Page 221 - Integrated Wireless Propagation Models

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M i c r o c e I P r e d i c t i o n M o d e I s 199

7. Find L8 from Fig. 4.2.1.2.2 curve b or from Eq. (4.2.1.4.1) for the value of Be ·
q
8. The predicted signal strength is expressed similarly to Eq. (4.2.1.2.1) as

( 4.2.1.4.2)

4.2.2 I n p ut Data for Microcell Prediction

4.2.2. 1 The Consideration of Input Data
Having reliable input data is always a challenge for the prediction model to work. If the
data are too coarse, the prediction cannot be accurate. However, detailed and accurate
data are not easy to get and are too expensive. Sometimes, even with data with very
high granularity, the prediction model may actually lose some accuracy and efficiency.
For the Lee microcell model to work, we need accurate building, terrain, and attribute
(e.g., water and foliage) data. As explained earlier, the building block data are the pri­
mary input to the Lee microcell model, such as in dense urban areas, due to the fact that
buildings impact propagation loss the most. Some flat-city microcell models are avail­
able. In most major dense urban cities, the terrain factor cannot be ignored. Even a ter­
rain variation of 3 to 5 m will impact the result quite drastically. This is based on the
drive test data from San Francisco, Tokyo, Seoul, and Los Angles, to name just a few. As
far as attribute (morphology) data are concerned, water and foliage data are two major
attributes that are needed to provide accurate predictions. In most cases, there are not
many trees in dense urban areas. However, water can make quite a difference for a city
like Amsterdam. Again, this is based on the analysis of measured data.
One issue to be noted here is that sometimes GPS does not work accurately in a
high-rise dense urban area. Recorded measured data need to be handled carefully as
input data so that these data and measured data can be lined up correctly to ensure the
accuracy of the prediction.
The flexibility of the Lee microcell model is that although building data are hard to
acquire, the model works well with street map data. It can simply assume that the area
bounded by four streets is a building block. This provides an efficient way to assess first­
order assessment on the coverage and interference at an urban base station antenna. As
shown in Fig. 4.2.2.1.1, the street data were actually used for the verification and bench­
mark with the Lee microcell model. The street information detailing certain street widths
is stored in the database for an area shown in Fig. 4.2.2.1.2. Since the data include street
classification, users can easily specify widths for varying kinds of streets (major, minor,
alley, and highway). Also, based on street data, a building database can be created.

4.2.2.2 Collecting and Leveraging Measured Data
Deriving the building loss curve and specifying the propagation characteristics of a
predicted area from the measured data is crucial. Especially, each city most likely has
different building materials, structures, and distributions of buildings.
To collect drive test data, it is more accurate to use ETAK and GPS data together, for
GPS might not be accurate, especially in dense urban areas. The ETAK data can be used
to correct the GPS error. The drive routes need to be predefined and explored to make
sure that they preset the characteristics of the predicted areas. Typically, routes selected
for the validation of the model are LOS, zigzag, staircase, and random. A typical drive
test route is shown in Fig. 4.2.2.2.1. Since many tall buildings are around, during the