臺灣博碩士論文加值系統

近年來，鑒於能夠有效提高通道容量(Capacity)以及提高室內覆蓋率(Coverage)，毫微微細胞(Femtocell)日漸受到重視。為了能夠有效利用頻譜效率(Spectrum efficiency)，毫微微細胞蜂巢網路使用與現存之無線通訊網路(Macrocell)相同頻帶。因此，如何有效控制毫微微細胞網路與現存網路之相互干擾成為目前急需克服的難題。

本文的主旨在於基於中斷機率限制之下控管網路下鍊之毫微微蜂巢網路與現存網路之間相互干擾，透過中斷機率管制毫微微細胞對於現存網路所造成的影響。換言之，當某毫微微細胞之中斷機率符合標準，此毫微微細胞方可與其服務之用戶通訊。反之，鑒於此毫微微細胞會對現存網路造成過大干擾，此毫微微細胞不允許服務其用戶。此外，由於採用中斷機率限制條件，毫微微細胞無須知曉現存網路用戶確切位置，此能有效降低毫微微網路與現存網路之間資料交換量。

為了能夠滿足中斷機率限制，毫微微細胞必須控制其資源分配。本文中，資源分配最佳化問題被切割成兩個下鍊資源分配問題：次載波分配問題，與傳輸功率分配問題。於本文中，筆者藉由半定放寬(Semidefinite Relaxation)將原問題簡化成半定規劃(Semidefinite Programming)，並且利用主偶型內點法(primal-dual interior-point method)解決此資源分配問題。透過數值結果，我們證明基於有中斷機率限制之資源分配可以在保證現存網路之通道容量之下，有效提高毫微微細胞之通道容量。

The femtocell technology is an attractive alternative to solve the capacity and coverage problems of the existing macrocellular networks. To fully exploit and realize the promised potentials, it has toovercome the interference issue when femtocells are to be deployed over a macrocell system sharing the same spectrum.

In this thesis, we adopt a resource allocation based interference control approach. We consider downlink transmissions of an orthogonal frequency division multiple access (OFDMA) based femtocell network. The interference constraint is manifested in the form of (average) outage probability requirement. In other words, a femto base station (BS) is allowed to use a certain spectrum only if such an use does not cause the outage of a macrocellular user within the coverage neighborhood. The advantage of using the outage probability constraint is that the knowledge of the locations of the primary (macrocellular) users is not needed.

To satisfy the average outage probability constraint, a femtocell BS has to allocates the downlink resource (subcarriers and power) properly. We divide the resource allocation problem into two subproblems. The semidefinete relaxation (SDR) is used to convert the non-convex subcarrier assignment problem into a convex semidefinete programming (SDP) which is then solved by the primal-dual interior-point method. Given the subcarrier assignment, we suggest an iterative water-filling method by solving the KKT conditions for the power allocation problem.

Chinese Abstract i
English Abstract ii
Acknowledgements iv
Contents v
List of Figures vii
List of Tables ix
1 Introduction 1
2 System Model and Problem Formulation 6
3 Average Outage Probability 12
3.1 Approximation using least square method with independence assumption 14
3.2 Least square approximation without independence assumption . . . . . . 19
4 Resource Allocation 23
4.1 Subcarrier Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.1.1 Primal-Dual Interior-Point Method . . . . . . . . . . . . . . . . . 27
4.2 Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5 Numerical Results and Discussions 42
5.1 Comparison between Worst Case and Average Case scheme . . . . . . . . 44
5.2 Dierent total power constraints . . . . . . . . . . . . . . . . . . . . . . . 46
5.3 Dierent outage probability threshold . . . . . . . . . . . . . . . . . . . . 49
6 Conclusion 52
Bibliography 54

[1] FCC Spectrum Policy Task Force. (2002, Nov.). ”Report of the spectrum
efficiency working group,” Tech. Rep. 02-135. [Online]. Available:
http://www.fcc.gov/sptf/files/SEWGFinalReport 1.pdf
[2] S. Haykin, ”Cognitive radio: brain-empowered wireless communications,” IEEE J.
Sel. Areas Commun. , vol. 23, pp. 201-220, Feb. 2005.
[3] Y. -Y. Li, M., M., E.S. Sousa, T. Sato, and M. Nanri, ”Cognitive interference management
in 3G femtocells,” in Proc. PIMRC Tokyo, Japan, pp.1118-1122, Sept. 2009
[4] I. Akyildiz, W. Lee, M. Vuran, and S. Mohanty, ”Next generation/ dynamic spectrum
access/cognitive radio wireless networks: a survey,” Elsevier Computer Networks,
Vol. 50, pp. 2127-2159, Sept. 2006
[5] S. Carlaw, A. Giustina, R. R. Bhat, V. S. Rao, R. Siegberg, and S.R. Saunders
Femtocells: opportunities and challenges for business and technology, Wiley, 2009
[6] H. Claussen, ”Performance of macro- and co-channel femtocells in a hierarchical cell
structure,” in Proc. PIMRC, Athens, Greece, pp.1-5, Sept. 2007
[7] V. Chandrasekhar, J. G. Andrews, and A. Gatherer, ”Femtocell networks: a survey,”
IEEE Commun. Mag. , vol.46, no.9, pp.59-67, Sept. 2008
[8] Y. Bai, J. Zhou, L. Liu, L. Chen, and H. Otsuka, ”Resource coordination and interference
mitigation between macrocell and femtocell,” in Proc. PIMRC , Tokyo,
Japan, pp.1401-1405 , Sept. 2009
[9] D. Lopez-Perez, A. Ladanyi, A. Juttner, and J. Zhang, ”OFDMA femtocells: a
self-organizing approach for frequency assignment,” in Proc. PIMRC, Tokyo, Japan,
pp.2202-2207, Sept. 2009
[10] K. Han, Y. Choi, D. Kim, M. Na, S. Choi, and K.Han, ”Optimization of femtocell
network configuration under interference constraints,” in Proc. WIOPT, Seoul,
Korea, pp.1-7, June 2009
[11] V. Chandrasekhar, J. G. Andrews, ”Uplink capacity and interference avoidance
for two-tier femtocell networks,” IEEE Trans. Commun. , vol.8, no.7, pp.3498-3509,
July 2009
[12] M. Haenggi, J.G. Andrews, F. Baccelli, O. Dousse and M. Franceschetti, ”Stochastic
geometry and random graphs for the analysis and design of wireless networks,” IEEE
J. Sel. Areas Commm. , vol.27, no.7, pp.1029-1046, Sept. 2009
[13] K. Hamdi, Wei Zhang, and K. Ben Letaief, ”Power control in cognitive radio systems
based on spectrum sensing side information,” in Proc. ICC, Glasgow, Scotland,
pp.5161-5165, June 2007,
[14] A. Goldsmith, Wireless communications, Cambridge University Press, 2005
[15] ”3GPP RAN4, simulation assumptions and parameters for FDD HeNB RF requirements,”
R4-092042, May 2009
[16] J. Fox Applied regression analysis and generalizFed linear models, 2nd ed. Sage
Publications, 2008
[17] L. Wasserman All of nonparametric statistics, Springer, 2005
[18] A. Papoulis, and S.U. Pillai Probability, random variables and stochastic processes,
4th ed., McGraw-Hill, 2002
[19] L. Vandenberghe and S. Boyd ”Semidefinite programming,” SIAM Review ,vol. 38,
no. 1, pp. 49-95, March 1996
[20] S. Boyd and L. Vandenberghe, Convex optimization, Cambridge University Press,
2004
[21] F.- J. Kuo, ”Interference control for OFDMA based overlay femto networks,” M.S.
thesis, Institute of Communication Engineering, NCTU, Hsinchu, Taiwan, 2009
[22] C. Helmberg, F. Rendl, R. Vanderbei, and H. Wolkowicz, ”An interiorpoint method
for semidefinite programming,” SIAM J. Optim., vol. 6, no. 2, pp. 342V361, 1996.
[23] Z.-Q Luo, W.-K Ma, A.M.-C. So, Y. Ye, and S. Zhang, ”Semidefinite relaxation of
quadratic optimization problems,” IEEE Signal Process Mag., vol.27, no.3, pp.20-34,
May 2010
[24] S. Haykin, Communication systems 4th ed. Wiley, 2001.
[25] D. Tse and P. Viswanath, Fundamentals of wireless communication, Cambridge
University Press, 2005