Breaking the Area Spectral Efficiency Wall in Cognitive Underlay Networks

In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network. The developed framework explicitly accommodates channel, topological and medium access uncertainties. The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks. To this end, we highlight two available degrees of freedom, i.e., shaping medium access or transmit power. While from the primary user's perspective tuning either to control the interference is equivalent, the picture is different for the secondary network. We show the existence of an area spectral efficiency wall under both adaptation schemes. We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance. But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks. We explore several design parameters for both adaptation schemes. Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies.