Comparative analysis of propagation models in LTE networks with spline interpolation

Sharma, Purnima K.; Sharma, Dinesh; Sau, Paresh Chandra; Gupta, Akanksha

doi:10.1109/ccintels.2016.7878189

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…University Uterim (SUI) [20], which was chosen because it is possible to model urban environments with shading if the correct parameters are utilized [21]. The signal strength received by the user is calculated using Equation (1), which assumes that all small cells transmit at maximum power PM with gain G, a signal that suffers from the fading (L SUI ) caused by the environment and obstacles.…”

Section: Propagation Model and Qos Assessment The Selected Propagatio...mentioning

confidence: 99%

Optimization Methods for Feasible Deployment Planning of Future Mobile Networks

Araújo

Vieira

Silva

et al. 2022

Wireless Communications and Mobile Computing

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The exponential growth of mobile traffic requires mobile operators to update their network infrastructure to provide greater capacity and better connections for end-users. A promising alternative is to deploy heterogeneous networks (HetNets) that combine macrocells and small cells; however, this alternative increases the complexity and cost of transport (connections between the small cells and the operator’s control center). Most planning strategies outlined in the literature are aimed at reducing the number of small cells without considering important aspects involving transport (access backbone). With the advent of centralized architectures, this point becomes essential, since it is necessary to consider the potential impact of the transport segment on the deployment cost of the network (with the advent of the fronthaul). In this sense, this work proposes an optimal multiobjective model of radio and transport allocation based on linear programming to minimize the total cost of the network and two efficient heuristics to obtain a near-optimal solution. Considering a real case study of the literature, we show the cost (financial and computational) of the optimal placement of radio and transport infrastructure and the limitations of the solution. We also compare the proposed function placement heuristic with the optimal solution in terms of cost efficiency and execution time and demonstrate that it can provide a good estimation of the deployment cost in a much shorter time, with an approximation of up to 10% in relation to the optimal model.

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Section: Propagation Model and Qos Assessment The Selected Propagatio...mentioning

confidence: 99%

Optimization Methods for Feasible Deployment Planning of Future Mobile Networks

Araújo

Vieira

Silva

et al. 2022

Wireless Communications and Mobile Computing

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“…Penelitian pada artikel [5][6][7] mengevaluasi model propagasi di wilayah Nigeria, sedangkan penelitian [8] mengevaluasi untuk wilayah Kanakata state India. Pada penelitian [9] dilakukan analisis komparatif dari beberapa model propagasi untuk jaringan LTE di NCR region Delhi India, dan didapatkan kesimpulan bahwa model Ericsson paling cocok untuk diterapkan di daerah tersebut.…”

Section: Pendahuluanunclassified

“…Pada artikel ini yang akan dioptimasi hanya Model Okumura Hata, karena memiliki standar deviasi yang terkecil diantara model lainnya, sehingga hanya perlu mengoptimasi rata-rata RMSE-nya saja. Optimasi dilakukan dengan cara menjumlahkan path loss yang diprediksi dengan nilai rata-rata RMSE [9]. Secara matematis dinyatakan dengan persamaan (25).…”

Section: Optimasi Modelunclassified

Komparasi dan Optimasi Model Propagasi Pada Sistem Komunikasi Seluler Di Kota Palembang

Fadhli

Soim

2020

JNTE

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Terdapat berbagai macam model propagasi yang dapat digunakan untuk memprediksi path loss. Setiap model propagasi diklasifikasikan berdasarkan kategori lingkungannya. Pembagian kategori lingkungan ini bersifat subjektif, sehingga untuk mendapatkan prediksi yang akurat diperlukan pemilihan kategori lingkungan yang tepat. Penelitian ini membandingkan empat jenis model propagasi dengan hasil pengukuran RSL pada lima site di Kota Palembang. Model yang dibandingkan adalah Okumura Hata, ECC-33, Ericsson dan SUI. Berdasarkan komparasi menggunakan parameter RMSE, Kota Palembang menurut model Okumura Hata termasuk dalam kategori large urban. Sedangkan pada model ECC-33 termasuk kategori medium urban. Pada model Ericsson termasuk kategori suburban, dan pada model SUI termasuk dalam tipe terrain A. Dari perbandingan keempat model berdasarkan RMSE dan standar deviasi, diketahui bahwa pada hasil pengukuran lima site di Kota Palembang, model ECC-33 memiliki akurasi yang tinggi dengan RMSE 3,28 dB dan standar deviasi 2,74 dB. Setelah dioptimasi, model Okumura Hata juga memberikan akurasi prediksi yang tinggi, dengan RMSE 1,75 dB dan standar deviasi 0,76 dB. Sehingga disarankan untuk menggunakan model ECC-33 medium urban atau model Okumura Hata large urban yang telah dioptimasi untuk mempredikasi path loss di Kota Palembang.

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“…It was assumed that all the SCs are transmitting with maximum power PS . The received power at user k from b(k) can be calculated by means of (2), which relates the received power to a node and is the result of the transmitted power and the fading of the signal calculated by the Stanford University Interim (SUI) model [ 57 ] This can be expressed as: …”

Section: Network Parametersmentioning

confidence: 99%

“…By correctly assigning the input parameters, it is possible to simulate urban and suburban environments with shading [ 57 ].…”

Section: Network Parametersmentioning

confidence: 99%

Deployment of small cells and a transport infrastructure concurrently for next-generation mobile access networks

et al. 2018

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The exponential growth of mobile traffic means that operators must upgrade their mobile networks to provide higher capacity to final users. A promising alternative is to deploy heterogeneous networks (HetNets) that combine macro Base Stations (BSs) and SmallCells (SCs), although this increases the complexity and cost of the transport (SCs to Fiber Access Point–FAP). Most of the planning strategies outlined in the literature are aimed at reducing the number of SCs and ignore the impact that the transport segment might have on the total cost of network deployment. In this paper, heuristics are used for the joint planning of radio (i.e., SCs) and transport resources (i.e., point-to-point fiber links). These were compared and examined to determine the advantages and disadvantages of each approach, and in some cases, this led to a 50% reduction in total costs, while still creating a non-scalable network.

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Comparative analysis of propagation models in LTE networks with spline interpolation

Cited by 8 publications

References 6 publications

Optimization Methods for Feasible Deployment Planning of Future Mobile Networks

Optimization Methods for Feasible Deployment Planning of Future Mobile Networks

Komparasi dan Optimasi Model Propagasi Pada Sistem Komunikasi Seluler Di Kota Palembang

Deployment of small cells and a transport infrastructure concurrently for next-generation mobile access networks

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