IJEETC. InternationalJournalof. ElectricalandElectronicEngineering& Telecommunications.

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1 IJEETC InternationalJournalof ElectricalandElectronicEngineering& Telecommunications

2 Int. J. Elec&Electr.Eng&Telecoms Ranjeeta Verma and Garima Saini, 2015 Research Paper ISSN Special Issue, Vol. 1, No. 2, July 2015 National Conference on Emerging Trends in Electronics & Communication (ETEC-2015) 2015 IJEETC. All Rights Reserved DIFFERENT PROPAGATION MODELLING TOOLS USED FOR VARIOUS INDOOR AND OUTDOOR SCENARIOS Ranjeeta Verma 1 * and Garima Saini 1 *Corresponding Author: Ranjeeta Verma, ranjeeta.verma.singhal@gmail.com In wireless communication the transmitted signal from the base station to the receiver may reach either through a direct Line of Sight or it may be obstructed due to the presence of vegetation, buildings, and mountains. The Propagation models provide a means to calculate the path loss incurred by the transmitted signal. However the predicted path loss by the exting propagation models different from the actual path loss measured. These propagation models are based on the terrain profiles and data measurements taken in countries different from India. In order to make these models provide actual results in India it needs to make adjustments. For finding the best suitable propagation model for an area various tools are available. Th paper focuses on the various tools available and their features which are used for propagation modelling for indoor and outdoor scenarios for urban, sub urban and rural areas. Keywords: Propagation models, Tools, Drive test INTRODUCTION With the ever increasing demand for mobile phone subscribers there a very strong need for efficient network planning which provides an efficient estimation of the path loss. In cellular communication the obstacles present in between the mobile subscriber and the base transmitter strongly affect the strength of the received signal. The path loss prediction models have a major role in the radio frequency coverage optimization, interference analys and efficient utilization of the available network resources [1]. It very important to estimate the channel charactertics accurately in order to limit the interference to a minimum value. The channels can be grouped into two broad categories indoor channels and outdoor channels. The indoor channels refer to the propagation channels inside the buildings and outdoor channels refer to the propagation channels outside the buildings periphery. The factors 1 NITTTR Chandigarh, India. 49

3 affecting the propagating signals in the two domains are entirely different. The various indoor and outdoor propagation models and the tools used for their optimization and tuning are presented. Indoor channels are different from traditional cellular radio channels in two approaches. One that the dtances covered smaller and second the variations in environment are much larger for small transmitter receiver separations. The propagating signal inside a building influenced by the layout of the building, construction materials and type of building whether a it factory, office, grocery store, sports arena or a home. Indoor propagation takes place due to reflection, scattering, diffraction. Indoor propagation depends on conditions like whether the doors/ windows are open or not, the antenna placed, number of floors in the building. Indoor channels can be categorized as Line of sight (LOS) and Obstructed (OBS) with different degrees of clutter. Outdoor Propagation totally different from indoor propagation. Here the propagating signal may incur different terrain profiles which must be taken into account while estimating the path loss. The terrain profile may vary from a simple curved profile to a highly mountainous profile. The presence of trees, buildings, and other obstacles also must be taken into account. A number of propagation models are available to predict path loss over irregular terrain. All these models aim to predict signal strength at a particular receiving point or in a specific local area but the methods may vary widely in their approach, complexity and accuracy. Channel modeling required to predict path loss associated with the design of cellular network base stations, as th informs the design engineers how much power a transmitter need to radiate so as to service a given cell site [2]. The propagation models are basically used to predict the path loss which vital for planning the placement of base stations and the area of coverage. The path-loss prediction models can be roughly divided into three types, i.e., the empirical, theoretical, and site-specific models [3]. The empirical models are based on a set of equations which are derived from field measurements. The empirical models are based on input parameters which are qualitative and are not specific like dense urban, urban, sub urban and rural. Site-specific models are based on numerical methods such as the ray-tracing method [4], [5] and the Finite- Difference Time-Domain (FDTD) method. Theoretical models are derived physically assuming some ideal conditions, for example, the over-rooftop diffraction model derived using physical optics assuming uniform heights and spacing of buildings [3]. OUTDOOR PROPAGATION MODELS Free Space Model Path loss in Free Space PL FS defines how much strength of signal lost during propagation from transmitter to receiver [11]. It calculated as: PL FS = log 10 (d) + 20 log 10 (f) f frequency in MHz, d dtance between Transmitter and receiver in meters and power in dbm. Okumura Model The Okumura Model [6] an empirical model 50

4 based on extensive measurements made in Japan at several frequencies in the range from MHz (it also extrapolated up to 3000 MHz). Okumura s model one of the most widely used models for signal prediction in urban areas. It applicable for transmitter receiver separation dtances of 1 Km to 100 Km. It used for base stations height in the range from 30 m to 1000 m. The path loss predicted by th model given by the following formula L the median path loss in decibel, LFSL the free space loss in decibel, AMU Median attenuation in decibel, H MG mobile station antenna height gain factor, H BG base station antenna height gain factor and K correction the Correction factor gain (such as type of environment, water surfaces, olated obstacle, etc.) Terrain information can be qualitatively included in the Okumura model. For example, the propagation environments are categorized as open area, quasi- open area, and suburban area [7]. Hata Model Hata model the most popular model that extensively used in Europe and North America [8]. Hata s Equation classified into three models [9, 10]: 1. Rural: open space, no tall trees or building in path. 2. Suburban area: Village Highway scattered with trees and house with some obstacles near the mobile but not very congested. 3. Urban area: Built up city or large town with large building and houses. The Hata model for urban areas formulated as following: For small or medium sized city, and for large cities, L U path loss in urban areas in decibel, h B height of base station antenna in meter, h M height of mobile station antenna in meter, f frequency of transmsion in MHz, C H antenna height correction factor, d Dtance between the base and mobile stations in km. Hata model for suburban areas formulated as, L SU path loss in suburban areas in decibel, L U average path loss in urban areas for small sized city in decibel and f frequency of transmsion MHz Hata model for open areas formulated as: L O path loss in open area in decibel, L U path loss in urban areas for small sized city in decibel, f frequency of transmsion in MHz COST Hata 231 Model It an extended version of Hata model. The proposed model for path loss 51

5 L median path loss in decibel, F frequency of transmsion in MHz, h B Base station antenna effective height meter, d link dtance in km, h R mobile station antenna effective height in meter, a(h R ) mobile station antenna height correction factor as described in the Hata model for urban areas. Th model used for MHz, h R from 1 m to 10 m, h B from 30 m to 200 m and d for 1 km to 20 km. INDOOR PROPAGATION MODELS Log-Dtance Path Loss Model The model predicts the path loss a signal encounters inside a building or densely populated areas over dtance. Log-dtance path loss model formally expressed as: P L the total path loss measured in Decibel the transmitted power in dbm, P Tx the transmitted power in watt. the received power in dbm, P Rx the received power in watt, P L0 the path loss at the reference dtance d 0 in Decibel, d the length of the path, d 0 the reference dtance, usually 1 km, the path loss exponent. Ericsson Multiple Breakpoint Model The model used for measurements in multi storey building. It provides a determintic limit on the range of path loss at a particular dtance. It uses a uniform dtribution to generate path loss values within the maximum and minimum range as a function of dtance. ITU Model Th a radio propagation model that estimates the path loss inside a room or a closed area inside a building build by walls of any form. It applies to frequency range from 900 MHz to 5.2 GHz and in a building with floors 1 to 3. The ITU indoor path loss model formally expressed as:, L the total path loss in decibel, f frequency of transmsion in MHz, and d dtance. In metre, N the dtance power loss coefficient, n the number of floors between the transmitter and receiver, P f (n) the floor loss penetration factor. TOOLS USED FOR OPTIMIZATION OF PROPAGATION MODELS Mentum Cell Planner It supports GSM/GPRS/EDGE, WCDMA with HSPA/EUL and MBMS, WiMAX, and LTE technologies. It can be used for planning and optimization of 2 G, 2.5 G, 3 G, WIMAX and LTE networks. It a commercially available tool and very expensive. TEMS Investigation TEMS Investigation the industry standard tool for troubleshooting, verification, optimization and maintenance of wireless networks. TEMS 52

6 Figure 1: Experimental Set Up for Drive Test Investigation supports all major technologies, making it the ideal testing tool at every stage of the network s life cycle. It collects field measurement data from actual Drive Test. The experimental set up used for conducting the Drive Test shown in Figure 1. TEMS Investigation the only mobile network Testing tool with integrated support for Phone. EDX SignalPro It can be used for planning and optimization of networks for Broadband, LTE, Mobile/Cellular, WiMAX, Mesh, in-building DAS, LMR and more. It can be integrated with Google Earth to vualize the system design. Adjustable parameters present allow adding trees, buildings or other environmental factors to enhance the accuracy of the path loss. It can be used for indoor, outdoor and indoor/ outdoor system design. SignalPro generates study results in KML, KMZ, MIG, and MID/MIF formats for use with Google Earth, MapInfo and can obtain in print form. and step-by-step transition to virtualized network functions. ASSET Th multi technology software solution facilitates planning of wireless networks including GSM, UMTS, LTE, Wi Fi, GPRS/ EDGE, AMPS, TDMA, TACS, PCS, PMR/ TETRA/iDEN, HSDPA, HSUPA, HSPA+, CDMA2000, EV-DO, DVB-H, Fixed WiMAX and Mobile WiMAX. Asset when combined with ArrayWizard can produce nationwide coverage plots and stattical reports which can also be printed. It also an expensive commercially available tool. G-NetTrack It a wireless network monitor and a drive test tool for Android OS devices. It allows monitoring and logging of mobile network parameters without using specialized equipment. It can be used to determine RXLEVEL that received signal level in decibels, data throughput, blocked calls, and dropped calls. It provides the Longitude and Latitude of the current location of mobile in decimal format, the Uplink and downlink data transfer speed in kbps. Its main features are Figure 2: Details of a Particular Cell NetAct Optimizer It provides measurement based optimization for GSM, WCDMA and LTE. With NetAct, Nokia Networks provides sustainable and pragmatic support for implementing hybrid networks. Th leads to very low entry costs 53

7 that it measures the wireless network parameters and also dplays them on Map View. The measured data by GNetTrack can be analysed by other tools like G-NetDiag, and G- NetEarth. The plots which are obtained from the screen are shown in Figure 2 and Figure 3. Figure 2 shows the details of a particular cell like Cell name, Cell Id, Azimuth, Technology and LAC (Location Area Code) of cell. Figure 3 shows the details of the parameters captured by the Logfile during a Drive Test. The highlighted path the path route followed during the Drive Test. It also shows the received signal strength with different colours. The G-NetTrack monitors the signal strength of at least six neighbouring cells and gives the details on the screen. It switches to the cell with the strongest signal strength at any moment of time. It has a MAP Tab which can be used to take geographical view of the measurements taken and mobile network base stations. The measurements taken in the form of Log files can be stored and exported in kml format which can be used for post processing. The other Figure 3: Details of a Logfile During a Drive Test Figure 4: Useful Information on Mobile Screen useful information which can be obtained from the mobile screen shown in Figure 4. The major attractive feature of th tool that it available for free. It can be freely downloaded on an Android phone and can be used for many wireless network related studies. CONCLUSION The propagation models are of keen importance for finding the path loss between the transmitter and receiver. They are helpful to calculate the path losses before their actual implementation in the field. Various tools are present for network planning and optimization but one selected depending upon the availability of funds, time, and resources. The tools dcussed have features which can be selected depending on the application and 54

8 area of use. Thus propagation models provide a solution to the radio network engineers in planning and optimization by using the various tools as dcussed in the paper. REFERENCES 1. Alotaibi F D and Adel A Ali (2006), TETRA Outdoor Large-Scale Received Signal Prediction Model in Riyadh City- Saudi Arabia, IEEE W ireless and Microwave Technology Conference (WAMICON), December, pp. 4-5, USA. 2. Gupreet Singh Bola and Gurpreet Singh Saini (2013), Path Loss Measurement and Estimation Using Different Empirical Models for WiMax in Urban Area, International Journal of Scientific & Engineering Research, Vol. 4, No Liang G and Bertoni H L (1998), A New Approach to 3-D Ray Tracing for Propagation Prediction in Cities, IEEE Trans. Antennas Propagat., Vol. 46, June, pp Magdy F Iskander and Zhengqing Yun (2002), Propagation Prediction Models for Wireless Communication Systems, IEEE Transactions on Microwave Theory and Techniques, Vol. 50, No. 3, pp Mardeni R and Kwan K F (2010), Optimization of Hata Propagation Prediction Model in Suburban Area in Malaysia, Progress in Electromagnetics Research C, Vol. 13, pp Micheal D Y (2000), Foundation of Mobile Radio Engineering, CRC Press Inc. 7. Okumura Y, Ohmori E, Kawano T and Fukuda K (1968), Field Strength Variability in VHF and UHF Land Mobile Service, Rev. Elect. Comm. Lab., Vol. 16, Nos. 9-10, pp Rappaport T S (2002), Wireless Communications Principles and Practice, Prentice Hall. 9. Segun Isaiah Popoola and Olasunkanmi Fatai Oseni (2014), Performance Evaluation of Radio Propagation Models on GSM Network in Urban Area of Lagos, Nigeria, International Journal of Scientific & Engineering Research, Vol. 5, June, pp Seidel S Y and Rappaport T S (1994), Site-Specific Propagation Prediction for W ireless in-building Personal Communication System Design, IEEE Trans. Veh. Technol., Vol. 43, November, pp

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