Modeling and Analysis for Spectrum Handoff in Cognitive Heterogeneous Wireless Network In cognitive heterogeneous wireless network where the open wireless network and the cognitive radio network coexist, we use the queuing theory to model the spectrum handoff of secondary user(SU), solving the problem of large secondary user service delay. First, a cognitive heterogeneous wireless network environment is established based on the shared unlicensed spectrum as well as the opportunity utilization authorized spectrum in cognitive radio network. Then, a novel dynamic data adjustment scheme is proposed based on the channel historical state information. Combining with the user classification and the PRPNPRP MG1 queue model, we analyze the secondary usersxe2x80x99 extened data transmission delay in spectrum handoff, and propose an adaptive frequency handoff strategy to minimize the delay. Finally, the simulation results show that the proposed cognitive heterogeneous wireless network can realize the network coexistence and improve the spectrum utilization efficiency. Meanwhile, the proposed adaptive spectrum handoff strategy can effectively reduce the transmission delay of delaysensitive secondary users, while improve the service performance of nondelaysensitive ones.

A Joint Design of User Modeling and Resource Management in Cognitive Radio Networks The limited available spectrum and inefficient spectrum utilization make it necessary to employ dynamic spectrum access techniques. The key enabling technology for dynamic spectrum access is cognitive radio (CR), which exploits the existing wireless spectrum opportunistically and utilizes the licensed spectrum when primary users are passive. Thus, the activities of primary radio (PR) and cognitive radio (CR) users play a major role in the performance of cognitive radio networks. In this paper, a joint solution for user modeling and spectrum management is offered. The activities of PR and CR users are modeled through threestate Markov chain as the spectrum resource blocks are utilized with help of servicelevel overbooking strategy. Simulation results show the performance of the booking limit with respect to the channel release ratio of CR users and consequently the improved revenue of the network.