Due to irregular deployment of small base stations （SBSs）, the interference in cognitive heterogeneous networks （CHNs） becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the ...Due to irregular deployment of small base stations （SBSs）, the interference in cognitive heterogeneous networks （CHNs） becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains （DTMCs）, the spectrum mobility with respect to the arrival and departure of macro base station （MBS） users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry （SG）. Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information （CSI）. Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.展开更多
基金Project supported by the National Basic Research Program （973） of China （No. 2012CB315801）, the National Natural Science Foundation of China （Nos. 61302089 and 61302081）, and the State Major Science and Technology Special Projects （No. 2013ZX03001025-002）
文摘Due to irregular deployment of small base stations （SBSs）, the interference in cognitive heterogeneous networks （CHNs） becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains （DTMCs）, the spectrum mobility with respect to the arrival and departure of macro base station （MBS） users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry （SG）. Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information （CSI）. Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.
