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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Channel Estimation in Massive MIMO: Algorithm and Hardware

verfasst von : Chuan Tang, Cang Liu, Luechao Yuan, Zuocheng Xing

Erschienen in: Computer Engineering and Technology

Verlag: Springer Berlin Heidelberg

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Abstract

Currently 5G is research hotspot in communication field, and one of the most promising wireless transmission technologies for 5G is massive multiple input multiple output (MIMO) which provides high data rate and energy efficiency. The main challenge of massive MIMO is the channel estimation due to the complexity and pilot contamination. Some improvement of traditional channel estimation methods to solve the problem in massive MIMO have been introduced in this paper. Besides, the hardware acceleration is useful for massive MIMO channel estimation algorithm. We discuss the relate work about hardware accelerator of matrix inversion and singular value decomposition which are the main complex operations of channel estimation. We find that the memory system, network of processing elements and the precision will be the main research directions for the hardware design of large-scale data size.

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Vorheriges Kapitel Designing Parallel Sparse Matrix Transposition Algorithm Using ELLPACK-R for GPUs
Nächstes Kapitel A ML-Based High-Accuracy Estimation of Sampling and Carrier Frequency Offsets for OFDM Systems
Literatur
1.
Wang, C.X., Haider, F., Gao, X., You, X.H., Yang, Y., Yuan, D., Aggoune, H., Haas, H., Fletcher, S., Hepsaydir, E.: Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun. Mag. 52(2), 122–130 (2014)CrossRef
2.
Han, C., Harrold, T., Armour, S., Krikidis, I., Videv, S., Grant, P.M., Haas, H., Thompson, J.S., Ku, I., Wang, C.X.: Green radio: radio techniques to enable energy-efficient wireless networks. IEEE Commun. Mag. 49(6), 46–54 (2011)CrossRef
3.
Marzetta, T.L.: Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wireless Commun. 9(11), 3590–3600 (2010)CrossRef
4.
Rusek, F., Persson, D., Lau, B.K., Larsson, E.G., Marzetta, T.L., Edfors, O., Tufvesson, F., Print, P., Rusek, F., Persson, D.: Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Sig. Process. Mag. 30(1), 40–60 (2013)CrossRef
5.
Marzetta, T.L., Caire, G., Debbah, M., Chih-Lin, I., Mohammed, S.K.: Special issue on massive MIMO. J. Commun. Netw. 15(4), 333–337 (2013)CrossRef
6.
Shin, C., Heath Jr., R.W., Powers, E.J.: Blind channel estimation for MIMO-OFDM systems. IEEE Trans. Veh. Technol. 2, 670–685 (2007)CrossRef
7.
Tu, C.C., Champagne, B.: Subspace blind MIMO-OFDM channel estimation with short averaging periods: performance analysis. In: Wireless Communications and Networking Conference, WCNC 2008, pp. 24–29. IEEE (2008)
8.
Shariati, N., Bjornson, E., Bengtsson, M., Debbah, M.: Low-complexity channel estimation in large-scale MIMO using polynomial expansion, ePrint arXiv, pp. 1157–1162 (2013)
9.
10.
Cands, E.J.: Compressive sampling. Marta Sanz Sol 25(2), 1433–1452 (2007)
11.
Renzo, M.D., Haas, H., Ghrayeb, A., Member, S., Sugiura, S., Member, S., Hanzo, L.: Spatial modulation for generalized MIMO: challenges, opportunities and implementation. Proc. IEEE 102(1), 56–103 (2013)CrossRef
12.
Lei, Z.D., Lim, T.J.: Simplified polynomial-expansion linear detectors for DS-CDMA systems. Electron. Lett. 34(16), 1561–1563 (1998)CrossRef
13.
Hoydis, J., Debbah, M., Kobayashi, M.: Asymptotic moments for interference mitigation in correlated fading channels. In: 2011 IEEE International Symposium on Information Theory Proceedings (ISIT), pp. 2796–2800 (2011)
14.
Kammoun, A., Muller, A., Bjornson, E., Debbah, M.: Linear precoding based on polynomial expansion: large-scale multi-cell MIMO systems. IEEE J. Sel. Top. Sig. Process. 8(5), 861–875 (2014)CrossRef
15.
Chen, Z., Hou, X., Han, S., Yang, C., Wang, G., Lei, M.: Low complexity channel estimation in TDD coordinated multi-point transmission systems. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 3128–3133 (2013)
16.
Yin, H., Fellow, D.G., Liu, Y.: A coordinated approach to channel estimation in large-scale multiple-antenna systems. IEEE J. Sel. Areas Commun. 31(2), 264–273 (2012)CrossRef
17.
Rao, X., Lau, V.K.N.: Distributed compressive csit estimation and feedback for fdd multi-user massive MIMO systems. IEEE Trans. Sig. Process. 62(12), 3261–3271 (2014)CrossRefMathSciNet
18.
Tse, D., Viswanath, P.: Fundamentals of wireless communication. Eth Zrich Lect. Script 3(5), B6–1–B6–5 (2005)
19.
Nguyen, S.L.H., Ghrayeb, A.: Compressive sensing-based channel estimation for massive multiuser MIMO systems. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2890–2895 (2013)
20.
Mller, R., Cottatellucci, L., Vehkapera, M.: Blind pilot decontamination. IEEE J. Sel. Top. Sig. Process. 8(5), 1016–1020 (2013)
21.
Ngo, B.Q., Larsson, E.G.: EVD-based channel estimation in multicell multiuser MIMO systems with very large antenna arrays. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3249–3252 (2012)
22.
Wu, X., Renzo, M.D., Haas, H., Wu, X.: Channel estimation for spatial modulation. IEEE Trans. Commun. 62(12), 4362–4372 (2013)CrossRef
23.
Chen, S., Sugiura, S., Hanzo, L.: Semi-blind joint channel estimation and data detection for space-time shift keying systems. IEEE Sig. Process. Lett. 17(12), 993–996 (2010)CrossRef
24.
Zhang, P., Dey, I., Sugiura, S., Chen, S.: Semi-blind adaptive space-time shift keying systems based on iterative channel estimation and data detection. IEEE Veh. Technol. Conf. 120(2226), 1–5 (2011)CrossRef
25.
Zhang, P., Chen, S., Hanzo, L.: Reduced-complexity near-capacity joint channel estimation and three-stage turbo detection for coherent space-time shift keying. IEEE Trans. Commun. 61(5), 1902–1913 (2013)CrossRef
26.
Yokoyama, S., Matsumoto, K. Sedukhin, S.G.: Matrix inversion on thecell/b.e. processor. In: 11th IEEE International Conference on High Performance Computing and Communications, HPCC 2009, pp. 148–153 (2009)
27.
Huang, Z.Y., Tsai, P.Y.: Efficient implementation of qr decomposition for gigabit MIMO-OFDM systems. IEEE Trans. Circ. Syst. I Regul. Pap. 58(10), 2531–2542 (2011)CrossRefMathSciNet
28.
Singh, C.K., Prasad, S.H., Balsara, P.T.: VLSI architecture for matrix inversion using modified Gram-Schmidt based QR decomposition. In: Proceedings of the IEEE International Conference on VLSI Design, pp. 836–841 (2007)
29.
Eilert, J., Wu, D., Liu, D.: Efficient complex matrix inversion for MIMO software defined radio. In: IEEE International Symposium on Circuits and Systems, ISCAS 2007, pp. 2610–2613 (2007)
30.
Ma, L., Dickson, K., Mcallister, J., Mccanny, J.: QR decomposition based matrix inversion for high performance embedded MIMO receivers. IEEE Trans. Sig. Process. 59(4), 1858–1867 (2011)CrossRef
31.
Zhang, L., Li, F., Shi, G.: Efficient matrix inversion based on VLIW architecture. J. Syst. Eng. Electron. 25(3), 393–398 (2014)CrossRef
32.
Arias-Garcia, J., Jacobi, R.P., Llanos, C.H., Ayala-Rincon, M.: A suitable FPGA implementation of floating-point matrix inversion based on Gauss-Jordan elimination. In: 2011 VII Southern Conference on Programmable Logic (SPL), pp. 263–268 (2011)
33.
Moussa, S., Razik, A.M.A., Dahmane, A.O., Hamam, H.: FPGA implementation of floating-point complex matrix inversion based on Gauss-Jordan elimination. In: Canadian Conference on Electrical & Computer Engineering, pp. 1–4 (2013)
34.
Arias-Garcia, J., Llanos, C.H., Ayala-Rincon, M., Jacobi, R.P. : FPGA implementation of large-scale matrix inversion using single, double and custom floating-point precision. In: 2012 VIII Southern Conference on Programmable Logic (SPL), pp. 1–6 (2012)
35.
Zhou, J., Dou, Y., Zhao, J., Xia, F., Lei, Y., Tang, Y.: A fine-grained pipelined implementation for large-scale matrix inversion on FPGA. In: Dou, Y., Gruber, R., Joller, J.M. (eds.) APPT 2009. LNCS, vol. 5737, pp. 110–122. Springer, Heidelberg (2009)CrossRef
36.
Wu, M., Yin, B., Vosoughi, A., Studer, C., Cavallaro, J.R., Dick, C.: Approximate matrix inversion for high-throughput data detection in the large-scale MIMO uplink. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2155–2158 (2013)
37.
Yin, B., Wu, M., Studer, C., Cavallaro, J.R., Dick, C.: Implementation trade-offs for linear detection in large-scale MIMO systems. In: 1988 International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1988, vol. 32, no. 3, pp. 2679–2683 (2013)
38.
Ylinen, M., Burian, A., Takala, J.: Direct versus iterative methods for fixed-point implementation of matrix inversion. In: Proceedings of the 2004 International Symposium on Circuits and Systems, ISCAS 2004, vol. 3, pp. III–225–8 (2004)
39.
Kaji, T., Yoshizawa, S., Miyanaga, Y.: Development of an ASIP-based singular value decomposition processor in SVD-MIMO systems. In: 2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp. 1–5 (2011)
40.
Bildosola, I., Martinez-Corral, U., Basterretxea, K.: Adaptive scalable SVD unit for fast processing of large LSE problems. In: 2014 IEEE 25th International Conference on Application-specific Systems, Architectures and Processors (ASAP), pp. 17–24 (2014)
41.
Brent, R.P., Luk, F.T., Van Loan, C.: Computation of the singular value decomposition using mesh-connected processors. J. VLSI Comput. Syst. 1(3), 242–270 (1983)
42.
Cavallaro, J.R., Luk, F.T.: Architectures for a CORDIC SVD processor. In: 30th Annual Technical Symposium, pp. 45–53 (1986)
43.
Sibul, L.H., Fogelsanger, A.L.: Application of coordinate rotation algorithm to singular value decomposition. In: IEEE International Symposium on Circuits and Systems, pp. 821–824 (1984)
44.
Bobda, C., Danne, K., Linarth, A.: Efficient implementation of the singular value decomposition on a reconfigurable system. In: Cheung, P.Y.K., Constantinides, G.A. (eds.) FPL 2003. LNCS, vol. 2778. Springer, Heidelberg (2003)CrossRef
45.
Ledesma-Carrillo, L.M., Cabal-Yepez, E., Romero-Troncoso, R.D.J., Garcia-Perez, A., Osornio-Rios, R.A. Carozzi, T.D.: Reconfigurable FPGA-based unit for singular value decomposition of large m\(\,\times \,\)n matrices. In: International Conference on Reconfigurable Computing and FPGAs, pp. 345–350 (2011)
46.
Milford, D., Sandell, M.: Singular value decomposition using an array of CORDIC processors. Sig. Process. 102(9), 163–170 (2014)CrossRef
47.
Martinez-Corral, U., Basterretxea, K., Finker, R.: Scalable parallel architecture for singular value decomposition of large matrices. In: 2014 24th International Conference on Field Programmable Logic and Applications (FPL), pp. 1–4 (2014)
Metadaten
Titel
Channel Estimation in Massive MIMO: Algorithm and Hardware
verfasst von
Chuan Tang
Cang Liu
Luechao Yuan
Zuocheng Xing
Copyright-Jahr
2016
Verlag
Springer Berlin Heidelberg
Buch
Computer Engineering and Technology

Print ISBN: 978-3-662-49282-6

Electronic ISBN: 978-3-662-49283-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-662-49283-3_8

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