This paper presents neural network based models for the prediction of propagation path loss in urban environment. The neural networks are designed separately for line-ofsight (LOS) and non-line-of-sight (NLOS) cases. The performance of the neural models is compared to that of the COST231-Walfisch-lkegami model, the WaGschBertoni model and the single regression model, based on the absolute mean error, standard deviation and the root mean squared error between predicted and measured values.

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Modern technology promises mobile users Internet connectivity anytime, anywhere, using any device. However, given the constrained capabilities of mobile devices, the limited bandwidth of wireless networks and the varying personal sphere, effective information access requires the development of new computational patterns. The variety of mobile devices available today makes device-specific authoring of web content an expensive approach. The problem is further compounded by the heterogeneous nature of the supporting networks and user behaviour. The notions of "typical user" and "typical user behaviour" are no longer applicable for many Internet applications. This research investigates the challenges posed by these problems, and proposes a context-aware adaptation framework (FAÇADE) to bridge the gap between the existing Internet content and today's heterogeneous computing environments. A pilot implementation of a facility for testing and performance evaluation of FAÇADE is also discussed.

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