The ray-tracing (RT) algorithm has been used for accurately predicting the site-specific radio propagation characteristics, in spite of its computational intensity. Statistical models, on the other hand, offers computational simplicity but low accuracy. In this paper, a new model is proposed for predicting the indoor radio propagation to achieve computational simplicity over the RT method and better accuracy than the statistical models. The new model is based on the statistical derivation of the ray-tracing operation, whose results are a number of paths between the transmitter and receiver, each path comprises a number of rays. The pattern and length of the rays in these paths are related to statistical parameters of the site-specific features of indoor environment, such as the floor plan geometry. A key equation is derived to relate the average path power to the site-specific parameters, which are: 1) mean free distance; 2) transmission coefficient; and 3) reflection coefficient. The equation of the average path power is then used to predict the received power in a typical indoor environment. To evaluate the accuracy of the new model in predicting the received power in a typical indoor environment, a comparison with RT results and with measurement data shows an error bound of less than 5 dB.Index Terms-Power coverage, power delay profile, probabilistic geometry, rat tracing, site-specific channel model, statistical indoor radio propagation, wireless deployment tool.

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Ray tracing (RT) software has been used to develop a site-specific autoregressive (AR) model for an indoor radio channel. A comparison is carried out between this model and that derived from the channel measurements. To enhance the congruence between the two models, an interpolation technique is applied to the RT raw data. It is concluded that a two-pole AR model can be derived from the RT software instead of the channel measurements. Results obtained from RT software avoid expensive and time consuming measurement process.Introduction: The indoor wireless systems are expected to be deployed on a large scale in the near future. Site-specific propagation characterisation will then be important for deployment planning and performance evaluation using software tools. Many wideband statistical models have been proposed for characterising the indoor radio channels in the time-and frequency-domains [ 11. These models are derived from the extensive propagation data for wide variety of buildings. Time-domain models represent the time response as a tapped delay line. The values and statistics of the model coefficients are derived from the measured time response of the channel profiles [ 11. Frequency-domain models represent the frequency response as a site-specific autoregressive (AR) system driven by white noise [2]. The locations and statistics of poles of the model are derived from the frequency response of the channel. The method for finding the poles of an AR model, given a channel profile of a radio channel, is presented in [l] where it is shown that an indoor channel could be modelled by up to 5 poles. Furthermore, results in [2] concluded that it is sufficient to use the two most significant poles only, thus, the number of coefficients required is less than for the time-domain models. Therefore, it is easier to implement this model in block oriented software. The AR model is further extended in [4] to include the Doppler spectrum to the AR model.Statistical models, in general, are simple and computationally efficient and therefore commonly used in computer simulation. However, they lack accuracy since they are not based on the specifics of the place where the deployment is intended. A good example of non-site-specific models is the JTC model 141. To improve the statistical models, we suggest deriving them from sitespecific propagation information, therefore, we call them site-specific statistical models. There are two main methods to attain the site-specific propagation information. The first is by carrying out numerous measurements for the time or frequency response of the site in question [l]. This is the most accurate method for characterising the channel, but it is very costly and time consuming. The second method is to use simulation software developed for calculating the radio channel profiles, such as the ray tracing (RT) soft-.

This paper provides the economical tradeoffs for supporting multiple services over an OFDMA cellular network. These tradeoffs are related to two conflicting goals: (1) maximizing coverage so as to lower the system cost; and (2) maximizing the number of end users so as to maximize revenues. This problem is formulated in terms of coverage spectral efficiency, which directly determines the system capacity. We will analyze how the coverage geometry and the service requirements affect the coverage spectral efficiency, which determines the revenue generation. This paper also shows how the channel impairments (shadow fading, fast fading, and co-channel inference) affect the economics of the OFDMA system.