Intelligent discovery of the capabilities of reconfiguration options in a cognitive wireless B3G context

Beyond 3G (B3G) wireless connectivity can efficiently be realized by exploiting cognitive networking concepts. Cognitive systems dynamically reconfigure the Radio Access Technologies (RATs) and the spectrum they use, based on experience, in order to adapt to the changing environment conditions. However, dynamic reconfiguration decisions call for robust discovery, i.e. radio-scene analysis and channel identification schemes. This paper intends to contribute in the areas of radio-scene analysis and channel identification: firstly, by providing an overview of interference estimation methods, and explaining how capacity estimations can be derived based on the measured interference levels; secondly, by specifying the information flow for the radio-scene analysis process of a cognitive radio system; and thirdly, by enhancing the above with a learning system, which is essential for obtaining a truly cognitive process. The proposed approach lies in the introduction of a robust probabilistic model for optimal prediction of the capabilities of alternative configurations, in terms of capacity.