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CLC number: TP393

On-line Access: 2014-12-23

Received: 2014-04-10

Revision Accepted: 2014-10-28

Crosschecked: 2014-12-12

Cited: 3

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Citations:  Bibtex RefMan EndNote GB/T7714


Jiang LIU


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


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)

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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.

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DOI - 10.1631/FITEE.1400129

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.




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[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.

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