streaming). The measurement data for characterising the performance of each MBB service has been collected using a dedicated smartphone handset. YouTube videos with 720p and 1080p resolutions have been sequentially streamed to assess the performance of MBB video-streaming services. Three distinct websites (Google, Instagram and mStar) have been accessed to evaluate the performance of MBB web-browsing services. The experimental methodology of this study integrates several diversified elements including four different urban states, four distinct KPIs, three main MNOs, two MBB services and two radio networks (4G and 3G), which are both accessible by the smartphones when available to mimic real-world scenarios. The results of this study reveal that the performance of 4G radio networks is generally superior to that of 3G. For instance, 4G networks achieved a vMOS score of more than 3 for both MBB video-streaming and web-browsing services, while 3G networks scored less than 3 across all four study areas. The analysed experimental results confirmed that compared to 3G networks, 4G technology presents an enhancement factor of up to 1.6 and 4.2 in download speed when streaming a video and browsing a web page, respectively. The study outcomes can contribute to the efficient planning of non-standalone (NSA) 5G networks in Malaysia where 5G networks will be aided by existing 4G infrastructures. Analysing the 4G coverage performance is the first step towards deciding the deployment rate of NSA 5G in Malaysia.

The goal of this review paper is to highlight the image processing techniques’ role in the Internet of Things (IoT), aiming to attain an energy-efficient and secure IoT. IoT-dependent systems (IoTSs) cause heavy usage of energy. This is one of the biggest issues associated with IoTSs. Another issue is that the security of digital content is a big challenge and difficulty. Image processing has recently played an essential role in resolving these difficulties. Several researchers have made efforts to improve future IoTSs, which are summarized in this article. Day-by-day, proposed methods are developed, and thus IoT deployment has been plainly engaged in our everyday activities. Several efficient image-processing techniques that can be utilized by IoTSs to overcome such issues have been proposed. This review paper aims to highlight those proposed methods that can make contributions in this direction. Thus, this study aims to review numerous research studies on this subject. This study looks at 36 publications relevant to image-processing techniques utilized by several types of IoTSs. The innovative work of this review paper is to provide readers with a map of suitable image processing techniques to be used with certain types of IoT systems (i.e., scenarios). Both methodology and analysis have come out with a suggested mind map highlighting a number of proposed solutions (i.e., image processing techniques) that can be suitable to help design an energy-efficient, secure, and intelligent IoT system. We have made some conclusions and projections for future research work.

In recent years, both the scientific community and the industry have focused on moving computational resources with remote data centres from the centralized cloud to decentralised computing, making them closer to the source or the so called “edge” of the network. This is due to the fact that the cloud system alone cannot sufficiently support the huge demands of future networks with the massive growth of new, time-critical applications such as self-driving vehicles, Augmented Reality/Virtual Reality techniques, advanced robotics and critical remote control of smart Internet-of-Things applications. While decentralised edge computing will form the backbone of future heterogeneous networks, it still remains at its infancy stage. Currently, there is no comprehensive platform. In this article, we propose a novel decentralised edge architecture, a solution called OMNIBUS, which enables a continuous distribution of computational capacity for end-devices in different localities by exploiting moving vehicles as storage and computation resources. Scalability and adaptability are the main features that differentiate the proposed solution from existing edge computing models. The proposed solution has the potential to scale infinitely, which will lead to a significant increase in network speed. The OMNIBUS solution rests on developing two predictive models: (i) to learn timing and direction of vehicular movements to ascertain computational capacity for a given locale, and (ii) to introduce a theoretical framework for sequential to parallel conversion in learning, optimisation and caching under contingent circumstances due to vehicles in motion.

This paper presents a multidimensional performance analysis of existing Mobile Broadband (MBB), Third Generation (3G) and Fourth Generation (4G) networks, of rural morphology in Malaysia. The MBB performance analysis is carried out based on measurement data obtained through Drive Tests (DT) conducted in rural areas located in three Malaysian states: Johor, Sarawak, and Sabah. The measurement data pertains to the performance of three national Mobile Network Operators (MNOs) in rural areas: Maxis, Celcom, and DiGi. The MBB performance measurement data was collected between January and February using modified Samsung Galaxy S6 smartphone handsets. The measurement data of the 3G and 4G MBB networks are associated with four performance metrics (coverage, latency, satisfaction, and speed) for two MBB services: web browsing and video streaming. During the measurements, each smartphone collected the performance data of only one MBB service. Several classifications were identified to comprehensively monitor the performance of the two MBB services. For the data measurement of the MBB video streaming service, the same YouTube video was alternately played by the same smartphone, but with two different resolutions: 720p (low) and 1080p (high). For the data measurement of the MBB video streaming service, three different webpages (i.e., google, Instagram, and mstar) are sequentially browsed in a loop using another smartphone. This research work is designed to mimic real scenarios where the smartphone in use is not exclusively locked to a single technology while streaming a video or browsing a website. This allows the identification of the coverage for 2G, 3G, and/or 4G technologies within the tested areas. Due to the small amounts of 2G data, we omitted the analysis of 2G technology in the present study. The MBB performance analysis shows that, on average, the 4G network performed much better than the 3G network for all three MNOs throughout all measurement areas considered in this research. For instance, the 4G technology achieved a minimum of 42.4 ms on the web ping average RTT latency, while the 3G only achieved a minimum of 69.9 ms. For the average E2E RTT ping server latency, 4G achieved as low as 33.27 ms, while 3G obtained a minimum of 122.98 ms. The vMOS scores for 4G technology for both web browsing and video streaming services are larger than 3, while the 3G technology had a score of less than 3. The 4G technology can provide an improvement up to a factor of 4.2 and 1.6 in the download speed when browsing a web and streaming a video, respectively, in comparison to the 3G technology. These observations were found to be consistent across all mobile operators. This is unsurprising because we would expect consumers to experience a noticeable improvement when using a mobile broadband service over a 4G network as compared to a 3G network. The presented results provide a general direction for efficiently planning the Fifth Generation (5G) network in rural areas.

Ensuring a reliable and stable communication throughout the mobility of User Equipment (UE) is one of the key challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. One of the main issues is the use of suboptimal Handover Control Parameters (HCPs) settings, which are configured manually or generated automatically by certain self-optimization functions. This issue becomes more critical with the massive deployment of small base stations and connected mobile users. This will essentially require an individual handover self-optimization technique for each user individually instead of a unified and centrally configured setting for all users in the cell. In this paper, an Individualistic Dynamic Handover Parameter Optimization algorithm based on an Automatic Weight Function (IDHPO-AWF) is proposed for 5G networks. This algorithm dynamically estimates the HCPs settings for each individual UE based on UE's experiences. The algorithm mainly depends on three bounded functions and their Automatic Weights levels. First, the bounded functions are evaluated, independently, as a function of the UE's Signal-to-Interference-plus-Noise-Ratio (SINR), cells' load and UE's speed. Next, the outputs of the three bounded functions are used as inputs in a new proposed Automatic Weight Function (AWF) to estimate the weight of each output bounded function. After that, the final output is used as an indicator for optimizing HCPs settings automatically for a specific user. The algorithm is validated throughout various mobility conditions in the 5G network. The performance of the analytical HCPs estimation method is investigated and compared with other handover algorithms from the literature. The evaluation comparisons are performed in terms of Reference Signal Received Power (RSRP), Handover Probability (HOP), Handover Ping-Pong Probability (HPPP), and Radio Link Failure (RLF). The simulation results show that the proposed algorithm provides noticeable enhancements for various mobile speed scenarios as compared to the existing Handover Parameter Self-Optimization (HPSO) algorithms.