Cognitive radio is considered as an efficient way to improve the spectrum efficiency. As one of its key technologies,spectrum handoff can guarantee the transmission continuity of secondary users(SUs). In this paper,we...Cognitive radio is considered as an efficient way to improve the spectrum efficiency. As one of its key technologies,spectrum handoff can guarantee the transmission continuity of secondary users(SUs). In this paper,we address a new and more generalized spectrum handoff problem in cognitive radio networks(CRNs),by considering simultaneously energy efficiency,multiple spectrum handoffs and multiple channels. Furthermore,effects of the primary users'(PUs')arrival and service rate on the target channel sequence selection are also considered. In order to obtain the energy-efficient target channel sequence,we firstly analyze the energy consumption and the number of delivered bits per hertz in the spectrum handoff process,and formulate a ratio-type energy efficiency optimization problem,which can be transformed into a parametric problem by utilizing fractional programming. Then,we propose an algorithm combining dynamic programming with bisection(DPB)algorithm to solve the energy efficiency optimization problem. Our simulation results verify that the designed target channel sequence has better performance than the existing algorithms in terms of energy efficiency.展开更多
In order to estimate the traffic arrival rate and service rate parameters of primary users in cognitive radio networks,a hidden Markov model estimation algorithm( HMM-EA) is proposed,which can provide better estimatio...In order to estimate the traffic arrival rate and service rate parameters of primary users in cognitive radio networks,a hidden Markov model estimation algorithm( HMM-EA) is proposed,which can provide better estimation performance than the energy detection estimation algorithm( ED-EA). Firstly,spectrum usage behaviors of primary users are described by establishing a preemptive priority queue model,by which a real state transition probability matrix is derived. Secondly,cooperative detection is utilized to detect the real state of primary users and emission matrix is derived by considering both detection and false alarm probability. Then,a hidden Markov model is built based on the previous two steps,and evaluated through the forward-backward algorithm. Finally,the simulations results verify that the HMM-EA algorithm outperforms the ED-EA in terms of convergence performance,and therefore the secondary user is able to access the unused channel with the least busy probability in real time.展开更多
基金Heilongjiang Province Natural Science Foundation(Grant No.F2016019)；National Natural Science Foundation of China(Grant No.61571162)；Major National Science and Technology Project(2015ZX03004002004)； China Postdoctoral Science Foundation(Grant No.2014M561347).
文摘Cognitive radio is considered as an efficient way to improve the spectrum efficiency. As one of its key technologies,spectrum handoff can guarantee the transmission continuity of secondary users(SUs). In this paper,we address a new and more generalized spectrum handoff problem in cognitive radio networks(CRNs),by considering simultaneously energy efficiency,multiple spectrum handoffs and multiple channels. Furthermore,effects of the primary users'(PUs')arrival and service rate on the target channel sequence selection are also considered. In order to obtain the energy-efficient target channel sequence,we firstly analyze the energy consumption and the number of delivered bits per hertz in the spectrum handoff process,and formulate a ratio-type energy efficiency optimization problem,which can be transformed into a parametric problem by utilizing fractional programming. Then,we propose an algorithm combining dynamic programming with bisection(DPB)algorithm to solve the energy efficiency optimization problem. Our simulation results verify that the designed target channel sequence has better performance than the existing algorithms in terms of energy efficiency.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61071104)
文摘In order to estimate the traffic arrival rate and service rate parameters of primary users in cognitive radio networks,a hidden Markov model estimation algorithm( HMM-EA) is proposed,which can provide better estimation performance than the energy detection estimation algorithm( ED-EA). Firstly,spectrum usage behaviors of primary users are described by establishing a preemptive priority queue model,by which a real state transition probability matrix is derived. Secondly,cooperative detection is utilized to detect the real state of primary users and emission matrix is derived by considering both detection and false alarm probability. Then,a hidden Markov model is built based on the previous two steps,and evaluated through the forward-backward algorithm. Finally,the simulations results verify that the HMM-EA algorithm outperforms the ED-EA in terms of convergence performance,and therefore the secondary user is able to access the unused channel with the least busy probability in real time.
