  In addition to serve as platforms for dynamic spectrum access, cognitive
radios can also serve as a method for improving the performance of wireless
communication systems by smartly adjusting their operating parameters according
to the environment and requirements. The uncertainty always present in the
environment makes the practical implementation of the latter application
difficult. In this paper, we propose a probabilistic graphical model, Bayesian
network that captures the causal relationships among the variables bit energy
to noise spectral density ratio (EbN0), carrier to interference ratio (C/I),
modulation scheme (MOD), Doppler phase shift (Dop_Phi), and bit error rate
(BER). BER indicates how the communication link is performing. The goal of our
proposed Bayesian network is to use the BER as evidence in order to infer the
behavior of the other variables, so the cognitive radio can learn how the
conditions of the environment are, and based on that knowledge make better
informed decisions. This model along with the method used to build it are
described in this paper.
