The need to capture the actual network traf c condition and fundamental queueing dynamics
in a massive cognitive radio network (CRN) is important for proper analysis of the intrinsic effects of spatial
distribution while capturing the essential temporal distribution properties of the network. In massive CRN,
many users, including primary and secondary users, transmit on scarce spectrum resources. While primary
users (PUs) are delay-sensitive users that require prioritized access over secondary users (SUs), carrying
out analysis that captures this property becomes imperative if users' service experience is to be satisfactory.
This paper presents priority conscious spatiotemporal analysis capable of characterizing users' experience
in massive CRN. Users in the primary priority queue were considered to have pre-emptive priory over users
in the virtual and secondary priority queues. A Geo/G/1 discrete-time Markov chain queueing system was
adopted to characterize both primary and secondary priority queues, while the virtual priority queue was
analyzed as part of the secondary priority queue. Using the tools of stochastic geometry and queueing
theory, the user's coverage probability was determined while the delay experienced by each class of users
in the network was obtained using existing results. Through the obtained delay for each class of users in the
network, the corresponding quality of service was also obtained. The results obtained show that the proposed
framework is capable of accurately characterizing users' service experience in massive CRN.