Fractal Modeling for Multi-Tier Heterogeneous Networks with Ultra-High Capacity Demands

Maksymyuk, Taras; Brych, Mykola; Strykhalyuk, Ihor; Jo, Minho

doi:10.6029/smartcr.2015.04.011

Cited by 15 publications

(6 citation statements)

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“…Thus, the above discussion highlights the relevance of a precise estimation of the propagation loss. Results in [24] presented a new deterministic approach for analysis of multi-tier heterogeneous network that was proposed in the research based on the fractal geometry. Also, two models were proposed for regular and non-regular environment.…”

Section: Results and Analysismentioning

confidence: 99%

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 Analysismentioning

confidence: 99%

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Zakaria

Ivanek

Glesk³

2017

KSII TIIS

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“…On the other hand, different from some well-known fractal structures with specific geometric shapes as studied in [5]- [7], the fractal social networks in this paper are those with general fractal characteristics. Within the framework of complex networks, the general fractal features have been well studied [8]- [10] and are mainly determined by the degree correlations in the connectivity of the networks, which can be characterized by some well-established models or distributions like [11]- [13].…”

Section: Introductionmentioning

confidence: 91%

On the Capacity of Fractal D2D Social Networks With Hierarchical Communications

Chen¹,

Li²,

Zhang³

et al. 2017

Preprint

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The maximum capacity of fractal D2D (deviceto-device) social networks with both direct and hierarchical communications is studied in this paper. Specifically, the fractal networks are characterized by the direct social connection and the self-similarity. Firstly, for a fractal D2D social network with direct social communications, it is proved that the maximumif a user communicates with one of his/her direct contacts randomly, where n denotes the total number of users in the network, and it can reach up to Θ 1 log n if any pair of social contacts with distance d communicate according to the probability in proportion to d −β . Secondly, since users might get in touch with others without direct social connections through the inter-connected multiple users, the fractal D2D social network with these hierarchical communications is studied as well, and the related capacity is further derived. Our results show that this capacity is mainly affected by the correlation exponent of the fractal structure. The capacity is reduced in proportional to 1 log n if 2 < < 3, while the reduction coefficient is 1 n if = 3.

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“…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]. In the recent literature, possible architectures for 5G networks have been discussed.…”

Section: Introductionmentioning

confidence: 99%

Massive MIMO: survey and future research topics

Araújo¹,

Maksymyuk²,

Almeida³

et al. 2016

IET Communications

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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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Fractal Modeling for Multi-Tier Heterogeneous Networks with Ultra-High Capacity Demands

Cited by 15 publications

References 0 publications

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

Propagation Measurements and Estimation of Channel Propagation Models in Urban Environment

On the Capacity of Fractal D2D Social Networks With Hierarchical Communications

Massive MIMO: survey and future research topics

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