Automatic Location of Base-Stations for Optimum Coverage and Capacity Planning of LTE Systems

8/11/2019 Automatic Location of Base-Stations for Optimum Coverage and Capacity Planning of LTE Systems

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Abstract — Optimum basestation positioning in the frameworkof LTE wireless networks is not an easy task since there areseveral factors that need to be taken into account. The everincreasing demand for geographically inhomogeneouscapacity, which has become the limiting factor in the networkplanning process, as well as the presence of relay nodes andmixed cell scenarios, must all now be considered along with thesystem’s constraints and capabilities . In this paper, theoptimization problem for an LTE system is described and

results are produced with a proposed partially combinatorialoptimization algorithm. The optimization analysis is based oncoverage, capacity and cost criteria. Example results areprovided for a typical case study that give useful planninginsights for LTE systems.

Index Terms — Radio network planning, Basestation location,LTE systems.

I. INTRODUCTION

RF planning is a multi-step process of a rather iterativenature. In the context of 2G wireless systems this processcould be divided in two main unrelated steps, i.e. thecoverage and capacity planning stages. This however is notthe case with 3G WCDMA and 4G LTE systems, wherecapacity and coverage planning are strongly interrelated andshould be treated simultaneously.

Current LTE deployments (Rel. 8/9) offer up to 100Mbpspeak rates with traffic that includes voice and data from cellphones and other mobile devices such as tablets. Theplethora of data services (web browsing, social networking,audio, video, software downloading) requires a soundestimation of the user throughput requirements which israther difficult to achieve. Additionally, at the end of 2012~10% of the population was under LTE while theprojections suggest that this percentage will climb up to

60% at the end of 2018 [1]. Key feature of Release 12 andbeyond is the utilization of heterogeneous deployments thatcomprise both high (macro) and low power nodes [2].

Finding the optimal LTE network deployment amounts tosolving an optimization problem of combinatorial nature.As a consequence, system modeling that allows for rapidperformance assessment and hence selection among atypically huge number of candidate solutions, is crucial.

The cost and complexity of a network is closely related tothe number of basestations required to achieve the operator'sservice objectives. The location of basestations is not aneasy task and there are numerous factors that must be takeninto account when deciding the optimum position.

Planning and performance optimization in cellular networksform a very active line of research with a considerableamount of published work in the open literature especiallyon UMTS network planning and optimization, ranging fromBS location and coverage planning to antenna parameterconguration and cell load balancing [3-5]. The problem ofbase station (BS) positioning planning becomes complicatedin the framework of 4G wireless networks since thepresence of relay nodes (RNs) and mixed cell structuresmust now be included in the planning process. Typicaloptimization algorithms (combinatorial, genetic, greedy,simulating annealing) have already been used for this typeof problems (e.g. [6]). However, the combined problem of‘optimized base -station and relay- node planning’ in order toimprove network coverage and capacity as well as cost,especially in 4G systems, has received little attention.

In this paper, we have developed a rigorous LTE systemmodel that works for a general network topology andexplicitly accounts for mixed cell structures and relay nodesas well as non- uniform trafc demand (hotspots). A

partially combinatorial optimization algorithm that exploitsspecific characteristics of the LTE system (CQI) has alsobeen developed in order to automatically identify anoptimum solution that meets the operator’ s requirements interms of coverage and capacity in a given area, with theminimum number of basestations and hence, cost.

The remainder of the paper is organized as follows: SectionII describes the general BS positioning problem in thecontext of an LTE network, section III describes theoptimization algorithm, section IV presents simulationresults for a case study and finally section V concludes thepaper.

II. THE PROBLEM

A number of discrete user supplied points or ‘control nodes’(CNs) are used to represent the capacity and coveragerequirements in the operational area [7]. The initial numberof possible BS locations is user supplied (i.e. locationswhere planning permission and/or agreements have beenmade). The initial selection of user supplied sites willdetermine to a great extent, the final outcome, since noadditional locations will be identified during theoptimization process, and the user selected positions willnot be modified.

A typical problem is shown in Figure 1, where a number ofcontrol nodes (CNs) have been distributed in an area. Anumber of possible base station (BS) locations are also

Automatic Location of Base-Stations for OptimumCoverage and Capacity Planning of LTE Systems

G.E. Athanasiadou, D. Zarbouti, G.V. TsoulosUniversity of Peloponnese, Department of Informatics and Telecommunications,

Tripoli 22100, Greece, E-mail: {gathanas, dzarb, gtsoulos}@uop.gr



displayed in the map. Each macro base station has threesectors, pointing towards 30 o, 150 o and 270 o in azimuth.The antenna pattern of [8] is used for each sector antenna.In the direction of maximum power, each sector can alsohave an omnidirectional Relay Node (RN) which isactivated only if a CN is connected to the basestation

through this relay node. In the simulation, the microbasestations are omnidirectional and they don’ t have relayany nodes. The propagation module is based on theWINNER model [9], which gives different path loss valuesfor macro, micro and relay node scenarios. The LOSdistance in the WINNER model is set to 500m andshadowing is not included in this stage, in order to be ableto check the validity of the simulation outcome.

0 500 1000 1500 2000 2500 30000

500

1000

1500

2000

2500

3000Test Area (axes in meters)

Figure 1: Test area of the case scenar io. Thick red dots arepossible locations for sector ized BSs, thick green dots a re

possible locations for micro BSs, black stars are controlnodes and thick magenta star s represent hot spot areas.

The propagation parameters of the simulation aresummarized in Table 1.

EIRP of a macro BS (sector) 46dBm + 18dBi = 64dBmEIRP of a micro BS 30dBm + 7dBi = 37dBmEIRP of a Relay Node 30dBm + 5dBi = 35dBmPropagation model used formacro base stations

WINNER Path LossModels: Scenario C2 NLOSwith hTx = 25m

Propagation model used for

micro BS

WINNER Path Loss

Models: Scenario B1 NLOSwith hTx =10m, d LOS =500m

Propagation model used forRelay Nodes

WINNER Path LossModels: Scenario B1 NLOSwith hTx = 5m, d LOS = 500m

User/micro/RN antennapattern

Omnidirectional

Sector antenna pattern 3GPP antenna pattern for120 o sector [8]

Total cable losses 2dBCentral frequency 2.12GHz

Table 1: Propagation parameters of the simulation

The capacity module that provides the LTE rate calculationfor each user is described with the following flow chartwhere the Rank Index (RI) and the number of RBs that willbe allocated to the user are known.

SIR mapping toCQI

T a b l e

7 .2 . 3 -1

Modulation Code Rate

Data_Bits(eq.1)

USER_RATE =

Data_Bits/1ms

RBs

RI

Figure 2: Rate calculation for each user according to LTE.(Table 7.2.3-1 can be found in [12]).

The user demands are represented by two different sets ofcontrol nodes. There is a set of CNs which require a relativesmall data rate (e.g. 1-3Mbps) and there are hot spot areaswhich require ten times more capacity. All bandwidthresources are available to each sector and microcell (reusepattern 1/3). Hence, each sector of a macrocell and eachmicrocell have 100 Resource Blocks (RBs) to allocate tousers. The number of RBs which are allocated to each CN isa function of the channel Signal-to-Noise-plus-Interference

Ratio (SINR) and the user demand for data rate. For theLTE rate calculations the authors use a similar approach asthe one presented in [11]. For determining the minimumnumber of RBs that need to be allocated by the serving nodeto the CN, the process described in Figure 2 is followed.More specifically,

a) the SINR that is derived at the CN is mapped to a singleChannel Quality Index (CQI) value using the SINR-to-CQImapping function given in [10].

b) the CQI is mapped to a proper modulation level (Q m)andcode rate (CR). This mapping is derived according to Table7.2.3-1 of [12].

c) equation 1 provides the required number of RBs (N RB)when the CN throughput (CN t) is known. In equation 1 N RE is the number of Resource Elements (REs) per RB that forthe purposes of this work is set to 126 (CFI=3,Transmission Mode 1), T slot is the time duration of the LTEslot, while the 24bits-CRC of LTE has also beenconsidered.

CRCCRQNNRITCN mRERBslott 81

8 (eq.1)

The RNs are considered to be outband, i.e. thecommunication of the RNs with the corresponding macro

basestation is not using these bandwidth resources. The RBs



needed by the users connected to an RN, are subtracted bythe pool of RBs of the corresponding macro basestation.

The algorithm must provide the bandwidth resourcesaccording to the SINR and the data rate request, as well ascoverage to all control nodes using the smallest sub-set ofpossible eNBs. Among solutions with equal number of basestations, those which provide better overall channel SINRare preferred.

III. THE OPTIMIZATION ALGORITHM

The proposed optimization algorithm follows a partiallycombinatorial approach. The combination approach fitsperfectly with the idea of pre-selected antenna positions.Similar approaches have been used for automaticallylocating basestations for 2G systems with coverage andtraffic criteria [13-14]. The idea is that if all possiblecombinations of basestation positions are examined theoptimum combination for this set of BSs will be found.

However, the number of combinations increasesdramatically as the number of possible locations isincreased. To avoid this problem this algorithm segmentsthe total number of possible locations into smaller groups,which are randomly selected, and for each smaller group anexhaustive search for solution is performed.

The test area is first divided into four equal subareas (e.g.1.5Kmx1.5Km for the area of Figure 1) where the partiallycombinatorial algorithm is applied. Half of the positions ofeach subarea form the set of BS positions to examine for thewhole area. All basestations which participate in any of theproduced solutions for the subareas form the group of

candidate positions for the total area. Hence, the number ofpossible locations reduces to half (or even more if required),improving dramatically the algorithm speed, making itpossible to examine scenarios with a large number ofcandidate positions.

The initial number of BSs for each segment is determinedby estimating the number of BSs needed to fulfill the capacitycriterion assuming a moderate channel quality in terms ofSINR for all links (CQI = 4). If the algorithm after goingthrough the produced combinations cannot find a solution, alower CQI is assumed (i.e. a higher number of requiredBSs) until a solution is found or CQI reaches its lowestvalue (CQI=1).

Also, a first estimate for the number of BSs needed to fulfillthe coverage criterion is found by applying a simple greedyalgorithm. Given a number of BSs and control nodes, thegreedy algorithm first selects the BS that covers the mostcontrol nodes. The BS and the control nodes are then removedfrom the area of study and the same procedure is repeated untilthere are no control nodes left to cover. The larger of these twoestimations (one for the coverage and one for the capacitycriterion) sets the initial number of locations in the examinedcombinations.

For each combination of possible basestations, thealgorithm first checks if all CNs are covered, i.e. the

received power is above the threshold of -95dBm, as it iscalculated by the LTE link budget. If the CNs are not

covered then the proposed solution is invalid and thealgorithm proceeds to the next combination of possible BSs.If the solution passes the coverage test, the SINR and therequired RBs for all possible links are calculated. The CNswhich are covered by only one BS, are the first to beallocated to an eNB. Then, starting from the CNs which

require the highest number of RBs, each CN is allocated tothe eNB where it has the highest SINR. If this eNB has notenough available RBs, the next eNB is considered, untilthere are not any eNbs left. If a CN cannot be served by anyof the basestations, the solution is invalid. If all CNs areallocated to an eNB, the combination is stored as onepossible solution and the algorithm continues the search (forother solutions with equal or smaller number ofbasestations) until a predefined number of trials (e.g. 100) isreached.

At the end of the search, the algorithm chooses the solutionwith the minimum cost. Given that macrocells are muchmore expensive to deploy, the cost of each microcell andRN is considered to be 1/10 and 1/20 of the cost of amacrocell, respectively [1]. Channel quality (i.e. requiredbandwidth resources) as well as 95% coverage of the totalarea, are also considered as secondary criteria.

IV. SIMULATION RESULTS FOR A CASE STUDY

In this section a case study is performed for a 3Km x 3Kmarea (Figure 1) where 80 CNs are randomly placed, 10% ofwhich represent hot spots. The required data rate for theCNs is 1Mbps, and for the hot spots 10Mbps. In the areathere are 40 randomly placed possible macrocells with threesectors each, as well as 40 microcells with omnidirectionalantennas. The distance between the RN and thecorresponding macrocell is 400m.

The solution shown in Figure 3.a has four sectorized macrobasestations (thick red dots in the figure) and one microbasestation (thick green dot). At all sectors the RNs (blackcircles) have been activated since they have CNs connectedto them (black lines), but there are also connections directlyto the sectors (cyan, green and magenta lines for the threedifferent sectors).

Note that according to the WINNER model, the channelgain reduces considerably beyond the LOS distance. Hence,the CNs connected to the RNs or the microcells are within arange of ~500m (e.g. in Figure 3.a the CN in the bottom leftcorner is served directly by the sector since it is out of themicrocell range). The algorithm chooses basestation/RNpositions close to the hotspots so that the channel quality ishigh and hence, less bandwidth resources (RBs) are used forthe link.

Another possible solution (Figure 3.b) is also the finaloutcome of the algorithm. Note that 3 out of 5 proposedbasestations are common for both solutions. However, thesecond solution is preferred (i.e. it has better fitnessfunction) because the usage of microcells, reduces the costof the solution considerably, and also improves the overall

channel quality of the CNs since the interference from themicrocells is much lower. From the same figure can benoticed that one of the basestations covers CN positions



almost 2Km away. This is not surprising, since the coverageof the employed macrocells is ~3Km. The reason why thecoverage of the chosen cells in this test scenario appearssmaller, is that it is a capacity limited scenario i.e. the cellrange remains small in order to have better channel qualityand also the eNBs only have enough bandwidth resources

(RBs) to serve the CNs which are closer to them.

0 500 1000 1500 2000 2500 3000

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Algorithm solution

(a)

0 500 1000 1500 2000 2500 3000

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Algorithm solution

(b)

Figure 3: (a) A possible solution produced by the partiallycombinatoria l algor ithm, (b) The final optimum solution.

V. CONCLUSIONS The positioning optimization problem of heterogenous cellswith relay nodes for an LTE system with non-uniformtraffic is described in this paper and results that give usefulinsights are produced with a partially combinatorialoptimization algorithm that exploits LTE characteristics.The optimization analysis is based on coverage, capacityand cost criteria.

ACKNOWLEDGEMENTS

The work in this paper has been co-financed by theEuropean Union (European Social Fund ESF) and Greek

national funds through the Operational Program Educationand Lifelong Learning of the National Strategic Reference

Framework (NSRF)-Research Funding Program: THALISNTUA, Novel Transmit and Design Techniques forBroadband Wireless Networks (MIS 379489).The authorswould also like to acknowledge discussions with colleaguesin COST VISTA IC1102.

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[2] Astely, D.; Dahlman, E.; Fodor, G.; Parkvall, S.; Sachs,J., "LTE release 12 and beyond" CommunicationsMagazine, IEEE, vol.51, no.7, pp. 154-160, July 2013

[3] Hurley S (2002) Planning effective cellular mobile radionetworks. IEEE Transactions on Vehicular Technology51(2):243 – 253

[4] E.Amaldi, A. Capone, F. Malucelli, ‘Radio planning and

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[5] Mathar RM, Niessen T (2000) Optimum positioning ofbase stations for cellular radio networks. WirelessNetworks 6:421 – 428

[6] Raisanen L, Whitaker RM (2005) Comparison andevaluation of multiple objective genetic algorithms forthe antenna placement problem. Mobile Networks andApplications 10:79 – 88

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[8] Spatial channel model for multiple input multiple output(MIMO) simulations, 3GPP TR 25.996 v.6.1.0, 2003.

[9] WINNER II WP1: ‘Channel models’, DeliverableD1.1.2, 30/11/2007.

[10] Ikuno, J.C.; Wrulich, M.; Rupp, M., "System LevelSimulation of LTE Networks," IEEE VTC 2010-Spring,2010 IEEE 71st, vol., no., pp.1,5, 16-19 May 2010.

[11] D.Zarbouti, G.V.Tsoulos, G.E.Athanasiadou,C.A.Valagiannopoulos, ‘The Effective Radiation Pattern

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[12] 3GPP TS 136.213: “ EVOLVED UniversalTerrestrial Radio Access (E-UTRA); Physical Layer

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[13] A.Molina, G.E.Athanasiadou and A.R.Nix, “TheAutomatic Location of Base-Stations for optimised cellularcoverage: A new combinatorial Approach”, IEEE VTC1999, pp. 606-610.

[14] A.Molina, A.R.Nix, G.EAthanasiadou, “ CellularNetwork Capacity Planning Using the CombinationAlgorithm for Total Optimisation ”, IEEE VTC 2000, pp.2512-2516.






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Automatic Location of Base-Stations for Optimum Coverage and Capacity Planning of LTE Systems