Stochastic Geometry Models for 5G Heterogeneous Mobile Networks

Maksymyuk, Taras; Brych, Mykola; Pelishok, Volodymyr

doi:10.6029/smartcr.2015.02.002

Cited by 29 publications

(20 citation statements)

References 2 publications

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“…By taking into account the results of COST 231 W-I model, it was noted that these results are in good agreement with the results of the same model which described in [22]. Also, attention should be paid for the results in [23] which stated that two different types of cells deployment which are random building environment and high urban environment with tall buildings have been compared and studied. Advanced network model for Device-to-Device heterogeneous network was developed based on the Poisson point process combined with K-means clustering method which is able to reflect the random user devices.…”

Section: Results and Analysissupporting

confidence: 66%

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Zakaria

Ivanek

Glesk³

2017

KSII TIIS

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Wireless communication is a telecommunication technology, which enables wireless transmission between the portable devices to provide wireless access in all types of environments. In this research, the measurements and various empirical models are analysed and compared in order to find out a suitable propagation model to provide guidelines for cell planning of wireless communication systems. The measured data was taken in urban region with low vegetation and some trees at 900 MHz frequency band. Path loss models are useful planning tools, which permit the designers of cellular communication to obtain optimal levels for the base station deployment and meeting the expected service level requirements. Outcomes show that these empirical models tend to overestimate the propagation loss. As one of the key outputs, it was observed that the calculations of Weissberger model fit with the measured data in urban environment.

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Section: Results and Analysissupporting

confidence: 66%

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Zakaria

Ivanek

Glesk³

2017

KSII TIIS

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“…The classification of mobility models is done according to the existing research work in D2D communication. We focus on mobility models and traces with regards to human and vehicle behavior according to their movement patterns [73], [210], [211], speed, geographic location [104], [212], social characteristics [53], [85], [213], stochastic data [214] and frequent visiting places [82]. Mobility models include random mobility model [61], [72], human mobility model [74], vehicular mobility model, dynamic graph model [97], social group based mobility model [5], [215] and geographic based mobility model.…”

Section: A Overview Of Mobility Assisted D2d Communicationmentioning

confidence: 99%

“…6) Stochastic Geometry Implementation: Stochastic geometry is also considered in many research article to focus on the mobility behavior of users [214]. The modeling of stochastic geometry is the significant part of the D2D network design and analysis in the mobile premise.…”

Section: B Key Lessons Learnedmentioning

confidence: 99%

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

Waqas

Niu

et al. 2020

IEEE Commun. Surv. Tutorials

110

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Device-to-device (D2D) communication proposes a new epitome in mobile networking to avail data exchange between physically proximate devices. The exploitation of D2D communication enables mobile operators to harvest short range communications for improving network performance and corroborating proximity-based services. In this article, we investigate mobility aspects of D2D communication, which are indispensable for the adoption and implementation of D2D communication technology. We present an extensive review of the state-of-theart problems and the corresponding solutions for encouraging the exploitation of mobility to assist D2D communication. Specifically, by identifying the mobility models, traces, problems, requirements, and features of different proposals, we discuss the lessons learned and summarize the advantages of mobility-aware D2D communication. We also present open problems and highlight future research directions concerning D2D communication applications in real-life scenarios. To the best of our knowledge, this is the first comprehensive survey to address mobility-aware D2D communication, which offers insight to the underlying problems and provides the potential solutions. Index Terms-Device-to-device communication, mobility, mobile communication, mobile data traffic. I. INTRODUCTION D EVICE-to-device (D2D) communication takes the advantage of opportunistic encounters by mobile users with each other [1]. These opportunistic encounters' information between users are highly related to their movement. By exploiting users' movement, D2D enabled applications and services visualize highly opportunistic and unpredictable human mobility. Therefore, the challenges of exploiting mobility resides in the inherent complexity of users' mobility. It is mainly concerns with predicting the establishment of communication links among D2D users. For instance, two mobile users can establish a link at the time when they are in close proximity to each other. However, there are two key questions have to M.

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“…A possible solution to cope with such a capacity demand is through network densification by adding small cells (SCs) (picocells and femtocells) that operate at high frequencies (e.g. 60 GHz) within the macro cell area [4, 5]. SCs that utilise the same band spectrum can increase the capacity of a mobile network from 10 to 100 times, depending on the number of SCs and frequency reuse method [6, 7].…”

Section: Introductionmentioning

confidence: 99%

Massive MIMO: survey and future research topics

Araújo¹,

Maksymyuk²,

Almeida³

et al. 2016

IET Communications

Self Cite

172

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Massive multiple-input multiple-output technology has been considered a breakthrough in wireless communication systems. It consists of equipping a base station with a large number of antennas to serve many active users in the same time-frequency block. Among its underlying advantages is the possibility to focus transmitted signal energy into very short-range areas, which will provide huge improvements in terms of system capacity. However, while this new concept renders many interesting benefits, it brings up new challenges that have called the attention of both industry and academia: channel state information acquisition, channel feedback, instantaneous reciprocity, statistical reciprocity, architectures, and hardware impairments, just to mention a few. This paper presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research.

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Stochastic Geometry Models for 5G Heterogeneous Mobile Networks

Cited by 29 publications

References 2 publications

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges

Massive MIMO: survey and future research topics

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