  In this paper, a new cooperation structure for spectrum sensing in cognitive
radio networks is proposed which outperforms the existing commonly-used ones in
terms of energy efficiency. The efficiency is achieved in the proposed design
by introducing random interruptions in the cooperation process between the
sensing nodes and the fusion center, along with a compensation process at the
fusion center. Regarding the hypothesis testing problem concerned, first, the
proposed system behavior is thoroughly analyzed and its associated
likelihood-ratio test (LRT) is provided. Next, based on a general linear fusion
rule, statistics of the global test summary are derived and the sensing quality
is characterized in terms of the probability of false alarm and the probability
of detection. Then, optimization of the overall detection performance is
formulated according to the Neyman-Pearson criterion (NPC) and it is discussed
that the optimization required is indeed a decision-making process with
uncertainty which incurs prohibitive computational complexity. The NPC is then
modified to achieve a good affordable solution by using semidefinite
programming (SDP) techniques and it is shown that this new solution is nearly
optimal according to the deflection criterion. Finally, effectiveness of the
proposed architecture and its associated SDP are demonstrated by simulation
results.
