How user throughput depends on the traffic demand in large cellular networks

Little's law allows to express the mean user throughput in any region of the network as the ratio of the mean traffic demand to the steady-state mean number of users in this region. Corresponding statistics are usually collected in operational networks for each cell. Using ergodic arguments and Palm theoretic formalism, we show that the global mean user throughput in the network is equal to the ratio of these two means in the steady state of the "typical cell". Here, both means account for double averaging: over time and network geometry, and can be related to the per-surface traffic demand, base-station density and the spatial distribution of the SINR. This latter accounts for network irregularities, shadowing and idling cells via cell-load equations. We validate our approach comparing analytical and simulation results for Poisson network model to real-network cell-measurements

Prediction of User Throughput in the Mobile Network Along the Motorway and Trunk Road

The main goal of this research is to create a machine learning model for predicting user throughput in the mobile 4G network of the network provider M:tel Banja Luka, Bosnia and Herzegovina. The geographical area of the research is limited to the section of Motorway "9th January" (M9J) Banja Luka - Doboj, between the node Johovac and the town of Prnjavor (P-J section), and the area of the section of trunk road M17, between the node Johovac and the town of Doboj (J-D section). Based on the set of collected data, several models based on machine learning techniques were trained and tested together with the application of the Correlation-based Feature Selection (CFS) method to reduce the space of input variables. The test results showed that the models based on k-Nearest Neighbors (k-NN) have the lowest relative prediction error, for both sections, while the model created for the trunk road section has significantly better performance

Wireless networks appear Poissonian due to strong shadowing

Geographic locations of cellular base stations sometimes can be well fitted with spatial homogeneous Poisson point processes. In this paper we make a complementary observation: In the presence of the log-normal shadowing of sufficiently high variance, the statistics of the propagation loss of a single user with respect to different network stations are invariant with respect to their geographic positioning, whether regular or not, for a wide class of empirically homogeneous networks. Even in perfectly hexagonal case they appear as though they were realized in a Poisson network model, i.e., form an inhomogeneous Poisson point process on the positive half-line with a power-law density characterized by the path-loss exponent. At the same time, the conditional distances to the corresponding base stations, given their observed propagation losses, become independent and log-normally distributed, which can be seen as a decoupling between the real and model geometry. The result applies also to Suzuki (Rayleigh-log-normal) propagation model. We use Kolmogorov-Smirnov test to empirically study the quality of the Poisson approximation and use it to build a linear-regression method for the statistical estimation of the value of the path-loss exponent

ANALISIS PERENCANAAN JARINGAN HETEROGEN LTE-ADVANCED SMALL CELL FREKUENSI 1800 MHz STUDI KASUS KOTA BANDUNG

LTE-Advanced merupakan teknologi berbasis IP yang dikeluarkan oleh 3GPP sebagai standar untuk komunikasi data nirkabel berkecepatan tinggi. Mobilitas user yang tinggi, persebaran user yang tidak merata, peningkatan coverage, dan cell throughput menjadi tantangan yang harus dihadapi oleh operator dalam merencanakan jaringan LTE-Advanced di suatu daerah. Salah satu cara untuk menghadapi tantangan tersebut adalah dengan melakukan perencanaan jaringan heterogen. Jaringan heterogen merupakan suatu penerapan suatu jaringan seluler dengan meletakkan small cell di dalam macro cell. Dalam tugas akhir ini dilakukan suatu perencanaan jaringan heterogen LTE-Advanced small cells menggunakan frekuensi 1800 MHz di Kota Bandung. Analisis dilakukan dengan meninjau tiga sel yang mewakili daerah sub urban, urban, dan dense urban dengan jumlah user tertinggi menggunakan dua skenario : sel dengan penambahan small cell Wi-Fi 802.11n pada frekuensi 2.4 GHz serta cell tanpa penambahan small cell Wi-Fi 802.11n sebagai pembanding performansi perencanaan jaringan heterogen. Perencanaan dilakukan menggunakan perhitungan berdasarkan pendekatan coverage planning dan capacity planning. Dalam perencanaan jaringan heterogen daerah sub urban mampu dilayani oleh 4 sel, daerah urban mampu dilayani oleh 6 sel, dan daerah dense urban mampu dilayani oleh 9 sel. Implementasi jaringan heterogen mampu menghasilkan nilai RSRP yang baik dengan nilai RSRP ? -100 dBm untuk 90% luas area di seluruh daerah tinjauan. Jaringan heterogen menghasilkan peningkatan nilai throughput sebesar 25 % sehingga mampu meningkatkan kapasitas jaringan yang diakibatkan oleh pengalihan trafik dari jaringan LTE-Advanced ke jaringan Wi-Fi 802.11n, sehingga jaringan heterogen dapat menangani jumlah user yang semakin meningkat. Sementara dari hasil simulasi yang dilakukan, performansi maksimal terjadi saat user saat kondisi diam dengan presentasi user connected 99%. Dari hasil tersebut maka penggunaan small cell Wi-Fi 802.11n pada jaringan heterogen LTE-Advanced layak untuk diimplementasikan Kata kunci : LTE-Advanced, Jaringan Heterogen, Small cell, Throughpu

What frequency bandwidth to run cellular network in a given country? - a downlink dimensioning problem

We propose an analytic approach to the frequency bandwidth dimensioning problem, faced by cellular network operators who deploy/upgrade their networks in various geographical regions (countries) with an inhomogeneous urbanization. We present a model allowing one to capture fundamental relations between users' quality of service parameters (mean downlink throughput), traffic demand, the density of base station deployment, and the available frequency bandwidth. These relations depend on the applied cellular technology (3G or 4G impacting user peak bit-rate) and on the path-loss characteristics observed in different (urban, sub-urban and rural) areas. We observe that if the distance between base stations is kept inversely proportional to the distance coefficient of the path-loss function, then the performance of the typical cells of these different areas is similar when serving the same (per-cell) traffic demand. In this case, the frequency bandwidth dimensioning problem can be solved uniformly across the country applying the mean cell approach proposed in [Blaszczyszyn et al. WiOpt2014] http://dx.doi.org/10.1109/WIOPT.2014.6850355 . We validate our approach by comparing the analytical results to measurements in operational networks in various geographical zones of different countries

Statistical learning of geometric characteristics of wireless networks

International audienceMotivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learning this function from the examples of marked point patterns in order to predict the marks of new point patterns. To approximate (interpolate) the marking function, in our baseline approach, we build a statistical regression model of the marks with respect some local point distance representation. In a more advanced approach, we use a global data representation via the scattering moments of random measures, which build informative and stable to deformations data representation, already proven useful in image analysis and related application domains. In this case, the regression of the scattering moments of the marked point patterns with respect to the non-marked ones is combined with the numerical solution of the inverse problem, where the marks are recovered from the estimated scattering moments. Considering some simple, generic marks, often appearing in the modeling of wireless networks, such as the shot-noise values, nearest neighbour distance, and some characteristics of the Voronoi cells, we show that the scattering moments can capture similar geometry information as the baseline approach, and can reach even better performance, especially for non-local marking functions. Our results motivate further development of statistical learning tools for stochastic geometry and analysis of wireless networks, in particular to predict cell loads in cellular networks from the locations of base stations and traffic demand