Big Data Assisted CRAN Enabled 5G SON Architecture

2019

Fifth generation mobile networks (5G) will rather supplement than replace current 4G networks by dramatically improving their bandwidth, capacity and reliability. This way, much more demanding use cases that simply are not achievable with today’s networks will become reality - from home entertainment, to product manufacturing and healthcare. However, many of them rely on Internet of Things (IoT) devices equipped with low-cost transmitters and sensors that generate enormous amount of data about their environment. Therefore, due to large scale of 5G systems, combined with their inherent complexity and heterogeneity, Big Data and analysis techniques are considered as one of the main enablers of future mobile networks. In this work, we recognize 5G use cases from various application domains and list the basic requirements for their development and realization.

Wireless Communications and Mobile Computing

Recent improvements in communication technology have undergone a significant shift over the last two decades, with state-of-the-art communication equipment, standards, and protocols simplifying the lives of consumers everywhere. For more than a decade, advancements in communication technology have mostly focused on increasing the speed with which information can be delivered and retrieved from anywhere in the globe at any time of day or night, regardless of location. Four-generation (4G) communication technologies, which have already been developed and implemented, are used to offer users with seamless access to multimedia content at transmission rates of 100 megabits per second (Mbps). It is becoming more vital to create new technologies in order to meet the growing need for faster speed as well as a variety of other advanced features. 5G networks have just recently been built as a result of extensive research and development. This has resulted in the gradual replacement of existin...

2021

The networks are evolving drastically since last few years in order to meet user requirements. For example, the 5G is offering most of the available spectrum under one umbrella. In this work, we will address the resource allocation problem in 5G networks, to be exact in the Cloud Radio Access Networks (C-RAN). The Radio Access Network (RAN) mechanisms involve multiple network topologies that are isolated based on the spectrum bands and it should be enhanced with numerous access technology in the deployment of 5G network. C-RAN is one of the optimal technique to combine all the available spectral bands. However, existing C-RAN mechanisms lacks the intelligence perspective on choosing the spectral bands. Thus, C-RAN mechanism requires an advanced tool to identify network topology to allocate the network resources for substantial traffic volumes. Therefore, there is a need to propose a framework that handles spectral resources based on user requirements and network behavior. In this wo...

ArXiv, 2020

The development of cellular technology will be directly proportional to the increasing requirement in various aspects, such as the speed of data transmission (velocity), data variations (variety), and data storage media (volume). The increase in various aspects will have a direct impact on the growth of internet users worldwide. In relation to that, the demand for increased capacity and coverage becomes a necessity because users access the networks using the same resources by utilizing resource sharing mechanism. Self Organizing Network (SON) is one of the solutions to make the system more efficient with guaranteed Quality of Experiment (QoE). SON can create an automated network with a self-optimization, self-configuration and self-healing mechanism. Big Data is leveraged in the network analytics as a reference for decision-making activities in the network automation process. In this paper, SON using Big Data analytics on 5G network is compared with SON on 4G network as the previous...

Journal of Network and Computer Applications, 2020

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non-Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions.

Journal of Physics: Conference Series, 2021

One of the potential top-level goals for 5G heterogeneous networks may be intellectual and perfect network which modifies consumer preferences in a proactive manner in addition to needs of channel. Research provides an interdisciplinary approach to e-health, primarily concern BDA, and radio space management inside a various level fifth generation network in the company of massive data. The growing need for and usage of big data fuelled digital transformation. The research focuses on the effect of Big Data on digital technologies during the 5G era. To carry out digital transformation, three machine learning (ML) algorithms are identified. In addition to decision tree DT the other algorithms used for the classification are NB, LR. These algorithms run on the large data processing engine they work. These algorithms serve as an ensemble tool for examining old records of stroke outpatients (OPs) and body built IOT based sensors [19]. These readings are available as Big Data. In the model which has been proposed here, OP-Centric Network Optimization Framework was presented before evaluating the machine learning algorithm function and all appropriate steps to plan massive data. An ensemble method in the company of NB classification device, decision tree classification device, and logistic classification device was used in this analysis. These entire classification devices are highly controlled and managed classification device. This method is based on the OP data set and feeds the predicted stroke probabilities to an SV classifier.

In the near future, i.e., beyond 4G, some of the prime objectives or demands that need to be addressed are increased capacity, improved data rate, decreased latency, and better quality of service. To meet these demands, drastic improvements need to be made in cellular network architecture. This paper presents the results of a detailed survey on the fifth generation (5G) cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of users. In this detailed survey, the prime focus is on the 5G cellular network architecture, massive multiple input multiple output technology, and device-to-device communication (D2D). Along with this, some of the emerging technologies that are addressed in this paper include interference management, spectrum sharing with cognitive radio, ultra-dense networks, multi-radio access technology association, full duplex radios, millimeter wave solutions for 5G cellular networks, and cloud technologies for 5G radio access networks and software defined networks. In this paper, a general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G cellular network architecture. A detailed survey is included regarding current research projects being conducted in different countries by research groups and institutions that are working on 5G technologies.

IRJET, 2020

In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and with the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean 5G RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture relies on a single general-purpose learning framework conceived for RRM directly using the data gathered in the network. The complexity of RRM is shifted to the design of the framework, whilst the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios and future directions on applications of machine learning to RRM are discussed. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. We conduct a survey on the role that big data analytics can play in the design of data communication networks

International Journal of Electrical and Computer Engineering (IJECE), 2021

The rapid growth of both mobile users and application numbers has caused a huge load on the core network (CN). This is attributed to the large numbers of control messages circulating between CN entities for each communication or service request, however, making it imperative to develop innovative designs to handle this load. Consequently, a variety of proposed architectures, including a software defined network (SDN) paradigm focused on the separation of control and data plans, have been implemented to make networks more flexible. Cloud radio access network (C-RAN) architecture has been suggested for this purpose, which is based on separating base band units (BBU) from several base stations and assembling these in one place. In this work, a novel approach to realize this process is based on SDN and CRAN, which also distributes the control elements of the CN and locates them alongside the BBU to obtain the lowest possible load. The performance of this proposed architecture was evaluated against traditional architecture using MATLAB simulation, and. the results of this assessment indicated a major reduction in signalling load as compared to that seen in the traditional architecture. Overall, the number of signalling messages exchanged between control entities was decreased by 53.19 percent as compared to that seen in the existing architecture.