Performance Analysis of Radio Propagation Modelsfor Smart Grid Applications

Christian Muller, Hanno Georg, Markus Putzke and Christian WietfeldTU Dortmund University

Communication Networks Institute (CNI)Dortmund, Germany

Email: {christian5.mueller, hanno.georg, markus.putzke, christian.wietfeld}@tu-dortmund.de

Abstract—Realizing future Smart Grid applications highlydepend on the communication technologies being used. For thispurpose, an ubiquitous communication infrastructure is essential,providing real time communication and reliable connectivity toSmart Grid components. Especially nowadays wireless radionetworks offer a cost-efficient, reliable and well-engineered so-lution. In this paper we present a performance analysis basedon a comparison of analytic channel models and Ray Tracingsimulations for wireless digital cellular networks with respectto actual Smart Grid deployment scenarios. Both methods areapplied especially under consideration of a typical outdoor-to-indoor transition models. Based upon these results a coverageanalysis for wireless digital cellular networks (e.g. GSM orUMTS) is shown considering different Smart Metering andenergy management application scenarios including position-based radio characteristics, like basement, indoor and outdoorinstallations. The network topology is described using a simula-tion environment including various analytic models in order toanalyze the capacity of the transmission technology in real-worldscenarios. Our analysis shows the impact on the path loss, causedby frequencies, geographical position and indoor deployment,which leads to an additional deviation up to 25 dB.

I. INTRODUCTION

In order to enable the essential functionalities of a SmartGrid, an ubiquitous Information and Communication Technol-ogy (ICT) network infrastructure is necessary, offering robustand secure communication between utilities, providers andhouseholds [1]. The integration of services and applicationslike Demand Side Management, Smart Metering, Decentral-ized Energy Generation and Electric Mobility into existingheterogeneous ICT infrastructures is currently investigatedby numerous research and pilot projects [2][3]. Within thiscontext, the usage of digital cellular radio networks provide ad-vantages compared to wired technologies, like well-establishedinfrastructures, lower installation costs, sufficient QoS anddata rates especially for subsequent or dedicated integrationof distributed energy components. As part of the release oflower frequency ranges for new services, like the reuse oftelevision broadcast radio spectrum for Smart Metering, apromising frequency range is available in order to overcomethe coverage lack caused by the prevailing in-house installationof the communication components.

Hence the work presented in this paper concentrates onthe coverage evaluation of digital cellular networks beingapplied in context of smart grid networks with strong respectto the actual deployment scenario including apartment anddown-to-earth installations. Therefore we provide a simulativeapproach comparing different existing analytic channel modelsand simulative Ray Tracing methods [4] for their application

in the smart grid context, where especially the installationslocated in the basements reduces the coverage and limits theavailable bandwidth. To accomplish a performance evaluationfor the scenarios shown in Figure 1, existing channel modelsare presented and analyzed. Furthermore, these models areapplied to a stochastic simulation environment, calculatingfrequency based attenuations and results, being representedusing Google-based maps. In addition, particular scenarios aremodeled by a Ray Tracing simulation environment in order tocompare the results of the analytic models with simulativeapproaches for outdoor-to-indoor building penetration.

The paper is structured as follows, a summary of existinganalytic channel models for building penetration is given inSection II. In order to provide variable large-scale simulationscenarios with detailed modeling of urban and suburban den-sities and appropriate house types, the performance evaluationhas been conducted in a simulation environment using RayTracing and analytic channel models. Section III introducesthe simulation environment, considering different in-housescenarios in Section III-A, as well as the scenario and topologygeneration process described in Section III-B. The simulationresults and comparison between the simulation and analyticapproaches are discussed in Section IV. Finally the papercloses with conclusions and an outlook on future work inSection V.

II. RELATED WORK

Channel models predicting scattering and propagation ofradio waves are capable to determine the received powerdepending on the environment. Nowadays many models areavailable as much research effort has been put into this field[5], [6], [7]. Nearly for every environment, like outdoor, indoorand transitions from outdoor-to-indoor and vice versa arecaptured by corresponding models [8], [9].

Here, within the scope of smart grid applications, especiallytransition models in outdoor-to-indoor scenarios [10], [11]are important for radio wave propagation, as Smart Metersand Energy Management HAN Gateways have to be placedin typical positions within houses. As we are presenting acoverage analysis for smart grids, we are only interested in thepath loss described by large-scale models. The following fouranalytic models are used in the analysis described in SectionIII.

All analytical channel models are simplifications of the realscenarios and do not cover windows in the exterior wall ofthe house. Therefore, the minimum of the path loss predictedby the models will not coincide with ray tracing results or

(a) Deployment Scenarios Front (b) Deployment Scenarios Back

Fig. 1: Deployment Scenarios with Different Smart Meter Locations

measurements, as the path loss through windows is lowerthan through walls, even if the path is longer. Otherwise,it is not meaningful to integrate those effects in analyticalchannel models as they increase the complexity and requiremore precise description of the environment.

A. Cost231 path loss model for building penetrationA basic approach, especially for transition models, has been

developed within the COST (COperation europenne dans ledomaine de la recherche Scientifique et Technique) project[12]. Based on measurements of Okumura, urban and subur-ban environments are analyzed. The model assumes isotropicantennas, carrier frequencies in the range of 900−1800MHzand base station height below 30m. The propagation loss isdivided into three parts, cf. Figure 2

Ltot = Lout + Ltw + Lin (1)

where Ltot represents the total propagation loss, Lout theoutdoor, Lin the indoor and Ltw the wall loss. Thereby theoutdoor loss is given by a corresponding approximation of thefree space loss

Lout = 32.4 + 20 log(f [GHz]) + 20 log(S + d) (2)

while the indoor loss is determined by

Lin = max{pWi, α(d− 2) (1− sin(θ))2

}(3)

where p is the number of penetrated indoor walls, Wi theloss per wall, d the perpendicular indoor distance, α the losscoefficient for indoor propagation and θ the incident angle tothe normal of the outdoor wall. In contrast, the wall loss oncondition of an arbitrary incident angle can be determined bythe special cases of perpendicular We and parallel penetrationWGe

Ltw =We +WGe (1− sin(θ))2. (4)

As can be seen the model determines the path loss by a su-perposition of free space loss between the outdoor transmitter

and indoor receiver and the wall losses by the external andinternal walls.

B. Extended Cost231 Walfisch-Ikegami modelIn order to allow higher frequencies than 1800MHz and

to be more accurate, the authors in [13] extended the basicCost231 model, cf. (1). Two modification are made, one forthe indoor loss Lin and one for the outdoor loss Lout. Theloss by the external wall (4) is untouched. For more realisticmodeling the outdoor path loss is replaced by the Walfisch-Ikegami model

Lout = 42.6 + 26 log(S[km]) + 20 log(f [MHz]) . (5)

As this model only uses the path loss between the base stationand the external wall S, it is more accurate as the basicCost231 building penetration (2), cf. [13]. To further improveand simplify the model the authors have modified the indoorloss

Lin = αd (6)

This modification is equivalent to a scenario where the indoorwalls are separated each time by 10m. A further advantage of

Fig. 2: Clarification of Parameters for Cost231 Channel Model

the simplification made in (6) is, that no detailed information

is needed about the location of the receiver. In summary theextended model is valid for frequencies between 800 and2000MHz, base station heights of 4 − 50 m and distancesbetween 0.02−5km. The model is usually referred as Cost231wi.

C. Winner II B4 LOS modelIn the context of Winner II (Wireless World Initiative New

Radio) many channel models for outdoor, indoor and transitionscenarios are developed, cf. [14]. An interesting transitionmodel assuming LOS conditions between the base stationand the incident point of the incoming ray at the externalwall is Winner II B4. The model require frequencies between2−6GHz, mobile station heights of 1−2m over floor and basestation heights of 5−15m below rooftop. In the same way asfor the other models the path loss is divided into three parts,cf. (1). In contrast to Cost231 and Cost231 wi the outdoor andindoor loss is modified while the through wall attenuation Ltw

is unchanged

Lout =max {41 + 20 log(f [GHz]/5) + · · ·22.7 log (S + d) , Lfree}

(7)

and

Lin = pWi . (8)

Herein S is the outdoor distance (cf. Figure 2) and Lfree is thefree space loss between the outdoor base station and indoorreceiver without considering the walls of the house. The indoorpath loss is modeled as a stepwise attenuation, which makesthe model more realistic for irregular houses, i.e. for spacingsbetween the interior walls not always the same. It has to benoted that the specified path loss is reciprocal, i.e. the sameis true for an indoor-to-outdoor transition.

D. Winner II C4 NLOS modelWinner II C4 represents another transition model for

outdoor-to-indoor propagation proposed within the Winner IIproject with respect to for non-line-of-sight (NLOS) outdoorscenarios. NLOS conditions mainly occur in urban scenarioswhich increase the path loss compared to Winner II B4 andreduce the coverage area of one base station. Assumptionsfor Winner II C4 are distances between the base and mobilestation of 50m−5km, base station position higher than 30mand frequencies between 2− 6GHz. Equally to the approachof the other channel models the path loss is described as asuperposition of three parts, cf. (1). Compared to Cost231wi, the indoor loss Lin and through wall attenuation Ltw

is unchanged, while the outdoor path loss Lout have to bemodified due to the changed outdoor scenario:

Lout = [44.9− 6.55 log(hBS)] log(S + d) + 26.46 + · · ·5.83 log(hBS) + 20 log(f [GHz]/2)

(9)

where S and d represent the outdoor and indoor distances inthe corresponding scenario, cf. Figure 3. GFH denotes thefloor height gain factor and accounts for receivers located ondifferent floors. From measurements within the Cost project,GFH is assumed to be 0.8 dB/m.

A general disadvantage of all presented channel models is,that they are only valid for a positive mobile station height.

Therefore they can not be applied for receiver below ground,i.e. Smart Meters which are located in the basement.

Fig. 3: Winner II C4 scenario

III. SMART GRID SIMULATION ENVIRONMENT

Digital cellular network planning strongly relies on giventopologies, building densities, mobility and distances to thecommunication nodes. Even if mobility can be neglected byconsidering radio networks for Smart Grid applications, therequirements for such systems are complicated by takinginto account the actual position of the Smart Meters andcontrollable loads in the households [15]. In order to providea close to reality simulation environment for an accurate radiopropagation prediction, a geo-based scenario generation to-gether with detailed in-house scenarios have been implementedand are described in the following section.

A. Inhouse Smart Grid ScenariosOne decisive factor besides the actual distance between

the base station and the houses are the particular positionsof the Smart Meter in or at the households. Influencingfactors on the signal path and attenuation of the radio signalat the houses are the position relative to the base station,including line-of-sight, non-line-of-sight and incidence angle,as well as the type of exterior walls and type and numberof internal walls which has to be penetrated by the radiowaves. Possible scenarios are shown in Figure 1, whereas two

Fig. 4: Inhouse Path Loss of Inhouse Scenarios

scenarios simulate the Smart Meter installation on the outsideof the exterior wall, which is typical for Meter installationsin e.g. America or Italy [16]. Both meters are installed onopposite sides to match the simulation of line-of-sights andnon-line-of-sights radio propagations. Furthermore, we assumethat outdoor installations are protected against environmentalinfluences and third party manipulation by a robust meterbox implying additional attenuation to the radio signal. Two

more scenarios are designed for simulating a Smart Meterinstallation in the basement, which is typical for Europeaninstallations. Here especially the attenuation caused by groundfloor, additional walls in the basement and surrounded terrainare taken into account. The last scenario covers the positionof a Smart Meter on a certain floor, which can be mappedto apartment houses with multiple apartments as well assingle family houses with fuse boxes installed in the corridor.Here especially the characteristics of in-house installations,like reflections brought by walls and the additional fuse box,partially penetrations and signal diffusions have to been takeninto account to simulate the resulting in-house attenuation forsmart meter installations.

Fig. 5: Variation of Housetypes

In order to get a more detailed analysis of outdoor-to-indoorradio wave propagation with respect to the incident angle andconsidering the actual wall attenuation caused by the materialof the wall, the previously described in-house installation sce-narios has been modeled in Radiowave Propagation Simulator(RPS)[17], which is shown in Figure 4.

All scenarios were simulated by moving the base stationaround the house to cover the influence of the incidence angleto the in-house radio propagation. Therefore the base stationis located in 1 km distance with 20m height and is moved inequidistant steps of one degree around the house model. Thetransmission power is set to 20 dBm and the receiver fieldsshown in Figure 4 are broken down to the actually Smart Meterpositions described before (see Figure 1). The variation of theattenuation of different house types is shown in Figure 5.

The influence of the distance between the base station andreceiver has been simulated and summarized for differenthouse types. Compared to previously mentioned analytic chan-nel models the simulative path loss depending on the distancecan be calculated by the line-of-sight models Cost231, Cost231wi and Winner II B4 model and for a non-line-of-sight scenarioby the Winner II C4 model.

B. Large-scale Smart Grid ScenariosBased upon the simulative results of Section III-A, a

coverage analysis for different frequency ranges has beenperformed by simulation. The wide area rural and (sub)urban

infrastructure models in the simulation are based on geo-graphic positions representing Dortmund, Germany in orderto meet real-world topologies. The coordinates of the basestations and houses are provided in latitude, longitude, altitudeand azimuth of orientation and reflect the actual density anddistribution of given infrastructure. On the basis of thesetopologies appropriate analytic channel models (see SectionII) are used to calculate the attenuation from the base stationto the houses by taking into account the distance, height ofbase station, receiver antenna and incidence angle. To easethe results, shown in Section IV-B, only one exemplary basestation is located in the center of the map and the radiopropagation is calculated using the most fitting channel modelachieved in Section IV.

IV. PERFORMANCE EVALUATION

The following section presents a comparison between theanalytical channel models and Ray Tracing results as well asa coverage analysis as a function of the carrier frequency. Forboth evaluations an urban scenario consisting of houses, whichare shown in Figure 1, is used.

A. Comparison between channel models and Ray Tracingresults

In order to evaluate the performance of the analyticalchannel models in Section II a comparison with a Ray Tracinganalysis has been conducted. An outdoor-to-indoor scenario ishighly dependent on the incident angle of the incoming raytowards the exterior wall of the house. Therefore, Figure 7shows the path loss obtained by Cost231, Cost231 wi andWinner II B4 as well as the path loss from the Ray Tracinganalysis. The frequency is set to 1800 MHz, the house inFigure 1 has a width of 10 m and a length of 8 m and thebase station height is 20m. Now the incident angle is changedfrom 0 − 360◦ according to the position of the base station.Hence, we get the attenuation dependent on the orientationof the house towards the base station. Furthermore, the smartmeter is positioned in scenario 5, cf. Figure 1.

Fig. 7: Variation of Incident Angles

As can be seen from the Figure 7, all channel models show asimilar performance. If the incident angle is between 0−225◦

or 315− 360◦, i.e. the ray incides on the front, right or back

(a) Cost231 wi @ 900 MHz (b) Cost231 wi @ 2100 MHz

Fig. 6: Coverage Analysis of Real-World Scenario using Cost231 wi Channel Model

of the house, we get the following behavior: The higher theincident angle, the higher the path loss due to the penetrationthrough the exterior wall. This behavior can be also seen fromLtw in (4). If the angle modulo 90◦ is greater than 45◦ theexterior wall, where the ray incides, is changed. Hence the pathloss decreases again until the incident ray reaches the normalof the corresponding wall. In contrast to that the minimum ofthe path loss is constantly reached within the interval 225 −315◦. For this incident angles the ray incides to the left wallof the house. Due to the reason that no interior wall is situatedbetween the left wall and the smart meter, the path loss reachesits minimum.

Fig. 8: Variation of Distance

The Ray Tracing results are slightly different from that ofthe channel models. As the analytical models only considerthe loss of the interior walls, which are crossed by the per-pendicular indoor distance p, which is not the actual numberof walls penetrated by the LOS ray, the Ray Tracing path lossdrops from 103dB to 91dB at an angle of 225◦. Furthermoreall channel models are simplifications as they do not consider

door and windows in the exterior and interior walls. As thepath loss is much less through a window or door compared tothe wall loss the Ray Tracing attenuation drops over 20 dB atan angle of 175◦. Finally the path loss is above 120dB within[45◦, 135◦], as for this angles the ray penetrates the maximumnumber of indoor walls.

Figure 8 shows the average path loss as a function of thedistance between the base station and an inhouse smart meterfor all channel models and the Ray Tracing analysis. All resultsshow the same expected behavior: the higher the distance thehigher the path loss. Due to penetrated indoor walls whichare not considered by analytical models, the attenuation of theRay Tracing analysis is always higher than Cost231, Cost231wi and Winner II B4. The only model predicting a path losshigher than the Ray Tracing analysis is Winner II C4, which isbased contrary to the three other models on a NLOS scenario.Therefore, it is intuitive that a NLOS scenario yield a higherattenuation than all other results.

B. Coverage Analysis

For evaluating the coverage area provided by the cho-sen channel models, frequencies at 900MHz, 1800MHz and2100MHz have been considered. The location and orientationof the households towards the base station are determined byreal-world scenarios described in Section III-B. The housetypes and characteristics are randomly assigned based on thein-house description in Section III-A.

As can be seen in Figure 8, the Cost231 wi model is the bestfit compared to the Ray Tracing result. Hence we used Cost231wi to perform the coverage analysis with a receiver sensitivityof −90 dBm. The parameters for the Cost231 wi model havebeen optimized using the Ray Tracing results in shown inSection IV-A. Therefore, the parameters for the external wallpenetration We have been set to 7.44dB for normal penetrationand 24.92dB for parallel penetration, whereas the internal wallattenuation was set to 4.68 dB.

The results in Figure 6a and Figure 6b show an geograph-ical scenario of Dortmund, Germany, where the path loss is

calculated by the Cost231 wi channel model and the givengeographical position. We assumed that the receiver has lostits connection, when the path loss is above 110 dB andremoved corresponding households from the plot. ThereforeFigure 6a shows the attenuation for a frequency of 900MHz,while Figure 6b uses a frequency of 2100MHz. A statisticalcomparison of these figures is shown in Figure 9.

Fig. 9: Coverage Analysis for Large-Scale Scenario usingCost231 WI channel model

The percentage of supplied houses is shown in dependencyof the housetype and used frequency. Therefore, a minimumreceive strength is assumed by a threshold of −90 dBm.As expected, the outdoor installations (Housetype 1 and 3)show the best performance result compared to the basement(Housetype 2 and 4) and indoor deployments (Housetype 5).In order to provide connectivity to the basement receiver, theusage of lower frequencies, offer an sufficient enhancement.Still the problem remains, that due to the pessimistic thresholdand the variation of the incident angles only a maximum of52% are supplied. Nevertheless our results represent a worst-case scenario with only a single base station in order to clarifythe impact of the evaluated model.

V. CONCLUSIONS AND OUTLOOK

The results presented in this paper have shown the impacton the reachability in a Smart Grid scenario depending on theactual position of the communication entities in the networktopology as well as the installation scenarios in-house. In orderto use wireless digital cellular networks for Smart Grid Appli-cations especially the basement and in-house deployments ofcommunication nodes are challenging tasks for sufficient radionetwork planning. Together with the current release of lowerfrequency ranges, like the previously used terrestrial analoguetelevision frequencies, the usage of these spectrum offers greatpotential for these applications. Our considerations have shownthat the characteristic of the radio channel for in-house instal-lations can add up to 25 dB at different frequency ranges. Wehave analyzed different analytic outdoor-to-indoor transitionradio propagation models and their performance compared toRay Tracing simulations, especially for the usability in a SmartGrid scenario.

Future works will focus on integrating existing radio tech-nologies models in order to analyze the link budget and

capacity of different technologies for the expected Smart Gridtraffic volume on the WAN interfaces. Additionally, real-world measurements from the model region of the E-DeMaproject [2] will be conducted for validating the theoreticalchannel models and depending on this information a proposalfor detailed network planning methodology for Smart GridApplications will be developed.

ACKNOWLEDGMENT

The work in this paper was partly funded by theGerman Federal Ministry of Economics and Technology(BMWi) through the projects E-DeMa (reference number01ME08019A). The authors would like to thank the projectpartners RWE, Miele, Siemens, ProSyst, SWK and ef.Ruhr.

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