Next generation wireless cellular networks: ultra-dense multi-tier and multi-cell cooperation perspective

Kazi, Baha Uddin; Wainer, Gabriel

doi:10.1007/s11276-018-1796-y

Cited by 51 publications

(24 citation statements)

References 86 publications

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“…In order to compare our survey to others, Table 1 summarizes relevant surveys of five past years, as [4,[33][34][35][36][37]. Others focus on understanding 5G background, its architectures, technologies, services and applications without addressing AI technology and especially ML technique, such as [1,6,10,11,[38][39][40][41][42][43][44][45].…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…In addition, it provides dynamic service provisioning without costly networking devices. Moreover, centralization and virtualization can reduce the number of processing units required in a small cell dense environment [6,16,40,143]. The use of MIMO technology in C-RAN minimize energy consumption and ensure a global optimal utilization of system resources [11,105].…”

Section: G Physical Architecturesmentioning

confidence: 99%

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

Mâaloul

Fourati

2020

Int. J. Mach. Learn. & Cyber.

115

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5G cellular networks are expected to be the key infrastructure to deliver the emerging services. These services bring new requirements and challenges that obstruct the desired goal of forthcoming networks. Mobile operators are rethinking their network design to provide more flexible, dynamic, cost-effective and intelligent solutions. This paper starts with describing the background of the 5G wireless networks then we give a deep insight into a set of 5G challenges and research opportunities for machine learning (ML) techniques to manage these challenges. The first part of the paper is devoted to overview the fifth-generation of cellular networks, explaining its requirements as well as its key technologies, their challenges and its forthcoming architecture. The second part is devoted to present a basic overview of ML techniques that are nowadays applied to cellular networks. The last part discusses the most important related works which propose ML solutions in order to overcome 5G challenges.

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Section: Motivation and Contributionsmentioning

confidence: 99%

Section: G Physical Architecturesmentioning

confidence: 99%

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

Mâaloul

Fourati

2020

Int. J. Mach. Learn. & Cyber.

115

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“…A cognitive radio (CR) based resource allocation scheme has been proposed by [14] that is shown to maximize the Femtocell throughput by minimizing intertier interference. In [15] a cooperative game theory based approach is taken to minimize interference in cooperative Femtocell networks [16]. It has been shown that throughput, fairness, spectrum spatial reuse and energy efficiency has been improved with the minimization of interference.…”

Section: Related Workmentioning

confidence: 99%

User Association, Power Control and Channel Access Schemes for Two-Tier Macro-Femto Networks: CDF of SINR Analysis

Ghosh

Gupta

Hacı

et al. 2020

IETE Technical Review

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In the rapid development of wireless communications Femtocells provide tremendous improvement in coverage and quality of service for users. Macro-Femto based networks are envisioned to be the de-facto solution for providing ultrahigh speed communications in next generation mobile wireless networks. This paper studies two-tier Macro-Femto networks and proposes a collection of novel technologies to address the interference problems. First, a novel user association scheme is proposed that aims to optimize the load among Femto base stations (FBSs). Second, a near-optimal ergodic search algorithm is proposed to regulate the power consumption at Macro base stations (MBSs) and improve energy efficiency. Third, a channel access mechanism is proposed for FBSs that aims to minimize inter-tier interference. For the proposed system, CDF of SINR is derived and used for performance investigation. Simulation results show that the proposed system can significantly outperform a popular, conventional cognitive radio based system for all the considered simulation scenarios.

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“…It will make mission-critical control applications attainable through low-latency communication links such as vehicle-to-vehicle (V2V) communications and the connection of the enormous number of Internet of Things (IoT) devices. In achieving these demands, some of the key technologies identified in the 5G protocol include massive multiple-input multiple-output (MIMO) implementation, millimeter wave (mmWave) transmissions, ultradense networks (UDNs), and multi-tier HetNets [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is necessary to decrease the distance between users and access nodes operating at the mmWave, making the small-cell network an optimal solution. These small-cell networks will be able to expand the coverage for every unit area via deployment in a highly concentrated manner to increase the network capacity [9,21,22], which requires an ultradense network.…”

Section: Introductionmentioning

confidence: 99%

Survey of Radio Resource Management in 5G Heterogeneous Networks

et al. 2020

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Radio resource management (RRM) for future fifth-generation (5G) heterogeneous networks (HetNets) has emerged as a critical area due to the increased density of small-cell networks and radio access technologies. Recent research has mostly concentrated on resource management, including spectrum utilization and interference mitigation, but the complexities of these resources have been given little attention. This paper provides an overview of the issues arising from future 5G systems and highlights their importance. The different approaches used in recently published surveys categorizing RRM schemes are discussed, and the survey method is presented. We report on a survey of HetNet RRM schemes that have been studied recently, with a focus on the joint optimization of radio resource allocation with other mechanisms. These RRM schemes were subcategorized according to their optimization metrics and qualitatively analyzed and compared. An analysis of the complexity of RRM schemes in terms of implementation and computation is presented. Several potential scopes of research for future RRM in 5G HetNets are also identified.

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Next generation wireless cellular networks: ultra-dense multi-tier and multi-cell cooperation perspective

Cited by 51 publications

References 86 publications

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

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

User Association, Power Control and Channel Access Schemes for Two-Tier Macro-Femto Networks: CDF of SINR Analysis

Survey of Radio Resource Management in 5G Heterogeneous Networks

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