Near optimal wireless data broadcasting based on an unsupervised neural network learning algorithm

Wireless data broadcasting (WDB) is proven to be an efficient information delivery mechanism of nearly unlimited scalability. However, successful performance of a WDB based system is not always guaranteed-it strongly depends on the system's ability to identify the most popular information (documents) among users and accurately estimate their actual request probabilities. In this paper, we argue that a recently proposed unsupervised neural network algorithm possesses the key properties of an ideal estimator of document request probabilities. Obtained simulation results support the theoretical assumptions and suggest a near optimal performance of a WDB based system employing the given algorithm.