隨著各式電子產品的蓬勃發展與人們對網路依賴感的提升，無線傳輸資料量以及對通訊品質的需求在近幾年內都有著指數倍地成長，也因此，找出有效率地使用頻寬，並加大通道的流量，成為現今最迫在眉睫的課題。在蜂巢式系統的架構中，為提升頻帶的使用率，普遍的作法是調高「頻率再用因子（frequency reuse factor）」，讓鄰近細胞內的用戶們能共用相同的頻帶，然而，這也卻衍生出另一項問題。在細胞邊界的用戶，因距離的關係很容易受到鄰近細胞中也使用相同頻帶的用戶或是基地台的干擾，我們稱之為同頻道干擾（co-channel interference）。同頻道干擾會嚴重地影響通訊品質，進而拖累系統整體的傳輸速度。為了解決同頻道干擾所造成的影響，在本篇論文中，我們透過干擾排列技術（interference alignment）盡可能地將干擾集中在一起，好讓接收端能多挪出些空間來接收資訊，使傳輸訊號擁有更高的自由度（degrees of freedom）。我們將針對雙細胞多天線式網路（two-cell MIMO interference network），分別以靜態（固定系統中的用戶數目）與動態（考慮用戶依序進入網路，且資源的分配有先後關聯）兩種不同的情境，分析其系統最大的自由度，並設計相對應的傳輸編碼、資源分配以及干擾排列的方式。
根據過往關於消息理論的研究，尋找多細胞多用戶網路的自由度一直是個尚待解決的課題，由於自由度的分析過於複雜，以往的研究僅能找出其理論的上界限與特定編解碼架構所達到的下界限，又或是僅限於某些系統參數底下的確切值。我們將這些有關自由度的過往研究，整理在本篇論文的第二章節，並詳述干擾排列技術的編解碼方式，探討其特性與優劣。
在本篇論文的第三章節，我們分析雙細胞多天線網路中靜態情境下的自由度。我們利用二維度的時空編碼（two-dimensional space-time spreading code, 2D-STSC）來表示有限的通道延展，並規劃一個「秩最小化（rank minimization）」的數學問題來尋找系統的自由度，最後透過我們所提出的組合理論（grouping algorithm）來求解。組合理論採用干擾排列技術的概念，將不同用戶們所造成的干擾，藉由設計過的組合盡可能地排列、集中在一起，使干擾矩陣的秩能達到最小。而利用組合理論所計算出的系統自由度，不僅比起過往的方法所能達到的下界限還要高，在某些特定的系統參數底下，甚至能比肩自由度理論值的上界限。除此之外，我們也推導出在有限通道延展下，系統最大的自由度，其等效於系統最多可容納的用戶數目。
在本篇論文的第四章節，我們探討雙細胞多天線網路中動態情境下的自由度。在動態的情境中，用戶會依序地進入網路，並從兩個基地台中選擇其一來傳送資訊，先來的用戶可優先享有此網路的資源，而後到的用戶只能使用前者所剩下的。我們分析了每個新進用戶所能使用的最大自由度，並發現其與之前用戶的基地台選擇（base station selection）有關。因此，我們建構了一幅樹狀圖，記錄每個用戶的選擇與對應的自由度。我們發現，當妥善地運用干擾排列技術時，每個用戶能享有更多的自由度，而系統中可容納的用戶數目也會隨之提升。我們也透過程式模擬，來比較傳統的基地台選擇方式與「以自由度為基準的基地台選擇方式」在不同的環境中的優劣。
在本論文的最後，我們透過上述章節的成果和結論，來強調自由度分析重要性以及其低複雜度的優勢，並試想將自由度的演算法運用在未來通訊系統的可能性，好比大規模天多線式系統（massive MIMO system）或小細胞網路（small cell network），藉以改善同頻道干擾所造成的影響，並提升頻帶的使用效率。

Due to the tremendous growth of data traffic over wireless communication networks and the exponentially increasing demands for higher throughputs by users, efficient use of resources to maximize capacity becomes utmost importance for system design. A key objective with respect to the deployment of future wireless cellular networks is to utilize a frequency reuse of one (or as close to one as is practical), i.e., the base stations (BS) in cells will operate on all available time-frequency resource blocks simultaneously such that the channel efficiency could be maximized. However, by increasing the frequency reuse, cell edge users may suffer more degradation in connection with BS due to the co-channel interference from other cells. Thus, finding the optimum interference management strategy becomes a pressing and challenging problem. In this dissertation, we consider an interference management technique, called interference alignment (IA), which main idea is to coordinate multiple transmitters so that their mutual interference may align together into a provably smaller dimension at the receivers. Our efforts primarily focused on verifying IA's ability to achieve the maximum degrees of freedom (DoF) in both static scenario (i.e., the number of users in the network is fixed) and dynamic scenario (i.e., users sequentially join into the network) of a two-cell MIMO interference network, developing algorithms for determining alignment solutions, and designing transmission strategies for DoF based BS selection. The proposed algorithms and results in this dissertation provide a promising solution for the spectrum scarcity in wireless systems.

The information-theoretic (optimal) DoF for the general multicell multiuser network is an open issue in the existing literature, since even the smallest gap between the best available DoF lower bound and theoretical DoF upper bound translates into an unbounded gap in the best available first-order capacity approximations. We summarize the preliminary studies about the investigations of DoF with the development of IA in different wireless systems (in Chapter 2).

We study the DoF focusing on an uplink two-cell multiuser MIMO interference network with asymmetric numbers of users in the two cells (in Chapter 3), where the results can be generally extended to the multi-cell networks. We propose an achievable scheme based on a two-dimensional space-time spreading code (2D-STSC) framework with linear precoding/decoding design and finite channel extension. The derivation of the sum DoF is shown related to a rank minimization problem, which corresponds to the minimization of the dimension of the interference subspace. The problem is solved by the proposed grouping algorithm (GA) based on aligning interfering data streams into a low-dimensional subspace as a group and attaining the minimum number of groups. The achievable sum DoF derived based on the proposed GA is shown greater than previous results and achieves the theoretic upper bound in some cases. We also give a closed-form expression of the maximum achievable sum DoF when there is the maximum number of admissible users in the considered finite diversity environment.

We further extend the DoF work (in Chapter 4) by considering a dynamic scenario of the uplink two-cell MIMO interference network, where users sequentially arrive into the network. We study the problem of sequential base station (BS) selection for the users, with the selection criterion based on the DoF available for the new arriving user. We find that different sequential BS selections affect individual and network performance in terms of the individual and network sum DoF as well as the number of admissible users in the network. We propose a method to build the tree structure for sequential BS selection, which carries trellis information for individual and system-wide selections. The properties of the tree are analytically studied. It turns out that by adopting an interference coordination strategy based on the concept of IA, a better individual and network performance can be achieved. Simulation compares the proposed DoF-based BS selection and traditional BS selection schemes (i.e., received signal strength indicator (RSSI) based and sum rate based BS selection criterions) and highlights the advantages of the proposed scheme.

Results in this dissertation demonstrate the superiority of the interference coordination scheme. Especially, the characteristic of low-complexity and the increasing advantages with growing number of antennas are benefical for the future massive MIMO system. The analysis of the DoF with both the maximum number of admissible users (static scenario) and the sequentially arriving users (dynamic scenario) provides a constructive solution for efficiently deploying the resource (e.g., frequency band, network infrastructures, and etc.) in small cell networks. Some conclusions and future researches are drawn (in Chapter 5).

[1] S. Ramanath, M. Debbah, E. Altman, and V. Kumar, “Asymptotic analysis of precoded small cell networks,” in the 30th Annual IEEE International Conference on Computer Communications (INFOCOM), San Diego, CA, USA, March 2010.
[2] D. Gesbert, S. Hanly, H. Huang, S. Shitz, O. Simeone, and W. Yu, “Multi-cell MIMO cooperative networks: A new look at interference,” IEEE J. Select. Areas Commun., vol. 28, no. 9, pp. 1380–1408, Dec. 2010.
[3] G. Boudreau, J. Panicker, N. Guo, R. Chang, N. Wang, and S. Vrzic, “Interference coordination and cancellation for 4G networks,” IEEE Commun. Mag., vol. 47, no. 4, pp. 74–81, April 2009.
[4] S.-M. Cheng, S.-Y. Lien, F.-S. Chu, and K.-C. Chen, “On exploiting cognitive radio to mitigate interference in macro/femto heterogeneous networks,” IEEE Trans. Wireless Commun., vol. 18, no. 3, pp. 40–47, June 2011.
[5] M. A. Maddah-Ali, A. S. Motahari, and A. K. Khandani, “Signaling over MIMO multi- base systems: Combination of multi-access and broadcast schemes,” in IEEE Inter- national Symposium on Information Theory Proceedings (ISIT), Seattle, Washington, USA, July 2006, pp. 2104–2108.
[6] V. R. Cadambe and S. A. Jafar, “Interference alignment and degrees of freedom of the K-user interference channel,” IEEE Trans. Inform. Theory, vol. 54, no. 8, pp. 3425– 2441, Aug. 2008.
[7] S. A. Jafar and M. J. Fakhereddin, “Degrees of freedom for the MIMO interference channel,” IEEE Trans. Inform. Theory, vol. 53, no. 7, pp. 2637–2642, July 2007.
[8] T. Gou and S. A. Jafar, “Degrees of freedom of the K-user M × N MIMO interference channel,” IEEE Trans. Inform. Theory, vol. 56, no. 12, pp. 6040–6057, Dec. 2010.
[9] A. Ghasemi, A. S. Motahari, and A. K. Khandani, “Interference alignment for the K- user MIMO interference channel,” in IEEE International Symposium on Information Theory Proceedings (ISIT), Austin, Texas, USA, June 2010, pp. 360–364.
[10] C. Suh and D. Tse, “Interference alignment for cellular networks,” in Proc. of 46th Annual Conference on Communication, Control and Computing, Monticello, IL, USA, Sept. 2008, pp. 1037–1044.
[11] S.-H. Park and I. Lee, “Analysis of degrees of freedom of interfering MISO broadcast channels,” in IEEE Global Telecommunications Conference (GLOBECOM), Honolulu, HI, USA, Nov. 2009.
[12] J. Sun, Y. Liu, and G. Zhu, “On the degrees of freedom of the cellular network,” in IEEE International Conference on Communications (ICC), Cape Town, South Africa, May 2010.
[13] W. Shin, N. Lee, J. B. Lim, C. Shin, and K. Jang, “On the design of interference align- ment scheme for two-cell MIMO interfering broadcast channels,” IEEE Trans. Wireless Commun., vol. 10, no. 2, pp. 437–442, Feb. 2011.
[14] S.-H. Park and I. Lee, “Degrees of freedom for mutually interfering broadcast channels,” IEEE Trans. Inform. Theory, vol. 58, no. 1, pp. 393–402, Jan. 2012.
[15] C. M. Yetis, T. Gou, S. A. Jafar, and A. H. Kayran, “On feasibility of interference alignment in MIMO interference networks,” IEEE Trans. Signal Processing, vol. 58, no. 9, pp. 4771–4782, Sept. 2010.
[16] ——, “Feasibility conditions for interference alignment,” in IEEE Global Telecommuni- cations Conference (GLOBECOM), Honolulu, Hawaii, USA, Nov. 2009.
[17] B. Nosrat-Makouei, J. G. Andrews, and R. W. Heath, “A simple SINR characterization for linear interference alignment over uncertain MIMO channels,” in IEEE International Symposium on Information Theory Proceedings (ISIT), Austin, Texas, USA, June 2010, pp. 2288–2292.
[18] C. Wang, T. Gou, and S. A. Jafar, “Subspace alignment chains and the degrees of free- dom of the three-user MIMO interference channel,” in IEEE International Symposium on Information Theory Proceedings (ISIT), Cambridge, MA, July 2012, pp. 2471–2475.
[19] G. Sridharan and W. Yu, “Degrees of freedom of MIMO cellular networks with two cells and two users per cell,” in IEEE International Symposium on Information Theory Proceedings (ISIT), Istanbul, July 2013, pp. 1774–1778.
[20] ——, “Degrees of freedom of MIMO cellular networks: two-cell three-user-per-cell case,” in IEEE Global Telecommunications Conference (GLOBECOM), Atlanta, GA, USA, Dec. 2013.
[21] G. Bresler and D. Tse, “3 user interference channel: degrees of freedom as a function of channel diversity,” in 47th Annual Allerton Conference on Communication, Control, and Computing, Monticello, IL, Sept. 2009, pp. 265–271.
[22] L. Li, H. Jarfarkhani, and S. A. Jafar, “Towards the feasibility conditions for linear interference alignment with symbol extensions: a diversity constraint,” in IEEE Global Telecommunications Conference (GLOBECOM), Anaheim, California, USA, Dec. 2012.
[23] G. Bresler, D. Cartwright, and D. Tse, “Feasibility of interference alignment for the MIMO interference channel: the symmetric square case,” in IEEE Information Theory Workshop (ITW), Paraty, Oct. 2011, pp. 447–451.
[24] Y. Ma, J. Li, R. Chen, and Q. Liu, “On feasibility of interference alignment for l- cell constant cellular interfering networks,” IEEE Commun. Lett., vol. 16, no. 5, pp. 714–716, May 2012.
[25] C. Wang, H. Sun, and S. A. Jafar, “Genie chains and the degrees of freedom of the K-user MIMO interference channel,” in IEEE International Symposium on Information Theory Proceedings (ISIT), Cambridge, MA, July 2012, pp. 2476–2480.
[26] M. Razaviyayn, G. Lyubeznik, and Z.-Q. Luo, “On the degrees of freedom achievable through interference alignment in a MIMO interference channel,” IEEE Trans. Signal Processing, vol. 60, no. 2, pp. 812–821, Feb. 2012.
[27] H.-J. Chou, C.-C. Chou, J.-M. Wu, and R. Y. Chang, “On the achievable degrees of freedom of two-cell multiuser MIMO interference networks,” IEEE Trans. Commun., vol. 62, no. 8, pp. 2880–2893, Aug. 2014.
[28] K. Gomadam, V. Cadambe, and S. A. Jafar, “Approaching the capacity of wireless net- works through distributed interference alignment,” in IEEE Global Telecommunications Conference (GLOBECOM), New Orleans, LA, USA, Dec. 2008.
[29] S. W. Peters and R. W. Heath, “Interference alignment via alternating minimization,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009, pp. 2445–2448.
[30] J. Kim, S.-H. Park, H. Sung, and I. Lee, “Sum rate analysis of two-cell MIMO broadcast channels: Spatial multiplexing gain,” in IEEE International Conference on Communi- cations (ICC), Cape Town, South Africa, May 2010, pp. 1–5.
[31] Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” IEEE Trans. Signal Processing, vol. 59, no. 9, pp. 4331–4340, Sept. 2011.
[32] J. Kim, S.-H. Park, H. Sung, and I. Lee, “Spatial multiplexing gain for two interfering MIMO broadcast channels based on linear transceiver,” IEEE Trans. Wireless Com- mun., vol. 9, no. 10, pp. 3012–3017, Oct. 2010.
[33] J.-M. Wu, T.-F. Yang, and H.-J. Chou, “MIMO active interference alignment for un- derlay cognitive radio,” in IEEE International Conference on Communications (ICC), Cape Town, South Africa, May 2010.
[34] H.-J. Chou, J.-M. Wu, J.-Y. Hsu, and P.-A. Ting, “Iterative interference alignment for joint degrees of freedom and sum capacity optimization,” in International ICST Con- ference on Communications and Networking in China (CHINACOM), Guilin, China, Aug. 2013, pp. 680–684.
[35] C.-C. Chou, H.-J. Chou, and J.-M. Wu, “A constraint relaxation version of the in- terference leakage minimization algorithm in MIMO interference channels,” in IEEE International Conference on Communications (ICC), Budapest, Hungary, June 2013, pp. 4860–4864.
[36] ——, “Exploiting the initial and the final conditions for the alternating minimization algorithm,” in IEEE International Conference on Communications (ICC), Budapest, Hungary, June 2013, pp. 5242–5246.
[37] H.-J. Chou and J.-M. Wu, “MIMO active interference cancellation for stealth cognitive radio in MB-OFDM UWB,” in International ICST Conference on Communications and Networking in China (CHINACOM), Hangzhou, China, Aug. 2008, pp. 173–177.
[38] B. Guler and A. Yener, “Selective interference alignment for MIMO cognitive femtocell networks,” IEEE J. Sel. Areas Commun., vol. 32, no. 3, pp. 439 – 450, March 2014.
[39] M. Amir, A. El-Keyi, and M. Nafie, “Constrained interference alignment and the spatial degrees of freedom of MIMO cognitive network,” IEEE Trans. Inform. Theory, vol. 57, no. 5, pp. 2994–3004, May 2011.
[40] F. Li and H. Jafarkhani, “Interference cancellation and detection for more than two users,” IEEE Trans. Commun., vol. 59, no. 3, pp. 901–910, March 2011.
[41] J. Kazemitabar and H. Jafarkhani, “Multiuser interference cancellation and detection for users with more than two transmit antennas,” IEEE Trans. Commun., vol. 56, no. 4, pp. 574–583, Apr. 2008.
[42] F. Li and H. Jafarkhani, “Multiple-antenna interference cancellation and detection for two users using precoders,” IEEE J. Sel. Topics Signal Process., vol. 3, no. 6, pp. 1066– 1078, Dec. 2009.
[43] M. Bhatnagar and A. Hjorungnes, “Improved interference cancellation scheme for two- user detection of alamouti code,” IEEE Trans. Signal Processing, vol. 58, no. 8, pp. 4459–4465, Aug. 2010.
[44] T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd ed. John Wiley and Sons, Inc., July 2006.
[45] R. G. Gallager, Information Theory and Reliable Communication. John Wiley and Sons, Inc., Jan. 1968.
[46] D. Astely, “LTE: The evolution of mobile broad- band,” IEEE Commun. Mag., vol. 47, no. 4, pp. 44–51, Apr. 2009.
[47] 3GPP Technical Specification Group Radio Access Network, “Requirements for further advancements for evolved universal terrestrial radio access (E-UTRA) (LTE-Advanced) (release 8),” Tech. Rep., 3GPP, 2009.
[48] ITU-R, “Recommendation ITU-R M.1645 framework and overall objectives of the future development of IMT-2000 and systems beyond IMT-2000,” in Tech. Rep., ITU-R, 2008.
[49] M. Hong and Z.-Q. Luo, “Distributed linear precoder optimization and base station selection for an uplink heterogeneous network,” IEEE Trans. Signal Processing, vol. 61, no. 12, pp. 3214–3228, June 2013.
[50] D. S. Papailiopoulos and A. G. Dimakis, “Interference alignment as a rank constrained rank minimization,” IEEE Trans. Signal Processing, vol. 60, no. 8, pp. 4278–4288, Aug. 2012.
[51] R. Doostnejad, T. J. Lim, and E. S. Sousa, “Space-time spreading codes for a mul- tiuser MIMO system,” in Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, vol. 2, CA, USA, Nov. 2002, pp. 1374–1378.
[52] B. Wang and K. J. R. Liu, “Advances in cognitive radio networks: A survey,” IEEE J. Sel. Topics Signal Process., vol. 5, no. 1, pp. 5–23, Feb. 2011.
[53] G. Caire and S. Shamai, “On achievable rates in a multi-antenna broadcast downlink,” in Proc. 38th Annu. Allerton Conf. Communication, Control and Computing, Monticello, Illinois, USA, Oct. 2000.
[54] S. Vishwanath, N. Jindal, and A. Goldsmith, “Duality, achievable rates, and sum-rate capacity of gaussian MIMO broadcast channels,” IEEE Trans. Inform. Theory, vol. 49, no. 10, pp. 2658–2668, Oct. 2003.
[55] P. Viswanath and D. Tse, “Sum capacity of the vector gaussian broadcast channel and uplink-downlink duality,,” IEEE Trans. Inform. Theory, vol. 49, no. 8, pp. 1912–1921, Aug. 2003.
[56] M. Costa, “Writing on dirty paper,” IEEE Trans. Inform. Theory, vol. IT-29, pp. 439– 441, May 1983.
[57] S. A. Jafar, Interference Alignment: A New Look at Signal Dimensions in a Communi- cation Network. Now Publishers Inc., 2011.
[58] S. A. Jafar and S. Shitz, “Degrees of freedom region of the MIMO X channel channel,” IEEE Trans. Inform. Theory, vol. 54, no. 1, pp. 151–170, Jan. 2008.
[59] O. Ayach, S. Peters, and R. Heath, “The practical challenges of interference the practical challenges of interference alignment,” IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 1536–1284, March 2013.
[60] S. A. Jafar, “Exploiting channel correlations - simple interference alignment schemes with no CSIT,” in IEEE Global Telecommunications Conference (GLOBECOM), Miami, FL, Dec. 2010.
[61] M.-J. Kim, H.-H. Lee, and Y.-C. Ko, “Robust transceiver design based on joint sig- nal and interference alignment for MIMO interference channels with imperfect channel knowledge design based on joint signal and interference alignment for MIMO interfer- ence channels with imperfect channel knowledge,” IEEE Commun. Lett., vol. 18, no. 11, pp. 2035 – 2038, Nov. 2014.
[62] H. Huang, V. Lau, Y. Du, and S. Liu, “Robust lattice alignment for K user MIMO in- terference channels with imperfect channel knowledge,” IEEE Trans. Signal Processing, vol. 59, no. 7, pp. 3315 – 3325, July 2011.
[63] D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press, 2005.
[64] D. A. Cox, J. B. Little, and D. B. O’Shea, Using Algebraic Geometry, 2nd ed. New York: Springer, 2005.
[65] T. Y. Li, “Solving polynomial systems by polyhedral homotopies,” Taiwanese J. Math., vol. 3, no. 3, pp. 251–279, Sept. 1999.
[66] M. Razaviyayn, M. Sanjabi, and Z.-Q. Luo, “Lineartransceiverdesign for interference alignment: complexity and computation,” IEEE Trans. Inform. Theory, vol. 58, no. 5, pp. 2896–2910, May 2012.
[67] M. Mesbahi and G. P. Papavassilopoulos, “On the rank minimization problem over a positive semidefinite linear matrix inequality,” IEEE Trans. Automatic Control, vol. 42, no. 2, pp. 239–243, Feb. 1997.
[68] B. Recht, M. Fazel, and P. Parrilo, “Gaurateed minimum-rank solutions of linear ma- trix equations via nuclear norm minimization,” in 45th Annual Allerton Conference on Communication, Control, and Computing, Illinois, USA, Sept. 2007, pp. 42–48.
[69] M. Fazel, H. Hindi, and S. Boyd, “Rank minimization and applications in system the- ory,” in Proceedings American Control Conference, Boston, Massachusetts, USA, June 2004, pp. 3273–3278.
[70] L. J. Cimini, G. J. Foschini, and L. A. Shepp, “Single-channel user-capacity calculations for self-organizing cellular systems,” IEEE Trans. Commun., vol. 42, no. 12, pp. 3137– 3143, Dec. 1994.
[71] D. Tse and S. Hanly, “Linear multiuser receivers: Effective interference, effective band- width and user capacity,” IEEE Trans. Inform. Theory, vol. 45, no. 2, pp. 641–657, March 1999.
[72] P. Viswanath, V. Anantharam, and D. Tse, “Optimal sequences, power control, and user capacity of synchronous CDMA systems with linear MMSE multiuser receivers,” IEEE Trans. Inform. Theory, vol. 45, no. 6, pp. 1968–1983, Sept. 1999.
[73] Z. Hasan, H. Boostanimehr, and V. K. Bhargava, “Green cellular networks: A survey, some research issues and challenges,” IEEE Commun. Surveys and Tutorials, vol. 13, no. 4, pp. 524–540, Fourth Quarter 2011.
[74] Q. Du and X. Zhang, “QoS-aware base-station selections for distributed MIMO links in broadband wireless networks,” IEEE J. Select. Areas Commun., vol. 29, no. 6, pp. 1123–1138, June 2011.
[75] E. Oh, B. Krishnamachari, X. Liu, and Z. Niu, “Toward dynamic energy-efficient op- eration of cellular network infrastructure,” IEEE Commun. Mag., vol. 49, no. 6, pp. 56–61, June 2011.
[76] Y. Yang, L. Chen, and W. Wang, “A novel energy saving scheme based on base stations dynamic configuration in green cellular networks,” in IEEE 78th Vehicular Technology Conference (VTC), Las Vegas, NV, Sep. 2013, pp. 1–5.
[77] E. Pollakis, R. Cavalcante, and S. Stanczak, “Base station selection for energy effi- cient network operation with the majorization-minimization algorithm,” in IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cesme, Turkey, June 2012, pp. 219–223.
[78] P. Gonzalez-Brevis, J. Gondzio, Y. Fan, H. V. Poor, J. Thompson, I. Krikidis, and P.-J. Chung, “Base station location optimization for minimal energy consumption in wireless networks,” in IEEE 73rd Vehicular Technology Conference (VTC Spring), Budapest, Hungary, May 2011, pp. 1–5.
[79] F. Adelantado, O. Sallent, and J. P.-Romero, “On WCDMA base station selection criteria,” IEEE Commun. Lett., vol. 10, no. 4, pp. 248–251, Apr. 2006.
[80] H. Takanashi and S. S. Rappaport, “Dynamic base station selection for personal commu- nication systems with distributed control schemes,” in IEEE 47th Vehicular Technology Conference (VTC), vol. 3, Phoenix, Arizona, USA, May 1997, pp. 1787–1791.
[81] Z. Zhu, P. Gupta, Q. Wang, S. Kalyanaraman, Y. Lin, H. Franke, and S. Sarangi, “Virtual base station pool: towards a wireless network cloud for radio access networks,” in Proceedings of the 8th ACM International Conference on Computing Frontiers, ser. CF’11, no. 34. New York, NY, USA: ACM, 2011, pp. 34:1–34:10.
[82] B. N.-Makouei, J. G. Andrews, and R. W. Heath, “User arrival in user arrival in MIMO interference alignment networks,” IEEE Trans. Wireless Commun., vol. 11, no. 2, pp. 842–851, Feb. 2012.
[83] J. Hoydis, S. ten Brink, and M. Debbah, “Massive MIMO in the UL/DL of cellular networks: How many antennas do we need?” IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 160–171, Feb. 2013.
[84] E. Altman, A. Kumar, C. Singh, and R. Sundaresan, “Spatial SINR games of base station placement and mobile association,” IEEE/ACM Trans. Netw., vol. 20, no. 6, pp. 1856–1869, Dec. 2012.
[85] L. E. Spence, A. J. Insel, and S. H. Friedberg, Elementary Linear Algebra: A Matrix Approach, 2nd ed. Upper Saddle River, NJ: Prentice Hall, June 2007.