  In cognitive radio networks, secondary users (SUs) may cooperate with the
primary user (PU), so that the success probability of PU transmissions are
improved, while SUs obtain more transmission opportunities. Thus, SUs have to
take intelligent decisions on whether to cooperate or not and with what power
level, in order to maximize their throughput subject to average power
constraints. Cooperation policies in this framework require the solution of a
constrained Markov decision problem with infinite state space. In our work, we
restrict attention to the class of stationary policies that take randomized
decisions in every time slot based only on spectrum sensing. The proposed class
of policies is shown to achieve the same set of SU rates as the more general
policies, and enlarge the stability region of PU queue. Moreover, algorithms
for the distributed calculation of the set of probabilities used by the
proposed class of policies are presented.
