Handbook of Propagation Effects for Vehicular and ... - Courses

Chapter 11
Theoretical Modeling Considerations
11.1 Background
Modeling serves a variety of purposes for characterizing land mobile satellite
propagation. Without the availability of data, a heuristic propagation model is desirable
in order to design propagation experiments that measure important signal characteristics
without imposing instrumentation limitations. Once data are available, specialized
models can be developed to explain the observed signal variations and their dependence
on a wide range of experimental parameters, such as the environment topography, link
elevation angle, vehicle speed, or receiver antenna pattern. As empirical or analytical
models reach maturity, they can be employed to evaluate system performance under
specified conditions or to simulate the actual operation for LMSS scenarios with a
particular choice of modulation and coding. Good models based on a thorough
understanding of the causes of signal degradation can then be used as aids in optimizing
system design and to explore fade mitigation strategies.
Much work has been done to characterize the signal variations observed in
terrestrial land mobile propagation at UHF [Jakes, 1974; Lee, 1986]. While some of the
same basic concepts of signal statistics apply also to LMSS, significant differences exist
and require the development of LMSS specific models. Satellite systems are usually
power limited because it is expensive and/or impractical to operate high-power
transmitters and high-gain antennas in space. Such systems therefore function with
relatively low fade margins at or near the line-of-sight signal level. On the other hand,
terrestrial systems can apply higher power levels and do not need to establish a line-ofsight
signal path. They normally operate by utilizing the scattered multipath signals. In
contrast, satellite systems must utilize the line-of-signal component for communications,
and multipath scattering represents interference.
In response to the needs of experimenters and system designers, several distinct
types of LMSS models have been developed. Three classes of models are described in
the following paragraphs. They are classified here as (1) empirical regression fits to data,
(2) probability distribution models, and (3) geometric-analytic models. The empirical