A FIELD-VALIDATED LOCATION CONSCIOUS QOS

PREDITION TOOL FOR WLL NETWORKS

Hicham Bouzekri, Tajjeeddine Rachidi

School of Science & Engineering

Alakhawayn University, Ifrane, Morocco

Yassine Moussaif, Tarik Janati

Wana Corporation, Morocco

Keywords: WLL QoS prediction, Field Measurement campaigns, Network Coverage Planning, GIS Map-based

propagation modeling.

Abstract: Wireless Local Loop (WLL) has become a viable alternative for the last mile problem, especially for

emerging countries. However the success of a large scale deployment of such a technology relies on careful

network planning taking into account existing building layout to achieve predictable quality of service

(QoS) for each customer. This paper proposes an approach where actual geographical layout of the area to

be covered is taken into account to distinguish line-of-sight (LOS), Obstructed Line-of-sight (OLOS) and

non-LOS (NLOS) regions and uses adequate propagation models for each case. By doing so a more accurate

prediction on achievable QoS is possible for each point of the area. The output of the tool is a point-by-point

quantitative measure of received average signal strength prediction and an optimized overall coverage

quality. Finally, field measurements and benchmarking were used to validate the approach adopted.

1 INTRODUCTION

In a net savvy world, broadband internet is as

important as having access to the power grid. Large

areas of the developing world still lack today the

wired infrastructure on which xDSL can run. Given

the prohibitive cost of running such installations, a

WLL solution is really the only viable alternative.

But unlike a twisted pair channel, which offers a

dedicated channel with highly predictable bandwidth

for a given distance from the local office, a WLL

operates over a channel which depends not only on

distance but also on actual obstacles between

transmitter and receiver. In order for an operator to

guarantee a QoS for its customers, an accurate

prediction of achievable performance for each

customer is required taking into account actual

position of base transceiver station BTS, customer

premises equipment (CPE) as well as landscape.

This paper proposes a field validated, benchmarked,

Geographic Information System (GIS) based tool

providing point by point received signal strength

prediction and a simulation-based BTS placement

optimization algorithm. Next, Section II discusses

the system model. Section III covers the propagation

models and tool description. Section IV presents

validation methodology and results, before the last

section draws some conclusions and proposes future

work directions.

2 SYSTEM DESCRIPTION

In order to accomplish the goals set for this research,

an awareness of the exact layout of propagation

environment between transmitter and receiver had to

be harnessed. This feat was accomplished in a two

stage process: first, natural elevation of the area had

to be taken into account and for the purposes of our

work the Digital Terrain Elevation Data (DTED)

files made available by National Imagery and

Mapping Agency (NIMA) were used. Second, actual

buildings laying in between transmitter and receiver

needed also to be taken into account, we made use of

a GIS map (in the publicly available ESRI shapefile

format; (ESRI shapefile 2008) of the desired area.

This map provides not only delimiting points of each

building in the area of interest but also their height.

157

Bouzekri H., Rachidi T., Moussaif Y. and Janati T. (2008).

A FIELD-VALIDATED LOCATION CONSCIOUS QOS PREDITION TOOL FOR WLL NETWORKS.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 157-162

DOI: 10.5220/0002024401570162

 SciTePress

The height information is not complicated to get as

cities obey strict zoning regulations that determine

for every zone building heights. By combining this

information we are able to accurately distinguish

LOS, OLOS and NLOS areas. The combination of

elevation information and GIS map gives the system

a complete knowledge of the propagation

environment as Figure1 shows.

Figure 1: Tool display of a Map of the Azrou Region

(Morocco).

3 PROPAGATION MODELS AND

TOOL DESCRIPTION

3.1 Propagation Models

Given that interest in this study is average received

signal strength in an urban environment, a

deterministic channel model approach (such as ray

tracing) is clearly unusable. Although Ray tracing is

very popular among researcher in this area, we think

that given the high number of reflections arriving at

the receiver and their dynamic nature, it would be

unreasonable to exhaustively enumerate and track

them. The assumption of a few dominant paths is

also inaccurate in the case of NLOS. The only valid

approach for a dynamic urban layout is to use

empirical propagation models (Rappaport, T, 1996

However the models for LOS, OLOS and NLOS

environment are vastly different. In our approach,

for every point in the map, a line-of-sight assessment

is applied between the location of the BTS and each

potential receiver antenna position. The tool uses an

OpenMap API to import ESRI shapefile of the area

augmented by DTED.

In case of a Line of Sight (LOS) environment,

the Free Space Loss (FSL) (Rappaport, T, 1996

)

model is used, provided the first Fresnel zone is

clear. This is a reasonable assumption as although

transmitted signal reflected replicas do arrive at the

receiver in the case of an urban environment, the

receiver will lock on the highest-power first arriving

path.

The second possible case is that of OLOS. In

case an obstruction is within the First Fresnel zone,

whose width depends on the transmission frequency,

neither the LOS nor the NLOS models are

appropriate. After a careful review of appropriate

models to be used for this case, the choice was made

to adjust the LOS path loss with an Excess Path Loss

(EPL). The most appropriate method for calculating

EPL was found to be the Deygout method

(Saunders, 2006 ). As Figure 2 shows all edges

(obstructions) in the Fresnel zone are taken into

account. In this illustration the main edge (closest to

the LOS path) defines two sub-paths. The method is

then recursively applied to these sub-paths

Figure 2: Edges inside Fresnel zone.

For a single edge in the Fresnel zone, the EPL is

given by the following formulas:

Gd = 0 for v <-1

Gd = 20 * Log

(0.5 – 0.62 * v) , -1 < v < 0 (1)

v= h/r sqrt(2) & r= sqrt (2(d1+d2)/(c/f)(d1.d2)

h being the negative height of the main edge

compared to the LOS path; d1 is the linear distance

from BS to the edge and d2 the distance from the

main edge to the CPE. f is the carrier frequency and

c the speed of light.

Naturally, in an urban deployment of a Wireless

Local Loop (WLL), some of the CPEs, if not most,

lack the privilege of having this clear, unobstructed

line of sight to the BTS. Consequently, empirical

propagation models have to be used in

(Abhayawardhana, 2005

), three models were

evaluated, namely the Stanford University Interim

(SUI) model, the COST-231 Hata model and the

ECC-33 model which showed the closest agreement

with the measurement results. In our paper both

models (ECC-33 and SUI), (IEEE 802.16 standards,

2008

) will be simulated to validate against measured

results.

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Figure 3: Predicted Coverage of the Azrou region (Normal Mode).

3.2 Tool Description

The Graphical User Interface (GUI) of the tool

allows the operator to enter all relevant information

for the simulation such as carrier frequency, height

of BTS and CPE, transmitter power and sensitivity

of the receiver as well as desired precision of the

output map. This last parameter directly impacts the

run time. The operator can then either pinpoint the

position of the BTS or make use of an optimization

algorithm for positioning the BTS.

Theoretically the algorithm would attempt all

possible locations for the BTS, however network

planning obeys business constraints. The tool

expects from the operator axes (typically along

major avenues) along which to search for optimum

positions. In the case of multiple BTSs, the

exponentially complex algorithm of finding joint

optimum positions can be replaced with disjoint

local optimum positions. This trade-off allows for

faster convergence of the algorithm at the expense of

the precision for the proposed placements.

QoS has taken various meanings depending on

the context it is being used in. For the work

presented in this paper, we are interested in the

average received signal strength as an indicator for

QoS, either the area average for the optimization

algorithm or individual for each point in the map.

For example the 802.16 standards (

Wimax Forum,

2008. ) tie the signal strength received to different

modulation formats used (from 64 QAM to QPSK)

translating into different data rates and different

error rates and hence different QoS.

4 SIMULATIONS AND TOOL

VA L I D AT I O N

This section presents results obtained: the first

subsection displays graphically the point-by-point

received signal strength prediction while the second

subsection details the approach and the results of the

tool validation.

4.1 Simulations

Figure 3 shows the output of the tool for the Azrou

region in Morocco. The different colors highlight

different received signal strengths with a scale

displayed in the bottom and a summary table on top

displaying BTS number, position (latitude and

longitude), height, frequency and overall area

average received signal strength.Figure. 4 shows the

output of the optimization algorithm for three BTS.

Each BTS position is optimized along a separate

axis to achieve overall best average Rx power.

4.2 Tool Validation

In order to validate the estimates provided by the

tool, field measurement campaigns had to be

conducted. This section details the approach used

and the results obtained.

Through a university-operator partnership a

street signal strength measurement campaign was

held in the city of Rabat during summer 2007. The

area of the city where measurements were conducted

A FIELD-VALIDATED LOCATION CONSCIOUS QOS PREDITION TOOL FOR WLL NETWORKS

159

Figure 4: Optimized positions for 3 BTS.

Figure 5: Comparison with measured values LOS areas.

is a medium density city with up to 5-floors

buildings. The interest was not in determining

instantaneous signal strength but rather time average

signal strength as these were influencing the QoS the

operator was able to provide.

The operator had several CPEs deployed around

the city and a centralized control and monitoring

system collected regularly signal strength signals

received at CPEs. Most of the installed CPEs were

in a LOS configuration, and the collected

measurements were used to validate the estimates

obtained by the tool. To complete this study a field

measurement campaign was conducted using a

spectrum analyzer and an omni-directional antenna

mounted on a vehicle. In addition to LOS areas, the

campaign targeted OLOS and NLOS areas in

increasing distances from the BS.

As the operator was using a commercial RF

planning tool (ATOLL), we have run a simulation to

obtain a commercial tool benchmark for our tool. In

order to validate each propagation model separately

the results were categorized as either LOS, OLOS or

NLOS.

For areas having an unobstructed LOS view of

the base station of the WLL, the results obtained by

the tool are very close to the benchmark commercial

tool and are almost identical to the field measured

values as can be seen from Figure 5.

The good match between these results can be

considered a validation of the FSL model of

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Figure 6: Comparison with measured values for OLOS areas.

Figure 7: Comparison with measured values for NLOS areas.

propagation. One possible justification for these

results is that even through reflected paths will

arrive at the receiver, the synchronization loop

within the receiver will lock on the first strongest

path, which is the LOS path, disregarding reflected

ones which will present minor interference.

Table 1: RMS error for OLOS areas.

Model used Error RMS

Commercial Benchmark tool 8.38

Developed Tool without EPL 9.89

Developed Tool with EPL 5.11

Areas having one or several obstructions within

their First Fresnel zone were classified as OLOS

areas. Figure 6 shows that the addition of the EPL

considerably improves the adherence of the results

compared to measured signal strengths.

Quantitatively the Root Mean Square (RMS) of the

error compared to measured values was assessed and

is given in Table 1.

Finally for NLOS area, two models were

potentially suitable: the ECC-33 and the SUI. Both

simulations were run to compare the adequacy for

our terrain. From Figure 7, ECC-33 seems to give

the closer results to the field measurements.

Quantitatively, Table 2 gives the average RMS error.

Table 2: RMS error for NLOS areas.

Model used Error RMS

Commercial Benchmark tool 12.94

Developed Tool ECC33 model 6.8

Developed Tool SUI model 8.11

5 CONCLUSIONS AND FUTURE

WORK

This paper presented the work conducted to develop

A FIELD-VALIDATED LOCATION CONSCIOUS QOS PREDITION TOOL FOR WLL NETWORKS

161

and validate a WLL planning tool. The approach

adopted in this paper was to use a GIS map of the

area to categorize the areas to be covered depending

on the presence of any obstructions between receiver

and transmitter. Three categories were retained:

LOS, OLOS and NLOS and appropriate propagation

models were used for each. The tool developed

provides a GUI based proposed BTS positions or a

simulation-based algorithm to propose optimized

placement. As with any QoS and prediction tool,

ultimate validation comes from field measurement

campaigns. Thanks to a partnership with a local

operator, access to collected measurements and

equipment to conduct further measurements

provided an environment for validating the tool. As

results showed, the tool prediction fared well and

gave in certain conditions better results than

benchmark commercial tools. The approach adopted

is hence validated by a 40% improvement in RMS

error for the OLOS case and a 47% improvement for

the NLOS case compared to commercial benchmark.

Another interesting observation is that SUI model

tend to give excessively optimistic results for

distances of less than 1 km and excessively

pessimistic results for distances above 10km

compared to ECC-33 model. Future work includes a

larger scale validation campaign in different

propagation environments and the introduction of

calibration parameters to better fit different terrains

and propagation environments.

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