Enhanced estimation of configuration capabilities in cognitive radio

Cognitive radio is a highly promising answer to the complexity and heterogeneity characterizing the beyond 3G wireless scenario. In this context, this article advances from the field of interference sensing to the fields of (basic) reasoning and robust reasoning. Interference sensing is concerned with the acquisition of interference related measurements for frequency bands of interest. The article describes how a cognitive radio system can reason on these measurements to obtain estimations for the capabilities of alternate configurations, especially in terms of achievable transmission capacity and coverage. Subsequently, it focuses on robust reasoning, namely, on enhancing these estimations by employing machine learning, which constitutes an important aspect of cognitive radio. Several relevant solutions are sketched and explained, with a view to providing a complete picture.