Optimal Primary-Secondary user Cooperation Policies in Cognitive Radio Networks

TL;DR

This paper develops optimal stationary cooperation policies for secondary users in cognitive radio networks, improving primary user stability and enabling distributed computation of SU transmission strategies.

Contribution

It introduces a class of stationary randomized policies that match the performance of more complex policies and expands the primary user stability region.

Findings

Proposed policies achieve the same SU rates as general policies.

Policies enlarge the stability region of the primary user.

Algorithms enable distributed calculation of policy probabilities.

Abstract

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.