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
在认知蜂窝异构网络中,针对大规模部署认知家庭基站带来的能量消耗问题,研究了两层异构网络上行链路的资源分配算法.提出了一种基于双循环迭代的资源联合分配算法,在实时用户服务质量（quality of service,QoS）需求约束和跨层干扰约束...在认知蜂窝异构网络中,针对大规模部署认知家庭基站带来的能量消耗问题,研究了两层异构网络上行链路的资源分配算法.提出了一种基于双循环迭代的资源联合分配算法,在实时用户服务质量（quality of service,QoS）需求约束和跨层干扰约束下最大化认知系统能量效率,将分数形式的能效函数等价转换为减数形式,使优化问题近似确定为凸优化形式,并通过迭代方法求解.仿真结果表明：该算法能够快速收敛到最优能效,并保证了实时用户的QoS需求,有效提高了系统能量效率.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.
文摘在认知蜂窝异构网络中,针对大规模部署认知家庭基站带来的能量消耗问题,研究了两层异构网络上行链路的资源分配算法.提出了一种基于双循环迭代的资源联合分配算法,在实时用户服务质量（quality of service,QoS）需求约束和跨层干扰约束下最大化认知系统能量效率,将分数形式的能效函数等价转换为减数形式,使优化问题近似确定为凸优化形式,并通过迭代方法求解.仿真结果表明：该算法能够快速收敛到最优能效,并保证了实时用户的QoS需求,有效提高了系统能量效率.
