Coverage Planning for LTE system Case Study

Similar documents
LTE RF Planning Training LTE RF Planning, Design, Optimization Training

Performance Comparison Based on SMG 2 Evaluation Reports: WCDMA vs. WB-TDMA/CDMA

Seasonal Pathloss Modeling at 900MHz for OMAN

Proposed Propagation Model for Dehradun Region

Adjustment of Lee Path Loss Model for Suburban Area in Kuala Lumpur-Malaysia

PROPAGATION PATH LOSS IN URBAN AND SUBURBAN AREA

Wireless Communications

Optimization of Base Station Location in 3G Networks using Mads and Fuzzy C-means

Table of Contents. Kocaeli University Computer Engineering Department 2011 Spring Mustafa KIYAR Optimization Theory

Dokumentnamn. Document - Ref PTS-ER-2004:32

COURSE DESCRIPTION LTE RADIO PLANNING AND OPTIMISATION. Format: Classroom. Duration: 5 Day

EELE 5414 Wireless Communications. Chapter 4: Mobile Radio Propagation: Large-Scale Path Loss

Performance of Path Loss Model in 4G Wimax Wireless Communication System in 2390 MHz

EE 577: Wireless and Personal Communications

Path Loss Prediction in Wireless Communication System using Fuzzy Logic

Investigating the Best Radio Propagation Model for 4G - WiMAX Networks Deployment in 2530MHz Band in Sub- Saharan Africa

2015 American Journal of Engineering Research (AJER)

ECE 5325/6325: Wireless Communication Systems Lecture Notes, Fall Link Budgeting. Lecture 7. Today: (1) Link Budgeting

Suburban Area Path loss Propagation Prediction and Optimisation Using Hata Model at 2375MHz

An Investigation on the Use of ITU-R P in IEEE N Path Loss Modelling

AN021: RF MODULES RANGE CALCULATIONS AND TEST

Indian Institute of Management Calcutta. Working Paper Series. WPS No. 764 June 2015

37th Telecommunications Policy Research Conference, Sept. 2009

Empirical Path Loss Models for n Wireless networks at 2.4Ghz in rural regions

EELE 6333: Wireless Commuications

I. INTRODUCTION II. COVERAGE AREA

LTE RF Optimization Training

Performance Evaluation of Channel Propagation Models and Developed Model for Mobile Communication

Pathloss and Link Budget From Physical Propagation to Multi-Path Fading Statistical Characterization of Channels. P r = P t Gr G t L P

Lecture 2: Wireless Propagation Channels

Review of Comparative Analysis of Empirical Propagation model for WiMAX

Measurement of Radio Propagation Path Loss over the Sea for Wireless Multimedia

SoftBank Group Corp. ANNUAL REPORT What about corporate data?

SPECIFICATION. : 2 x 4G/3G/2G MIMO Antenna (698~960MHz, 1710~2170MHz,2300~2700MHz, MHz) MIMO1 antenna MIMO2 antenna

Propagation Path Loss Measurements for Wireless Sensor Networks in Sand and Dust Storms

Path Loss Measurements for a Non-Line-of-Sight Mobile-to-Mobile Environment

Unit 1: The wireless channel

Hata-Okumura Model Computer Analysis for Path Loss Determination at 900MHz for Maiduguri, Nigeria

Optimization of Path Loss Models Based on Signal Level Measurements in 4G LTE Network in Sofia

Computer Engineering and Intelligent Systems ISSN (Paper) ISSN (Online) Vol.4, No.9, 2013

[Ekeocha*, 5(5): May, 2016] ISSN: Impact Factor: 3.785

CDMA2000 Network Planning. cdma university. Student Guide X3

Path Loss Models and Link Budget

IJESRT. Scientific Journal Impact Factor: (ISRA), Impact Factor: 2.114

IJEETC. InternationalJournalof. ElectricalandElectronicEngineering& Telecommunications.

Optimizing the Existing Indoor Propagation Prediction Models

Comparison Between Measured and Predicted Path Loss for Mobile Communication in Malaysia

Edinburgh Research Explorer

EE6604 Personal & Mobile Communications. Week 7. Path Loss Models. Shadowing

ECE 5325/6325: Wireless Communication Systems Lecture Notes, Spring 2013

Radio Path Loss and Penetration Loss. Measurements in and around Homes. and Trees at 5.85 GHz. Mobile and Portable Radio Research Group

Statistical Tuning of Hata Model for 3G Communication Networks at GHz in Porth Harcourt, Nigeria

Path Loss Modeling Based on Field Measurements Using Deployed 3.5 GHz WiMAX Network

Path Loss Model Using Geographic Information System (GIS)

Development of Propagation Path Loss Prediction Model for Mobile Communications Network Deployment in Osogbo, Nigeria

PERFORMANCE ANALYSIS OF INDOOR WLAN MOBILITY

COMPARATIVE ANALYSIS OF PATH LOSS PREDICTION MODELS FOR URBAN MACROCELLULAR ENVIRONMENTS

Performance Evaluation of Hata-Davidson Pathloss Model Tuning Approaches for a Suburban Area

Computer Simulation of Path Loss Characterization of a Wireless Propagation Model in Kwara State, Nigeria

Near Ground Path Loss Prediction for UMTS 2100 MHz Frequency Band Over Propagating Over a Smooth-Earth Terrain

Project: IEEE P Working Group for Wireless Personal Area Networks (WPANs)

Realignment of the / MHz Band To Create a Private Enterprise Broadband Allocation

Mobile and Wireless Compu2ng CITS4419 Week 2: Wireless Communica2on

Volume 4, Number 2, 2018 Pages Jordan Journal of Electrical Engineering ISSN (Print): , ISSN (Online):

INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

EE Large Scale Path Loss Log Normal Shadowing. The Flat Fading Channel

A Novel Hybrid Approach For Path Loss Exponent Estimation In Vanet Application

P. Thomas 1, D. Fisher 1 & F. Sheikh 2. Abstract

Empirical Characterization of Propagation Path Loss and Performance Evaluation for Co-Site Urban Environment

COMPARISON OF RADIO PROPAGATION CHARACTERISTICS AT 700 AND 2,500 MHz PERTAINING TO MACROCELLULAR COVERAGE

EENG473 Mobile Communications Module 3 : Week # (11) Mobile Radio Propagation: Large-Scale Path Loss

Real-Time Path Loss Modelling for a More Robust Wireless Performance

ISSN: Guizhen * et al., 6(11): November, 2017] Impact Factor: 4.116

Wireless communication for Smart Buildings Kortrijk, 07/04/2017

Intelsat Technical Seminar Lease Transmission Plan Program (LST5) 101 Overview. Robert Girard Customer Solutions Engineering

A Measurement-Based Model For The Analysis Of Pathloss In A Given Geographical Area

ECE6604 PERSONAL & MOBILE COMMUNICATIONS. Lecture 3. Interference and Shadow Margins, Handoff Gain, Coverage

SPECIFICATION. Product Name : 4G/3G/2G Cellular Hinged SMA(M) Mount Monopole

Optimization of Empirical Pathloss Models of WiMax at 4.5 GHz Frequency Band

The Wireless Communication Channel. Objectives

ISSN: [Chinedu, Nkwachukwu, Cosmas* et al., 6(5): May, 2017] Impact Factor: 4.116

International Islamic University Malaysia (IIUM), Gombak, Kuala Lumpur, Malaysia

Radio Propagation Modelling

Indoor Measurement And Propagation Prediction Of WLAN At

Link Budget Calculation. Ermanno Pietrosemoli Marco Zennaro

Implementation of Path Loss Model in Wireless Network Anupa Saini 1 MsVarsha Chauhan 2

Multi-beam Antenna Combining for 28 GHz Cellular Link Improvement in Urban Environments

arxiv: v2 [cs.it] 22 Feb 2016

Path Loss at the Exact Location of TV inside Residences using Digital Terrestrial Television Signal at 677 MHz

A Model of Coverage Probability under Shadow Fading

Tuning and Cross Validation of Blomquist-Ladell Model for Pathloss Prediction in the GSM 900 Mhz Frequency Band

Information Memorandum

International Journal of Innovative Research in Science, Engineering and Technology

CSP Algorithm In Predicting And Optimizing The Path Loss Of Wireless Empirical Propagation Models

The SoftBank Group s History 1980s 1990s

ITRAINONLINE MMTK RADIO LINK CALCULATION HANDOUT

PATH LOSS PREDICTION FOR GSM MOBILE NETWORKS FOR URBAN REGION OF ABA, SOUTH-EAST NIGERIA

Application of Artificial Neural Network For Path Loss Prediction In Urban Macrocellular Environment

White Paper: Comparison of Narrowband and Ultra Wideband Channels. January 2008

Analysis of kurtosis-based LOS/NLOS Identification based on indoor MIMO Channel Measurements

Transcription:

Coverage Planning for LTE system Case Study Amer M. Daeri 1, Amer R. Zerek 2 and Mohammed M. Efeturi 3 1 Zawia University. Faculty of Engineering, Computer Engineering Department Zawia Libya Email: amer.daeri@ Zu.edu.ly / ibnjiubair1@yahoo.co.uk 2 Zawia University. Faculty of Engineering Electrical and Electronic Eng. Department Zawia Libya Email: anas_az94@yahoo.co.uk 3 Authority of National Research and Technology Tripoli, Libya Email: melfituri@yahoo.com / mohamedelfituri@gmail.com Abstract: Long Term Evolution (LTE) that provides higher throughputs and lower latencies. LTE brings many technical benefits to cellular networks and improves the spectral efficiency in previous networks, allowing carriers to provide more services. In this paper a through dimensioning analysis has been done through calculation of link budget in order to calculate the coverage for the city of Tripoli in Libya, where a COST321 propagation model was used to do the coverage planning in order to find out the required number of sites for the various parts of the city based on population distribution. From the analysis it was found that the dense urban area needed 48 sites, urban area requires 81 sites and the suburban area sits were found to be 46 sites. KeyWords : Urban LTE, coverage, link budget, propagation model, sepectral efficiency. I. Introduction LTE framework basic objectives is to build up a system that meets high data rate demands, low latency and optimization for packet-domain traffic. LTE system is intended to have a peak data rate of 100 Mbps in DL and up to 50 Mbps in the UL[1]. To improve service provisioning and reduce user/operator costs, LTE must fulfill some key requirements and capability targets which are [2]: Low latency : for both user plane and control plane, with a 5MHz spectrum allocation the latency target is below 5 ms Bandwidth Scalability : different bandwidths can be used depending upon the requirements (1.25 to 20 MHz) Peak Data Rates : 100 Mbps for DL, 50 Mbps for UL 2 to 3 times capacity over existing Release 6 scenarios with HSUPA 2 to 4 times capacity over existing Release 6 scenarios with HSDPA Only Packet Switched Domain support Improved Cell edge performance Inter-working with the existing 2G and 3G systems and non-3gpp systems Optimized for low mobile speed but also support high mobile speeds Reduction of complexity in both system and terminals Ease of migration from existing networks Simplification and minimization of the number of interfaces Network planning requires initially a dimensioning phase that provides the first estimate of the network element count as well as the capacity of those elements. The purpose of dimensioning is to estimate the required number of radio base stations needed to support a specified traffic load in an area [3]. Dimensioning provides the first, quick assessment of the probable wireless network configuration [4]. Dimensioning is a part of the whole planning process, which also includes, detailed planning and optimization of the wireless cellular network. Wireless cellular network dimensioning follows these basic steps: Data/Traffic Analysis that gives an estimate of the traffic to be carried by the system. Coverage estimation is used to determine the coverage area of each base station Capacity evaluation Capacity planning deals with the ability of the network to provide services to the users with a desired level of quality Transport dimensioning Transport dimensioning deals with the dimensioning of interfaces between different network elements.[5]

II. Link Budget The main purpose of link budget in LTE network planning is to use such factors as building penetration loss, feeder loss, antenna gain, and the interference, margin of radio links to calculate all gains and losses that will affect the final cell coverage, To estimate the maximum link loss allowed based on the maximum transmit power of the terminal and enodeb transmit power allocation and coverage radius of a base station can be obtained according to the maximum link loss allowance under a certain propagation model. The radius can be used in subsequent design. The link budget parameters are grouped as follows:- Propagation transmission related parameters, such as the penetration loss which indicates the fading of radio signals from an indoor terminal to a base station due to obstruction by a building, where the building penetration loss ranges from 5 db to 40 db, body loss indicates the loss generated due to signal blocking and absorption when a terminal antenna is close to the body, feeder loss indicates the signal loss caused by various devices that are located on the path of the antenna to the receiver, and background noise[6]. Equipment dependent parameters, such as the transmit power, receiver sensitivity, and antenna gain. LTE specific parameters, such as the pilot power boosting gain, (MIMO) gain, edge coverage rate, repeated coding gain, interference margin, and fast fading margin. System reliability parameters, such as slow fading margin. Specific features that will affect the final path gain [7]. III. Propagation Model The radio propagation model plays a key role in the link budget. Propagation models according to the area and frequency band should be selected and modified if needed. This is necessary for coverage estimation. Every propagation model is different. Some offer conservative high path loss predictions for a section of the spectrum, some offer optimistic low path loss predictions for most of the spectrum. Some extend a previous model to work at higher frequencies. COST231 Hata extends Hata to work beyond 100 to 2000MHz. Figure (1) shows the most common models, by representing the modes as relation between frequency and db. Especially COST231 Figure (1) The most common models. Hata where it appears in blue line. That Cost231- Hata model can be used in macro cells as the propagation model. The basic path loss equation for this COST-231 Hata Model can be expressed as [8]:- PL= Where (f) indicates the working frequency of the system, in MHz. indicates the height of the base station antenna, in m. indicates the height of the terminal antenna, in m. (d) indicates the distance between the terminal and the base station, in km. indicates the terminal gain function. This function is related to the antenna height, working frequency of the terminal and the environment. Parameters equipment affecting the quality of the path include the base station antenna and terminal, transmit power, receiver sensitivity, noise figure, gains MIMO antenna In addition to link budget parameters vary with the base stations antennas and terminals of different vendors. These parameters affect the link budget results and also on the path. IV.Coverage Planning Analysis Tripoli is the capital of Libya and it is considered an over population city compared to the rest of most of the country. Efficient radio network planning is 1

obviously a big challenge here with the optimal utilization of limited resources. In this part of the work, coverage analysis along with link budget preparation and capacity analysis will be performed. Calculations will be made specifically for Tripoli city. As a result, it will be included for a complete Tripoli city radio network planning, the link budget calculations estimate the maximum allowed signal attenuation, called path loss between the mobile and the base station antenna. The maximum path loss allows the maximum cell range to be estimated with a suitable propagation model, such as Cost231 Hata model. The cell range gives the number of base station sites required to cover all Tripoli geographical area. The link budget calculation can also be used to compare the relative coverage of the different systems depending on the assumptions and considerations planning system and less trouble with procedure as these coverage planning and described briefly in the following points. In this point, the spectrum is managed; the available frequency band is chosen to be the frequency band for (LTE) network, the bandwidth, duplex mode SFR and Cyclic Prefix (CP) are specified as:- o System bandwidth is 20 MHz. o Cyclic prefix is chosen to be normal. o Frequency band number 11 and therefore duplex mode is (FDD). o Soft frequency reuse using (SFR 1*3*1). System bandwidth, channel model, (MIMO) scheme, cell edge rate, and the modulation coding scheme are shown in Table (1) for each case the coverage planning, and link budget is calculated for each scenario separately, for both cases, upload and download, these planning procedure steps are:- 1. Calculate Maximum Allowed Path Loss (MAPL) for DL and UL. 2. Calculate the DL and UL cell radiuses by the propagation model equation and the MAPL. 3. Determine the appropriate cell radius by balancing the DL and UL radiuses. 4. Calculate the site coverage area and the required sites number. Maximum Allowed Path Loss has different values for dense urban, urban and suburban UL and DL. So the calculation must be done to every condition and case apart, and from these results the cell radius can be calculated for each case. At the end, the minimum cell radius from UL and DL cell radiuses is chosen for each case. There are three different cell radiuses based on the capacity of area cases, each regions from cases has own cell radius [9]. Calculations Effective Isotropic Radiated Power (EIRP). transmit EIRP:- Table (1) Input parameters for each case Statement Dense urban Urban Suburban minimum required receiver signal strength (MRRSS) Ch. Type UL DL UL DL UL DL MIMO 1 2 2 2 1 2 2 2 1 2 2 2 Cell Edge Rate (kbps) 256 1024 256 1024 256 1024 MCS QPSK QPSK QPSK QPSK QPSK QPSK 3/4 1/2 3/4 1/2 3/4 1/2 Total Gain = Antenna Gain + Other Gains Total Loss = Cable loss + Body Loss + Interference Margin + Other Gains and Losses Extra Gain: Extra Gain =Hard Handoff gain + MIMO Gain + Other Gain extra margin and loss: Extra margin and Loss = Shadow Fading Margin + Penetration Loss + Other Loss.

Cell Radius Site Number Maximum Allowed Path Loss: The number of calculated site for the different area in the city can be seen in fig. (3) MAPL = EIRP - MRRSS + Extra Gain - Extra Margin and Loss Balancing cell radius by using cost231-hata model equations, with choosing the minimum distance between the terminal and the base station for Both types cell radius uplink and downlink. Cell radius is calculated by using equation 1 Balancing the cell radiuses is to choose the smallest cell radius from UL and DL cell radius calculations to ensure the UL and DL power coverage to the required area. The results of the cell radius calculation can be seen in fig. (2) 90 80 70 60 50 40 30 20 10 0 81 48 43 Dens Urban Suburban 2.5 2.21 Type of Area Fig. (2) Cell Radius distribution for Tripoli city The classification of Tripoli city and its suburbs areas are:- 2 1.5 1 0.5 0 0.6 o dense urban 35.6 km 2. o urban 127.4 km 2. o suburban 407.9 km 2. After determining the cell radius for different cases, sites number and sites coverage areas are calculated by the following equations: Site coverage area = Required sites number = 0.9 Dens Urban Suburban Type of Area Fig (3) Site Number distribution V. Conclusion In this paper the coverage range for proposed LTE network for the city of Tripoli was calculated based on the COST 321 Hata propagation model since this model is used the frequency range 100 to 2000 MHz, which includes the frequency band 11 which used for calculation. From the obtained results it can be seen that base station cell radius for the three selected areas were 0.6 km for dense urban, 0.9 km for urban and 2.21 km for suburban. Also the number of needed sites to cover this proposed networks found to be 48 for the dense area, 81 for the urban area and 43 for the suburban area...references 1. Partnership Project Description, Slide set, available at http://www.3gpp.org/about/3gpp.ppt 2. 3GPP TR 25.913, Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E- UTRAN), V7.3.0, 3. Olin, B.; Nyberg, H.; Lundevall, M., A novel approach to WCDMA radio network dimensioning, IEEE 60th Vehicular Technology Conference, vol 5, pp. 3443-3447, Sep. 2004 4. Harri Holma and Antti Toskala, HSDPA / HSUPA for UMTS, High Speed Radio Access for Mobile Communications, John Willey and Sons, 2006

5. Jaana Laiho, Achim Wacher and Tomas Novosad, Radio Network Planning and Optimisation for UMTS, John Willey and Sons, 2002 6. "LTE Radio Coverage Dimensioning" Huawei, 2013 7. Abdul Basit, Syed, "Dimensioning of LTE Network Description of Models and Tool, Coverage andcapacity Estimation of 3GPP Long Term Evolutionradio interface", Master's Thesis, HELSINKI University of Technology, 2009 8. Nafiz ImBin Hamid, Yahia Lawane, Mugumya Twarik Harouna, Nafiu Salele,"Towards an Efficient Radio Network Planning of LTE and Beyond in Densely Populated Urban Areas", International Journal of Computing and Digital Systems, No.2, 2015 9. Xavier, D. and Venes,J., (2014), LTE Radio Network Planning, Statistics Engineering Business Technology [Online]. Available at: http://www.slideshare. net/tharinduwije/lte-radionetwork-planning..