臺灣博碩士論文加值系統

本論文致力於下世代前瞻無線通訊技術設計，其中下世代前瞻無線通訊系
統在本文中指的是第四代行動通訊技術之後的行動通訊技術。本文將點出在下世代前瞻無線通訊系統中的挑戰為何，並提出相對應的解決方法。首先，本文將介紹空間偏移調變，空間偏移調變是一個在近期提出的基頻調變技術，利用了天線的指標當作調變空間，並且因為其低硬體複查度的特性吸引了很多研究探討。可以說空間偏移調變是一個可能在下世代前瞻無線通訊中被採用的技術。然而，空間偏移調變的低頻譜效率是一個應當被注意的問題。其次，本文將探討由另一種方式來增加空間偏移調變的頻譜效率以及能量使用效率。第三，本文將探討另一個在下世代前瞻無線通訊系統中被廣為關注的問題，就是如何在頻分雙工系統中實現巨量多輸入多輸出。因為在頻分雙工巨量多輸入多輸出系統中，訓練(training)無線通道所需要的時間資源將超過無線通道的同調時間(channel coherence time)，這會令現有的傳輸模式無法正常運作，本文提出的設計致力於降低訓練無線通道所需要的時間資源。第四，本文將探討基地台網路維護問題，在基地台網路維護中，如何降低偵測覆蓋空洞所需的花費在下世代前瞻無線通訊中是一個重要的問題，本文將提出相對應的解決方法並對其驗證。

This thesis focuses on the advanced wireless communication techniques design for the Beyond fourth Generation of mobile phone mobile communications standards (B4G), where the new design challenges are elaborated and the corresponding solutions are given. First, the Space Shift Keying (SSK) is introduced; SSK is a recently-proposed baseband modulation technique employing antenna index as modulation alphabet, which attracts large research attentions for its low hardware complexity. As a result, SSK is a promising modulation for B4G wireless communications. However, the low spectral efficiency of SSK is a concern. Second, the corresponding designs for SSK are thus proposed to enhance the spectral efficiency and power efficiency in SSK. Third, the other apparent challenge for B4G is to implement massive Multiple-Input Multiple-Output (MIMO) system, and one of the challenges to implement massive MIMO in Frequency Division Duplexing (FDD) is the training for wireless channel. The temporal overhead to estimate the wireless channel in massive MIMO exceeds the
channel coherence time, which makes wireless transmission impractical; the proposed design targets to reduce the training overhead for FDD massive MIMO. Fourth, for the network level coverage management, how to lower the cost in detecting coverage hole is a tough challenge in B4G; the corresponding solution is also proposed and verified in this thesis.

1 Introduction 1
1.1 The Potential SSK Modulation for Future Transceiver . . . . . . . 1
1.2 The Challenges in Using SSK . . . . . . . . . . . . . . . . . . . . 3
1.3 Training Overhead in FDD Massive MIMO . . . . . . . . . . . . . 6
1.4 Network Level Challenges . . . . . . . . . . . . . . . . . . . . . . 10
2 System Model 14
2.1 Mathematical Description of SSK . . . . . . . . . . . . . . . . . . 14
2.2 Blind Detection System Model . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Block Fading SSK . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Clustering Problem . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Modeling Massive MIMO Channel . . . . . . . . . . . . . . . . . . 18
2.3.1 MIMO OFDM . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.2 Channel Reciprocity . . . . . . . . . . . . . . . . . . . . . 18
2.3.3 DL Transmission . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Modeling Network Coverage . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 Network Propagation . . . . . . . . . . . . . . . . . . . . . 20
2.4.2 RLF Report . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3 The Proposed Wireless Techniques 24
3.1 Baseband Modulation: Bi-Space Shift Keying . . . . . . . . . . . 24
3.1.1 Proposed BiSSK . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Baseband Detection: Coding-aided K-means Clustering Blind Transceiver 25
3.2.1 KMC Detector . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.2 Depermutation via Channel Coding . . . . . . . . . . . . . 30
3.3 MAC layer CSI acquisition: Reverse Training via Channel Reciprocity
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 Proposed FDD-RT . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Network level CHD: Measurements Feedback Based CHD in Multicell
Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4.1 Proposed MCPL-CHD . . . . . . . . . . . . . . . . . . . . 40
4 Analytical Performance Derivation 49
4.1 For BiSSK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 For CKMC Detector . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2.1 Error Rate and Diversity Order . . . . . . . . . . . . . . . 52
4.2.2 The Tradeoffs in Using CKMC . . . . . . . . . . . . . . . 57
4.3 For FDD-RT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.4 For MCPL-CHD . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5 Numerical Performance Study 66
5.1 For BiSSK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.2 For CKMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2.1 Performance of KMC Detector . . . . . . . . . . . . . . . . 71
5.2.2 Performance of CKMC Blind Communication . . . . . . . 75
5.3 For FDD-RT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.3.1 Calibration Resolution in FDD-RT . . . . . . . . . . . . . 81
5.3.2 Sum-rate Comparisons . . . . . . . . . . . . . . . . . . . . 82
5.3.3 Spectral Efficiency Comparison . . . . . . . . . . . . . . . 88
5.4 For MCPL-CHD . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6 Conclusions 104
7 Bibliography 108

[1] J. Jeganathan, A. Ghrayeb, L. Szczecinski, and A. Ceron, “Space shift keying modulation for MIMO channels,” IEEE Trans. Wireless Commun., vol. 8, no. 7, pp. 3692–3703, 2009.
[2] M. Di Renzo, H. Haas, and P. M. Grant, “Spatial modulation for multipleantenna wireless systems: a survey,” IEEE Commun. Mag., vol. 49, no. 12, pp. 182–191, Dec. 2011.
[3] S. Sugiura, X. Chao, N. Soon Xin, and L. Hanzo, “Reduced-complexity coherent versus non-coherent qam-aided space-time shift keying,” IEEE Trans.
Commun., vol. 59, no. 11, pp. 3090–3101, 2011.
[4] M. Renzo and H. Haas, “Improving the performance of space shift keying
(SSK) modulation via opportunistic power allocation,” IEEE Commun.
Lett., vol. 14, no. 6, pp. 500–502, 2010.
[5] N. Serafimovski, M. D. Renzo, S. Sinanovic, R. Y. Mesleh, and H. Haas,
“Fractional bit encoded spatial modulation (FBE-SM),” IEEE Commun.
Lett., vol. 14, no. 5, pp. 429–431, May 2010.
[6] R. Y. Mesleh, H. Haas, S. Sinanovic, C. W. Ahn, and S. Yun, “Spatial
modulation,” IEEE Trans. Veh. Technol., vol. 57, no. 4, pp. 2228–2241,
July 2008.
[7] J. Jeganathan, A. Ghrayeb, and L. Szczecinski, “Generalized space shift keying
modulation for MIMO channels,” Proc. IEEE 19th Int. Symp. Personal,
Indoor and Mobile Radio Commun., pp. 1–5, Sep. 2008.
[8] R. Y. Chang, S.-J. Lin, and W.-H. Chung, “New space shift keying modulation
with Hamming code-aided constellation design,” IEEE Wireless Com-
mun. Lett., vol. 1, no. 1, pp. 2–5, Feb. 2012.
[9] G. Foschini, “Layered space-time architecture for wireless communication in
a fading environment when using multi-element antennas,” Bell labs tech. j.,
vol. 1, no. 2, pp. 41–59, 1996.
[10] D. Tse and P. Viswanath, Fundamentals of wireless communication. Cambridge
university press, 2005.
[11] P. Yang, M. Di Renzo, Y. Xiao, S. Li, and L. Hanzo, “Design guidelines for
spatial modulation,” IEEE Commun. Surveys Tuts., vol. 17, no. 1, pp. 6–26,
Firstquarter 2015.
[12] M. Di Renzo, H. Haas, A. Ghrayeb, S. Sugiura, and L. Hanzo, “Spatial
modulation for generalized MIMO: Challenges, opportunities, and implementation,”
Proc. IEEE, vol. 201, no. 1, pp. 56–103, Jan. 2014.
[13] M. Di Renzo, D. De Leonardis, F. Graziosi, and H. Haas, “Space Shift Keying
(SSK-) MIMO with practical channel estimates,” IEEE Trans. Commun.,
vol. 60, no. 4, pp. 998–1012, 2012.
[14] F. S. Al-Qahtani, S. Ikki, M. Di Renzo, and H. Alnuweiri, “Performance
analysis of space shift keying modulation with imperfect estimation in the
presence of co-channel interference,” IEEE Comm. Lett., vol. 18, no. 9, pp.
1587–1590, 2014.
[15] F. Rusek, D. Persson, L. Buon Kiong, E. G. Larsson, T. L. Marzetta, O. Edfors,
and F. Tufvesson, “Scaling up MIMO: Opportunities and challenges
with very large arrays,” IEEE Signal Process. Mag., vol. 30, no. 1, pp. 40–
60, 2013.
[16] W. Xiping, H. Claussen, M. Di Renzo, and H. Haas, “Channel estimation for
spatial modulation,” IEEE Trans. Commun., vol. 62, no. 12, pp. 4362–4372,
2014.
[17] B. Hassibi and B. M. Hochwald, “How much training is needed in multipleantenna
wireless links?” IEEE Trans. Inform. Theory, vol. 49, no. 4, pp.
951–963, 2003.
[18] M. Abuthinien, C. Sheng, A. Wolfgang, and L. Hanzo, “Joint maximum
likelihood channel estimation and data detection for MIMO systems,” in
Proc. IEEE Int. Conf. Commun. 2007, June 2007, pp. 5354–5358.
[19] S. Chen, W. Yao, and L. Hanzo, “Semi-blind adaptive spatial equalization
for MIMO systems with high-order QAM signalling,” IEEE Trans. Wireless
Commun., vol. 7, no. 11, pp. 4486–4491, 2008.
[20] S. Chen, S. Sugiura, and L. Hanzo, “Semi-blind joint channel estimation and
data detection for space-time shift keying systems,” IEEE Signal Process.
Lett., vol. 17, no. 12, pp. 993–996, Dec. 2010.
[21] S. Sugiura and L. Hanzo, “Effects of channel estimation on spatial modulation,”
IEEE Signal Processing Letters, vol. 19, no. 12, pp. 805–808, 2012.
[22] S. Sugiura, C. Sheng, and L. Hanzo, “Coherent and differential space-time
shift keying: A dispersion matrix approach,” IEEE Trans. Commun., vol. 58,
no. 11, pp. 3219–3230, 2010.
[23] C. Xu, S. Sugiura, S. X. Ng, and L. Hanzo, “Reduced-complexity noncoherently
detected differential space-time shift keying,” IEEE Signal Process.
Lett., vol. 18, no. 3, pp. 1505–1510, 2011.
[24] C. Pirak, J.Wang, K. J. R. Liu, and S. Jitapunkul, “A data-bearing approach
for pilot-embedding frameworks in space-time coded MIMO systems,” IEEE
Trans. Signal Process., vol. 54, no. 10, pp. 3966–3979, 2006.
[25] C. Chin-Te and C. C. Fung, “Robust training sequence design for spatially
correlated MIMO channel estimation,” IEEE Trans. Veh. Technol., vol. 60,
no. 7, pp. 2882–2894, 2011.
[26] Z. Xu and M. Davies, “Coding-assisted blind MIMO separation and decoding,”
IEEE Trans. Veh. Technol., vol. 59, no. 9, pp. 4408–4417, 2010.
[27] M. Di Renzo and H. Haas, “Space shift keying (SSK) modulation with partial
channel state information: Optimal detector and performance analysis over
fading channels,” IEEE Trans. Commun., vol. 58, no. 11, pp. 3196–3210,
2010.
[28] T. Y. Al-Naffouri, A. A. Dahman, M. S. Sohail, X. Weiyu, and B. Hassibi,
“Low-complexity blind equalization for OFDM systems with general constellations,”
IEEE Trans. Signal Process., vol. 60, no. 12, pp. 6395–6407,
2012.
[29] R. Xu, D. Wunsch et al., “Survey of clustering algorithms,” IEEE Trans.
Neural Networks, vol. 16, no. 3, pp. 645–678, 2005.
[30] T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu,
“An efficient k-means clustering algorithm: analysis and implementation,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 881–892, July
2002.
[31] G. Castagnoli, S. Brauer, and M. Herrmann, “Optimization of cyclic
redundancy-check codes with 24 and 32 parity bits,” IEEE Trans. Com-
mun., vol. 41, no. 6, pp. 883–892, 1993.
[32] 3GPP TR 37.868 V11.0.0, “Study on RAN improvements for machine-type
communications,” Sep. 2011.
[33] A. Richardson, WCDMA design handbook. Cambridge University Press,
2005.
[34] IEEE Std 802.11a, “Part 11: Wireless LAN medium access control (MAC)
and physical layer (PHY) specifications, high-speed physical layer in the 5
ghz band,” 1999.
[35] T. L. Marzetta, “Noncooperative cellular wireless with unlimited numbers
of base station antennas,” IEEE Trans. Wireless Commun., vol. 9, no. 11,
pp. 3590–3600, 2010.
[36] J. Jose, A. Ashikhmin, T. L. Marzetta, and S. Vishwanath, “Pilot contamination
and precoding in multi-cell TDD systems,” IEEE Trans. Wireless
Comm., vol. 10, no. 8, pp. 2640–2651, 2011.
[37] C. Shepard, H. Yu, N. Anand, E. Li, T. Marzetta, R. Yang, and L. Zhong,
“Argos: Practical many-antenna base stations,” in ACM 18th int. conf. Mo-
bile computing and networking, 2012, pp. 53–64.
[38] R. Rogalin, O. Y. Bursalioglu, H. Papadopoulos, G. Caire, A. F. Molisch,
A. Michaloliakos, V. Balan, and K. Psounis, “Scalable synchronization and
reciprocity calibration for distributed multiuser MIMO,” IEEE Trans. Wire-
less Comm., vol. 13, no. 4, pp. 1815–1831, 2014.
[39] N. Hoondong, K. Younsun, L. Juho, and L. Chungyong, “Codebook design
of generalized space shift keying for FDD massive MIMO systems in spatially
correlated channels,” IEEE Trans. Veh. Technol., vol. 64, no. 2, pp. 513–523,
2015.
[40] A. Papadogiannis, H. J. Bang, D. Gesbert, and E. Hardouin, “Efficient selective
feedback design for multicell cooperative networks,” IEEE Trans. Veh.
Technol., vol. 60, no. 1, pp. 196–205, 2011.
[41] B. N. Bharath and C. R. Murthy, “Channel training signal design for reciprocal
multiple antenna systems with beamforming,” IEEE Trans. Veh.
Technol., vol. 62, no. 1, pp. 140–151, 2013.
[42] H. Hoon, A. M. Tulino, and G. Caire, “Network MIMO with linear zeroforcing
beamforming: Large system analysis, impact of channel estimation,
and reduced-complexity scheduling,” IEEE Trans. Inform. Theory, vol. 58,
no. 5, pp. 2911–2934, 2012.
[43] N. Junyoung, A. Jae-Young, A. Adhikary, and G. Caire, “Joint spatial division
and multiplexing: Realizing massive MIMO gains with limited channel
state information,” in IEEE 46th Annual Conf. Inform. Sci. and Syst, 2012,
pp. 1–6.
[44] K. Ping-Heng, H. T. Kung, and T. Pang-An, “Compressive sensing based
channel feedback protocols for spatially-correlated massive antenna arrays,”
in IEEE Wireless Commun. and Networking Conf. ,2012, pp. 492–497.
[45] R. Xiongbin and V. K. N. Lau, “Distributed compressive CSIT estimation
and feedback for FDD multi-user massive MIMO systems,” IEEE Trans.
Signal Process., vol. 62, no. 12, pp. 3261–3271, 2014.
[46] J. Choi, D. Love, and T. Kim, “Trellis-extended codebooks and successive
phase adjustment: A path from LTE-Advanced to FDD massive MIMO
systems,” [Online]. Available: http://arxiv.org/abs/1402.6794.
[47] J. Choi, D. Love, and P. Bidigare, “Downlink training techniques for FDD
massive MIMO systems: Open-loop and closed-loop training with memory,”
IEEE J. Sel. Topics Signal Process., vol. PP, no. 99, pp. 1–1, 2014.
[48] G. C. Raleigh, S. N. Diggavi, V. K. Jones, and A. Paulraj, “A blind adaptive
transmit antenna algorithm for wireless communication,” in IEEE Int. Conf.
Commun., vol. 3, Jun. 1995, pp. 1494–1499 vol.3.
[49] B. M. Hochwald and T. L. Maretta, “Adapting a downlink array from uplink
measurements,” IEEE Trans. Signal Process., vol. 49, no. 3, pp. 642–653,
2001.
[50] D. Linglong, W. Zhaocheng, and Y. Zhixing, “Spectrally efficient timefrequency
training OFDM for mobile large-scale MIMO systems,” IEEE J.
Sel. Areas Commun., vol. 31, no. 2, pp. 251–263, 2013.
[51] A. Alexiou, D. Avidor, P. Bosch, et al., “Duplexing, resource allocation and
inter-cell coordination: design recommendations for next generation wireless
systems,” Wirel. Commun. Mob. Comput., vol. 5, no. 1, pp. 77–93, 2005.
[52] G. J. Foschini, H. C. Huang, S. J. Mullender, et al., “Physical-layer design
for next-generation cellular wireless systems,” Bell Labs Technical Journal,
vol. 10, no. 2, pp. 157–172, 2005.
[53] U. Salim, D. Gesbert, and D. Slock, “Combining training and quantized
feedback in multiantenna reciprocal channels,” IEEE Trans. Signal Process.,
vol. 60, no. 3, pp. 1383–1396, 2012.
[54] N. Hien Quoc, E. G. Larsson, and T. L. Marzetta, “Massive MU-MIMO
downlink TDD systems with linear precoding and downlink pilots,” in IEEE
51st Annual Allerton Conf. on Commun, Control, and Computing, 2013, pp.
293–298.
[55] Y. Wang, K. Pedersen, F. Frederiksen et al., “Detection and protection of
macro-users in dominant area of co-channel csg cells,” in Proc. IEEE Vehic-
ular Technology Conference (VTC Spring), May 2012, pp. 1–5.
[56] Y. Zhou, Z. Luo, and H. Zhuang, “Sensor-assisted coverage self-optimization
for wireless local area network,” in Wireless and Optical Communication
Conference (WOCC), May 2013, pp. 444–448.
[57] L. S. Muppirisetty, T. Svensson, and H. Wymeersch, “Spatial wireless channel
prediction under location uncertainty,” IEEE Trans. Wireless Commun.,
vol. 15, no. 2, pp. 1031–1044, Feb 2016.
[58] W. A. Hapsari, A. Umesh, M. Iwamura, M. Tomala, B. Gyula, and B. Sebire,
“Minimization of drive tests solution in 3GPP,” IEEE Commun. Mag.,
vol. 50, no. 6, pp. 28–36, June 2012.
[59] 3rd Generation Partnership Project (3GPP), “Universal Mobile Telecommunications
System (UMTS); LTE; Universal Terrestrial Radio Access (UTRA)
and Evolved Universal Terrestrial Radio Access (E-UTRA); Radio measurement
collection for Minimization of Drive Tests (MDT); Overall description;
Stage 2,” 3GPP, TS 37.320 V12.2.0, Sep. 2014.
[60] ——, “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved
Universal Terrestrial Radio Access Network (E-UTRAN); Overall description;
Stage 2,” 3GPP, TS 36.300 V12.4.0, Feb. 2015.
[61] M. Molinari, M.-R. Fida, M. K. Marina, and A. Pescape, “Spatial interpolation
based cellular coverage prediction with crowdsourced measurements,”
in Proceedings of the 2015 ACM SIGCOMM Workshop on Crowdsourcing
and Crowdsharing of Big (Internet) Data, 2015, pp. 33–38.
[62] B. Sayrac, J. Riihijぴarvi, P. Mぴahぴonen, S. Ben Jemaa, E. Moulines, and S. Grimoud,
“Improving coverage estimation for cellular networks with spatial
bayesian prediction based on measurements,” in Proc. ACM SIGCOMM
workshop on Cellular networks: operations, challenges, and future design,
2012, pp. 43–48.
[63] A. Galindo-Serrano, B. Sayrac, S. Ben Jemaa, J. Riihijarvi, and P. Mahonen,
“Automated coverage hole detection for cellular networks using radio environment
maps,” in International Symposium Modeling and Optimization in
Mobile, Ad Hoc and Wireless Networks (WiOpt2013), May 2013, pp. 35–40.
[64] H. Braham, S. Ben Jemaa, B. Sayrac, G. Fort, and E. Moulines, “Low complexity
spatial interpolation for cellular coverage analysis,” in International
Symposium Modeling and Optimization in Mobile, Ad Hoc, and Wireless
Networks (WiOpt2014), May 2014, pp. 188–195.
[65] H.-W. Liang, C.-H. Ho, L.-S. Chen, W.-H. Chung, S.-Y. Yuan, and S.-Y.
Kuo, “Coverage hole detection in cellular networks with deterministic propagation
model,” accepted by International Conference on Intelligent Green
Building and Smart Grid (IGBSG2016).
[66] Y. Chen and A. Terzis, “On the implications of the log-normal path loss
model: An efficient method to deploy and move sensor motes,” in Proceedings
of the 9th ACM Conference on Embedded Networked Sensor Systems, ser.
SenSys ’11. ACM, 2011, pp. 26–39.
[67] G. Jie, O. Can Ozdural, S. H. Ardalan, and L. Huaping, “Performance modeling
of MIMO OFDM systems via channel analysis,” IEEE Trans. Wireless
Comm., vol. 5, no. 9, pp. 2358–2362, 2006.
[68] A. Vattani, “k-means requires exponentially many iterations even in the
plane,” Discrete and Computational Geometry, vol. 45, no. 4, pp. 596–616,
2011.
[69] F. Kaltenberger, J. Haiyong, M. Guillaud, and R. Knopp, “Relative channel
reciprocity calibration in MIMO/TDD systems,” in Future Network and
Mobile Summit, 2010, 2010, pp. 1–10.
[70] M. Guillaud and F. Kaltenberger, “Towards practical channel reciprocity
exploitation: Relative calibration in the presence of frequency offset,” in
IEEE Wireless Commun. and Networking Conf., 2013 (WCNC 2013), 2013,
pp. 2525–2530.
[71] J. G. Proakis, Digital Communications, 4th ed. McGraw-hill, New York,
2001.
[72] M. Alouini and A. Goldsmith, “A unified approach for calculating error rates
of linearly modulated signals over generalized fading channels,” IEEE Trans.
Commun., vol. 47, no. 9, pp. 1324–1334, 1999.
[73] H.-W. Liang, R. Y. Chang, W.-H. Chung, and S.-Y. Kuo, “A reducedcomplexity
blind detector for MIMO system using k-means clustering algorithm,”
in Proc. IEEE Veh. Tech. Conf. (VTC Spring) 2013.
[74] P. Sweeney, Error Control Coding-From Theory to Practice. New York:
Wiley, 2002.
[75] D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,”
in IEEE Computer Society Conf. Computer Vision and Pattern Recognition
(CVPR), vol. 2, 2006, pp. 2161–2168.
[76] W. Jing, W. Jingdong, K. Qifa, Z. Gang, and L. Shipeng, “Fast approximate
k-means via cluster closures,” in IEEE Conf. Computer Vision and Pattern
Recognition (CVPR), June 2012, pp. 3037–3044.