A survey of 5G network systems: challenges and machine learning approaches

Mâaloul, Rihab; Fourati, Lamia Chaari

doi:10.1007/s13042-020-01178-4

Cited by 107 publications

(62 citation statements)

References 204 publications

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“…Moreover, 5G will provide nearly 100 percent availability and geographical coverage with improved security and privacy. Furthermore, 5G will consume ten times less energy while extending the battery life for devices by ten times [13][14][15][16] 5G technology implementation focuses on key technologies such as new radio access, massive Multiple Input Multiple Output (MIMO), heterogeneous ultra-densification, channel coding and decoding, and Millimeter Wave (mmWave) [17]. Further details on the technologies used by 5G are out of the scope of the paper.…”

Section: The 5gmentioning

confidence: 99%

The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System

2021

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A smart city is an urban area that collects data using various electronic methods and sensors. Smart cities rely on Information and Communication Technologies (ICT) and aim to improve the quality of services by managing public resources and focusing on comfort, maintenance, and sustainability. The fifth generation (5G) of wireless mobile communication enables a new kind of communication network to connect everyone and everything. 5G will profoundly impact economies and societies as it will provide the necessary communication infrastructure required by various smart city applications. Intelligent Transporting System (ITS) is one of the many smart city applications that can be realized via 5G technology. The paper aims to discuss the impact and implications of 5G on ITS from various dimensions. Before this, the paper presents an overview of the technological context and the economic benefits of the 5G and how key vertical industries will be affected in a smart city, i.e., energy, healthcare, manufacturing, entertainment, and automotive and public transport. Afterward, 5G for ITS is introduced in more detail.

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Section: The 5gmentioning

confidence: 99%

The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System

2021

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“…Supervised learning is basically classified into regression-where the predicted output is continuous-, and classification-where the predicted output is discrete or categorical. Examples of supervised learning algorithms include: artificial neural networks (ANN), support vector machine (SVM), extreme gradient boosting (XGBOOST), k-nearest neighbour (kNN), decision tree, random forest, etc, [124]. Supervised learning algorithms can help provide user mobility information through prediction of future location, trajectory, cell, etc., which is needed for proactive HO optimization and efficient resource allocation in 5G and B5G networks order to enhance the QoS of users [34].…”

Section: ) Supervised Learningmentioning

confidence: 99%

“…ML-based implementations rely on the availability of sufficient 12 and quality data 13 for model training. However, ML based mobility and HO optimization require data set containing user mobility history which is usually very difficult to obtain due to various data protection regulations [124]. Hence, synthetic data via network simulations are normally used for model training.…”

Section: A Data Set Availabilitymentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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“…The 5G cellular system uses it to create a more flexible environment by partitioning the network function from its hardware components. Nonetheless, the virtualized network of NFV causes unauthorized intrusion or data leakage [245]. It is a severe security threat, and to furnish secure processing of every data, a suitable machine learning mechanism is highly desirable.…”

Section: A Heterogeneous Networkmentioning

confidence: 99%

Interference Management in 5G and Beyond Network: Requirements, Challenges and Future Directions

et al. 2021

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In the modern technological world, wireless communication has taken a massive leap from the conventional communication system to a new radio communication network. The novel concept of Fifth Generation (5G) cellular networks brings a combination of a diversified set of devices and machines with great improvement in a unique way compared to previous technologies. To broaden the user's experience, 5G technology provides the opportunity to meet the people's potential necessities for efficient communication. Specifically, researchers have designed a network of small cells with unfamiliar technologies that have never been introduced before. The new network design is an amalgamation of various schemes such as Heterogeneous Network (HetNet), Device-to-Device (D2D) communication, Internet of Things (IoT), Relay Node (RN), Beamforming, Massive Multiple Input Multiple Output (M-MIMO), millimeter-wave (mm-wave), and so on. Also, enhancement in predecessor's techniques is required so that new radio is compatible with a traditional network. However, the disparate technological models' design and concurrent practice have created an unacceptable intervention in each other's signals. These vulnerable interferences have significantly degraded the overall network performance. This review article scrutinizes the issues of interferences observed and studied in different structures and techniques of the 5G and beyond network. The study focuses on the various interference effect in HetNet, RN, D2D, and IoT. Furthermore, as an in-depth literature review, we discuss various types of interferences related to each method by studying the state-of-the-art relevant research in the literature. To provide new insight into interference issue management for the next-generation network, we address and explore various relevant topics in each section that help make the system more robust. Overall, this review article's goal is to guide all the stakeholders, including students, operators, engineers, and researchers, aiming to explore this promising research theme, comprehend interferences and their types, and related techniques to mitigate them. We also state methodologies proposed by the 3 rd Generation Partnership Project (3GPP) and present the promising and feasible research directions toward this challenging topic for the realization of 5G and beyond network.

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A survey of 5G network systems: challenges and machine learning approaches

Cited by 107 publications

References 204 publications

The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System

The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

Interference Management in 5G and Beyond Network: Requirements, Challenges and Future Directions

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