Please wait a minute...
FITEE
Front. Inform. Technol. Electron. Eng.  2015, Vol. 16 Issue (1): 1-11    DOI: 10.1631/FITEE.1400129
    
理想/非理想感知条件下认知异构网络的容量分析
Tao Huang, Ying-lei Teng, Meng-ting Liu, Jiang Liu
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing
Tao Huang, Ying-lei Teng, Meng-ting Liu, Jiang Liu
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
 全文: PDF 
摘要： 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。
关键词： 认知异构网络;  马尔可夫链;  随机几何;  齐次泊松点过程    
Abstract: 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.
Key words: Cognitive heterogeneous networks    Markov chain    Stochastic geometry    Homogeneous Poisson point process (HPPP)
收稿日期: 2014-04-10 出版日期: 2014-12-23
CLC:  TP393  
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Tao Huang
Ying-lei Teng
Meng-ting Liu
Jiang Liu

引用本文:

Tao Huang, Ying-lei Teng, Meng-ting Liu, Jiang Liu. Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing. Front. Inform. Technol. Electron. Eng., 2015, 16(1): 1-11.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/FITEE.1400129    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2015/V16/I1/1

[1] Li-ming Yang, Wei Zhang, Yun-fang Chen. 基于社会网络整体模糊化度量的时序预测方法[J]. Front. Inform. Technol. Electron. Eng., 2015, 16(10): 805-816.