Optimal Base Station Placement and Fixed Channel AssignmentApplied to Wireless Local Area Network Projects1

Ricardo C. Rodrigues Geraldo R. Mateus Antonio A. F. LoureiroDepartamento de Ciência da Computação

Universidade Federal de Minas Gerais - Brazil{rick, mateus, loureiro}@dcc.ufmg.br

1 This work has been partially supported by Project SIAM/DCC/UFMG, grant MCT/FINEP/PRONEX, number 76.97.1016.00.

Abstract

The project of a wireless local area network (WLAN)has two major issues: determining the best placement ofthe base stations (BS) and assigning the frequencychannels for the stations. The correct BS placementminimizes the number of stations necessary to cover thedesired attendance area, reducing installation costs. Thechannel assignment determines the frequency band to beused by each BS, minimizing interference signal betweenthem and improving the network throughput.

This work relates a real experience where we appliedthe concepts of two outdoor environment classicproblems, the optimal base placement problem for indoorenvironments and the fixed channel assignment problem,to build a wireless local area network in an indoorenvironment. We describe the hardware we used, theinteger linear programming models developed and theresults obtained.

1. Introduction

The great advance on wireless communications duringthe last years must continue in the future. Cellular phonesand paging systems are good samples of wirelessapplications and several others are being developed. Thetendency is that these applications will not be offeredisolated as today but integrated. The objective is toprovide cellular telephony, paging, Web access and e-mail, among other services, integrated on a uniquecomputational device. Although wireless networks areclearly the present objective, the study of the wirelesstechnology does not apply only to the actual systems, butto a large variety of applications that will be available inthe coming years.

The first experiences on wireless local area networksused proprietary hardware [2]. The recent publication ofIEEE 802.11 specification regulating the media accessmethod on WLAN started an industry technologycompetition to adapt their products to the new standard.The first IEEE 802.11 devices were available on themarket by early 1998 and some projects have just begunto migrate to these new products [3].

In this work, we report a real experience where weinstalled an IEEE 802.11 compliant WLAN into an indoorenvironment. We studied two phases of the installationprocess: choosing the best placement for the base stations(BS) and assigning the frequency channels to thesestations. This work intends to share our experience oninstalling an IEEE 802.11 WLAN and stimulateinformation exchange between various research groups.

This paper is organized as follows. Section 2 describesthe acquired hardware we have used. Section 3 presents asolution to the base station (BS) placement problemdescribing the chosen attendance area, the computationalmodel developed and the obtained results. Section 4presents the solution for the channel assignment problem,its computational model and obtained results. Finally, wepresent the conclusions and future work in Section 5.

2. Hardware description

We used three WavePOINT-II Access Points, or basestations, that work as Ethernet bridges by receiving datafrom the wired backbone through an UTP connection andbypassing the received packets through a wirelessnetwork interface card (NIC). The mobile users receivethese packets through another NIC attached to its laptop.

The used hardware is IEEE 802.11 compliant. Thisstandard defines the media access method and thephysical layer specifications of a WLAN. It defines twomodulation techniques: DSSS (Direct Sequence Spread

Spectrum) and FHSS (Frequency Hopping SpreadSpectrum). These modulation techniques are incompatiblewith each other and it should be observed whenexpanding the wireless network.

Our wireless network operates at 2.4 GHz (microwaveband), provide a bandwidth of 2 Mbits/s, DSSSmodulation and CSMA/CA access method (Carrier SenseMultiple Access/Collision Avoidance). The mechanism toavoid collision is needed on a wireless network as a hostcannot detect a collision after it sends a packet, differentfrom an Ethernet network.

3. Base station placement

In this section we present the process to choose thebest placement for the transmission antennas or basestations.

3.1. Mapping the demand area

As being a pilot experience and due to a limitation ofhaving only three base stations available, we consideredthe third and fourth floors of our building as ourattendance area selected. Figure 1 shows the demand areamapping of each floor. The dark areas correspond to thefaculty rooms and labs. They are, effectively, our demandareas. The lighter areas correspond to aisles. It wasexcluded from the demand area because it is a circulationarea and its signal level are usually better than innerrooms (less obstacles). Thus, the impact caused by itsremoval is irrelevant.

Laboratory rooms mainly occupy the third floor whilefaculty rooms mainly occupy the fourth floor. The set ofrooms forms a rectangular area with an inner free space.

The demand area mapping was done by dividing thetotal area into small quadrangular pieces of demandpoints. Due to the variable size of the rooms, we definedthe square size in such a way that the area of each room isan integer multiple of the square area. It avoids situationswhere a square belongs to two adjacent rooms. With thismapping, we got 1144 square of 0,70 x 0,70 m2, being592 on the 3rd floor and 552 on the 4th floor.

3.2. Choosing candidate locations

In the next stage, we had to choose candidate locationsto the BS. A good candidate square must offer low cost ofinstallation and good attendance area. Mainly on indoorenvironments, questions like physical security, availableinfrastructure and flexibility are also relevant.

Choosing candidate locations with differentcharacteristics let us understand the BS behavior and itsreach, trying always opportunities that could give us abetter coverage. So, we chose three candidate locations onlaboratories at the third floor, one on a faculty room at the

4th floor and two on the aisles (3rd and 4th floors). Chosenlocations are shown on Figure 1.

X

Caption:

- Aisles

- Demand Area

- Base Station Candidate Position

X

X

XX

Third Floor

X

X

Fourth Floor

Figure 1: Attendance area of 3rd e 4th floors

3.3. Signal measurement

After choosing the candidate locations, we mustcalculate or measure the signal level received by eachcandidate BS on each demand point. On outdoorenvironments, this is calculated through signal predictionalgorithms. In our work, however, we preferred tomeasure the signal received on each demand point.

The great number of demand points and the small areaof each point (0,49 m2) would make very difficult tomeasure the signal level on each demand point. Theproblem was solved by grouping the demand points intosmall but larger groups. Usually, we formed groups offour or six demand points. For each group, we made onlyone measurement and we assumed that the measuredsignal level had the same value for all the group elements.With this simplification, the number of necessarymeasurements for each BS was reduced from 1144 to 253.

The communication signal quality is measured indecibel (db). Higher value means better signal quality.Values over 20 db indicate excellent quality. Valuesbetween 11 and 20 db indicate acceptable quality. Andvalues under 10 db means poor or no communicationcapacity.

The signal measurement was done using the softwareWaveManager/Client IEEE, implemented by theWavePOINT-II manufacturer. This software lets usregister the received signal of each BS simultaneously andsave the information on a log file for later treatment.

During the measurement, we verified that the signallevel on each point was very sensible to obstacles andhighly dependent of the mobile unit orientation. Given thesame demand point as reference, if we turn the mobileunit to left or right by 90 degrees, we will get a signallevel completely different. Figure 2 shows the signal levelvariation of two distinct BS that arrives to the samedemand point while the mobile unit is turned around by360 degrees.

Signal Level Variation Along Time

0

5

10

15

20

25

30

35

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73

Sample Sequence

Sig

nal L

evel

(db)

Figure 2: Signal level variation along time

The signal level variability according to the mobileunit orientation introduced a new component to theproblem. It is not sufficient to measure the signal level ofthe BS on each point, but it is also necessary to choose thesignal level that best represents the signal quality in thatpoint.

We defined a method for measuring the signal thatcould provide the most representative signal level for eachdemand point. The data was collected while the mobileunit was turned around slowly until it completed 360degrees. Thus, we tried to represent all possible locationsof the mobile unit in that point. With this methodology,we obtained approximately 80 signal level values fromeach BS for each demand point.

The next step was to analyze and treat the collectedinformation. The BS signal level on a demand point is adiscrete function. This behavior is due to the fact that thesignal is very susceptible to environment variations. Wedefined, thus, a methodology for treating the signalsamples. For each set of BS signal samples on a point, weordered the signal in an crescent orientation. After that,we discarded 40% of the samples, 20% referring to thelower values and other 20% referring to the higher values.Although this discard can look very high, it is important

to minimize the influence of sporadic values of themeasured signal on the point.

3.4. Computational model

The optimal base station placement problem consistsof distributing BS through the demand area in order toassist the desired cover objective using the minimalnumber of base stations. The cover objective variesaccordingly with the necessity. Usual objectives are thedemand area total coverage, partial coverage withmaximum economical return and coverage with demandsupply guaranteed. The solution is found through use ofinteger linear programming models able to inform theminimal number of BS needed to meet the desiredcoverage. This approach is traditionally used on cellularphone systems.

In our environment, however, we have a fixed numberof available base stations: three. Our problem is to knowwhat is the best BS placement to meet our demand areaand what is the percentage of the total area covered withthese BS.

The used solution develops an integer linearprogramming model that, given the number of availableBS and the signal level of each candidate BS on eachdemand point, provides the best station placement and thetotal area covered. This approach provides moreflexibility than the traditional one:

• Allows us to inform the number of available BS. Thisis a typical situation where there is a limited budget;

• Allows us to dimension the number of BS dependingon the desired coverage. On pilot installations, it canbe sufficient to cover only part of the demand area;

• Allows us to asses the obtained gain when installingnew BS on the system; and

• If one or more BS fail, allows us to asses theplacement changes to be applied to the working BSso that the WLAN can work, even without its fullcapacity, until the BS is reinstalled.

The developed integer linear programming model isdescribed as follows. Let M be the demand points set, Nthe candidate BS set, S the number of available basestations and Ak the set of mutually exclusive base stations.Given two or more mutually exclusive stations, only onecan be selected (installed). Le aj be the station j, a booleanvariable that assumes value 1 if the station j will beselected and 0 otherwise. Also, let Nj be the set of pointsattended by station j, sij the signal of station j on point i,wi the attendance priority of point i, and zi the area ofpoint i. Let also xij be a decision variable that assumesvalue 1 when the point i is assigned to station j and 0otherwise. The model is given by:

(1) ∑∈∈

NjMi

ijiiij xzwsmax

subject to:

(2) ; ,1 Mi

Njijx ∈∀≤∑

∈

(3) ; SNj

ja ≤∑∈

(4) ; ,|| jeNNj ax j

Niij

j

∀≤∑∈

(5) ; ,1 jeNkAi

ja ∀≤∑∈

The objective function (1) must be maximized as afunction of the points that have higher signal level,attendance priority and physical area. The attendancepriority stimulates the attendance of the most importantdemand points, such as points where it is more usual theuse of portable computers, meeting rooms, etc. In ourexperiments, all the attendance points have the sameattendance priority.

Through restriction (2), a point can be attended by onlyone station. Although this situation do not occur in thereality, this restriction is necessary to let the chosenstation cover the largest number of distinct pointspossible, increasing the total area coverage.

Restriction (3) limits the number of available stationsfor installation. Restriction (4) states that, if station aj isselected, the number of points attributed to it must be lessor equal to the maximum number of points that can beassigned to it. Finally, restriction (5) limits in only one thenumber of mutually exclusive stations that can be selectedfor each set Ak. The computational model wasimplemented using the AMPL language [5] integrated toCPLEX [4].

3.5. Results obtained

The signal level distribution on an indoor environmentcan be observed through signal quality maps. These mapsshow the arriving signal level from each candidate BS toeach demand point.

Figure 3 shows the signal quality maps of a basestation located on an aisle at 3rd floor. The X pointindicates the BS location. Dark colors represent the bestsignal level inside rooms and laboratories. Aisles signalswere not measured.

Note that the signal level inside a room isapproximately constant. It occurs because the biggestsignal obstacles are walls, metal materials (elevators) and

the concrete between floors. Another obvious factorresponsible for signal level fall is the distance between thebase station and the mobile unit.

X

Caption:

- Aisles

- Demand area with signal > 20db

- Base Station Candidate Position

- Demand area with signal < 10db

- Demand area with signal between 10 and 20db

X

Figure 3: Sample of a signal quality map

We made two measurement series with the basestations installed on candidate locations, so obtaining dataabout six candidate BS. Although having only three BSavailable, we simulated the use of 1 through 6 stations toobserve the increase on covered demand area.

Figure 4 shows two important characteristics of theresults. The first one is that the increase on total coveredarea attenuates as we increase the number of availablestations. Although having high increasing factor for littleBS, the environment saturation due to the installation ofvarious BS tends to stabilize the total covered area.

0,00

20,00

40,00

60,00

80,00

100,00

1 2 3 4 5 6

Base Station 1Base Station 2

Base Station 3Base Station 4

Base Station 5Base Station 6

Total Area

Number of Base Stations

Covered area by each Base Station (%)

Base Station 1Base Station 2Base Station 3Base Station 4Base Station 5Base Station 6

Total Area

Figure 4: Covered area by each base station

The second characteristic refers to the reduction on thearea covered by each BS as we increase the number ofBS. This way, we reduce the number of users attended byeach BS and, consequently, the demand for thecommunication media.

Figure 5 presents the last result. The three chosen BSare installed on 3rd floor, exactly where the rooms arebigger and have less obstacle interference. This figurealso shows the signal level by which each demand point isattended. The attendance on third floor is excellent whileon fourth floor it is only reasonable. It occurs because theBS are installed on 3rd floor and the great variety ofobstacles on 4th floor. Despite these problems, we wereable to cover 86% of our demand area with signal levelover 10db using only the three BS available.

X

Caption:

- Aisles

- Demand area with signal > 20db

- Base Station Candidate Position

- Demand area with signal < 10db

- Demand area with signal between 10 and 20db

X

X

X

Third Floor

Fourth Floor

Figure 5: Map of the covered demand area

4. Channel assignment

This section presents the channel assignment problem,its importance on WLAN projects and the methodologyused in our experiments.

4.1. Interference

When installing the WLAN, it is necessary to specifythe channel frequency to be used by the BS. The stationsused on our work allow us to choose one (and only one)

channel between 11 available. If two BS use the same ornear channels, they can interfere and degrade the networkperformance.

The solution to these problems consists in sorting thecommunication channels according to its frequency bandand allocate them to the base stations obeying theminimal channel distance. Operating on distant frequencybands, the stations do not cause interference with eachother. In our tests, the minimal distance to avoidinterference is three channels.

We run some tests to verify the influence ofinterference on a IEEE 802.11 WLAN. We used twolaptops to download two identical big files (20 Mb) ondistinct servers of our wired LAN. We run the testsaccording to six typical scenarios of operation andcompared the results. The scenarios are described below:

Scenario 1. Each laptop downloaded the file ondifferent times through the same basestation;

Scenario 2. Each laptop downloaded the filesimultaneously through the same BS;

Scenario 3. Each laptop downloaded the filesimultaneously through different BS usingthe same channel frequency. However, theBS was too far so that there were nointerference between them;

Scenario 4. Each laptop downloaded the filesimultaneously through different BS usingthe same channel frequency. The BS wasclose enough so that there was interferencebetween them;

Scenario 5. Each laptop downloaded the filesimultaneously through different BS withchannel distance equal to 1. As the BS wasvery close, there was interference betweenthem; and

Scenario 6. Each laptop downloaded the filesimultaneously through different BS withcannel distance equal to 3. Although thebase stations were very close, the distancechannel of 3 avoided interference betweenthem.

The results provided on Table 1 show the importanceof an adequate channel assignment for having goodperformance on the wireless network.

4.2. Computational model

The fixed channel assignment problem consists inallocating frequency channels to base stations in order toattend the demand on each cell and minimize interferencebetween near BS. On traditional cellular phone systems, itis allocated various non-interfering channels to each BS.

The number of allocated channels to each BS isproportional to the demand on that cell. In our system,however, we must assign only one frequency channel toeach BS, conserving the minimal channel distancebetween near BS.

Scenario Laptop 1 Laptop 2Scenario 1 151,32 172,92Scenario 2 86,65 93,19Scenario 3 151,73 170,77Scenario 4 94,24 92,25Scenario 5 96,37 90,92Scenario 6 151,09 172,39

Table 1: Transfer performance (Kb/s)according to the scenario operation

Although there are various solutions proposed to thisproblem, we decided to implement a very simple butefficient solution. The main idea is to represent oursystem through a non-oriented graph where nodesrepresent the chosen base stations. The existence of anedge between two nodes means that the nodes causeinterference between them and, so, they must obey theminimal channel distance.

Our objective is to maximize the sum of distancechannels between interfering stations preserving a channeldistance of at least 3. This way, we try to allocatechannels in a most sparse way and thus facilitating thewireless backbone expansion.

We implemented the solution through an integer linearprogramming model. The model is described below.

Let G(N,E) be a non oriented graph with nodesbelonging to set N and edges belonging to set E. Let M bethe set of placed base stations, K the ordered set ofavailable channels, cik the decision variable that assumesvalue 1 when channel k is assigned to station i and d theminimal distance channel for the system. The model isgiven by:

(6) ∑ ∑ −∑=

−

=∈

||

2

1

1),(

maxK

k

k

ljlik

Ejijckc

subject to:

(7) ; ,1 MiKk

ikc ∈∀≤∑∈

(8) ;),(, ,1

|)}|1()11(|}1..1{{

GjiKkKdkdk

dkdkljlik cc ∈∀∈∀≤+ ∑

<=−+∧>=+−−++−∈

The objective function (6) must be maximized as afunction of the distance channels between interfering basestations. Restriction (7) states that each BS must have oneand only one channel allocated to it. Restriction (8) statesthat if a channel k is allocated to BS i, their interfering BScannot use channels from range k-d+1 to k+d-1. In otherwords, this restriction forces base stations to obey theminimal channel distance. The computational model wasimplemented using the AMPL language [5] integrated toCPLEX [4].

4.3. Results

In our work, each base station should be configured touse only one of the 11 channels available for operation,respecting the minimal distance of three channels betweeninterfering stations. We ordered the available channels,numbering it from 1 to 11, and executed the assignmentmodel for our three placed BS. The allocated channelswere 11, 8 and 1.

Although we made real tests with only three basestations, we simulated the model for more complexstructures and obtained very good results. Being a simplemodel, the time required to run the model is very short(less than 10 seconds for some complex networkstructures). We hope to test our model on really largenetworks as our wireless backbone network increases.

5. Conclusions

Wireless local area networks provide great flexibilityfor their users and introduce lots of new questions nottreated by wired networks. The first question is theWLAN installation itself. It is necessary to measure orpredict the signal on each point of the demand area, locatethe base stations and then assign one frequency channel toeach base station.

This work presented simple and efficient proposals tosolve placement and channel assignment problems onindoor environments. Starting from models used onoutdoor systems, we developed two integer linearprogramming models that demonstrated being simple andhaving good performance.

Our future work will be the analysis of signal curvesand try to develop a model where it could be possible tomeasure the signal in only a few points and expand thesemeasurement to obtain the whole demand area signallevels.

References

[1] Wireless Andrew Project at Carnegie MellonUniversity, USA.http://www.ini.cmu.edu/WIRELESS/Wireless_Infrastructure.html

[2] Bernard. J. Bennington and Charles. R. Bartel. WirelessAndrew: Experience Building a High Speed, Campus-Wide Wireless Data Network. Third Annual ACM/IEEEInternational Conference on Mobile Computing andNetworking (Mobicom), Budapeste, Hungary, 1997. Pages55-65.

[3] Bernard. J. Bennington and Charles. R. Bartel. WirelessAndrew: Building a High Speed, Campus-WideWireless Data Network. Paper submitted to Special Issueon Wireless Internet and Intranet Access of theACM/Baltzer Journal Mobile Networks and Applications(MONET). 1998.

[4] CPLEX Optimization. Using CPLEX callable libraryand CPLEX mixed integer library. Version 5.0. 1997.

[5] R. Fourer, D. M. Gay, and B. W. Kernighan. AMPL – AModeling Language for Mathematical Programming.Duxbury Press / Brooks / Cole Publishing Company,1993.

[6] IEEE 802.11 - IEEE Standard for Wireless LANMedium Access Control (MAC) and Physical Layer(PHY) Specifications. 1997.

[7] G. R. Mateus. Exact Algorithm and Heuristics forLocation Problem. PhD thesis, COPPE/UFRJ, Rio deJaneiro, RJ, 1986.(In Portuguese)

[8] G. R. Mateus and A. A. F. Loureiro. Introduction toMobile Computing. 11th Computing Workshop, Rio deJaneiro, RJ, 1998. (In Portuguese)

[9] D. Stamatelos and A. Ephremides. Spectral Efficiencyand Optimal Base Placement for Indoor WirelessNetworks. IEEE Journal on Selected Areas inCommunications, 14(4):651-661, 1996.

[10] N. Ziviani et all. SIAM – Information Systems onMobile Computing Environments. DCC/UFMG, 1999.http://www.dcc.ufmg.br/siam

[11] H. H. Xia, A. B. Herrera, S. Kim, and F. S. Rico. ACDMA Distributed Antenna System for In-BuildingPersonal Communications Services. IEEE Journal onSelected Areas in Communications, 14(4):644-650, 1996.