Evaluation of a Terrain-based Point-To-point Propagation Model in the 900 Mhz Band

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, VOL. 10, 65–71 (1997)

EVALUATION OF A TERRAIN-BASED POINT-TO-POINT PROPAGATION MODEL IN THE 900 MHz BAND f. lazarakis*, a. a. alexandridis, k. dangakis and p. kostarakis Institute of Informatics and Telecommunications, N.C.S.R. “Demokritos”, Greece

and g. s. tombras Laboratory of Electronics, Department of Physics, University of Athens, Greece

SUMMARY The accuracy of a semi-empirical point-to-point propagation model, based on terrain data information close to the receiver, is tested. The evaluation is performed through extended transmission loss measurements taken in an urban environment (Athens region) in the 900 MHz band. The prediction error is calculated for each measurement point and coordinated with detailed terrain information. Specifically, the evaluation of the model is separately performed for various categories of measurement data with respect to the measurement point’s effective height and line-of-sight conditions. 1997 by John Wiley & Sons, Ltd. Int. J. Commun. Syst., 10, 65–71 (1997) No. of Figures: 5 key words:

No. of Tables: 3 No. of References: 9

mobile communications; propagation model; terrain database

1. INTRODUCTION Mobile radio prediction models have been studied for over 40 years as a topic of special interest in the area of mobile communications. The development of reliable prediction models has been proved to be essential for designing and installing a mobile cellular radio system, especially for areas characterized by non-uniform terrain and environmental features. To achieve this end the acquisition of field strength or signal loss measurements is necessary for studying the impact of a mobile environment on signal variations. During the past, the use of terrain and environmental databases, updated for a specific area, has been addressed as a necessity for the detailed analysis of signal variations.1–5 In References 4 and 5 the employment of a geographical information system (GIS) has been presented for the coordination of measured signal values with detailed topographical data, forming a propagation and topographical information database. The integrated procedure had been proved to be particularly user-friendly, offering simplicity in estimating crucial propagation parameters. On the basis of extended measurements in the 900 MHz band, taken in the region of Athens, a semi-empirical model had been developed for the specific area.5 In this paper, we examine the validity of the

*Correspondence to: F. Lazarakis, Institute of Informatics and Telecommunications, N.C.S.R ‘Demokritas’, GR-153 10, Aghia Paraskeui, Athens, Greece. Email: [email protected]

CCC 1074–5351/97/020065–07 $17.50  1997 by John Wiley & Sons, Ltd.

above-mentioned semi-empirical model by processing and statistical analysis of propagation measurements that form a second set of data, for the same test area and the transmitting antenna located at a different site. Moving the transmitting antenna to a new location, every reception point has a totally new path profile. Nevertheless, building environmental characteristics and other parameters related to the specific urban environment remain unchanged. The aim of the processing of the new data set is, first, to test the accuracy and applicability of the previously mentioned prediction model. Second, to enrich the available signal measurements set for extensive statistical analysis and improvement of the prediction model for the region of Athens. A summary of the results presented in Reference 5 is given in Section 2 where the data that were used for the derivation of the propagation model will be referred to as the first set of measurements. Moreover, the criteria to be used in this paper for the evaluation of the model are set out at the end of the section. The detailed evaluation of the model by means of the new (second) data set is analysed in Section 3. 2. OVERVIEW OF THE FIRST SET OF MEASUREMENTS Propagation measurements are obtained by means of a measuring system. This consists of a mobile unit for signal reception and the recording and storage of instantaneous signal power values. Throughout the measuring procedure, loss deviation, local Received December 1996 Revised February 1997

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mean, median transmission loss and location variability information are available. The system operates in full coordination with a GIS, providing, for each reception point, topographical information together with local mean value. Following the guidelines in Reference 6, an unmodulated carrier, at 888 MHz, of known and fixed power, was transmitted from an appropriately sited base unit. A total of 16,351 measurement points was obtained, covering an area extending up to 6 km from the transmitting antenna. 2.1. Derivation of the prediction model Based on the signal loss measurements obtained from the procedure described above, a semi-empirical point-to-point prediction model has been derived. This model is based on detailed topographical information in order to return a prediction value for each measurement point. A parameter that best describes local topographical features close to the receiver is the effective transmission antenna height or the socalled, effective height (heff ).7 An analytical expression for effective height estimation using terrain morphology data, can be found in Reference 5. Considering the definition of heff, a negative heff value practically means a non-line-of-sight (NLOS) path because of local terrain morphology and high slope values. A different manipulation of measurement points relative to the heff sign, should thus be established. The proposed point-to-point prediction model, introduced in Reference 5, therefore includes two expressions: loss (dB) = 31·52 + 40 log(d) − 20 log(heff ), heff . 0 and

(1a)

(1b)

where d and heff are measured in meters. For positive heff values, the expression for loss prediction is a proper modification of the standard Lee’s model8 in order to apply for the specific area, and thus it is based on a two-ray model. For negative heff values, loss prediction is obtained through curve fitting of the appropriate measurement points. Moreover, urban environment and traffic effects are embodied as an average factor for the specific mobile environment. A comparison of actual and predicted median transmission loss versus propagation distance is depicted in Figure 1. Nevertheless, the detailed evaluation of models is usually performed through the calculation of prediction error, i.e. measured loss minus predicted loss, for each measurement point. Throughout this paper, the statistical analysis is given in terms of average and standard deviation of the prediction error. The average prediction error represents the systematical error induced by the Int. J. Commun. Syst., 10, 65–71 (1997)

model, indicating the general trend of the model to overestimate (negative value of average prediction error) or underestimate (positive value of average prediction error) path loss. The standard deviation of the prediction error represents the model’s deficiencies if the systematical error is removed. In Reference 9, it is noticed that current models, based on terrain data and without environmental information, appear with a standard deviation of error in the range from 6 dB up to 12 dB. Considering the model under discussion, an average error of 0·8 dB and a standard deviation of 9·73 dB have been obtained. Therefore, although the model does not take into account obstructions that interrupt line-ofsight (LOS) from the receiver, its accuracy proves that terrain morphology, close to the receiver, plays an important role in propagation loss. 3. SECOND SET OF MEASUREMENTS— MODEL EVALUATION

loss (dB) = 55 + 20 log(d) + 10 log(uheffu), h eff , 0

Figure 1. Comparison of measured and predicted loss for the total of measurement points of the first data set

A second set of propagation measurements has been obtained for the same region of Athens, with the transmitting antenna installed at a new location. The measuring set-up configuration was kept identical to the one for the first set of data on purpose. A total of 18,838 measurement points was taken. The threedimensional terrain elevation map of the area under study is shown in Figure 2, where the old and the new site of the transmitting antenna are indicated. Furthermore, it can readily be seen that the propagation path profiles of the new measurement points are different from those of the first set of measurements. Measurement data collection, acquisition and processing have been implemented using an integrated system, as described in Reference 4, based on the utilization of a GIS and digital maps of the area under study. Following the developed model, the whole procedure makes it easy to produce loss curves and derive predicted loss for each measurement point. Figure 3 depicts a comparison of the median transmission loss (versus propagation  1997 by John Wiley & Sons, Ltd.

propagation model in the 900 MHz band

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Figure 2. Three-dimensional terrain elevation map of the area under study with indications for the first and the second antenna positions

Figure 3. Comparison of measured and predicted loss for the total of measurement points of the second data set

distance) between measured and predicted loss values. Evaluation of the model’s performance can then be obtained through prediction error calculation. The resultant average error is found to be −1·62 dB and the standard deviation equal to 11·02 dB. In comparison with the corresponding error values of the first set, the reliability of the model has been slightly decreased because the standard deviation of error has been increased by only 1·3 dB. Further investigation and detailed processing of measurements led to useful results. As mentioned above, large terrain obstructions that lie far from the receiver and interrupt line-of-sight causing diffraction loss were not taken into consideration in the derivation of the model. The evaluation of the  1997 by John Wiley & Sons, Ltd.

model is thus performed separately for points with and without diffraction loss. The following analysis, by means of the available terrain database, is based on the detailed projection of the path profile, with a 10 m step for each measurement point. Hereafter, LOS and NLOS, as well as diffraction phenomena terms, will refer only to terrain morphology and not to obstructions by buildings or other human-made structures. Nevertheless, in an urban environment such as the area under study, ‘pure’ LOS communication can rarely be found. However, this does not affect the validity of our analysis because terrain morphology plays a major role in all cases in signal propagation. In Table 1, the accuracy of the model is tested for LOS and NLOS locations for the first and the second set of measurements, in terms of average value of prediction error (av) and error standard deviation (stdev), both expressed in dB. The column ‘Counts’ indicates the number of the corresponding measurements in each category whereas their percentage with respect to the total measurement points, is shown within parentheses. At this point it should be noted that, hereafter, the analysis results for the first set of measurements will be used only as a reference. The evaluation of the model will be performed by means of the second set of measurements. As can be seen, when applying the model to the new data set for LOS locations, the average error increases by 4·6 dB whereas the standard deviation decreases by 0·6 dB. For NLOS locations, the average error increases by 3·9 dB and the standard deviation increases by 2 dB. Therefore, when difInt. J. Commun. Syst., 10, 65–71 (1997)

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f. lazarakis et al. Table I. Prediction error for the first and the second set of measurements

Path LOS NLOS

Counts 1st set

2nd set

10,358 (63·4%) 5994 (36·6%)

13,507 (71·7%) 5331 (28·3%)

fraction loss does not exist the standard deviation of prediction error will assume an acceptable value. On the other hand, an obvious reduction of the model’s accuracy is noticed for locations where diffraction loss exists. As mentioned in the previous section, the model under discussion handles, in a different way, locations with positive and negative values of effective height. For further testing of the model, loss values with respect to the heff sign and information for LOS and NLOS conditions will be considered in the following paragraphs. In Table 2, statistical analysis results of prediction error are presented for measurements data corresponding to locations with positive heff values, distinguished according to LOS and NLOS conditions for both measurement sets in two main categories. The first category includes the majority of the measurement points, corresponding to LOS locations with positive heff values. For these points belonging to the first data set, the average error and standard deviation values indicate a good approximation of actual transmission loss. For the second set, the average error increases by 4·15 dB and standard deviation decreases by 0·35 dB with respect to the first set. Despite the increase of average prediction error, its value, together with the error standard deviation, indicates that predicted loss is still in good approximation with measured loss.9 The second category includes measurement points with NLOS paths and positive heff values. For these points, even for the data for which the model was originally developed (first set), the average and standard deviation of error show a degradation of the model’s accuracy compared with the first category. A further increase for both statistical values is observed for the second set, indicating that the model fails for this category of measurement points. This is expected because the model does not encoun-

av (dB) 1st set 2nd set

stdev (dB) 1st set 2nd set

−0·04 2·24

9·42 10·08

−4.68 6·13

8·82 12·18

ter diffraction phenomena for locations with positive heff values. For the first data set, it can be proved that NLOS locations have a general trend to suffer more attenuation than the corresponding LOS locations. Therefore, because the model has been developed for points with positive heff unifying NLOS and LOS locations, loss prediction results in the overestimation of LOS loss and the underestimation of NLOS loss. This is clearly proved by the statistical analysis for the second set where, for LOS paths, the average error is −4.54 dB (loss overestimation), whereas, for NLOS paths, it is 8.81 dB (loss underestimation). Next, the case of negative heff values, for LOS and NLOS conditions, is investigated. Statistical analysis results are summarized in Table 3 for both measurement sets. According to the definition of heff it could be expected that negative heff values occur at shadowed parts of the area under study. However, Table 3 reveals that, in practice, there is a small number of measurements in which, under marginal terrain conditions and due to mobile antenna height, LOS paths can occur even for negative heff values. Inspecting the analysis results for the first set of measurements in Table 3, the model approximates the actual transmission loss for points with negative heff and NLOS conditions pretty well, whereas for LOS paths the performance is worse with an absolute increase of the average error by 2·9 dB and of the standard deviation by 2·3 dB. This is expected because the model has been derived through curve fitting of measurement points with heff , 0 without distinction for NLOS and LOS conditions. Among these points, locations with NLOS paths are the majority (71·8%) and thus, for the case of heff , 0, the model is more accurate for NLOS than for LOS paths. This is verified by the second set of measurements where, for LOS paths, the model

Table II. Prediction error analysis for locations with heff . 0 heff . 0 LOS

Counts av (dB) stdev (dB)

NLOS

1st set

2nd set

1st set

2nd set

9432 (57·6%) −0·39 9·15

12,853 (68·2%) −4·54 8·80

3632 (22·2%) 4·10 10·37

3590 (19%) 8·81 12·18

Int. J. Commun. Syst., 10, 65–71 (1997)

 1997 by John Wiley & Sons, Ltd.

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propagation model in the 900 MHz band Table III. Prediction error analysis for locations with heff , 0 heff , 0 LOS

Counts av (dB) stdev (dB)

NLOS

1st set

2nd set

1st set

2nd set

925 (5·6%) 3·61 11·20

654 (3·4%) −7·38 8·83

2362 (14·4%) −0·62 8·90

1741 (9·2%) 0·62 10·15

overestimates path loss by 7·38 dB. On the other hand, a fairly good performance is indicated for points with NLOS conditions with an average error of 0·62 dB and standard deviation of 10·15 dB. In conclusion, the evaluation of the model can be summarized as follows. When diffraction loss does not exist and effective height has a positive value, equation 1a of the derived model will offer a good approximation to actual transmission loss (average error = −4.5 dB, standard deviation of error = 8·8 dB). Figure 4 depicts a comparison of the median transmission loss (versus propagation distance) between measured and predicted loss values for heff . 0 and LOS paths, as obtained for the second set of measurements. Further improvement can be achieved by processing only the data that corresponds to heff . 0 with LOS conditions. Generally, for designing a mobile cellular system in an urban environment, a criterion for the selection of base station sites should be the establishment of LOS paths (considering terrain profile) for most parts of the cell. In these parts, the majority of the points correspond to positive heff values and thus the model under consideration, being sufficiently accurate for such signal reception conditions, is a useful tool to get an insight into the functionality of the system. When diffraction loss exists and effective height has a negative value, equation 1b of the derived model will sufficiently approximate measured transmission loss (average error = 0·62 dB, standard

Figure 4. Comparison of measured and predicted loss for points with heff . 0 and LOS path, as obtained for the second data set  1997 by John Wiley & Sons, Ltd.

deviation of error = 10·1 dB). In Figure 5(a) comparison of the median transmission loss between measured and predicted loss values is presented, for heff , 0 and NLOS paths, as obtained for the second set of measurements. In this case, the model approximates diffraction loss based only on terrain morphology close to the receiver. A more accurate prediction is expected if detailed path profile information is taken into account. In order to improve the accuracy of the model, by considering the diffraction loss aspects, we tried to incorporate in it some known diffraction loss algorithms based on multiple knife-edge models (such as the one proposed by ITU). Nevertheless, the application of such algorithms resulted in an unacceptable loss overestimation because a single terrain irregularity is usually taken as a number of isolated edges. A better approximation of actual transmission losses is expected through the establishment of a more realistic criterion for edge determination and selection. 4. CONCLUSION Evaluation of the model proposed in Reference 5 has been presented based on extensive measurements and statistical analysis of the collected data. The measurement data were divided into four categories according to effective height sign and characterization of propagation path (LOS or NLOS). Statisti-

Figure 5. Comparison of measured and predicted loss for points with h eff , 0 and NLOS path, as obtained for the second data set Int. J. Commun. Syst., 10, 65–71 (1997)

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cal analysis of the prediction error for each category was performed. The model is easily implemented in many cases of combined urban and rural environments, being reasonably accurate. It enables a sufficient approximation of transmission loss in the absence of diffraction loss when terrain morphology forms a positive effective height, and in the presence of diffraction loss when effective height takes a negative value. Further investigation of diffraction phenomena is needed for the case of distant terrain obstructions along the path profile. In particular, a more sophisticated interpretation of terrain data is needed in order to extract the appropriate number of isolated edges to be used by multiple knife-edge models for the prediction of the diffraction loss. Moreover, the information on the terrain morphology close to the receiver has to be taken into account because the above analysis proved its importance in the total propagation loss. REFERENCES 1. J. T. Hviid, J. B. Andersen, J. Toftgard and J. Bojer, ‘Terrainbased propagation model for rural area – an integral equation approach’, IEEE Trans. Antennas and Propagation, 43 (1), pp 41–46 (1995). 2. M. Feistel and A. Baier, ‘Performance of a three-dimensional propagation model in urban environments’, 6th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’95), vol. 2, Toronto, 1995, pp. 402–407. 3. G. C. Hess, Land Mobile Radio System Engineering, ch. 16, Artech House Inc., 1993. 4. F. Lazarakis, K. Dangakis, A. Alexandridis and G. S. Tombras, ‘Field measurements and coverage prediction model evaluation based on a geographical information system’, 5th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’94), vol.1, September 1994, pp. 274–279. 5. F. Lazarakis, K. Dangakis, A. Alexandridis and G. S. Tombras, ‘A point-to-point mobile radio prediction model based on effective height estimation for the region of Athens’, 5th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’94), vol. 1, September 1994, pp. 258–262. 6. ‘Coverage prediction for mobile radio systems operating in the 800/900 MHz frequency range’, IEEE Trans. Veh. Technol., 37 (1), February 1988. 7. W. C. Y. Lee, Mobile Communications Engineering, ch. 1, 3, 4, McGraw Hill, New York, 1982. 8. W. C. Y. Lee, Mobile Communications Design Fundamentals, 2nd edn. ch. 2, John Wiley and Sons Inc., New York 1993. 9. J. P. Linnartz, Narrowband Land-mobile Radio Networks, ch. 2, Artech House Inc., 1993.

Authors’ biographies: Fotis Lazarakis was born in Pireas, Greece in 1968. He graduated from the Department of Physics, Aristotle University of Thessaloniki, Greece, in 1990. He is currently completing his work towards a PhD degree in the area of mobile communications. Since 1991 he has been with the Institute of Informatics and Telecommunications of the National Center for Science Research “Demokritos”, Athens, first as a postgraduate student and now as a researcher in mobile communications R&D projects. His Int. J. Commun. Syst., 10, 65–71 (1997)

current interests in mobile communications include propagation models, digital modulation techniques, diversity techniques and fading channels capacity. Antonis A. Alexandridis was born in Rovies, Greece in 1962. He received the diploma in electrical engineering from the Technical University of Patras, Greece, in 1985, and the PhD degree from the University of Patras, Greece, School of Electrical Engineering, in 1992. From 1986 to 1990 he was working on his PhD degree in the Digital Communications Lab of the National Center for Science Research “Demokritos”. During this period he was involved with the design and development of hardware and simulation software in order to evaluate a CDMA voice communication network. Since 1991 he has been with the Institute of Informatics and Telecommunications of the NCSR “Demokritos”, first as an R&D engineer in the Digital Communications Laboratory R&D projects (Stimulation project, Esprit II FCPN project) and now as a researcher in the Mobile Communications Laboratory. His current interests include mobile communications, propagation models, digital modulation techniques, and specifically, spread spectrum systems and CDMA techniques. Kostas P. Dangakis was born in Kavala, Greece in 1950. He received the diploma in electrical engineering from the National Technical University of Athens (1973) and the PhD degree from the University of Patras, School of Electrical Engineering (1984). From 1974 to 1976, during his military service, he worked as an engineer at the Research Center for National Defence. Since 1977, he has been a researcher at the Institute of Informatics and Telecommunications of the National Center for Science Research “Demokritos”, Athens, and is now head of the Mobile Communications Laboratory. He has been project leader of several mobile communications R&D projects. His current interests include mobile communications and, specifically, digital modulation and data transmission techniques, spread spectrum systems and CDMA techniques. George S. Tombras was born in Athens, Greece in 1956. He received the BSc degree in physics from the Aristotelian University of Thessaloniki, Greece, the MSc degree in electronics from the University of Southampton, UK, and the PhD degree from the Physics Department of the Aristotelian University of Thessaloniki, in 1979, 1981, and 1988, respectively. From 1981 to 1989 he was a teaching and research assistant, working on adaptive delta modulation techniques, and from 1989 to 1991, a lecturer at the Laboratory of Electronics, Physics Department, Aristotelian University of Thessaloniki. During 1985–86, he served his military service as a research assistant at the Research Center for National Defence. From 1990 to 1991 he was on sabbatical leave at the Institute of Informatics and Telecommunications of the National Center for Science Research “Demokritos”, Athens, where he was involved in various EC cofunded R&D projects related to mobile communication systems. Since 1991, he has been Assistant Professor at the Laboratory of Electronics, Physics Depart 1997 by John Wiley & Sons, Ltd.

propagation model in the 900 MHz band ment, University of Athens. His current interests include mobile communications, analogue and digital circuits and systems, as well as electro-acoustics and audio engineering. Dr Tombras is a member of IEEE, AES, and the Helenic Physicists Association.

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opean Research Center, CERN, in Geneva, Switzerland, he joined “Demokritos” in 1985. Dr Kostarakis is actively involved in testing and type approval and he is Lloyd’s registered auditor for ISO 9000 and a member of the total quality forum.

Panos Kostarakis is research director at the Institute of Informatics and Telecommunications at the Greek National Research Center, “Demokritos”, in Athens, Greece. Dr Kostarakis graduated in physics from the Aristotelian University of Thessaloniki and got his PhD from the University of Strasbourg. After 10 years of working experience in fast electronics and computers in the Eur-

 1997 by John Wiley & Sons, Ltd.

Int. J. Commun. Syst., 10, 65–71 (1997)