Modeling and analysis of mobility stochastic properties in cognitive radio networks

In wireless networks, the movements of Primary Users (PUs) have a great impact on ongoing sessions of the Secondary Users (SUs) due to the dynamic spectrum access strategy. Precisely analyzing the mobility model plays an important role on efficiency of spectrum utilization and protocol design. Different from the traditional mobile networks, the mobility of cognitive radio networks is more challenging, which includes the temporal mobility, spatial mobility and spectrum mobility and so on. To this end, this paper focuses on the mobility stochastic properties of cognitive radio networks and makes a complete model for them based on the tools of probability theory. In particular, we analyze the mobility stochastic properties of cognitive radio networks under two important mobility models, namely, the Random Direction (RD) model and the Random Waypoint (RWP) model respectively. More specifically, we drive the closed-form expressions of expected meeting time and expected contact time between PU and SU, which can be used to forecast the communication status. To evaluate the accuracy of our models, extensive comparisons are conducted by comparing the theoretical results with the simulation ones under different mobility scenarios. The results show that the analytical results fit with the simulation results, which indicates that the proposed models can guide further researches on the spectrum-efficient cognitive radio networks.