Measurement and Prediction of Indoor Signal Propagation for ISM Band

Nusrat Tanzim, Khandkar M. Rashid, Shazzad HosainDepartment of Electrical Engineering and Computer Science, North South University, Dhaka, Bangladesh

E-mail: nusrat812@northsouth.edu, khandkar130@northsouth.edu, shazzad@northsouth.edu

ABSTRACTThis paper is focused on the measurement and prediction of indoor signal propagation for ISM band system in frequency bands 2.4 GHz and 5.3 GHz. In this research, two basic radio propagation models are studied and compared with theoretical and practical data. This comparison result is implemented on the test indoor wireless network. Based on the consideration, this paper proposes an enhancement to the path loss model in the indoor environment for improved accuracy in the relationship between distance and received signal strength. The model can be used as a prediction model that can be further developed to fit in other indoor scenarios too.

KEY WORDS: WLAN, ISM Band, Indoor Propagation Model, Path Loss

1. INTRODUCTIONThe past decade has witnessed a phenomenal growth in wireless communication. Over the last few years, WLANs have gained strong popularity in a number of vertical markets which have profited from the productivity gains of using hand-held terminals and notebook computers to transmit real-time information to centralized hosts for data processing. Today WLANs are becoming more widely recognized as a general-purpose connectivity alternative for a broad range of business customers.

Indoor scenarios are usually very complicated and due to people movement environment changes rapidly. The need for an efficient way to evaluate radio propagation in buildings is increasing. It is also critical to optimize the locations of the base stations required to ensure satisfactory system performance. Consequently, radio-propagation prediction for indoor environments, which forms the basis for optimizing the location of the base stations, has become an important research topic. Indoor radio propagation is not influenced by weather conditions, such as rain, snow, clouds etc as is outdoor propagation, but it can be affected by the layout of a building, and especially by the use of different building materials. Owing to the reflection, refraction and diffraction of radio waves by objects such as walls, windows, doors and furniture inside the building, the transmitted signal often reaches the receiver through more than one path. Due to multipath propagation, where several waves arrive at

the receiver via different paths and with different phases, rapid variations (fading) of the received signal envelop occur. Time variations of the received signal and wide bandwidth of the transmission are the reasons why the statistical evaluation of measurement result is necessary. There are many different ways how the signal coverage in buildings can be determined. In our research we focused on a processing of measured values, the optimization of parameters for COST231 Multi-Wall model, which allow mean signal level prediction for initial coverage planning.

For simulation and analysis of different propagation model, number of test wireless networks has been created to meet the requirement of basic propagation mechanism. To observe and aid the simulation process we used Frequency Spectrum Analyzer (SPECTRAN HF-2025E). With spectrum analyzer we took many test point data from our test-bed wireless networks to identify different variations in our test parameters. Test network mapping has been done with the help of GIS mapping software to feed map information into Ekahao Heat Mapper software, which generates SNR information as gradient over the map. For simulation hardware preparation, we modified some commercially available ISM band wireless antennas to serve our purpose and frequency needs. Moreover, to generate user defined wireless output power we used customized access point firmware that enabled the facility to have more precious control over transceiver device.

2. PROBLEM ANALYSISThe existing challenge for indoor environments is that the signal propagated from the transmitter antenna will experience many different signal transformations and paths with a small portion reaching the receiver antenna. Awareness of this process will assist the user in the process of better understanding radio performance limitations.The indoor propagation channel differs considerably from the outdoor one. The distance between transmitter and receiver is shorter due to high attenuation caused by the internal walls and furniture’s. Most often, the lower transmitter power is also a cause of it. The short distance implies shorter delay of echoes and consequently a lower delay spread. The path loss and the statistical characteristics of the received signal envelop play an important role in coverage planning applications.

The considered propagation models in the literature are divided into two groups: empirical models and deterministic models. Earlier models were expressed in form of simple mathematical equations that give the path loss as the output. The equations were obtained by fitting the model measurements results at 2.451GHz.

Fig. 1, Overlapping 2.4 GHz Channels in ISM band

Between these two groups, the empirical ones minimize the computation time in order to give results even if their accuracy is low. For this reason, the empirical models have been adopted to be introduced in the WLAN models. [1], [2], [3]

3. DATA COLLECTION AND MEASUREMENT CAMPAIGN

3.1 Site SurveySite survey campaign was performed on the 10th floor of the North South University South Academic Tower. It is a typical modern multi-floored building. Many walls and partitions divide measured floor and there are four metal lifts in the scenario as well (Fig. 2). Access point was mounted at the roof with two Omni-directional 12 dB antennas whose radiation pattern is shown in Fig. 3.

Fig. 2, Floor layout with signal level distribution

Fig. 3, Radiation Pattern of Omni Antenna

3.2 Data Collection PreparationAccess point SSID was set as NSU-test-network, operating at channel 11 (start frequency: 2451 MHz, Center frequency: 2462 MHz, Stop frequency: 2473 Mhz) with access point output power: 150 mW.

At Spectrum analyzer, all the frequency parameters were synchronized to the access points operating frequency as shown in Fig. 4(a).

Fig. 4(a), Spectrum analyzer preparation

3.3 Narrowband Data Collection and MeasurementA narrowband system was developed at theDepartment of Electrical Engineering and Computer Science consisting of one Access Point, USB transceiver, Spectrum Analyzer and two wire Omni-directional ground-plane antennas. The system wasdesigned for 2.451 GHz ISM band. Full computercontrol is made through the USB interface of anotebook computer. A value of the signal level wastaken automatically every second and saved in the computer while the receiver was moving along ameasured path. More information about system and measurement method can be found in [4], [5], [10].

Fig. 4(b), Measurement with SPECTRAN HF-2025E spectrum analyzer

3.4 Wideband Data Collection and MeasurementA simple Wireless LAN peer-to-peer connection was built up using two notebooks - both suppliedwith identical standard WLAN USB cards [8].Omni-directional antennas are integrated in the cards. Ekahau Heat Mapper and Net Stumbler software package was used to measure the signal power level and QoS.

In the case of narrowband measurements, one of the notebooks was situated at the same positions as the transmitter. The second notebook was placed with the help of tripod; so that it can be moved aroundthe azimuth in all 360 degrees may have been

scanned with 5-degrees step. At several locations, the signal power level was measured for a few hours. Three of the locations are shown in Fig. 14.

The main goal of the wideband measurement was to find cumulative distribution functions of the receivedsignal level for each location. These functions describe time variations of the received signal. It canbe used for calculations of the signal level above themean for an acceptable percentage of time. This waythe corresponding QoS is determined. Detailed information on used calculation procedures can befound in [6], [7].

4. MODELING METHODOLOGIESSoftware planning (using a propagation model) is much more convenient and cost-effective way to deploy a wireless network than a site survey with lots of measurements and empirical decisions. Using simulations many different configurations of the network can be tested with no expenses to find an optimal solution. As it was stated the indoor propagation modeling isone of the most complicated tasks in this field. In addition, a detailed description of an indoor scenario including furnishing, doors, constitutive electrical parameters of used materials etc. is almost impossible. To find a balanced trade-off between the model complexities (computation time, requirements on input data etc.) and reasonable accuracy is a challenge. Quite a large number of indoor propagation models can be found in literature; among them we are using Cost 231 one slop and multi wall model.

Fig 5, Experiment Geomentry

4.1 Empirical Modeling ApproachThe empirical and semi-empirical models are primarily based on statistically processed representative measurements. One-Slope and Multi-Wall model are very easy and fast to apply because the prediction is usually obtained from simple closed expressions. Also requirements on the input environment description are “reasonable”. But, at the same time, only the propagation loss without great site-specific accuracy can be predicted.Total path loss LTOT (dB) can be expressed,

Where L(P) (dB) is the average loss based on the position P only, and χ (dB) is random fading with a zero-mean statistical distribution. The empirical and semi-empirical models are able to predict the average path loss L(P). The random fading has to be considered as a fade margin in the power budget of a wireless link.4.1.1 One-Slope ModelThe One-Slope Model (1SM) [11] is the easiest way to compute the average signal level within a building without detailed knowledge of the building layout. The path loss in dB is a function of just a distance between transmitter and receiver antennas:

Where L0 (dB) is a reference loss value for the distance of 1 m, n is a power decay factor (path loss exponent) defining slope, and d (m) is a distance. L0

and n are empirical parameters for a given environment, which fully control the prediction. As an example table. 1 presents a few values taken from various reference points.4.1.2 Multi-Wall ModelA semi-empirical Multi-Wall Model (MWM) provides much better accuracy than 1SM. The results are site-specific but at the same time floor plan description is needed as an input.

Fig. 6, Multi Wall Model Geometry

The basic idea of MWM is illustrated in Fig. 6. The path loss between a transmitter and receiver LMW is given by

where LFSL (dB) is the free space loss for the distance d (m) between transmitter and receiver antennas, which is in fact1SM prediction with power decay factor n = 2.0, kwi is a number of walls of i-th type between transmitter and receiver antennas, Lwi (dB) is attenuation factor for i-th wall type, N is a number of wall types, kf is a number of floors between transmitter and receiver and Lf (dB) is the floor attenuation factor. Since the floor attenuation is not dealt with in this paper the original MWM [11] floor attenuation calculation was simplified in above equation. Floor attenuation analysis can be found in [4].

(3)

(1)

(2)

5. DISCUSSION OF RESULTS

5.1 One Slope Model Data ProcessingIt can be clearly seen that the value of the power decay factor n is highly dependent on the type of building or structure of the indoor environment and so it has the major influence on the resulting determination of the signal level coverage. A typical example of a coverage prediction using 1SM is shown in Fig. 7.

Comparing Fig. 2 and Fig. 7 it is apparent that 1SM prediction considers only the change of the signal level with distance between transmitter and receiver regardless of the actual structure of the indoor environment. The 1SM provide only a rough estimate (standard deviation usually greater than 10 dB) and the selection of proper power decay factor n is crucial.

Fig. 7, One Slope Model Coverage Prediction

f [GHz]

Lo[dB]

n [-]

Comment

1.9 38.00 3.5 Office Space

1.9 38.00 2 Open Space

1.9 38.00 1.3 Corridor

2.45 40.2 1.2 Corridor

2.5 40.0 3.7 Office Space

2.45 40.2 4.2 Office Space

Table 1, One Slope Model Empirical Parameters

The values of the power decay factor n vary depending on the type of building and indoor environment. The value n =2 corresponds to the propagation in free space. Values smaller than 2 are utilized for prediction of the signal propagation in corridors, where the decrease of the power decay factor is caused by a wave-guiding effect. In an office environment with walls and furniture n is usually between 3 and 6. The 1SM gives the best results for environment with more or less uniformly distributed walls and obstacles.

The 1SM performance and the importance of proper parameter n selection are demonstrated in Fig. 8, Fig. 9 and Fig. 10.

A transmitter was located on the left side in the corridor so that strong wave-guiding effect along the corridor can be observed in Fig. 8. Then two 1SM predictions were performed and compared with the measurement. For corridors n = 1.4 was utilized (Fig. 9) and very good agreement between measurement and prediction was seen in the corridor. On the other hand very poor prediction accuracy was provided for the offices since the signal attenuation was much stronger than in the corridor and the prediction was overestimated. If the n = 4.0 suitable for office environment was used instead (Fig. 10), the coverage prediction was perfect in the offices but strongly underestimated in the corridor. To handle this problem either the different parameter n can be used for corridor and offices or an averaged n value between 1.4 and 4.0 can used as a tradeoff, then the prediction will be valid for the whole floor but at the lower accuracy.

Fig. 8, Measured Signal Level

Fig. 9, Prediction error (difference between 1SM prediction and measurement) for n=1.4

Fig. 10, Prediction error (difference between 1SM prediction and

In spite of the strong dependence of 1SM on used empirical parameters, it provides an excellent tool when no information on an indoor scenario is available or when a very fast draft design is needed.

5.2 Multi-Wall Model Data ProcessingFig. 11 presents an example of a coverage prediction using MWM. The MWM can be marked as site-specific since particular walls are considered during the prediction. But still, it must be understood that the MWM introduces only an estimate of the real

wave propagation. In Eq. 3, only walls and obstacles located directly between transmitter and receiver are considered with their attenuation factors. Particular reflections and diffractions are not taken into account so the accuracy is limited in certain cases. As an example the wave-guiding effect of bending corridor cannot be modeled in Fig. 11.

Fig. 11, Multi Wall Model Coverage Prediction

A comparison with the 1SM shows a significant improvement of the site-specific accuracy; see Figs. 8, 9, 10, 12. No change of model parameters is needed.For good prediction accuracy the proper wall attenuation factors Lw - empirical parameters for Eq. 3 must be used. The attenuation factors do not represent actual physical attenuations of the walls but statistical values obtained from representative measurement campaigns. It means if the receiver is hidden behind a metal wall with limited dimensions, the prediction cannot result in an infinite attenuation, even so metal itself can be considered as a total reflector of the electromagnetic energy. But in the real scenario the wave can find its way around the metal obstacle due to reflection, diffraction and diffuse scattering, while the MWM considers only walls along a line connecting the transmitter and receiver.

Fig. 12, MWM prediction error - difference between MWM

Even though there are a lot of building materials, due to the statistical nature of the wall attenuation factors in Eq. 3, only a very few wall types are necessary to define for MWM. In fact in [11] only two wall types are considered: Light wall (L1) - a light wall or partition, and Heavy wall (L2) – a structural thick wall. Of course more wall types can be introduced for a specific application or software tool (metal walls, glass, etc.). Some empirical parameters of MWM for miscellaneous types of interiors are summarized in Table 2.

f [GHz]

Lo

[dB]

L1

[dB]

L2

[dB]

L3

[dB]

γ[-]

Comment

1.9 38.0 2.1 4.4 13.6 2 Office Space

1.9 38.0 0.5 4.2 2 Half Open Space

2.45 40.2 5.9 8.0 2 Office Space

2.45 40.2 6.0 2 Office Space

2.5 40.0 5.4 Dry Wall

Table 2, Multi Wall Model Parameters

As a result the planning based on propagation modeling is recognized as a highly preferable approach for designing large WLANs in an indoor scenario to provide optimal and cost effective solutions.

Fig. 13, Multi Wall Model Data Processing-Prediction Compared to Measurement

Fig. 14: Minimum received signal power level forprobability of 50, 90 and 99% as functions of thenotebook location and orientation

The cumulative distribution functions of the received signal power level in each location of the observation points were investigated with respect tothe tripod top rotation. It defines minimum received

signal level for probability of 50, 90 and 99% as functions of the measurement notebook location and orientation. The influence of the notebook azimuthal orientation on the standard WLAN transmission can be nicely demonstrated. Using the results, the gain of eventual angle diversity can be determined as well.

6. COMPARISON WITH LOG DISTANCE PATH LOSS MODELLog distance path loss model [8] predicts the signal strength without considering site environmental factors and mainly focuses on outdoor models. So outcome from this model becomes inapplicable and inaccurate for no line of sight cases. This is unsuitable for indoor 802.11 networks.

The One slope and Multi wall empirical propagation model considers attenuation in an indoor site environment by including various wall attenuation factors. This provides increased accuracy in both line of sight and non line of sight cases.

Variation between predicted signal strength by Log Distance Path Loss model[9] that we considered is as much as 9 dB for the case of non line of sight. So, the accuracy of our prediction is much higher with a variance of ± 10% compared to the Log Distance Path Loss Model with a higher variance of around ± 20% between predicted and measured actual signal in the indoor site environment.

7. CONLUSION AND FUTURE WORKMeasurements taken at North South University, Dhaka were compared against predictions made by two empirical propagation models.The cost 231 model, in general over estimated the path loss, especially at greater antenna heights.

In our study, the 10th floor of NSU south academic tower was investigated for the propagation prediction to see how the signal will be affected in such complex floor with different types of building material. This study can be carried out in the future to see how the multi-floor factor can affect the signal by analyzing study for other multistory building.In future, the signal propagation prediction can be extend to larger area with a number of single and multistory buildings and simulate for outdoor transmitters to indoor receivers and vice versa. Comparison between different indoor propagation models can also be done. And from the study a new channel model can be developed which could be used for wireless networks to provide optimal performance in a local indoor environment.

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http://www.awecommunications.com/Propagation/Indoor/Empirical/index.htm