Page 191 - Integrated Wireless Propagation Models

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M a c r o c e I P r e d i c t i o n M o d e I s - P a r t 2 : P o i n t - t o - P o i n t M o d e I s 169

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3 . 3 Enhanced Lee M a croce l Prediction Model

3 . 3 . 1 I n troduction
This chapter describes the enhancements added to the Lee macrocell prediction model,
which has been well recognized by the wireless industry as one of the most accurate
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propagation prediction models. 3-2 5 This section discusses innovative approaches deal­
ing with a pile of rough digital samples of terrain data and the enhancements to the Lee
model during the validation process.
In general, the Lee model is composed of two parts: the impact of human-made
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structures and the impact of the natural terrain variation. • • 6 Other authors discuss
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innovative algorithms for calculating effective antenna gain 7• 8 and diffraction loss 9•3
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as well as for enhancing the Lee modeP3• 5•3 This section focuses on the natural (terrain)
factor. The new algorithm presented in this section is quite different from the others. It
integrates the calculation on both LOS and shadow loss. First, in the LOS scenario, it
addresses the issue of the big swing of effective antenna gain due to noncontinuous ter­
rain data. Second, in the obstructive situation, the effective antenna gain is integrated
with shadow loss. Both single- and multiple-knife-edge scenarios are discussed. The
new algorithm is developed based on the analysis of measured and predicted (calculat­
ing the theoretical shadow loss and effective antenna gain) data. The new algorithm
involves more calculation but improves the accuracy of the predicted value.
This algorithm was implemented and verified using field terrain and measured
signal data from a variety of environments in different countries, including Italy, the
United States, Spain, Japan, South Korea, Taiwan, and Romania.

3.3.2 The Algorithm
As we have mentioned, the new algorithm covers two areas. First, it addresses the big
swing of effective antenna gain due to noncontinuous terrain data. There are several
ways to handle this, such as averaging the terrain data along the radial path around the
reflection point, averaging the effective height gains from all potential reflection points,
and so on. Based on data from the large volume of measured data, the biggest gain must
compare with the value from free space loss as a cap value.
Second, effective antenna gains need to be integrated with knife-edge diffraction loss.
This section proposes an integrated solution that effectively combines both the effective
gain and the shadow loss for each knife-edge situation. Section 3.3.2.1 discuss single
knife-edge integration, and Sec. 3.3.3 addresses the integration of multiple knife edges.

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3.3. . 1 Effective Antenna Gain Algorithm
In order to work on the original effective antenna height gain (in the LOS case) calculation, • 1 2
we have to find the reflection point on the ground first. If the ground is not flat, there
are two reflection points between the base station and the mobile receiver. Connecting
the image of the transmit antenna height at the base station below the ground to the
actual receiving antenna at the mobile, the intercept point on the ground is the first
reflection point. Connecting the actual transmit antenna height of the base station to the
image of the mobile receiving antenna below the ground, the intercept point on the
ground is the second reflection point. Between the two points, the one that is close to
the mobile receiver is chosen as the effective reflection point to be used. Take a tangen­
tial line on the curved terrain at the reflection point and extend the line to intercept at the