论文部分内容阅读
目的:针对认知异构网络中频谱的动态性和干扰的复杂性,利用随机几何理论进行干扰分析并提出网络容量的闭式表达式,为认知异构网络的性能分析提供理论基础。创新:利用随机几何理论分析认知异构网络中复杂的干扰问题,并提出干扰和网络容量的闭式表达式。方法:采用“分而治之”的方法,一方面,针对频谱使用的动态性,利用离散时间马尔可夫链对主用户(宏基站用户)进行建模,得到主用户离开和到达概率,并考虑理想/非理想感知情况,分别计算出两种情况下主用户和次用户的数量;另一方面,针对用户位置的随机性,利用齐次泊松点过程对基站和用户进行建模,再采用随机几何理论对干扰进行分析,提出计算干扰的闭式表达式。最后,利用以上两部分结果分别求出理想/非理想感知情况下网络容量的闭式表达式。结论:利用随机几何理论分析认知异构网络中的干扰和容量具有可行性和准确性;理想感知情况下得到的网络容量要大于非理想感知的情况。
Aims: According to the dynamics of spectrum and the complexity of interference in cognitive heterogeneous networks, we use the stochastic geometry theory to analyze the interference and propose the closed-form expression of network capacity, which provides a theoretical basis for the performance analysis of cognitive heterogeneous networks. Innovations: Using stochastic geometry theory to analyze complex interference problems in cognitive heterogeneous networks, and presenting closed-form expressions for interference and network capacity. On the one hand, aiming at the dynamic of spectrum usage, the main user (macro base station user) is modeled by the discrete-time Markov chain to get the departure probability and arrival probability of the main user, and on the one hand, Ideal / non-ideal perception, and calculate the number of primary and secondary users in two cases respectively. On the other hand, for the randomness of user location, we use the homogeneous Poisson point process to model base stations and users and then use Stochastic geometry theory analyzes the interference and puts forward a closed-form expression to calculate the interference. Finally, we use the above two results to find out the closed-form expressions of the network capacity under ideal / non-ideal perception. Conclusion: It is feasible and accurate to analyze the interference and capacity in cognitive heterogeneous networks by using stochastic geometry theory. The network capacity obtained under ideal perception is greater than the non-ideal perception.