Full Text:   <981>

Summary:  <663>

CLC number: TP393

On-line Access: 2014-12-23

Received: 2014-04-10

Revision Accepted: 2014-10-28

Crosschecked: 2014-12-12

Cited: 3

Clicked: 2924

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jiang LIU

http://orcid.org/0000-0002-0729-1299

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.1 P.1-11

http://doi.org/10.1631/FITEE.1400129


Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing


Author(s):  Tao Huang, Ying-lei Teng, Meng-ting Liu, Jiang Liu

Affiliation(s):  State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Corresponding email(s):   liujiang@bupt.edu.cn

Key Words:  Cognitive heterogeneous networks, Markov chain, Stochastic geometry, Homogeneous Poisson point process (HPPP)


Share this article to: More |Next Article >>>

Tao Huang, Ying-lei Teng, Meng-ting Liu, Jiang Liu. Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(1): 1-11.

@article{title="Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing",
author="Tao Huang, Ying-lei Teng, Meng-ting Liu, Jiang Liu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="1",
pages="1-11",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1400129"
}

%0 Journal Article
%T Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing
%A Tao Huang
%A Ying-lei Teng
%A Meng-ting Liu
%A Jiang Liu
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 1
%P 1-11
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400129

TY - JOUR
T1 - Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing
A1 - Tao Huang
A1 - Ying-lei Teng
A1 - Meng-ting Liu
A1 - Jiang Liu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 1
SP - 1
EP - 11
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1400129


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.

This paper employs the divide-and-conquer strategy, deriving the spectrum usage opportunity by the DTMC, and managing to approximate the aggregated interference by an integral method. The idea of the application of stochastic geometry in HetNet is timely and interesting.

理想/非理想感知条件下认知异构网络的容量分析

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

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Akoum, S., Zwingelstein-Colin, M., Heath, R.W., et al., 2010. Cognitive cooperation for the downlink of frequency reuse small cells. Proc. 2nd Int. Workshop on Cognitive Information Processing, p.111-115.

[2]Akoum, S., Kountouris, M., Heath, R.W., 2011. On imperfect CSI for the downlink of a two-tier network. Proc. IEEE Int. Symp. on Information Theory, p.553-557.

[3]Andrews, J.G., Baccelli, F., Ganti, R.K., 2011. A tractable approach to coverage and rate in cellular networks. IEEE Trans. Commun., 59(11):3122-3134.

[4]Bennis, M., Perlaza, S.M., Blasco, P., et al., 2013. Self-organization in small cell networks: a reinforcement learning approach. IEEE Trans. Wirel. Commun., 12(7):3202-3212.

[5]Dhillon, H.S., Ganti, R.K., Baccelli, F., et al., 2012a. Modeling and analysis of K-tier downlink heterogeneous cellular networks. IEEE J. Sel. Area Commun., 30(3):550-560.

[6]Dhillon, H.S., Novlan, T.D., Andrews, J.G., 2012b. Coverage probability of uplink cellular networks. Proc. IEEE Global Communications Conf., p.2179-2184.

[7]ElSawy, H., Hossian, E., 2013. Two-tier HetNets with cognitive femtocells: downlink performance modeling and analysis in a multichannel environment. IEEE Trans. Mob. Comput., 13(3):649-663.

[8]ElSawy, H., Hossain, E., Kim, D.I., 2013a. HetNets with cognitive small cells: user offloading and distributed channel access techniques. IEEE Commun. Mag., 51(6):28-36.

[9]ElSawy, H., Hossain, E., Haenggi, M., 2013b. Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: a survey. IEEE Commun. Surv. Tutor., 15(3):996-1019.

[10]Fehske, A.J., Viering, I., Voigt, J., et al., 2014. Small-cell self-organizing wireless networks. Proc. IEEE, 102(3):334-350.

[11]Gelabert, X., Sallent, O., Pérez-Romero, J., et al., 2010. Spectrum sharing in cognitive radio networks with imperfect sensing: a discrete-time Markov model. Comput. Netw., 54(14):2519-2536.

[12]Heath, R.W., Kountouris, M., Bai, T., 2013. Modeling heterogeneous network interference using Poisson point processes. IEEE Trans. Signal Process., 61(16):4114-4126.

[13]Huang, Y.C., Ko, K.T., Huang, Q., et al., 2011. An efficient method for performance evaluation of femto-macro overlay systems. Proc. IEEE Int. Conf. on communications, p.1-6.

[14]Hwang, I., Song, B., Soliman, S.S., 2013. A holistic view on hyper-dense heterogeneous and small cell networks. IEEE Commun. Mag., 51(6):20-27.

[15]Kelif, J.M., Alman, E., 2005. Downlink fluid model of CDMA networks. Proc. IEEE 61st Vehicular Technology Conf., p.2264-2268.

[16]Khawam, K., Samhat, A.E., Ibrahim, M., et al., 2007. Fluid model for wireless adhoc networks. Proc. IEEE 18th Int. Symp. on Personal, Indoor and Mobile Radio Communications, p.1-5.

[17]Khoshkholgh, M., Navaie, K., Yanikomeroglu, H., 2013. Outage performance of the primary service in spectrum sharing networks. IEEE Trans. Mob. Comput., 12(10):1955-1971.

[18]Madhusudhanan, P., Restrepo, J.G., Liu, Y., et al., 2012. Heterogeneous cellular network performance analysis under open and closed access. Proc. IEEE Globecom Workshops, p.563-568.

[19]Meerja, K.A., Ho, P.H., Wu, B., 2011. A novel approach for co-channel interference mitigation in femtocell networks. Proc. IEEE Global Telecommunications Conf., p.1-6.

[20]Nakamura, T., Nagata, S., Benjebbour, A., et al., 2013. Trends in small cell enhancements in LTE advanced. IEEE Commun. Mag., 51(2):98-105.

[21]Stoyan, D., Kendall, W.S., Mecke, J., 1995. Stochastic Geometry and Its Applications (2nd Ed.). John Wiley & Sons, Chichester.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE