Propagation path loss models for mobile communication

Klozar, Lukas; Prokopec, Jan

doi:10.1109/radioelek.2011.5936478

Cited by 38 publications

(18 citation statements)

References 4 publications

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“…The received results are spread out in the wide range of path losses values and seem as the certain "swarm" when the sufficient number of experimental measurements are carried out in the microcells [12][13][14][15]. Such "swarm" is also observed at short distances between BS and UE (less than 200 m) when the frequency is high enough (> 20 GHz) [16][17][18].…”

Section: Related Workmentioning

confidence: 99%

Mobile Signal Path Losses in Microcells behind Buildings

Japertas¹,

Grimaila²

2017

RADIOENGINEERING

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Section: Related Workmentioning

confidence: 99%

Mobile Signal Path Losses in Microcells behind Buildings

Japertas¹,

Grimaila²

2017

RADIOENGINEERING

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“…In turn, the SINR of any user u l is a function of the total interference received,

i_{R, u_{l}}

, which in turn depends on the path loss

L_{l z}^{PM}

(dB) between the involved antennae (those of u l and B z ) and on the propagation model (PM) used. We compute

L_{l z}^{PM}

by using either the “one‐slope” PM for LTE macrocells or the “two‐slope” PM for small cells:

\begin{matrix} L_{l z}^{PM} & = A + B normall normalo normalg f (MHz) - 13.82 normall normalo normalg h_{B_{z}} (m) \\ - a \cdot h_{u_{l}} (m) + ς \cdot normall normalo normalg d_{l z} (km) + L_{CLU}, \end{matrix}

where A and B are frequency related path loss coefficients, f is the carrier frequency,

h_{B_{z}}

is the height of eNB B z ,

h_{u_{l}}

is the height of user u l , ς is the slope parameter, and a and L CLU are the correction factors depending of the “clutter type.” The term “clutter” refers in this context to different environments (dense urban, urban, suburban, and rural) in which there are different propagation conditions (building density, street structure, obstacles characteristic, etc) and different user densities (much higher in dense urban than in rural scenarios). Table 2 in the experimental Section 5 lists the values used for these parameters.…”

Section: Problem Formulationmentioning

confidence: 99%

Near‐optimal user assignment in LTE mobile networks with evolutionary computing

Arco-Vega

Cuadra

Portilla-Figueras

et al. 2016

Trans. Emerging Tel. Tech.

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This paper tackles the problem of users assignment to evolved NodeBs in long‐term evolution mobile networks. We propose a model that aims at assigning users by minimizing a function called download time of the complete system, defined as the minimum time required for all the users in the system to complete their downloads. This strategy helps the network use its resources in a more balanced way, by assigning users from cells (which otherwise would be overloaded when using a simple signal‐to‐interference‐plus‐noise ratio‐based approach) to others with less load. The user‐cell association problem is, in general, a combinatorial NP hard problem. Although other approaches indirectly tackle this complexity by transforming the problem into another whose optimal solution is less difficult (but still with very high computational cost), we tackle directly our download time of the complete system minimization problem by finding an approximate solution provided by a specific implementation of an evolutionary algorithm, which is based on a long‐term evolution domain‐specific initial population along with fully tailored operators adapted to this assignment problem. A variety of experiments in different realistic scenarios point out that the proposed method outperforms the conventional signal‐to‐interference‐plus‐noise ratio‐based and load balancing approaches, specially in urban and dense urban environments (where the assignment is more critical in terms of capacity). We have also tested our approach in simple scenarios that mimic heterogeneous networks and ultra‐dense networks in which the number of users is similar to that of nodes, proving its beneficial properties. Our method is aimed at the design/dimensioning stage and not to the operation stage, and in this sense, it is an off‐line algorithm that could be useful to make better decisions on cost‐effective network dimensioning and upgrading.

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“…All new wireless systems undergo careful planning [2] [3] process, where coverage, capacity and cost-efficiency issues are investigated and optimized. Coverage is an essential part of wireless system design and it is used to verify whether or not the system under design is capable of meeting the given coverage requirements.…”

Section: Supporting Both Frequency Division Duplex (Fdd) and Time DIVmentioning

confidence: 99%

“…Okumura-Hata model [4] uses empirical data to determine the path loss (L P ) in dB given by equation (2). C m =0 for median sized cities and suburban areas.…”

Section: Okumura-hata Modelmentioning

confidence: 99%