Channel fading attenuation based on rainfall rate for future 5G wireless communication system over 38-GHz

In this paper, the effect of heavy rainfall on the propagation of a 38-GHz in a tropical region was studied and analyzed. Real measurement was collected, with a path length of 300 meters, for a (5G) radio linkage in Malaysia, installed at the Universiti Teknologi Malaysia (UTM) Johor Bahru campus. The employed system entails an Ericsson MINI-Link 38 E-0.6 mm, with a horizontal polarization (HP) antenna at the top integrated with a rain gauge and a data logger. Daily registered samples with a single minute span, for a full study period of 1 month, were collected and evaluated. The obtained rain rate was found as 56 mm/hr with a specific rain attenuation of 18.4 dB/km for 0.01% of the time. In addition to that, a calculated average rain attenuation of 5.5 dB for the transmission path of 300 meters length, was calculated. Based on these findings, a recommendation to update the International Telecommunication Union (ITU) specification of the rain attenuation for Malaysia is proposed. Based on the results, we suggest shifting the zone classification of Malaysia from zone P to zone N-P. Therefore, accurate design for future 5G systems would rely on more precise estimated attenuation levels leading to enhanced performance

Efficient radio resource allocation scheme for 5G networks with device-to-device communication

A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases

Key challenges, drivers and solutions for mobility management in 5G networks: a survey

Ensuring a seamless connection during the mobility of various User Equipments (UEs) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operations, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs' mobility as well as future research directions on mobility management in 5G networks and beyond

Spectrum gap analysis with practical solutions for future mobile data traffic growth in Malaysia

In this paper, an efficient spectrum forecasting model is developed to estimate the required spectrum and calculate the spectrum gap in future. This developed model is essentially based on five main metrics and one constant. The five main metrics are the currently available spectrum, sites number growth, data traffic growth, average network utilization, and spectrum efficiency growth. The constant metric is considered to give a space for our model to be used in another country or when a new technology is coming. This developed model is then used to forecast the required spectrum and spectrum gap for Malaysia in 2020. The estimation is performed based on the input market data of four main mobile telecommunication operators in Malaysia: Maxis, Celcom, Digi, and U-Mobile. The input data for this model are collected from various sources, such as the Malaysian Communications and Multimedia Commission, OpenSignal, Analysys Mason, GSMA, and HUAWEI. The results indicate that by 2020, Malaysia will require around 307 MHz of additional spectrum to fulfill the enormous increase of mobile data demands. Addressing this increment can be achieved by launching additional spectrum bands, enhancing spectrum efficiency, off-loading mobile data to unlicensed bands or deploying more site numbers

Handover parameters optimisation techniques in 5G networks

The massive growth of mobile users will spread to significant numbers of small cells for the Fifth Generation (5G) mobile network, which will overlap the fourth generation (4G) network. A tremendous increase in handover (HO) scenarios and HO rates will occur. Ensuring stable and reliable connection through the mobility of user equipment (UE) will become a major problem in future mobile networks. This problem will be magnified with the use of suboptimal handover control parameter (HCP) settings, which can be configured manually or automatically. Therefore, the aim of this study is to investigate the impact of different HCP settings on the performance of 5G network. Several system scenarios are proposed and investigated based on different HCP settings and mobile speed scenarios. The different mobile speeds are expected to demonstrate the influence of many proposed system scenarios on 5G network execution. We conducted simulations utilizing MATLAB software and its related tools. Evaluation comparisons were performed in terms of handover probability (HOP), ping-pong handover probability (PPHP) and outage probability (OP). The 5G network framework has been employed to evaluate the proposed system scenarios used. The simulation results reveal that there is a trade-off in the results obtained from various systems. The use of lower HCP settings provides noticeable enhancements compared to higher HCP settings in terms of OP. Simultaneously, the use of lower HCP settings provides noticeable drawbacks compared to higher HCP settings in terms of high PPHP for all scenarios of mobile speed. The simulation results show that medium HCP settings may be the acceptable solution if one of these systems is applied. This study emphasises the application of automatic self-optimisation (ASO) functions as the best solution that considers user experience

Edge on Wheels With OMNIBUS Networking for 6G Technology

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

A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems

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

Adaptive Handover Decision Algorithm Based on Multi-Influence Factors through Carrier Aggregation Implementation in LTE-Advanced System

Although Long Term Evolution Advanced (LTE-Advanced) system has benefited from Carrier Aggregation (CA) technology, the advent of CA technology has increased handover scenario probability through user mobility. That leads to a user’s throughput degradation and its outage probability. Therefore, a handover decision algorithm must be designed properly in order to contribute effectively for reducing this phenomenon. In this paper, Multi-Influence Factors for Adaptive Handover Decision Algorithm (MIF-AHODA) have been proposed through CA implementation in LTE-Advanced system. MIF-AHODA adaptively makes handover decisions based on different decision algorithms, which are selected based on the handover scenario type and resource availability. Simulation results show that MIF-AHODA enhances system performance better than the other considered algorithms from the literature by 8.3 dB, 46%, and 51% as average gains over all the considered algorithms in terms of SINR, cell-edge spectral efficiency, and outage probability reduction, respectively