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Abstract
There is an ever increasing demand on wireless communication systems for improved indoor coverage, greater capacity and enhanced user services. Energy efficient, small scale femtocell networks deployed in home, office and other indoor environments are one of the promising means of satisfying these demands. They can provide substantial benefits to both operators and users, such as a reduction in capital expenditures and operational expenditures, easy plug-and-play and optimized indoor coverage. Because of the random deployment behavior by the users, femtocell base stations (FBSs) should have cognitive position awareness, automatic parameter tuning and learning abilities. This will allow for self-optimization of the indoor coverage with the minimal interference to outdoor users. This paper first proves that the optimal coverage radii for FBS exist in typical indoor environments with position awareness enabled technology. Then, a dynamic power allocation technique for FBS is designed and analyzed in this paper by considering the path loss and different wall penetration conditions. To further enable the intelligent coverage self-optimization for randomly deployed indoor FBSs, a novel joint dynamic power allocation and antenna pattern selection scheme using the artificial neural network is proposed. Performance results are presented to illustrate the effectiveness and accuracy of the proposed coverage technique for randomly deployed femtocell networks.
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References
Mansfield, G. (2008, June). Femtocells in the US market-business drivers and consumer propositions. InProceedings of the femtocell Europe, London, UK.
Cullen, J. (2008, June). Radioframe presentation. InProceedings of the femtocell Europe, London, UK.
Zhang, J., & de la Roche, G. (2010).Femtocells: Technologies and deployment. New York: Wiley.
Saleh, A. A. M., Rustako, A. J, Jr, & Roman, R. S. (1987). Distributed antennas for indoor radio communications.IEEE Transactions on Communications,35(12), 1245–1251.
Edwards, C. (2008). The future is femto.IET Engineering & Technology,3(15), 70–73.
Chandrasekhar, V., Andrews, J. G., & Gatherer, A. (2008). Femtocell networks: A survey.IEEE Communications Magazine,46(9), 59–67.
Femtoforum.http://www.femtoforum.org
Chandrasekhar, V., & Andrews, J. G. (2009). Spectrum allocation in tiered cellular networks.IEEE Transactions on Communications,57(10), 3059–3068.
Lopez-Perez, D., Valcarce, A., de la Roche, G., & Zhang, J. (2009). OFDMA femtocells: A roadmap on interference avoidance.IEEE Communications Magazine,47(9), 41–48.
Claussen, H., Ho, L., T., W., & Samuel, L. G. (2008, April) Self-optimization of coverage for femtocell deployments. InProceedings of the wireless telecommunications on symposium, pp. 278–285.
Ashraf, I., Claussen, H. & Ho, L. T. W. (2010, May). Distributed radio coverage optimization in enterprise femtocell networks. InProceedings of the IEEE international conference on communications, pp. 1–6.
Barbieri, A., Damnjanovic, A., Ji, T. F., et al. (2012). LTE femtocells: System desgn and performance analysis.IEEE Journal on Selected Areas in Communications,30(3), 586–594.
Yun, J.-H., & Shin, K. G. (2011). Adaptive interference management of OFDMA femtocells for co-channel deployment.IEEE Journal on Selected Areas in Communications,29(6), 1225–1241.
Chandrasekhar, V., Andrews, J. G., Muharemovie, T., Shen, Z., & Gatherer, A. (2009). Power control in two-tier femtocell networks.IEEE Transactions on Wireless Communications,8(8), 4316–4328.
Yavuz, M., Meshkati, F., Nanda, S., Pokhariyal, A., Johnson, N., Raghothaman, B., et al. (2009). Interference management and performance analysis of UMTS/HSPA+ femtocells.IEEE Communications Magazine,47(9), 102–109.
Jo, H.-S., Mun, C., Moon, J., & Yook, J.-G. (2009). Interference mitigation using uplink power control for two-tier femtocell networks.IEEE Transactions on Wireless Communications,8(10), 4906–4910.
Arulselvan, N., Ramachandran, V., Kalyanasundaram, S., & Han, G. (2009, April). Distributed power control mechanisms for HSDPA femtocells. InProceedings of the IEEE vehicular technology conference, pp. 1–5.
Claussen, H. (2007, September). Performance of macro- and co-channel femtocells in a hierarchical cell structure. InProceedings of the IEEE international symposium on personal, indoor and mobile radio communications, pp. 1–5.
Ho, L. T. W., & Claussen, H. (2007, September). Effects of user-deployed, co-channel femtocells on the call drop probability in a residential scenario. InProceedings of the IEEE international symposium on personal, indoor and mobile radio communications, pp. 1–5.
Knisely, D. N., Yoshizawa, T., & Favichia, F. (2009). Standardization of femtocells in 3GPP.IEEE Communications Magazine,47(9), 68–75.
Knisely, D. N., & Favichia, F. (2009). Standardization of femtocells in 3GPP2.IEEE Communications Magazine,47(9), 76–82.
Claussen, H., & Pivit, F. (2009, June). Femtocell coverage optimization using switched multi-element antennas. InProceedings of the IEEE international conference on communications, pp. 1–6.
Chandrasekhar, V., Kountouris, M., & Andrews, J. G. (2009). Coverage in multi-antenna two-tier networks.IEEE Transactions on Wireless Communications,8(10), 5314–5327.
She, J., Suo, S. Q., Quan, H. Y., et al. (2008).3GPP long term evolution: Principle and system design. Peking: Posts & Telecom Press.
Goldsmith, A. (2005).Wireless communications. Cambridge: Cambridge University Press.
Tsagkaris, K., Katidiotis, A., & Demestichas, P. (2008). Neural network-based learning schemes for cognitive radio systems.Computer Communications,31(14), 3394–3404.
Hopfield, J. J. (1988). Artificial neural networks.IEEE Circuits and Devices Magazine,4(5), 3–10.
Cybenko, G. (1989). Approximation by superposition of sigmoidal functions.Mathematics of Control, Signals and Systems,2, 303–314.
Haykin, S. (2009).Neural networks and learning machines (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996).Neural network design. Boston, MA: PWS Publishing.
Chen, T. P., Chen, H., & Liu, R. (1995). Approximation capability in\(C(\bar{ R}^n)\) by multilayer feedforward networks and related problems.IEEE Transaction on Neural Networks,6(1), 25–30.
Mornik, K. M., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators.Neural Networks,2(5), 359–366.
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Authors and Affiliations
Key Lab. of Universal Wireless Communications Ministry of Education, Information and Telecommunication Engineering of Beijing University of Posts and Telecommunications (BUPT), Xitucheng Rd, Haidian Dist., Beijing, 100876, People’s Republic of China
Qixun Zhang & Zhiyong Feng
Department of Electrical and Computer Engineering, University of Victoria, Victoria, V8W 3P6, Canada
Wei Li
- Qixun Zhang
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- Zhiyong Feng
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- Wei Li
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Corresponding author
Correspondence toQixun Zhang.
Additional information
This work was supported by the National Natural Science Foundation of China (61201152, 61227801, 61421061), the National High Technology Research and Development Program of China (863 Program 2014AA01A707) and Key Laboratory of Universal Wireless Communications (KFKT-2013103).
Appendix
Appendix
1.1Lemma 1
When\(0 < R_f \le D_s /2\),\(\delta \) is a decreasing function of\(R_f\) because
Thus, the minimum value of\(\delta \) is given by\(\delta _1^{\min } = \delta _1 (D_s /2) = 1 - \pi /4 = 0.21\) when\(R_f = D_s /2\). For\(D_s /2 < R_f < \sqrt{2} D_s /2\), define\(\alpha = \arccos [D_s /(2R_f )],0 < \alpha < \pi /4\) and substitute\(D_s = 2R_f \cos \alpha \) into (8). After some manipulation, we obtain
From
and
the minimum value of\(\delta _2\) is given by\(\delta _2^{\min } \left| {_{\alpha = \pi /8} } \right. = 1 - 2\tan \alpha = 0.17\) with\(\alpha = \pi /8 = 0.393\) and\(D_s = 2R_f \cos \alpha = 1.85R_f\). When\(R_f \ge \sqrt{2} D_s /2\),\(\delta \) is an increasing function of\(R_f\) according to
Thus, the minimum value of\(\delta \) is given by\(\delta _3^{\min } = \delta _3 (\sqrt{2} D_s /2) = \pi /2 - 1 = 0.57\), with\(R_f = \sqrt{2} D_s /2\). The minimum value of\(\delta \) is then\(\delta ^{\min } = \min (\delta _1^{\min } ,\delta _2^{\min }, \delta _3^{\min } ) = \delta _2^{\min } = 0.17\) with an optimal FBS coverage radius of\(R_f^{Opt} = \arg \min \delta = 0.54D_s\).
1.2Lemma 2
When\(0 < R_f \le D_s\),\(\delta \) is a decreasing function of\(R_f\) because
Thus, the minimum value of\(\delta \) is given by\(\delta _1^{\min } = \delta _1 (D_s ) = 1 - \pi /4 = 0.21\) when\(R_f = D_s\).
For\(D_s < R_f < \sqrt{2} D_s\), define\(\alpha = \arccos [D_s/(R_f )], 0 < \alpha < \pi /4\) and substitute\(D_s=R_f\cos \alpha \) into (15). After some manipulation, we obtain
Based on the results in (52) and (53), the minimum value of\(\delta _2\) is given by\(\delta _2^{\min } \left| {_{\alpha = \pi /8} } \right. = 1 - 2\tan \alpha = 0.17\) when\(\alpha = \pi /8 = 0.393\) and\(D_s=R_f\cos \alpha =0.92R_f\). When\(R_f \ge \sqrt{2} D_s\),\(\delta \) is an increasing function of\(R_f\) according to
Thus, the minimum value of\(\delta \) is given by\(\delta _3^{\min } = \delta _3 (\sqrt{2} D_s ) = \pi /2 - 1 = 0.57\) when\(R_f = \sqrt{2} D_s\). The minimum\(\delta \) is then\(\delta ^{\min } = \min (\delta _1^{\min } ,\delta _2^{\min } ,\delta _3^{\min } ) = \delta _2^{\min } = 0.17\) with an optimal FBS coverage radius of\(R_f^{Opt} = \arg \min \delta = 1.08D_s\).
1.3Lemma 3
When\(0 < R_f \le D_s /2\),\(\delta \) is a decreasing function of\(R_f\) according to
Thus, the minimum value of\(\delta \) is given by\(\delta _1^{\min } = \delta _1 (D_s /2) = 1 - \pi /8 = 0.61\) when\(R_f = D_s /2\). When\(D_s /2 < R_f < D_s\), define\(\alpha = \arccos [D_s /(2R_f )],0 < \alpha < \pi /3\) and substitute\(D_s=2R_f\cos \alpha \) into (24). After some manipulation,\(\delta _2\) is given by
Based on
and
The minimum value of\(\delta _2\) is given by\(\delta _2^{\min } \left| {_{\alpha = \pi /4} } \right. = 1 - 0.5\tan \alpha = 0.5\) when\(\alpha = \pi /4 = 0.79\) and\(D_s = 2R_f \cos \alpha = 1.41R_f\). When\(D_s \le R_f < \sqrt{5} D_s /2\), define\(\alpha = \arccos (D_s/R_f), \beta = \arctan (1/2) - \alpha , \mu = \arccos [D_s/(2R_f)], \gamma = \arctan (2) - \mu , 0 \le \alpha < \arctan (1/2), \pi /3 \le \mu < \arctan (2)\) and substitute\(D_s = R_f \cos \alpha = 2R_f \cos \mu , \mu = \arccos [(\cos \alpha )/2]\) into (24). After some manipulation, we obtain
Using\(\zeta = \sqrt{4 -\cos ^2 \alpha } /(2\cos \alpha ), \xi = 2\alpha + 2\arccos \left[ {(\cos \alpha )/2} \right] - \pi /2\), (62) can be simplified to
Based on
and
Thus\(\delta _3\) is an increasing function of\(R_f\) with minimum value\(\delta _3^{\min } \left| {_{\alpha = 0,\mu = \pi /3} } \right. = 1 - \sqrt{3} /2 + \pi /6 = 0.66\) when\(\alpha =0\),\(\mu = \pi /3\) and\(D_s=R_f\).
When\(R_f \ge \sqrt{5} D_s /2\),\(\delta \) is an increasing function of\(R_f\) according to
Thus, the minimum value of\(\delta \) is given by\(\delta _4^{\min } = \delta _4 (\sqrt{5} D_s /2) = 5\pi /8 - 1 = 0.96\) when\(R_f = \sqrt{5} D_s /2\). The minimum value of\(\delta \) is then\(\delta ^{\min } = \min (\delta _1^{\min } ,\delta _2^{\min } ,\delta _3^{\min } ,\delta _4^{\min } ) = \delta _2^{\min } = 0.5\) with an optimal FBS coverage radius of\(R_f^{Opt} = \arg \min \delta = 0.71D_s\).
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Zhang, Q., Feng, Z. & Li, W. Coverage Self-Optimization for Randomly Deployed Femtocell Networks.Wireless Pers Commun82, 2481–2504 (2015). https://doi.org/10.1007/s11277-015-2360-z
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