EDGE Downlink Throughput Performance

In a Forest Environment

MALEK SRAJ

Master’s Degree ProjectStockholm, Sweden 2006

EDGE Downlink Throughput Performance

In a Forest Environment

MALEK SRAJ

Master’s Degree ProjectStockholm, Sweden 2006

COS/RCS 2006-2

KTH, Communication SystemsSE-164 40 Kista

SWEDEN

c© Malek Sraj, Februari 2006

Tryck: Tryckeriet Ladan AB

Master Thesis

EDGE Downlink Throughput Performance in

a Forest Environment

The Royal Institute of Technology

Radio Communication Systems Lab

Author: Malek Sraj

Advisor: Mats Nilson

Examiner: Jens Zander

February 10, 2006

Abstract

Mobile Telephony has witnessed considerable growth over the past fewyears. Since the dawn of the first analogue systems, the volume ofsubscribers was constantly increasing.

In recent years GSM networks have witnessed an unparalleledgrowth and today they are available in almost all countries and regionsaround the globe. Although EDGE technology has been available sincesome time, it has not witnessed considerable spread among operators.However, as a step towards complying with regulators, operators needto cover rural areas with 3G services. From hence, EDGE was thetechnology of choice since it provides coverage and high data rates ina scenario where the population is scarce and high throughput is wanted.

This thesis investigates the throughput coverage performance of re-cently deployed EDGE networks in rural forest areas. Thereby providingan insight as to what bit rates to expect on the downlink in a scenariowhich is to be carried out in the 3GSM rollout plan for several operators.

A case study is carried out in the forest areas around Jonkopingwhere EDGE capabilities are being deployed. A selection of typical for-est environments and different sites have been chosen so as to provide asample of forest areas across Sweden. Measurements in those test areashave been carried out and later extracted and analyzed. The results re-veal that the throughput in those areas was below expectations and thatthe main reason lied in the high block error rate experienced even whenthe signal to interference-noise ratio is high. The MCS selection was alsonot the same as anticipated by simulations, due to the high Block ErrorRate which forced the base station to choose lower Modulation CodingSchemes than the ones perferred at a specific signal level. In the enda rough expectation of the throughput coverage is concluded from theresults acquired by this thesis and those provided by a thesis carried inparallel, which involved a parallel study of the propagation model in thesame areas.

Acknowledgements

A teacher of mine once told me that whoever you meet and whateveryou do becomes part of who you are and you become part of them. Inthese words I can summarize my experience in Sweden and the loveand appreciation I have for my friends. I would also like to extendmy deepest appreciation and my sincerest love to my family, who werealways there..for better and for worse. It would not have been possibleif it were not for my father’s struggle to educate me, my sister, andmy brothers... It would not have been possible if it were not for mymother’s patience and support throughout the years, while my fatherwas working abroad.

This thesis would not have been accomplished if it were not for thesupport of many people. I would like from hence, to extend my specialgratitude to my advisor, Mats Nilson, for his discussions and guidanceduring the course of the thesis. I would also like to thank my sponsorsat Skogforsk, represented by Bertil Liden, for accepting me to carryout this thesis and lending me their help. Also appreciation to MagnusSommer and Jan Loven from Telia for their help in testing their network.Also I would like to thank Prof. Jens Zander for agreeing to be myexaminer, fine tuning my thesis, and providing a fruitful atmosphere at”Wireless@KTH”. Finally special thanks to Prof. Johan Montelius forproviding the tools necessary to carry out the thesis.

Contents

Table of Contents iv

List of Figures v

1 Introduction 11.1 A Brief History of Mobile Telephony . . . . . . . . . . . . 11.2 Positioning EDGE in the 3G Rollout Plan . . . . . . . . . 21.3 Wireless and The Forest Industry . . . . . . . . . . . . . . 31.4 A Practical Case Study . . . . . . . . . . . . . . . . . . . 41.5 Problem Definition . . . . . . . . . . . . . . . . . . . . . . 41.6 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Technical Background 92.1 General Packet Radio Services Components . . . . . . . . 92.2 Enhanced Data Rates for GSM Evolution . . . . . . . . . 10

2.2.1 Physical Layer . . . . . . . . . . . . . . . . . . . . 102.2.2 RLC/MAC Layers . . . . . . . . . . . . . . . . . . 11

2.3 TCP/IP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.3.1 Slow Start and Congestion Avoidance . . . . . . . 142.3.2 Fast Retransmit and Fast Recovery . . . . . . . . 15

3 Data Collection and Analysis 163.1 Forest Environment Overview . . . . . . . . . . . . . . . . 163.2 EDGE Cells Overview . . . . . . . . . . . . . . . . . . . . 193.3 Signal Quality . . . . . . . . . . . . . . . . . . . . . . . . . 203.4 Measurement Procedure . . . . . . . . . . . . . . . . . . . 223.5 Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.5.1 TEMS . . . . . . . . . . . . . . . . . . . . . . . . . 233.5.2 TCP Settings . . . . . . . . . . . . . . . . . . . . . 25

3.6 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 253.6.1 Logged Data Specifics . . . . . . . . . . . . . . . . 263.6.2 Data Processing . . . . . . . . . . . . . . . . . . . 27

4 Results 304.1 Throughput and Distance . . . . . . . . . . . . . . . . . . 304.2 Throughput, SINR, and RXLEV . . . . . . . . . . . . . . 324.3 BLER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.4 MCS Utilization . . . . . . . . . . . . . . . . . . . . . . . 384.5 Sample-Cell Coverage Anticipation . . . . . . . . . . . . . 39

5 Conclusion 42

6 Future Work 44

Bibliography 47

List of Figures

2.1 Architecture top view of EDGE/GPRS. . . . . . . . . . . 102.2 8-PSK constellation. . . . . . . . . . . . . . . . . . . . . . 102.3 Segmentation procedure of an LLC frame. . . . . . . . . . 122.4 Payload relationship for MCS belonging to family B. . . . 13

3.1 Tree Type Distribution . . . . . . . . . . . . . . . . . . . . 173.2 Trunk Width Distribution . . . . . . . . . . . . . . . . . . 183.3 Tree Age Distribution . . . . . . . . . . . . . . . . . . . . 183.4 Antenna pattern Type-1 used at Sites 1,2, and 3. . . . . . 203.5 Antenna pattern Type-2 used at Site 4, vertical polarization. 213.6 Data Analysis Relationships. . . . . . . . . . . . . . . . . 26

4.1 Throughput vs. Distance . . . . . . . . . . . . . . . . . . 314.2 Throughput measured against the perceived SINR and

RXLEV at each site. . . . . . . . . . . . . . . . . . . . . . 334.3 Mean throughput of all sites measured against the per-

ceived SINR and RXLEV. . . . . . . . . . . . . . . . . . . 344.4 BLER of all sites measured against the perceived SINR

and RXLEV. . . . . . . . . . . . . . . . . . . . . . . . . . 364.5 Mean BLER of all sites measured against the perceived

SINR and RXLEV. . . . . . . . . . . . . . . . . . . . . . . 374.6 Modulation Coding Scheme (MCS) distribution over

SINR and RXLEV. . . . . . . . . . . . . . . . . . . . . . . 394.7 RXLEV coverage in a cell with radius of 5 Km. . . . . . . 41

Acronyms

3G Third Generation

3GPP Third Generation Partnership Project

3GSM Third Generation Global System for Mobile

ACK Acknowledgment

AMPS American Mobile Phone System

ARQ Automatic Repeat Request

BLE Block Error

BLER Block Error Rate

BSS Base Station Subsystem

BSSGP Base Station System GPRS Protocol

BTS Base Transceiver Station

CIR Carrier to Interference Ratio

CW Congestion Window

EDGE Enhanced Data Rates for GSM Evolution

ETSI European Telecommunication Standards Institute

FTP File Transfer Protocol

GERAN GSM EDGE Radio Access Network

GGSN Gateway GPRS Support Node

GMSK Gaussian Minimum Shift Keying

GPRS General Packet Radio Service

GSM Global System for Mobile

HTTP Hyper Text Transfer Protocol

IP Internet Protocol

IR Incremental Redundancy

LA Link Adaptation

LLC Logical Link Control

LQC Link Quality Control

MAC Medium Access Control

MCS Modulation Coding Scheme

MS Mobile Station

MSC Mobile Switching Center

MSS Maximum Segment Size

NMT Nordic Mobile Telephony

PDC Personal Digital Cellular

PDCH Packet Data Channel

PDU Packet Data Unit

PSK Phase Shift Keying

RLC Radio Link Control

RTT Round Trip Time

SGSN Serving GPRS Support Node

SINR Signal to Interference and Noise Ratio

SNR Signal to Noise Ratio

SST Slow Start Threshold

TACS Total Access Cellular System

TBF Temporary Block Flow

TCP Transmission Control Protocol

TDMA Time Division Multiple Access

TS Time Slots

UDP Universal Datagram Protocol

VLR Visitor Location Register

WCDMA Wideband Code Division Multiple Access

Chapter 1Introduction

According to the statistics [1], there are 620 live GSM networksspanning 210 countries and serving more than 1.5 billion subscribers.The increased demand for packet data services has pushed the wirelessindustry to seek new technologies that would satisfy this growingdemand. A fruit of this search was GPRS (General Packet RadioService), which offers IP service for GSM networks.

GPRS can provide a throughput of 115 Kbps, almost the sameeffective access speed of a modem, it was the second milestone along theevolution path towards UMTS. The next natural step of this technologywas EDGE (Enhanced Data Rates for Global Evolution), today itis an official 3G technology, which is a powerful enhancement to theGSM/GPRS networks. EDGE is merely a software enhancement overGPRS and from hence it is also referred to as EGPRS (EnhancedGPRS), allowing high spectral efficiency and a peak data rate of473.6 Kbps. Today almost 130 GSM operators are using EDGEtechnology, 51 of which are offering commercial services. Nokia expectsthe number of subscribers to rise up to 200 million by the end of 2005and reach above 700 million by 2010 [1].

To have better understanding of the answer sought by this thesis,some background information and a brief history of mobile telephony isnecessary. This chapter aims at presenting the problem to be investi-gated as well as providing related background information.

1.1 A Brief History of Mobile Telephony

The first generation of cellular systems to come to existence offered widearea coverage for voice services based on analogue modulation. Suchsystems are the American Mobile Phone System (AMPS), the TotalAccess Cellular System (TACS), and the Nordic Mobile Telephonysystem (NMT). The capacity of these systems was not designed to cope

1.2 Positioning EDGE in the 3G Rollout Plan 2

with the increasing popularity of the technology. The main problemthat limited the capacity in such systems was due to the sensitivity ofthe analogue signal to interference. The reuse of the radio resources waslimited and the frequency channels could not be repeated very often,thus each base station would be limited to a few channels and thus lowcapacity.

With the introduction of digital modulation it was possible to haveinterference robust signals, which enhanced the capacity of the cellularsystem. Such second generation systems are the Global System forMobile (GSM) and the Personal Digital Cellular (PDC) system.

As the mobile technology was evolving, the usage of Internet serviceswas booming, as a result the idea of merging these technologies seemedextremely attractive. However the transmission pattern of the Internetservices rendered a need for packet switched bearer services instead ofthe circuit switched ones in the second generation systems. As a resultGPRS was developed to serve the purpose of packet based transmissions.

However, capacity and service level were still limited, a result ofwhich current third generation systems are currently being researched.In order to comply with the mentioned demands, diversity techniquesand adaptive resource management schemes were introduced. Upgradeshave been made to second generation systems such as EDGE, which isto provide third generation capacity and service capabilities.

1.2 Positioning EDGE in the 3G Rollout Plan

EDGE technology has been available since some time; however, onlyover the past two years has it become a matter of strategic thinkingto the operators. Today EDGE is being placed complementary to3GSM, deploying 3GSM (which is 3G based on UMTS) in dense urbanareas and EDGE in rural and less populated areas. Such a methodof deployment provides a cost effective solution to deliver high speedservices. The fact that 3GSM would be deployed in the 1900 MHzband tends to make it a less attractive solution for high speed datain rural areas due to the need for more sites as higher frequenciestend to attenuate faster. EDGE, unlike 3GSM, can be deployed in the900 MHz band and still provide high speed data. Also there is noneed to deploy 3GSM which will have higher capacity in areas withlow population density. The most significant factors that would makean operator opt for such a rollout strategy will mainly depend on thedemand for high data rate services and the regulatory pressure faced.Another perspective on this case can be summarized in what MartaMunoz Mendez-Villamil, an analyst with Ovum said as an explanationfor the increased interest in EDGE.

1.3 Wireless and The Forest Industry 3

”EDGE has been neglected in the past. The general view of theindustry was that GPRS was going to be a huge success and thenimmediately afterwards it was going to start moving on to 3GSM andall the operators would be offering these high data rates. However,the success of GPRS somehow has not turned out to be as good as weexpected and suddenly the operators and vendors have realized thatit will be some time before 3GSM starts to get into the market to thepoint where it is delivering proper services. So, in the meantime, Ithink a number of operators have turned their eyes at EDGE and theyare beginning to look at EDGE as an intermediate technology to offerhigher data rates to their customers until 3GSM comes along.” [2].

In [3] the author points out that EDGE is not just about speed.The GERAN network is standardized to be compatible with WCDMAarchitecture. Thus EDGE is not simply a GPRS enhancement but itis also a complement to future WCDMA deployment where EDGE and3GSM are designed to coexist. Also, the typical combination scenario ofEDGE and 3GSM results in 50 % reduction in CAPEX compared to anationwide deployment of a 3GSM network.

1.3 Wireless and The Forest Industry

The wireless boom has opened many opportunities for an increasingprofit through Wireless E-Business applications. These types of ap-plications have been introduced to the forest industry in Finland andSweden. The real time information available for large planning andoptimization process for cutting down and collecting wood improvedthe overall efficiency of wood procurement.

An increased margin of profit as well as a reduction in man powercosts has been considerable [4]. The functions of these applications aremainly the just-in-time delivery of raw material to production sites andthe initial planning of routes that harvesters and gatherers would take.After having optimized routes, this routing information is sent to theharvester in advance and it can be accessed during the harvest.

Measurements of wood quality and quantity are communicated backto the main office by the harvester. The harvesting information is sentwirelessly to forwarders that transport the wood to roadside stocks. Themain office is thus able to keep track of the amount of wood available ateach roadside stock as well as their location. Another route optimizationis performed for transport from the roadside stocks to the reception sites.

The current applications typically running on GSM/GPRS wherepossible or nationwide on MobiTex, which is a wireless packet switching

1.4 A Practical Case Study 4

technology that operates in the 80 MHz band. The wireless compo-nent has handed a remarkable efficiency enhancement via the real timeplanning, execution and optimization of the overall wood procurementoperation.

1.4 A Practical Case Study

NMT450 has a significantly larger coverage than GSM in Sweden. Teliais in the progress of increasing GSM coverage to meet the coveragedemand when NMT450 will be closed down in 2 years. In addition tothe promised GSM coverage increase, GSM/EDGE for higher data rateswill also be implemented. Telia is also carrying out its 3G rollout planin a similar fashion as described earlier. EDGE is to be deployed evenin scarcely populated and rural areas while 3G services are to compriseurban and densely populated areas.

The wood procurement process in Swedish forestry operates morethan 100, 000 different harvesting sites each year. Access to Internetand e-mail are essential services for the future in the working-places,in harvesters, forwarders and trucks. Reports on production from theharvesters and forwarders sent at least every day in order to update thestocks of round wood at roadside are crucial for the efficient managingof the transports by trucks and railroad.

As a consequence the demand for packet switched services withacceptable bit rates is present and is represented by the wood procure-ment companies in Sweden. Thus, Telia has found the demand it waslooking for and found the opportunity to gracefully comply with theregulatory conditions associated with the licenses.

In co-operation with other KTH exam thesis projects theGSM/EDGE data rate and coverage performance for users in vehiclesused by the forest industry shall be investigated and optimized. Thetypes of vehicles involved are harvesters, forwarders, trucks and ordi-nary cars. In order to judge expected performance, a desk top analysisof the colleted data and the path loss model from Part 1 shall be usedto have a moderate expectation of the coverage. The main part of theproject will consist of field trials to verify the EDGE service in a relevantenvironment for the forest industry. A proper formulation of the thesisobjective is discussed in the next section.

1.5 Problem Definition

EDGE research has mainly been done by simulating the protocol stackand by using a propagation model recommended by ETSI. They weremeant to provide an expectation of the possible performance under

1.5 Problem Definition 5

different conditions. Different means of simulation may provide differentresults or may even assume a simplified model at other layers than theone studied. Currently EDGE networks are being deployed in ruralareas as stated before and little is known about the actual performanceof these networks in terms of throughput.

Simulations try to describe the operation as close as possible to areal life scenario, they are also time efficient when it comes to largescale assessment of a certain model. Although real-time measurementsare more indicative of the real operation they tend to be rigorous anda huge amount of data will have to be gathered to have as clear apicture. Thus the latter option would require more time and resources.The results obtained from both methods should enable having morereliable results than when using only one of the methods. Previoussimulations have been carried out in the literature for internet accessand throughput performance at the system level and at the physicallayers. Also the results of the simulations were used as input to othersimulations, such as the expected throughput or block error probabilitymapped to the signal to interference ratio.

This thesis has the objective of providing a view of what throughputperformance to expect out of EDGE by carrying out measurementson live networks. There has been claims that EDGE can provideupto 200 Kbps over most of the cell area. This throughput maynot be possible in a forest with slow mobile users. Operators do notpossess much information on what would be the performance in suchenvironments since mainly drive tests are carried out on roads withemphasis on population coverage. Different reasons may influence thethroughput, such as channel conditions, errors in channel estimation,and delays due to the packet handling protocols. These can be studiedat lower levels in the light of the results obtained. It also does notattempt to explain the specific reasons behind the obtained results,however based on previous research it will attempt to explain thepossible reasons. Thus it serves as a framework for a more detailedresearch at either the network protocols or radio interfaces. The testswill verify the bit rate capabilities, excluding thorough capacity issues,in recently deployed networks which have EDGE enabled. The test siteswill be those deployed in rural areas, since the demand lies within theforest industry, they will be tested within a forest environment.

Thereafter, the question that this thesis attempts to answer is:”What downlink bit rates and coverage can be expected of EDGE in aforest dense environment?”. The answer will be provided by conductinga case study on the deployed EDGE network in Sweden. Some sitesshall be selected to carry out a set of measurements such that these sitesreflect most of Sweden’s forests and EDGE networks in those areas. The

1.6 Previous Work 6

throughput shall not only be considered in respect to distance but alsoin respect to measured SINR and RXLEV values. These values wouldhelp in anticipating the throughput coverage when the distribution foreach is known in a cell. The results will be analyzed later to provideinformation that would allow answering the above question. Also BLERand MCS performance shall be recorded in order to have a clearer ideaof the operation of EDGE in the light of the throughput measured.

Uplink throughput is not going to be tested, despite its relevance tothe question on coverage, due to the resource and time limitations onan exam thesis. Since the cellular design in rural areas involves a largecell radius with no frequency hopping, the user’s moving at slow speedsare expected to experience performance below optimum. The expectedperformance is to be below the forecasted throughput for EDGE, dueto the fact that with a large cell radius and slow mobility in forests,the user maybe located at unfavorable conditions for a long time. Thiswould cause a degradation in signal quality and thus throughput, wherethe maximum throughput possible with EDGE may not be feasible atall times. The next section shall discuss what previous work has beendone and compare it to the work done in this thesis.

1.6 Previous Work

This thesis attempts, as explained above, to try to examine thethroughput of EDGE after deployment in the GSM networks. Its resultswill be of interest to other operators in the process of deploying EDGEin similar areas. Previous work has mainly been focused on studyingEDGE performance by the use of simulations and a combination ofmeasurement based on simulation results.

Although it has been explained that the system level performanceis not a problem to be studied herein. It should be mentioned thatprevious simulations carried out in the literature tended to examinethe impact of increased load on performance perceived by the users.The main problem of increased loads is the increased RTT (RoundTrip Time) experienced by users as a result of queuing as well as theincreased interference on the uplink. Such examination can be foundfor GPRS and EDGE in [5], [6], [7], [8], [9].

In [10] the authors have simulated EDGE in attempt to compareit against the throughput performance of other technologies. Thesesimulations have been done for different environments and the resultshave shown the effect of each type on the throughput of EDGE asdistance increases. Although the results of throughput against distancehas not been provided in the article for other than urban cases, themean throughput for a rural environment at 3Km/Hr was found to be

1.6 Previous Work 7

around 46.3 Kbps per time slot. However, the rural model provided byETSI is not specific to a forest dense environment. It is possible thatthroughput within such an environment may differ significantly. Alsomind that the simulation took into account overall network performancewhich would include cell reselection and other users sharing resources.

During the early development phases of EDGE, Nokia has conductedintensive simulations for the purpose of investigating the throughputthat EDGE would provide given the enhancements introduced to GPRS.As such, some throughput and BLER graphs in function of CIR or SNRhave been generated as in [11]. Using the results of the simulations,Nokia conducted a measurement campaign [12] that aimed at collectingsignal level measurements in an urban city followed by calculating theinterference; thereafter, mapping it to the corresponding throughputfrom the simulated results. This showed that users can achieve ratesclose to the theoretical values; however, it was considered that LA/IRwas perfect and that the throughput is influenced only by the CIR,which has a predefined BLER distribution. Nokia did not have theoption at the time to test for deployed EDGE networks and the drivetests they did were in an urban environment.

There has not been previous studies of EDGE throughput per-formance after deployment, the study was conducted by carryingout simulations away from a real planned network. Today EDGEis being deployed in rural forests using the existing GSM plannednetwork. There exists no knowledge of how would EDGE perform insuch a scenario. As such this work shall carry out measurements thatwould allow interested parties in evaluating EDGE services in thoseareas. The throughput against signal quality would help operatorsin forecasting the coverage when they have knowledge of the distri-bution of the signal quality in a cell. By including BLER and MCSselection, the measurements can also help in supplying an estimate ofthe possible maximum rates attainable under the current network status.

Differences from previous work include the following:1- The deployment of EDGE would allow the testing of the technologyin rural forest dense areas, where it is likely to be deployed. Thethroughput as such would be measured in terms of data received overtime and not based on CIR graphs.2-Also any particular effect of the environment would be reflected inthe throughput, recorded effective BLERs, and MCS selection process.3- The work is not concerned with capacity related problems and doesnot examine the overall network performance, i.e. it is a coverage studyof EDGE.4- The use of available measurement tools to measure deployed EDGEnetworks in relevant areas as discussed and not based on previous

1.6 Previous Work 8

simulation results and CIR measurements only.

Together with simulations that have been performed it would supplya feedback and a better expectation of this technology’s performance.

Chapter 2Technical Background

This chapter gives an overview of the GSM/EDGE Radio Access Net-work. Some basic architecture and service features will be first presentedfollowed by some details on the key-elements for enhancing data ratescompared to standard GPRS.

2.1 General Packet Radio Services Components

To support EGPRS/GPRS, some functional network element enhance-ments to the GSM networks need to be introduced. These two elementsare the serving GPRS support node (SGSN) and the gateway GPRSsupport node (GGSN).

The SGSN controls the user’s access to the GPRS network byauthenticating the mobile stations (MS) and keeping track of theirlocation. It is in this sense similar to the mobile switching center (MSC)and the visitor location register (VLR) for GSM. It is an essentialcomponent in the GPRS system as it provides the connection betweenthe radio sub-system and the backbone network.

The GGSN is the interface to external private or public networks.Both the SGSN and GGSN are connected to an IP-based GPRSbackbone network. This is achieved through the base station systemGPRS protocol (BSSGP). The BSSGP handles connectionless transferof LLC frames between the base station subsystem (BSS) and theSGSN, which allows for signaling and user data transfer between theradio RLC/MAC interface and the SGSN.

The GPRS network subsystem as such offers independence from theradio subsystem. This would allow the use of different radio interfacetechnologies such as GSM 900/1800/1900, EDGE, and WCDMA. Fig-ure 2.1 shows how the GPRS network is separated from the radio inter-face where EDGE is implemented. Further architecture description of

2.2 Enhanced Data Rates for GSM Evolution 10

GERAN is available from 3GPP [13].

GGSN

EDGE/GPRS EDGE Transceiver Packet Control SGSN MS Base Station

Internet

Figure 2.1: Architecture top view of EDGE/GPRS.

2.2 Enhanced Data Rates for GSM Evolution

In what follows enhancements introduced to GPRS to create EDGE willbe highlighted. Focus will be on the EGPRS air interface in terms ofphysical layer and RLC/MAC layer improvements.

2.2.1 Physical Layer

One of the main improvements in EDGE is the introduction of ninemodulation and coding schemes (MCS 1-9) whereas GPRS used only fourcoding schemes (CS 1-4). The difference is that in EDGE a new modula-tion method 8-PSK (Phase Shift Keying) is used, whereas GPRS simplyused GMSK (Gaussian Minimum Shift Keying). The former would allowthe transmission of 3 bits over one symbol unlike GMSK which is limitedto one bit per symbol. The use of 8-PSK would result in an eight pointconstellation diagram as show in Figure 2.2.

Q

(0, 1, 0)

(0, 0, 0)(0, 1, 1)

(0, 0, 1) (1, 1, 1)I

(1, 0, 1) (1, 1, 0)

(1, 0, 0)

Figure 2.2: 8-PSK constellation.

Despite the increase in bit rate by three folds the higher the numberof transition states in the constellation diagram would also result in anincrease in the symbol error rate and thus the block error rate (BLER).Thereafter higher protection is required for 8-PSK, which would resultin a data rate that is less than three folds. Table 2.1 shows the differentMCS used in EDGE. More about the physical layer in EDGE can befound in [14] and [15].

2.2 Enhanced Data Rates for GSM Evolution 11

Scheme Modulation MaximumRate(Kbps) CodeRate Family

MCS − 9 8− PSK 59.2 1.00 A

MCS − 8 54.4 0.92 A

MCS − 7 44.8 0.76 B

MCS − 6 29.6 0.49 A

MCS − 5 22.4 0.37 B

MCS − 4 GMSK 17.6 1.00 C

MCS − 3 14.8 0.85 A

MCS − 2 11.2 0.66 B

MCS − 1 8.8 0.53 C

Table 2.1: EDGE Modulation Coding Schemes (MCS).

2.2.2 RLC/MAC Layers

The main purpose of these layers is the transfer of the LLC PDUs acrossthe air interface to and from the user. RLC includes the segmentationand reassembly of LLC PDUs, LQC (Link Quality Control) and ARQ(Automatic Repeat Request). While the MAC controls channel access,resource allocation and management, thus enabling fixed or multiplexeduse of multiple time slots (TS).

Figure 2.3 represents a high level overview of the method by whichan LLC packet data unit (PDU) is mapped onto GSM TDMA bursts.The LLC PDU is divided into 20ms RLC data blocks so as to matchthe TDMA burst structure as in GSM. The amount of bits that are tofill the RLC data block depend on the coding scheme selected. Themodulation scheme is selected by a link adaptation (LA) algorithmto cope with varying channel conditions. RLC/MAC headers containinformation that is necessary for the code soft combining process of theincremental redundancy (IR), method which allows a reduction in theamount of retransmissions. Check sequences for the user data and theheader are added to the RLC block before being passed to the physicallayer, where the user data and the header data are coded separatelyand mapped to 2 or 4 TDMA bursts, as the coding scheme dictates.

In the RLC/MAC block structure described earlier, the header isencoded separately and usually has a higher channel protection than theuser data. The reason behind this is that information incorporated inthe header is necessary for the proper functioning of the IR mechanism.

The LA and IR mechanisms (Hybrid Type II/III ARQ) are the maincomponents of the LQC necessary for a multicode packet data system.For GPRS the LQC is achieved by selecting the code rate that bestcopes with the prevailing radio channel condition.

2.2 Enhanced Data Rates for GSM Evolution 12

LLC Frame

Segment Segment Segment

RLC Data

Header Data

Tail Data

Encoded Data (MCS, Code Rate, Puncturing, and Interleaving)

Burst Burst Burst Burst

LLC Layer

RLC Block

RLC/MAC Layer

Physical Layer

Figure 2.3: Segmentation procedure of an LLC frame.

IR in EDGE was added so that if a data block is not decodedsuccessfully more redundant information is sent in retransmissions. Theerroneous blocks are saved and combined with each retransmission untilsuccessful decoding of the block is achieved. In a practical system, theindependent usage of LA or IR will result in a less spectrally efficientsystem than the one where both are combined. This hybrid mode ofoperation allows a given coding scheme to operate over a larger portionof a cell, since IR tends to reduce the effective BLER. For further insighton the effect of IR refer to [11].

Upon establishing a connection, before any transfer of data a MCSneeds to be selected and this is done after estimating the channel state.The better the channel is, the less coding needed and thus the higher theMCS. During the connection period the code selection is re-evaluatedtaking into consideration changes in the channel conditions. Theswitching point within the LA algorithm may influence the utilizationof the codes. However, it has been shown in [6] that the LA switchingpoint may not have a great impact on throughput performance evenwhen IR is not used. Also other enhancements in EDGE help notdegrade the overall performance, such as better quality channel metrics,block resegmentation, IR and a larger number of coding schemes wouldimprove the granularity of the LA algorithm.

Quality messages which are estimates of the channel conditionsare usually reported by the users upon request from the network.However, it must be noted that some errors in the LA/IR code selectionprocedure may result due to the transfer dynamics in packet switchedcommunication and inaccurate estimation at the receiver. This wouldresult in the selection of a suboptimal MCS, leading to a loss inthroughput.

Re-segmentation is used in EDGE and is achieved by grouping the

2.3 TCP/IP 13

MCS into families as shown in Figure 2.4. Such grouping allows theretransmission of a block with more or less protection by moving upor down within the same family group. Different MCS are groupedinto families depending on payload size. The reason for introducingthis concept was due to a problem that was present in GPRS, whereif an error occurs it is not possible to retransmit with a different CSwhich would imply a possibility of another error occurring. Frequentretransmissions may cause an increased RTT or force the connectionto stall. The same or a more robust MCS belonging to the same MCSfamily may be used in the retransmissions and the IR can still com-bine these received blocks despite the fact that they have different MCSs.

2288 OOcctteettss2288 OOcctteettss2288 OOcctteettss2288 OOcctteettss

MCS-7

MCS-2 MCS-5 Family B

Figure 2.4: Payload relationship for MCS belonging to family B.

During a connection the RLC/MAC blocks are transmitted sequen-tially unless retransmission needs arise due to negatively acknowledgedblocks by the use of selective ARQ. The negatively acknowledged blocksare transmitted with higher priority than new blocks. Additionaldescription of LQC mechanisms is available in the standards related tothe RLC/MAC [16] and channel coding [14].

The MAC layer assigns temporary block flows (TBFs) for dataand signaling transfer between the MS and the network. The TBF isused by each entity to communicate LLC PDUs on the packet dataphysical channels (PDCH) between the RLC entities on each side of thecommunication link. Two accessing modes are offered:1- Dynamic allocation, where by users can be multiplexed onto a singleTS.2- Fixed allocation, where a user is reserved resources upon establishinga connection.

In general the MAC functionality is similar to that of GPRS. Moreinformation can be found in the specifications [16].

2.3 TCP/IP

Although the thesis is not concerned in studying the way the Trans-mission Control Protocol (TCP) protocol interacts within EDGE. Yetit is imperative that the reader get to know its dynamics in order tounderstand later the throughput results which are generated by a TCPconnection via EDGE.

2.3 TCP/IP 14

The TCP is designed to provide a reliable connection between twocommunicating devices. TCP has the purpose of guaranteeing thedelivery of packets across the network next to reordering the packetsthat arrive at the host. ARQ is implemented in TCP, when packets arecorrectly received at the destination an acknowledgment is sent backto the receiver. If the acknowledgement is not received within a giveretransmit time value the packet is retransmitted.

Apart from the guarantees above, TCP also handles data flow andcongestion control. Data flow control is used to avoid overflow at thereceiver due to a fast transmitter. The receiver tends to advertisea maximum amount of data that can be sent by some sender. Thisamount of data is set by the advertised window size; by setting it tozero the receiver can stop the sender from sending any more data.

Another issue that TCP attempts to control is congestion on thenetwork. While transmitting if a packet loss is detected, it is attributedto a congestion point on the network. This is due to the fact thaterroneous data is scarce on wired links. Every time a packet is lost,the transmitter tends to slow down its transmission. Mind that suchinterpretation of a packet loss is not sensible in a wireless networkwhere packet retransmissions are often due to errors introduced by abad channel condition.

To initiate a TCP connection a three way handshake is required tosynchronize the receiver and sender as well as determine if the receiveris ready to receive data.

Since the major cause for a decrease in throughput when usingTCP is due to congestion, it is necessary to understand the congestionavoidance strategies.

TCP uses four different algorithms to control congestion problems, acloser look into these algorithms can be found in [17]. Two of them arebriefly explained below.

2.3.1 Slow Start and Congestion Avoidance

Each sender in TCP uses an indicator, the congestion window (CW), todetermine how many bytes can be in flight and unacknowledged. Theinitial value is usually set to twice less than maximum sender segmentsize (MSS) and is increased for each successful transmission by doublingthe previous CW value. Once the maximum CW size reaches the slowstart threshold (SST) the CW is increased linearly. In case of a conges-tion occurring during the slow start or congestion avoidance phases, the

2.3 TCP/IP 15

CW is reset and a new SST is calculated (usually to half the currentCW).

2.3.2 Fast Retransmit and Fast Recovery

When a TCP segment is lost in transmission, all the following TCPsegments cannot be delivered to higher layers until the lost segmentis retransmitted. When the retransmission timer expires the senderwould retransmit the lost segment. This would introduce delay sincethe receiver would have to wait.

Acknowledgements usually contain the sequence number expectednext. Thus it is possible for the sender to know that segments thatfollowed the lost one have been received. It is also possible to sendduplicate ACKs to the sender in case of a reordering of the segmentswithin the network. Once three duplicate ACKs are received the senderwould assume that the segment was lost and would retransmit it beforethe retransmission timer expires. Also upon such loss of segments slowstart is not used to recover and the CW is set to be the SST. This isknown as fast recovery whereby duplicate ACKs indicate that congestionhas been noted only for the given segment and not all those that followed.

Chapter 3Data Collection and Analysis

Since this thesis is considered to be a case study, it is imperative toproduce the details of the test environment and network settings. Thesedetails are necessary for any conclusions to be drawn by operators whomay be in the process of deployment as well as any party who is interestedin utilizing EDGE services. This chapter provides a description of thetest areas, which includes the type of forest and terrain, as well as thesite settings. The equipment used with their settings and the methodsof analysis is also supplemented.

3.1 Forest Environment Overview

In order for the reader to assess the relevance of the information, it isnecessary to have an adequate description of the types of forests thatexist and the type of terrain. The information that will be presentedhere mainly concerns the Swedish forests. Based on this, the selectionof the test areas was carried out.

The majority of forest types in Europe are coniferous forests andare mainly located in northernmost Europe (north-western Russia,Finland and Sweden). Sweden has a forest cover of about 60% ofits total area, these forests are divided into different categories. Thetwo major categories comprise the boreal and the boreo-nemoralforests. Boreal forests cover northern Sweden, mainly from the northof Stockholm and towards the northern mountains and arctic regions.They consist of Spruce and Pine which are evergreen needle leaf trees.The areas surrounding Stockholm and onwards south are categorizedas boreo-nemoral forests. These forests are a combination of trees thatare found in the north and to the extreme south were broad leavedtrees may be found. In addition, other features of these two forestlandscapes are the presence of marshes, lakes and watercourses. Figure3.2(b) shows the distribution of tree types in Sweden, 81% of the totalamount is needleaf (Spruce and Pine) while only 16% are trees with

3.1 Forest Environment Overview 17

broad leaves. The terrain type in most of Sweden can be described ashilly, with mountains only in the north.

Spruce53.5%

Pine 29.7%

Birch8.9%

Other6.2%

Dead/Windthrown1.8%

(a) Jonkoping

Spruce41.7%

Pine 38.7%

Birch11.3%

Other5.8%

Dead/Windthrown2.5%

(b) Sweden

Figure 3.1: Tree Type Distribution

As such, the test sites have been selected to reflect the abovementioned details so as to have a valid sample of the Swedish forest.The test areas were chosen to be located in the southern part ofSweden, (Vaggeryd and Aneby in Smaland), where we have a mix ofbroad and needle leaf trees. According to the data available in [18],the tree distribution in Jonkoping 1 is depicted in Figure 3.2(a). Thetree distribution is almost the same in Jonkoping as it is for the wholecount over Sweden; thereafter, the forest in those areas seems to be aproper sample of Swedish forests. The terrain variation is also similarto the case in most of Sweden. At the test sites, elevation above sealevel varied between 200 m and 300 m, noted using GPS. Concerningthe relevance of the test area, Bertil Liden a researcher at Skogforskasserted: ”The forests of the test area consist mainly of Spruce and Pinewhich is relevant for the main part of the forests in Sweden. The testarea is also representative for most of the Swedish forests concerningthe type of hilly terrain.”

It needs to be stated that the results of the thesis are mainly forneedle leaf trees and not broad leaf trees. The leaf size may influencethe propagation properties of radio waves and may cause scattering.Besides the effect of leaves, tree height and trunk diameter may alsohave a similar effect and may influence the amount of attenuationobserved by the transmitted wave as it passess through the foliage. Itis needed to remind the reader that the purpose of the thesis is notto investigate tree effects on radio waves2; however, these effects may

1Vaggeryd and Aneby are located within the district of Jonkoping2This task is to be carried out by another thesis, which is being carried out at the

3.1 Forest Environment Overview 18

0-9 5.64%10-14

10%

15-1914.4%

20-24 17.45% 25-29

17.6%

30-3414.1%

35-4416.7%

45-4%

(a) Jonkoping

0-9 8.56%

10-1413%

15-19 17.63%

20-24 18.35%

25-29 15.84%

30-3411.7%

35-44 11.43%

45- 3.47%

(b) Sweden

Figure 3.2: Trunk Width Distribution

0-2 3.56%

3-10 10.68%

11-209.25%

21-309.96%

31-40 10.81%

41-507.25%

51-606.97%

61-70 8.53%

71-80 9.82%

(a) Jonkoping

0-2 3.79%

3-108.8%

11-2011.6%

21-30 10.42%

31-409.33%

41-507.47%

51-605.87%

61-705.64%

71-806.04%

(b) Sweden

Figure 3.3: Tree Age Distribution

degrade performance and as such decrease the theoretically achievablethroughput. Figures 3.2 and 3.3 depict these tree properties, they areprovided for the test areas as well as over Sweden. The figures showthat the test areas again share the same forest properties.

It maybe the case that this data does not indeed exactly describe theareas were the tests were carried out. Thereafter, there lies a doubt asto whether the test sites are a sample of the Swedish Forest. However,an exact match of the Swedish Forest can not be precisely described bya few locations. Nevertheless, it is not possible to test for a large areaand over the course of the thesis, so the sites were selected based on theabove described data and time and resource constraints.

same time, for Skogforsk and Telia.

3.2 EDGE Cells Overview 19

3.2 EDGE Cells Overview

In what follows is a description of the test sites’ serving base stations.The details pertaining to these sites will provide the reader with an ideaof how the base stations are planned in Sweden’s rural areas. Thus, thiswill also help as mentioned in Section 3.1 in assessing the relevance ofthese sites.

Networks planned in rural areas mainly focus on providing cover-age to major roads and residential areas, where the majority of thesubscribers are located. The fact that the subscribers’ speeds are highon roads and their density is low in towns, allows for a higher cellreuse distance. A higher cell reuse distance means that a single cellcan cover a large area since capacity is not a major problem. Thusto cover this large area, antennas’ heights and the transmitted powercan be large; typical antenna heights in rural areas are higher than 30 m.

The test sites that are studied herein comprise of a mix of differentantenna heights, output power, and antenna patterns. From hence,the results will not be for a specific site setting, but they will includethe results for different combinations, which would provide a bettersample of the deployed cell sites. Table 3.1 provides the details of thesedifferent sites.

Location Site Height Elevation Power Antenna Channels

V aggeryd 1 53 −55.5 m

197 m 41 dBm Type −1

2

Aneby 2 59.4 −62.4 m

310 m 46 dBm Type −1

1

V aggeryd 3 42.2 −44.8 m

230 m 41 dBm Type −1

2

Aneby 4 59.2 −62.4 m

283 m 44 dBm Type −2

2

Table 3.1: EDGE Cell Information.

The sites, designed for GSM coverage, have EDGE installed withLA/IR enabled and utilize the full MCS range. However, frequencyhopping is not enabled mainly due to the unavailability of the equipmentnecessary. According to Telia there is no sites in similar forest environ-ment and configuration that supports frequency hopping; thereafter,the absence of frequency hopping is not a specific of the sites studiedherein. From the data provided by Telia concerning the service areaand interference effects, it is expected that the test areas shall mainlybe noise limited with a coverage of 4− 6 Km. A further argument thatsupports the generality of the tested sites is a statement from Telia stat-

3.3 Signal Quality 20

ing that the sites tested are typical sites regarding the configuration, theequipment used, and positioning for rural forest environments in Sweden.

From Figures 3.4 and 3.5, it is clear that for the case of the tri-sectored sites, which have directional antennas the power is concentratedin the horizontal plane with no significant side lobes. Sidelobes may af-fect the analysis of the gathered results, since it is difficult to distinguishbetween having low signal levels, and thus throughput, due to the ef-fects of the forest on the signal’s propagation or to being located atan antenna’s null. Also it should be noted that the vertical patternis extremely narrow, this will influence the area covered by the mainbeam, which may not reach ground surface, depending on the antenna’sheight, until more than 500 m away from the BSS. For the case of theomni-directional antennas, the horizontal pattern is uniform and thiswould allow conducting the experiment over a wider range. However,the vertical pattern is also narrow, which would imposes performing themeasurements at a distance that would be within the main beam, againin the order of more than 500 m.

A-PanelDual PolarizationHalf-power Beam Width

936.

2360

/a

S

ubje

ct t

o al

tera

tion.

Input 2 x 7-16 female

Connector position* Bottom or top

Weight 22.6 kg

Wind load Frontal: 470 N (at 150 km/h)Lateral: 280 N (at 150 km/h)Rearside: 1040 N (at 150 km/h)

Max. wind velocity 200 km/h

Packing size 2721 x 302 x 172 mm

Height/width/depth 2580 / 262 / 116 mm

Mechanical specifications

page 1 of 2 739 650

Horizontal Pattern Vertical Pattern

3 dB

10

0

88°

165°

8°

dB

10

3

0

880 – 960 MHz: +45°/–45° Polarization

Horizontal Pattern Vertical Pattern

3 dB

10

0

85°

160°

8.5°

dB

10

3

0

806 – 894 MHz: +45°/–45° Polarization

X

88°

* Inverted mounting:Connector position top: Change drain hole screw.

806–960–45°

806–960+45°

7-16 7-16

806–960

739 650Type No.

Frequency range806 – 894 MHz 880 – 960 MHz

Polarization +45°, –45° +45°, –45°

Gain 2 x 16.5 dBi 2 x 16.7 dBi

Half-power beam width Horizontal: 85° Horizontal: 88°Copolar +45°/ –45° Vertical: 8.5° Vertical: 8°

Sidelobe suppression for ≥ 18 dBfirst sidelobe above horizon

Front-to-back ratio, copolar > 25 dB

Isolation > 30 dB

Impedance 50 Ω

VSWR < 1.5

Intermodulation IM3 < –150 dBc(2 x 43 dBm carrier)

Max. power per input 600 W (at 50 °C ambient temperature)

XPol A-Panel 806–960 88° 17dBi

806–960

KATHREIN-Werke KG . Anton-Kathrein-Straße 1 – 3 . PO Box 10 04 44 . D-83004 Rosenheim . Germany . Telephone +49 8031 1 84-0 . Fax +49 8031 1 84-9 73

Internet: http://www.kathrein.de

Figure 3.4: Antenna pattern Type-1 used at Sites 1,2, and 3.

3.3 Signal Quality

The operation of GSM/EDGE relies on reports about the signal leveland signal quality between the MS and the BSS. The received signal ismeasured in this case at the MS, where it is assumed that the MS usedin the experiment statisfies the 3GPP recommendations concerningsignal level and quality measurements.

The signal level measured is the R.M.S value and is measured at

3.3 Signal Quality 21

−11.25

−2.5

6.25

15

60

120

30

150

0

180

30

150

60

120

90 90

(a) Horizontal Pattern

−11.25

−2.5

6.25

15

60

120

30

150

0

180

30

150

60

120

90 90

(b) Vertical Pattern

Figure 3.5: Antenna pattern Type-2 used at Site 4, vertical polarization.

the receiver’s end by either the MS or the BSS over the range specifiedbetween −110 dBm and −38 dBm. The 3GPP specifies an accuracyrequirement for signal levels in the lower range and for those above−48 dBm. As such an absolute accuracy of 4 dB is tolerated forvalues between −110 dBm to −70 dBm under normal conditions and9 dB for values between −48 dBm and −38 dBm. The measurementsare calculated over all the active time slots and are averaged over areporting period of 480 ms (104 TDMA frames). The received signal,denoted as RXLEV, is averaged over a single reporting period whilemeasurements over other periods are discarded.

In GSM, signal level and signal quality are used as inputs to vari-ous decisions that invlove power control and handover processes. Thesignal quality is also used in EDGE as a way to adapt the MCS tosuite the channel quality at hand. The signal quality measurements relymainly on calculating the expected BER, based on that, judgments aremade on how to adapt to achieve the maximum possible performance.The main quality measures that are used by EDGE, when selecting theMCS, are the mean bit error probability (Mean BEP) and the coefficientof variation of the bit error probability (CV BEP). These two values arecalculated such that an average is calculated from the BEP of each burst,as in Equation 3.1, to get the BEP over each block; following that, thesevalues are averaged for each TS, shown in Equation 3.2, and finally overthe reporting period, as in Equation 3.3. The averaging does not includevalues calculated in previous reporting periods. For the case of the co-efficient of variation a similar method is carried out with Equation 3.1being replaced by Equation 3.4. Concerning the accuracy of these mea-surements, more information can be found in [19], where a detailed tableis provided for 8-PSK and GMSK signals. The averaging calculationsare shown below.

3.4 Measurement Procedure 22

Mean BEPblock =14.

4∑i=1

BEPburst i (3.1)

Mean BEP TNn = (1−e.xn

Rn).Mean BEP TNn−1+e.

xn

Rn.Mean BEPblock,n

(3.2)

n Iteration index per blocke A forgetting factorRn Denotes the reliability of the filtered quality parametersxn Quality parameter

Mean BEPn =

∑j Rj

n.Mean BEP TN jn∑

j Rjn

(3.3)

n Iteration index at reporting timej Channel (TS) number

CV BEPblock =

√13 .

∑4k=1 (BEPburst k − 1

4 .∑4

i=1 BEPburst i)2

14 .

∑4i=1 BEPburst i

(3.4)

3.4 Measurement Procedure

The main task of this work is to collect data from live network reportsin the test areas. These live network reports are indicators of thecurrent network status related to resource management, performance,and signaling. The main focus will be on the overall performance ofEDGE indicated by the data rate. The test areas are scarcely inhabitedand the load on the network is expected to be low, this would provide agood case study as to what would be the data rates when there is not astrong competition over the resources. From hence, the results can beanalyzed without having to attribute them to resource management is-sues. The only reasons might either be at the radio layer or higher layers.

To gather these reports and extract relevant information from them,TEMS Investigation 5.1.2, a test tool from Ericsson, will be used. Themain issue, concerning the collected data, is that TEMS is not designedto display all the needed information and does not accurately correlatedifferent data sets. It is necessary to write some scripts which wouldallow proper extraction of the data of interest from the logged text files.Once this data is extracted it is possible to correlate them in differentways and extract relevant information, this is discussed in more detailin Section 3.6.

TEMS has the option of downloading or uploading an FTP file,accessing HTTP services, or even sending and receiving E-mails. These

3.5 Equipment 23

services are mainly TCP based, it is expected that the throughputwill be below the optimal value which can be achieved with UDP.Nevertheless, testing for UDP would seem irrelevant since the main useof EDGE in those areas will be for data transfer which needs to bereliable.

To mitigate the effects of slow start in TCP, bulk download offiles will be executed, this would allow to study the performance whenthroughput is, to some extent, stable, this is shown in [20]. FTP willbe used for this transfer in order to have comparable results with thesimulations that tested for Internet access via FTP traffic models inprevious work on EDGE.

The measurements will be carried out over four different cells atdifferent times and locations so as to have time and location indepen-dence. The cells were chosen so as to have a forest outstretch withminimum residential and open spaces. The measurements are to beconducted by combining two methods. The first is by walking within theforest for distances in the range of 300 − 500 m inside the forest Thesemeasurements would be collected at regular distance intervals from theserving base station, the mobile speed would be around 3 Km/Hr.The other method is by driving at 10 Km/Hr on ”black” roads [21].The black roads are narrow and the trees are high enough to give theimpression of doing the measurements within the forest. These speedsare the expected speeds of the majority of the users (forest mobilemachinery) to be in those areas. It is also necessary to remain as muchas possible within the half beam width so that the results may not bedependent on the antenna gain significantly.

The network is currently being deployed and thus it is not possible toinspect the impact of cell reselection, which may incur delays, since notall base stations have EDGE capability. The base stations may not haveEDGE but they do have GPRS; as a result, when doing the measure-ments, in order not to switch to GPRS a channel lock is forced. Lockingonto a channel may not be a proper method to avoid this problem inurban areas where the frequency reuse is high; however, in rural areaswere the reuse is relatively low this is not an issue. This would allow thetesting to be carried out to the full extent of the cell border.

3.5 Equipment

3.5.1 TEMS

TEMS is a tool, provided by Ericsson that allows the mobile phone tocommunicate with the base station and process a lot of the information.The information is acquired by analyzing the received information in

3.5 Equipment 24

packet headers as well as those received by ordinary network reportswhile the TEMS generated reports are logged for later analysis.

The data that has been gathered was calculated by TEMS in thefashion to be described below in addition to what was mentionedbefore. However, the methods described have not been obtained fromTEMS manuals since they do not provide this information. A detailedobservation of the saved data showed how they were being calculatedand the intervals over which they were averaged.

TEMS tends to calculate throughput at all levels (application, linklayer, radio link) by counting the number of bytes received within thelast throughput reporting period, which is about one second. Thereported throughput is averaged over the current and last throughputreporting periods. This is the throughput that is used as input to theresults obtained in the next chapter; however TEMS also reports athroughput that has been averaged over all the past reporting periods.The latter throughput is not of interest since it is not possible tocorrelate the throughput to other collected data, such as the RXLEV(Received Power Level).

There have been no BLEs (Block Errors) reported at the LLC,yet some existed at the radio link level. The BLER was calculated bycounting out the number of blocks received in error to the total numberof sent blocks and reporting the result every second. The received powerlevel was also recorded in a similar fashion to that described in Section3.3, they were collected every 480 ms. The CIR that is reported byTEMS is actually the received carrier signal to the totally perceivedinterference and noise. The latter data was averaged and reportedapproximately every 2 seconds, therefore, dips due to fast fading wouldnot be visible due to the large averaging period. In Chapter 2, it wasshown that the type of MCS is incorporated in the header of the RLCblocks, thus it is possible for TEMS to extract the information on MCSfrom the RLC blocks received. TEMS records what the MCS was sinceit last changed as a result of a command from the base station that isbased on the quality parameters discussed in Section 3.3.

Concerning the accuracy of the reported CIR, the TEMS manual[22] states that the measurement range extends from −5 dB to +25 dB,where it is unlikely to experience a CIR lower than −5 dB. The largerthe number of frequencies used (in frequency hopping) the less accurateis the CIR measures, in the experiment only one frequency is used. Togive an example of the range of error possible, if the measured valueswere between 0−15 dB and four frequencies were utilized the error wouldbe no more than 1 dB.

3.6 Data Analysis 25

3.5.2 TCP Settings

It is of importance to provide the reader with the TCP settings thathave been used in the experiment since these settings may have a directinfluence on the throughput perceived by the user. The settings thatwere used in the collection of data were the default settings configuredfor the Windows XP OS. The default settings were found to be belowoptimal as will be seen in Chapter 4, this is due to the fact that theTCP Advertised Window (AWND) was set to 16 KB. This value wouldnot allow the sender on the other side of the network to have more than16 KB of data in transit, thus limiting the maximum throughput to128 Kbps.

Thereafter, it is necessary to increase the AWND size to a preferredvalue of 32 KB, which would allow, if possible, to reach the maximumthroughput available with EDGE. Concerning other measurements thatwere collected, such as BLER or MCS selection, and the method ofanalysis, the AWND does not have any influence on them. The onlyeffect is that the recorded throughput during the session will be belowoptimal.

3.6 Data Analysis

Recalling that the goal is to provide a case study of the expectedthroughputs in a forest environment in Sweden. The measurementcampaign’s results shall be used to have experimental data that cangive a picture of what to expect out of EDGE if used in a ruralbut forest-dense environment. In order to study the throughput, anFTP file download was initiated in order to record the throughputlevel using TEMS, along with the throughput other data have beenrecorded such as BLER, MCS, RXLEV, and CIR. The informationcollected would make it possible to compare the recorded throughputsto the possibly attainable throughputs. It would also allow to checkhow much does the BLER influence the perceived throughput andhow are the MCSs utilized. The CIR is needed so as to observeits effect on the throughput, since lower CIR would cause a higherBER which may cause an increased BLER. The CIR (or RXLEV)would also provide an input to predict the coverage of EDGE inthe cell depending on the CIR (or RXLEV)distribution in similarenvironments. It is also possible, when plotted against RXLEV, to seehow the throughput may be influenced by increasing transmission power.

This section shall pass over how the gathered data was extracted,analyzed, and normalized to extract relevant information in attempt tohave the results sought.

3.6 Data Analysis 26

3.6.1 Logged Data Specifics

As mentioned in Section 3.5.1 the data was logged into TEMS filesperiodically according to the arrival or generation of the report by eitherTEMS software or the MS. Unfortunately the data that is reportedand logged in the files are not synchronized with respect to time, thisis due to the fact that the information that is extracted from themeasurements by the software and those arriving from the MS mayhave different periods. Yet it is possible to average the small reportsin a larger report interval, that would allow us to correlate betweenthe values measured. Another issue that is not evident in TEMS isthat some recorded measurements are measured at time t seconds butreported for the following t seconds. Although the effect of this may notbe drastic, it was opted to shift the values for each collected sample upby one reporting period. These modifications would allow for a bettercorrelation between the different data sets. Figure 3.6 shows how thedifferent data sets collected are related in terms of reporting periodsand how they were shifted by one period.

Averaging Period

SINR (CIR)

R1 R2 R3

Throughput

BLER

RXLEV

MCS

Figure 3.6: Data Analysis Relationships.

Another problem may arise due to the fact that different data sets donot have synchronized starting points, i.e. if the CIR reporting periodis 2 seconds it may happen that we do not have 2 full reporting periodsfor throughput which has a period of about 1 second. Therefore it wasopted to average the values with a smaller reporting period within thelarger period of the data set to be compared with. Although a value fora particular measurement is reported once by TEMS it is logged as isfor any other reports of other measurements. This poses another issue

3.6 Data Analysis 27

to be considered, that is counting the reports generated by TEMS andbasing on them the relationships between different data sets will providean inaccurate relationship. The reason is that for a particular data set,the value x may be reported 5 times over 0.2 seconds while that of ymay have been reported once over 0.8 seconds, thus counting reportsis not necessarily indicative of the results. Thereafter, the averagedvalues are based on the interval of time that a measurement value didnot change within the larger reporting period over the total time of thelatter. Figure 3.6 also shows, for the case between CIR and throughputmeasurements, how the reported throughputs were averaged over thereporting period of the CIR based on time ratio.

Concerning BLER measurements it was found that, although bothdepended on the same information when calculated, there was a timedescrepancy between the throughput reports and the BLER reports.The BLER was reported fractions of seconds after the throughput reportwas logged, thereafter there was a need to shift the BLER values so asto properly associate them with the corresponding throughput value.Figure 3.6 again shows how the BLER measures were linked to theircorresponding throughput report.

3.6.2 Data Processing

After the data has been extracted into files that would allow inspectingthe relationships between them, it was interesting to know how closewas the throughput measured to the maximum attainable. What ismeant by maximum attainable, is the throughput that can be attainedgiven the reported usage of the MCS and the effect of the BLER. Sinceit was not possible to do another set of measurements for UDP, theinformation extracted from the MCS and BLER values would give anidea of what throughput to expect for UDP and compare it to thevalues recorded for TCP. It would also show the effect of BLER on thethroughput, so as to observe how much the BLER experienced degradesthe performance.

It was chosen to use the maximum MCS throughput, provided inSection 2.2.2, when finding this maximum attainable throughput, for itis not possible to know the exact value for each MCS using TEMS. TheMCS was changing constantly and rapidly, this was reflected in the num-ber of switches within the reporting period of the measured throughput.The throughput reporting period was chosen to be the period withinwhich the MCS usage should be considered and mapped to an attain-ble throughput by Equation 3.5. The calculation of the throughput assuch does not consider protocol overhead and does not consider any BLEexperienced on the radio link. In other words, this throughput would in-dicate the maximum throughput that could have been attained if effectsof protocol overhead were absent and the LA/IR algorithm was adapt-

3.6 Data Analysis 28

ing properly to the changing environment thus reducing the BLE to 0 %.By stating that the LA/IR is adapting properly, it is assumed that it isadapting to the environment properly and supposedly this should reduceBLER. However, it is not meant that the LA/IR algorithm is workingperfectly as expected by research, in other words it does not assumethat, for instance MCS-9 is being used constantly at high SINR. MCSsother than MCS-9 were being used at the same time at high SINR, butit is assumed, by the calculations, that this was happening in attemptto reduce BLEs by the BSS. Another throughput value was calculatedby using the above calculated throughput and incorporating the valuesof the BLER as in Equation 3.6. The BLER values were mapped to thematching report period as explained previously. Thus the inefficiency ofthe LA/IR in coping with the environment (represented by the BLER)may be visible and comparable with that of a proper adaptation.

Thr No BLERn =1

tn + tn−1.

9∑i=1

Thrmcsi .ti+Thr No BLERn−1.tn−1

(3.5)

Thr BLERn =1

tn + tn−1.BLERn.

9∑i=1

Thrmcsi .ti+Thr BLERn−1.tn−1

(3.6)n Iteration index at reporting timei The MCS number (1-9)t Time

Each of the above calculated throughputs and the measuredthroughput values were mapped against other measured values suchas distance, CIR and RXLEV. Thus it is possible to view how thethroughput is affected by each, and would allow an anticipation ofthe throughput coverage. Equations 3.7 and 3.8 show how the av-eraging was done for each of the CIR report interval and that of RXLEV.

Thr CIRn =1tn

.k∑

i=1

Ri.ti (3.7)

n CIR iteration index at reporting timek Index of the reported throughput valuesR Reported throughput value

Instead of calculating the throughput under each RXLEV value, theaverage RXLEV value was calculated for each throughput reportingperiod as follows:

3.6 Data Analysis 29

RXLEV Thrn =1tn

.k∑

i=1

ri.ti (3.8)

n Throughput iteration index at reporting timek Index of the reported RXLEV valuesr Reported RXLEV value

After the values have been calculated, the mean over each CIR andRXLEV value was obtained in order to have the throughput againstdifferent CIR and RXLEV values. Also the throughput mentioned inEquations 3.7 and 3.8 involve those calculated in Equations 3.5 and 3.6and those directly measured and logged by TEMS.

Chapter 4Results

This chapter presents the results that have been obtained in the mea-surement campaign after they have been extracted and analyzed. Thechapter starts with the obtained throughput when considered over dis-tance and signal quality, followed by an inspection of the BLER and itssignificance. The MCS distribution is also revealed in attempt to obtaina better view of the reason behind the results presented herein. Finallyan expectation of the throughput coverage in a cell in the light of theobtained throughput measures is presented.

4.1 Throughput and Distance

Figures 4.1 show how the calculated and measured throughputs decreaseas the distance from the serving BSS increases. It is evident that thecalculated throughputs, considering the perfect LA case and that withBLER, tend to diverge drastically as the distance increases. When closeto the BSS, at about 700 m, the loss in throughput for Figure 4.1(a) isaround 9 Kbps yet it rapidly increases to a loss of more than 43 Kbpsas soon as the distance becomes almost 2 Km away. The measuredthroughput seems to be very low compared to the theoretical maximumthat is achievable in such a situation, where the loss is around 60 Kbps,yet the further away from the BSS the measured throughput tends toconverge to the upper limit.

The reason behind the low measured throughput when close to theBSS is that the advertised window size (AWND) by the operating systemis below optimal. Thus, the settings do not allow the sender on the otherside of the network to utilize the bandwidth properly. Utilizing thebandwidth properly under the used settings should allow a maximumthroughput of 128 Kbps, which is still not fully achieved when close tothe BSS and that is due to the BLER as will be seen later. It should benoted that there were no BLE at the link layer nor at the applicationlayer, which means that the effects of TCP, concerning congestion

4.1 Throughput and Distance 31

0.5 1 1.5 2 2.5 3 3.5 4 4.50

20

40

60

80

100

120

140

160

180

Distance (Km)

Th

rou

gh

pu

t (K

bp

s)

Throughput vs. Distance

MeasuredLA/IR with BLEPerfect LA/IRMeasuredPerfect LA/IRLA/IR with BLE

(a) Site 1

0 1 2 3 4 5 60

20

40

60

80

100

120

140

160

180

Distance (Km)

Th

royg

hp

ut

(Kb

ps)

Throughput vs. Distance

MeasuredLA/IR with BLEPerfect LA/IRMeasuredPerfect LA/IRLA/IR with BLE

(b) Site 2

Figure 4.1: Throughput vs. Distance

mechanisms do not influence the perceived throughput but the BLEon the radio link may cause delays, this has been shown for GPRS in [23].

The Figures in 4.1 were based on the average throughput at eachdistance and thus it involves an averaged effect of BLE and SINR (fromhere on, CIR shall be referred to as SINR). The reason why there areless points in Figure 4.1(b) is due to the fact that the throughput isaveraged over a larger distance than that of Figure 4.1(a). Distance waschosen to observe how the throughput may vary as the cell boundariesare approached, yet this will not provide a better insight as to why thethrouhgput is low and will not have a sense of generality. Thereafter,the throughput was plotted against the measured SINR and the RXLEV,which would provide more information.

4.2 Throughput, SINR, and RXLEV 32

4.2 Throughput, SINR, and RXLEV

As mentioned earlier, the throughput against distance may not providemuch information as to the coverage expected in a cell. The reasonis due to the fact that to have a better estimate of the throughputagainst distance, more measurements need to be done in more than onedirection in a cell. Thereafter, throughput if plotted against SINR orRXLEV would provide more information, since the measurements donot depend on the terrain effects but more on the measured signal level.Thus if the signal quality distribution is known, the throughput can bemapped to the corresponding signal value; thus providing an estimateof the coverage.

When plotted against SINR and RXLEV, the throughput had thebehaviour depicted in Figures 4.2. It can be noted in both Figures thatall the throughputs, calculated and measured, for the different testedsites are characterized by a similar behaviour and almost equal values.Figures 4.3 show the average throughput when all the results for the dif-ferent sites are incoporated into one data set and plotted, the bounds forthe confidence interval of 95% is also shown. Confidence and predictionbounds define the lower and upper values of the associated interval, anddefine the width of the interval. The width of the interval indicates howuncertain you are about the fitted coefficients, the predicted observation,or the predicted fit. For example, a very wide interval for the fitted co-efficients can indicate that you should use more data when fitting beforeyou can say anything very definite about the coefficients.

The bounds are the level of certainty that is specified for the givendata. The level of certainty in this case is chosen to be 95%, whichmeans that there is a chance of 5% being incorrect about predictinga new observation. Thereafter the 95% interval indicates that thereexists a 95% chance that the new observation lies within the predictionbounds displayed in the figures. In order to find these bounds thecommercially available Matlab tool, cftool, was used; the tool allowsfor the analysis of an adequate fit for the data collected. The for-mulas for calculating the prediction bounds can be found in detail in [24].

For Figure 4.3(a) the calculated throughput with no BLE tends tostart with low values at a SINR below 15 dB and increases rapidly inthe range of 15 − 25 dB and then achieves the maximum throughputaround 170 Kbps.

However, when the BLE effect is incorporated the throughput tendsto increase from lower values, slowly at the beginning but faster and ina more linear fashion after 15 dB. The measured throughput increaselinearly except for the case of one site, this site also shows some differencefrom the general trend of the three others. The reason behind this is

4.2 Throughput, SINR, and RXLEV 33

10 15 20 25 3020

40

60

80

100

120

140

160

180

200

CIR ( dB )

Th

rou

gh

pu

t (

Kb

ps

)

Throughput vs. CIR

Site1 (No BLER)Site2 (No BLER)Site3 (No BLER)Site4 (No BLER)Site1 (With BLER)Site2 (With BLER)Site3 (With BLER)Site4 (With BLER)Site1 (Measured)Site2 (Measured)Site3 (Measured)Site4 (Measured)

(a) Throughput vs. SINR

−100 −95 −90 −85 −80 −75 −70 −65 −6020

40

60

80

100

120

140

160

180

200

Received Power ( dBm )

Th

rou

gh

pu

t (

Kb

ps

)

Throughput vs. Received PowerSite1 (No BLER)Site2 (No BLER)Site3 (No BLER)Site4 (No BLER)Site1 (With BLER)Site2(With BLER)Site3 (With BLER)Site4 (With BLER)Site1 (Measured)Site2 (Measured)Site3 (Measured)Site4 (Measured)

(b) Throughput vs. RXLEV

Figure 4.2: Throughput measured against the perceived SINR andRXLEV at each site.

due to the less BLERs it faces when the SINR is higher but the morewhen the SINR is lower. It can also be seen again, how the measuredthroughput follows closely the maximum throughput possible with theBLER. The maximum possible throughput attainable by EDGE is takenfrom the research on EDGE in [11], it assumes a perfect estimation ofthe channel and perfect adaptation. Compared to this curve, the ”NoBLER” curve is far below, the reason is due to the high BLER that isbeing experienced with some MCS, forcing the BSS to opt for a lowerMCS resulting in a lower throughput. For the case of Figure 4.3(b), thethroughput seems to follow for each case a linear behaviour and reachesthe limit around −75 dBm. It can also be seen that the huge differencebetween the measured throughput and the calculated one is large forRXLEV above −80 dBm. It is also interesting to note that under betterconditions it is possible to provide a maximum rate above 140 Kbps fora large range of RXLEV, but given the pathloss in the forest it does notinclude a large cell area. It can also be seen how increasing the receivedpower level can help in increasing the area covered by higher throughput,for instance using high gain receiving antennas.

4.2 Throughput, SINR, and RXLEV 34

10 15 20 25 30

0

50

100

150

200

SINR (dB)

Th

rou

gh

pu

t (K

bp

s)

Avg. over all Sites (No BLER)95% Prediction Bounds Avg. over all Sites (With BLER)95% Prediction Bounds Avg. over all Sites (Measured)95% Prediction Bounds Perfect LA/IR Performance

(a) Throughput vs. SINR

−100 −95 −90 −85 −80 −75 −70 −65 −60 −55

20

40

60

80

100

120

140

160

180

RXLEV (dBm)

Th

rou

gh

pu

t (K

bp

s)

Avg. over all Sites (No BLER) 95% Prediction BoundsAvg. over all Sites (With BLER) 95% Prediction BoundsAvg. over all Sites (Measured) 95% Prediction Bounds

(b) Throughput vs. RXLEV

Figure 4.3: Mean throughput of all sites measured against the perceivedSINR and RXLEV.

4.3 BLER 35

4.3 BLER

The effect of BLER has been significant on the overall performance ofthe throughput. Thereafter, the BLER reports were matched againstthe SINR and the RXLEV to try to understand how much is the BLERaffecting the perceived throughput. Figure 4.4(a) shows the variation ofthe BLER as the SINR changes; the BLER, from 10%, steadly increasesas the SINR decreases from 30 dB until it reaches a maxima of 45% ataround 23 dB. The cause is mainly due to the fact that for the MCSsthat were being used in that range mostly have 8-PSK modulation(MCS-5-6-7-8-9). As the conditions get worse the BLER increasesrapidly until it reaches a certain threshold beyond which the BSSchooses to switch to lower MCS, most of them are GMSK modulatedand thus are more robust. The result of this switch is that the BLERdecreases and reaches a minima of 30% at around 15 dB where thelower MCS can no longer decrease the BLER anymore and thus theBLER increases back again to reach around 45 %. The same trend isevident in the plots against the RXLEV in Figure 4.4(b), yet the firstmaxima appears at around −83 dBm. The Figures 4.5(b) show theaveraged BLER for all the sites along with the confidence interval of 95%.

The reason behind such a large BLER is due to the scatteringproperty of the forest environment causing the connection to go intolong Rayleigh fading dips, due to the slow speed of the MS, with whichthe MCS used can not cope. The BSS senses a very good channelcondition, based on that it selects a suitable MCS that can maximize thethroughput. However, the MS experiences a sudden drop in the signallevel and the data being transmitted is lost and sent again. The thesis[25] studied the effect of the forest on the signal and showed that thepathloss represneted more of a suburban pathloss model than rural andthat the environment causes high scattering of the signal. Thereafterthe reason behind the high BLER and thus the low throughput ismainly due to the incapability of the BSS in adjusting properly tothe channel conditions. To combat the effect of slow mobility on theperceived channel quality, employing frequency hopping instead mayhelp in reducing the BLER. If the channel conditions are mitigated, theMCSs will be utilized more properly, thus increasing throughput, bydecreasing BLE and retransmissions with lower MCS. As a result thecurves provided in Sections 4.1 and 4.2 will have higher throughputsand the MS will experience a significant improvement.

The data in Table 4.1, taken from [26], shows a maximum errorrate enforced on each MCS used on a packet data channel in EDGE.The maximum error rate specified is not meant to dictate a minimumquality for the packet services, but as a reference for the manufacturersof the mobile equipment. The BLERs witnessed in the study show that

4.3 BLER 36

10 15 20 25 30

10

15

20

25

30

35

40

45

50BLER vs. CIR

BL

ER

( %

)

CIR ( dB )

Site1Site2Site3Site4

(a) BLER vs. SINR

−100 −95 −90 −85 −80 −75 −70 −65 −600

10

20

30

40

50

60

Received Power ( dBm )

BL

ER

(%

)

BLER vs Received Power

Site1Site2Site3Site4

(b) BLER vs. RXLEV

Figure 4.4: BLER of all sites measured against the perceived SINR andRXLEV.

4.3 BLER 37

10 15 20 25 30

0

10

20

30

40

50

SINR (dB)

BL

ER

(%

)

Mean BLER for all data95% Prediction Bounds

(a) BLER vs. SINR

−100 −95 −90 −85 −80 −75 −70 −65 −60 −55

0

5

10

15

20

25

30

35

40

45

50

RXLEV (dBm)

BL

ER

(%

)

Mean BLER for all data95% Prediction Bounds

(b) BLER vs. RXLEV

Figure 4.5: Mean BLER of all sites measured against the perceived SINRand RXLEV.

4.4 MCS Utilization 38

a high BLER is reached once the RXLEV is below −80 dB, yet it isbelow 30 % when above that value.

Service BLER BTS (dBm) MS (dBm)PDTCH −MCS1 < 10% −103PDTCH −MCS2 < 10% −101PDTCH −MCS3 < 10% −96.5PDTCH −MCS4 < 10% −91PDTCH −MCS5 < 10% −97 −94PDTCH −MCS6 < 10% −94.5 −91.5PDTCH −MCS7 < 10% −88.5 −84PDTCH −MCS8 < 30% −84 −83PDTCH −MCS9 < 30% −80 −78.5

Table 4.1: Minimum Sensitiviy Requirements.

If compared with the specification, it can be noticed that the BLERvalues comply with the specs for MCS-9 and MCS-8 which require aBLER less than 30 % for a sensitivity level of around −80 dB. However,the data collected can not be used to state for MCS-9 and MCS-8 thatthe specs are met. The reason is that MCS-6 and others were being usedin combination for a RXLEV above −80 dB. Thereafter, the reasonbehind the BLER being below 30 % may be due to a combination oflower MCSs being used. It is possible to conclude that for a RXLEVbelow −80 dB for all combinations of MCSs, the maximum specifiedBLER of 10 % is crossed. Still it can not be said that the bounds arecrossed for each and every MCS, it can only be stated that for the rangeof RXLEV that encompasses the sensitivity levels of MCS-7 to MCS-1,the maximum BLER is crossed for a combination of these MCSs.

4.4 MCS Utilization

To support the argument discussed in Section 4.3, a look at the MCSusage may help in understanding how the BLER curves behave. InFigures 4.6, the MCS distribution is provided for the SINR and RXLEVrespectively. It can be seen that at high SINR the MCS used aremainly the 8-PSK modulated schemes, MCSs 5, 6, and 9 are the majorcontributors. Although the high SINR values should allow the usage ofMCS-9 alone, the IR algorithm, upon errors, tends to re-transmit witheither a higher puncturing scheme or with MCS-6, which belongs tofamily A.

Thus the usage of MCS-6, in the light of the BLER measured, mayalso be due to switching within the same family. It can also be noticedthat as the SINR decreases the difference between the amount of usage of

4.5 Sample-Cell Coverage Anticipation 39

2 7 12 17 22 27 320

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

CIR( dB )

Per

cen

tag

e o

f U

se p

er C

IR In

terv

al(

% )

MCS Distribution over different CIR Intervals

MCS1MCS2MCS3MCS4MCS5MCS6MCS7MCS8MCS9

(a) MCS vs. SINR

−97 −92 −87 −82 −77 −72 −67 −62 −57 −520

0.1

0.2

0.3

0.4

0.5

Received Power ( dBm )

Per

cen

tag

e o

f U

se p

er A

bci

ssa

Inte

rval

( %

)

MCS Distribution over different Received Power Intervals

MCS1MCS2MCS3MCS4MCS5MCS6MCS7MCS8MCS9

(b) MCS vs. RXLEV

Figure 4.6: Modulation Coding Scheme (MCS) distribution over SINRand RXLEV.

MCS-9 approaches that of MCS-6 due to the increased errors. The peakthat was talked about in Section 4.3 is at the same point when MCS-6exceeds MCS-9, in other words when most of the data transmitted withMCS-9 is retransmitted with MCS-6. It can also be observed how thelower MCSs come into play, and their role becomes more significant asthe SINR decreases. It is interesting to note that MCSs 7 and 8 are notused often while MCSs 1 and 2 are rarely used. Better adjustments tothe channel will allow for a better utilization of MCS-9 at high SINRand that of MCS-4, thus allowing higher throughputs.

4.5 Sample-Cell Coverage Anticipation

This section shall present an expectation of the throughput coveragein a sample cell setting. The cell parameters are depicted in Table4.2 and the path loss model to be used herein is provided in Equation4.1, which has been emperically derived in [25]. The transmitter andreceiver antennas are assumed isotropic and the cell radius is assumedto be 5 Km. In the calculations, it is assumed that the mobile has abody loss of 10 dB, which is an average loss found in most GSM 900MS devices as shown in [27].

4.5 Sample-Cell Coverage Anticipation 40

Lpath = K + n ∗ (10 ∗ Log10(r)− S) (4.1)n Power loss factorK Clutter factorS Data centering parameterr Distance from transmitter

Substituting the values given in [25] for a receiver antenna elevationof 1 m into 4.1, we obtain 4.2.

Lpath = 119.7557 + 3.8680 ∗ (10 ∗ Log10(r)− 32.7622) (4.2)

Power Transmitted 43 dBm

Cable Losses 3 dB

Transmitter Omni− directional Antenna Gain 11 dB

Receiver Antenna Gain 0 dBi

Body Loss 10dB

Cell Radius 5 Km

Table 4.2: Link parameters.

It needs to be mentioned that the purpose behind this section isnot to provide a definite expectation of the throughput coverage in acell, since this is dependant on the distribution of the SINR or RXLEV,which is not covered in this study. To have a better expectation thedistribution of the signal quality in a similar cell needs to be done bya more intensive measurement campaign that spans the whole areaof several cells. Nevertheless, it would still provide some anticipationof the expected throughput coverage in those areas. Assuming thatno interference is present, i.e. a noise limited scenario, the RXLEVcalculated for different cell radii is mapped to the throughput levelsobserved and depicted in Figure 4.3(b). The throughput curve that wasused is the green curve that shows the maximum throughput possiblyattainable under the BLER experienced. The measured throughputvalues are not used due to the AWND size problem and the red curvecan not be attained under the conditions at hand.

It can be seen from Figure 4.7 that the area covered by a RXLEVabove −80 dBm lies below the 2 Km belt which constitute a mere 17 %of the total area. This specific area is where the highest throughputsabove 100 Kbps can be attained, reaching a maximum of 160 Kbps atthe closest measured point around 600 m from the BSS. If the next cellbound is set at a RXLEV of −90 dBm at a radius of 3.7 Km, wherethe GMSK MCSs come into play, the throughput is limited between

4.5 Sample-Cell Coverage Anticipation 41

100 Kbps and 60 Kbps for around 45 % of the total area. The rest ofthe cell area would experience throughputs below 60 Kbps. Thereafter,high throughputs attainable by EDGE under the current situation canbe reached, if the AWND size is set to 32 KB for less than 20% ofthe total area and a maximum of 160 Kbps. While around 80% ofthe cell area is confined to throughputs below 100 Kbps. Despite thisfact, performance for EDGE can be greatly improved if the BLEs andbody loss are mitigated, the farther areas of the cell will then have theperformance of the ones closer if not even better.

Most previous work results have found that the theoretical through-puts for EDGE can be attained [12] or that an average throughput of46.3 Kbps per TS can be obtained in a semi-open rural environment.The results presented herein show that these throughputs are optimisticcompared to the actual case in a GSM planned network in a rural forestenvironment.

−5000 −4000 −3000 −2000 −1000 0 1000 2000 3000 4000 5000−5000

−4000

−3000

−2000

−1000

0

1000

2000

3000

4000

5000

R < 2 Km Area is 17%100− 160 Kbps

2 Km < R < 3.7 Km Area is 45 %60− 100 Kbps

3.7 Km < R < 5 KmArea is 38 %40− 60 Kbps

− 90 dBm

− 80 dBm

Figure 4.7: RXLEV coverage in a cell with radius of 5 Km.

Chapter 5Conclusion

The purpose of this thesis was to conduct a case study on the through-put performance of the newly deployed EDGE capabilities in forestdense areas of Sweden. A selection of cell sites was carried out toprovide a sample of the sites deployed and the forest environment thatcharacterizes most of the Swedish forests. A measurement campaignwas conducted over these areas and the data was collected and an-layzed to extract information on the performance and coverage of EDGE.

It has been shown that the throughput was below expectations dueto several reasons. One of the reasons was the usage of the defaultsettings for the data connection on Windows XP OS. However, theextracted data did not comprise solely of the measured throughput,for the data gathered on the usage of MCSs and BLER was able tocompensate for this error. Thereafter, the results also showed that evenif the settings were changed the throughput would still be significantlybelow the maximum.

The main reason for the low throughput was the high BLERexperienced. The BLERs were above the acceptable levels dictatedby the standards, which may have been due to the slow mobility andmultipath effects. A possible solution to mitigate this problem wouldbe to deploy frequency hopping. The high BLER has also caused aselection of MCSs not anticipated by previous research, where MCS-6was being used in a region that should be dominated by MCS-8 andMCS-9. This selection of MCSs has caused a further decrease from themaximum theoretical throughput possible by EDGE.

From hence, a rough anticipation of the coverage was concluded basedon the results obtained by this thesis and [25]. The coverage studyshowed that, for a hand-held ”phone”, a rough 20% of the total area of acell would be covered by throughputs above 100 Kbps, while 80% wouldexperience throughputs below that level. Nevertheless, a reduction in

43

BLER would greatly improve the overall performance of the throughputand allows higher than 100 Kbps for most of the cell area. Thus, theclaim that 200 Kbps can be provided in those areas is not feasible underthe current circumstances.

Chapter 6Future Work

In an experimental study it is always better to have more collected data,as such it would be to an advantage to carry out more measurements inthe light of the results obtained so far. Future work may also includea study of the uplink throughput, which has not been covered by thisthesis due to the time and weather limitations. The uplink throughputis relevant since data shall also need to be transferred to the centralserver through the GERAN network.

The data has shown that high throughput coverage is limited ina cell, an increase in received power may be implemented by a highgain receiving antenna. However, it has been shown in [25] that theenvironment is scatterous, a directive high power gain antenna may notgreatly improve the performance. Thereafter, a further study of signalpropagation in the forest may need to be studied.

It has been suggested that the possible reason behind the highBLER may be due to the slow speed of the mobile in such environments.An implementation of frequency hopping may be tested to observe itseffect on the reduction of the BLER. If employing frequency hoppingmay be expensive, it is possible to observe the BLER distribution foreach MCS in this environment. This observation would allow for theselection of the more robust scheme, which would reduce BLER andimprove to some extent the throughput.

The data collected did not test for the case of a fully deployed EDGEnetwork, future work may be to conduct the measurement campaignsuch that the mobile would be able to switch from a site to another.The handover may induce some delays but may enhance coverage sincethe mobile will connect to the site that can provide a better quality ofservice. Also future work may involve characterizing the antenna gainand pattern of the mobile station in use, in this case the Nokia 6230TEMS phone.

45

Simulations can be carried out as part of the future work, wherebyEDGE is simulated given a detailed propagation model of the forest.The simulator would be able to simulate the EDGE network protocolstack and the LA/IR algorithm. This would allow a better comparisonwith the measured data collected in this thesis.

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