A Predictive Model for Cognitive Radio

Advances in process technology, manufacturing, and architecture have ushered in an age of faster, smaller, and cheaper electronic devices. Emerging processor technology has made it possible to migrate applications that were traditionally implemented in custom silicon to general purpose processors (GPP). In the area of wireless communication, this transition has given birth to the field of software-defined and cognitive radio. Theses smart radios, or cognitive/software-defined radios (C/SDR), can potentially make more efficient use of the available RF spectrum and adapt to a wide range of protocols and environments. One of the key benefits of having a C/SDR is its ability to change communication parameters in response to changes in application needs and the radio frequency (RF) landscape. While understanding the effects of changing communication parameters is a critical precursor to the development of a predictive model, it is not the focus of this paper. This research builds upon our investigation of the effects of varying these communication parameters through the development of a predictive model. This model allows a C/SDR to dynamically modify its configuration in order to improve system performance