Skip to main content
SpringerLink
Log in
Book cover

CCF Conference on Computer Engineering and Technology

NCCET 2015: Computer Engineering and Technology pp 69–84Cite as

Channel Estimation in Massive MIMO: Algorithm and Hardware

  • Conference paper
  • First Online:

  • 841 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 592))

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Marzetta, T.L.: Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Trans. Wireless Commun. 9(11), 3590–3600 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  9. Shepard, C., Yu, H., Zhong, L.: Argosv2: a flexible many-antenna research platform, pp. 163–166. (2013). http://nms.csail.mit.edu

  10. Cands, E.J.: Compressive sampling. Marta Sanz Sol 25(2), 1433–1452 (2007)

    Google Scholar 

  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)

    Article  Google Scholar 

  12. Lei, Z.D., Lim, T.J.: Simplified polynomial-expansion linear detectors for DS-CDMA systems. Electron. Lett. 34(16), 1561–1563 (1998)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  18. Tse, D., Viswanath, P.: Fundamentals of wireless communication. Eth Zrich Lect. Script 3(5), B6–1–B6–5 (2005)

    Google Scholar 

  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)

    Google Scholar 

  20. Mller, R., Cottatellucci, L., Vehkapera, M.: Blind pilot decontamination. IEEE J. Sel. Top. Sig. Process. 8(5), 1016–1020 (2013)

    Google Scholar 

  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)

    Google Scholar 

  22. Wu, X., Renzo, M.D., Haas, H., Wu, X.: Channel estimation for spatial modulation. IEEE Trans. Commun. 62(12), 4362–4372 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  31. Zhang, L., Li, F., Shi, G.: Efficient matrix inversion based on VLIW architecture. J. Syst. Eng. Electron. 25(3), 393–398 (2014)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Google Scholar 

  42. Cavallaro, J.R., Luk, F.T.: Architectures for a CORDIC SVD processor. In: 30th Annual Technical Symposium, pp. 45–53 (1986)

    Google Scholar 

  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)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Google Scholar 

  46. Milford, D., Sandell, M.: Singular value decomposition using an array of CORDIC processors. Sig. Process. 102(9), 163–170 (2014)

    Article  Google Scholar 

  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)

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by the NSF of China (Grant No. 61170083, 61373032) and Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20114307110001).

Author information

Authors and Affiliations

  1. Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China

    Chuan Tang, Cang Liu, Luechao Yuan & Zuocheng Xing

Authors
  1. Chuan Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luechao Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zuocheng Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuan Tang .

Editor information

Editors and Affiliations

  1. National Univ Defense Tech, Changsha, China

    Weixia Xu

  2. National Univ. of Defense Technolog, Changsha, China

    Liquan Xiao

  3. School of Computer Science, National Univ. of Defense Technolog, Changsha, China

    Jinwen Li

  4. National Univ Defense Tech, Changsha, China

    Chengyi Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tang, C., Liu, C., Yuan, L., Xing, Z. (2016). Channel Estimation in Massive MIMO: Algorithm and Hardware. In: Xu, W., Xiao, L., Li, J., Zhang, C. (eds) Computer Engineering and Technology. NCCET 2015. Communications in Computer and Information Science, vol 592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49283-3_8

Download citation

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

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49282-6

  • Online ISBN: 978-3-662-49283-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation