=== Modeling the Wireless Propagation Channel===
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The rapid growth of wireless communications along with the high costs associated with the design and deployment of effective wireless infrastructures underline a persistent need for computer aided communication network planning tools. Wireless engineers have long used simplistic statistical prediction models based on measurements that often exhibit considerable errors especially in areas having mixed building sizes.
Every wireless communication system involves a transmitter that transmits some sort of signal (voice, video, data, etc.), a receiver that receives and detects the transmitted signal, and a channel in which the signal is transmitted into the air and travels from the location of the transmitter to the location of the receiver. The channel is the physical medium in which the electromagnetic waves propagate. The successful design of a communication system depends on an accurate link budget analysis that determines whether the receiver receives adequate signal power to detect it against the background noise. The simplest channel is the free space. Real communication channels, however, are more complicated and involve a large number of wave scatterers. For example, in an urban environment, the obstructing buildings, vehicles and vegetation reflect, diffract or attenuate the propagating radio waves. As a result, the receiver receives a distorted signal that contains several components with different power levels and different time delays arriving from different angles.
The rapid growth of wireless communications along with the high costs associated with the design and deployment of effective wireless infrastructures underline a persistent need for computer aided communication network planning tools. The different rays arriving at a receiver location create constructive and destructive interference patterns. This is known as the multipath effect. This together with the shadowing effects caused by building obstructions lead to channel fading. The use of statistical models for prediction of fading effects is widely popular among communication system designers. These models are either based on measurement data or derived from simplistic analytical frameworks. The statistical models often exhibit considerable errors especially in areas having mixed building sizes. In many wireless applicationssuch cases, one needs to perform a physics-based, site-specific analysis of the total received power by propagation environment to accurately identify and establish all the possible signal paths from the transmitter to the receiver is all that matters. In some others, the angle of arrival This involves an electromagnetic analysis of the rays as well as their polarization are scene with all of immense interestits geometrical and physical details.
===EM.Terrano in a Nutshell ===
In ground-based systems, the presence of the ground as a very large reflecting surface affects the signal propagation to a large extent. Along the path from a transmitter to a receiver, the signal may also encounter many obstacles and scatterers such as buildings, vegetation, etc. In an urban canyon environment with many buildings of different heights and other scatterers, a line of sight between the transmitter and receiver can hardly be established. In such cases, the propagating signals bounce back and forth among the building surfaces. It is these reflected or diffracted signals that are often received and detected by the receiver. Such environments are referred to as âmultipathâ. The group of rays arriving at a specific receiver location experience different attenuations and different time delays. This gives rise to constructive and destructive interference patterns that cause fast fading. As a receiver moves locally, the receiver power level fluctuates sizably due to these fading effects.
The use of statistical models for prediction of fading effects is widely popular among communication system designers. These models are either based on measurement data or derived from simplistic analytical frameworks. The statistical models often exhibit considerable errors especially in areas having mixed building sizes. In such cases, one needs to perform a physics-based, site-specific analysis of the propagation environment to accurately identify and establish all the possible signal paths from the transmitter to the receiver. This involves an electromagnetic analysis of the scene with all of its geometrical and physical details. Â Link budget analysis for a multipath channel is a challenging task due to the large size of the computational domains involved. Typical propagation scenes usually involve length scales on the order of thousands of wavelengths. To calculate the path loss between the transmitter and receiver, one must solve Maxwell's equations in an extremely large space. Full-wave numerical techniques like the Finite Difference Time Domain (FDTD) method, which require a fine discretization of the computational domain, are therefore impractical for solving large-scale propagation problems. The practical solution is to use asymptotic techniques such as SBR, which utilize analytical techniques over large distances rather than a brute force discretization of the entire computational domain. Such asymptotic techniques, of course, have to compromise modeling accuracy for practical computation feasibilitycomputational efficiency.
=== The SBR Method ===