Skip to main content
SpringerLink
Log in

MIMO Modeling Based on Extreme Learning Machine

  • Conference paper
  • First Online:
Proceedings of ELM-2015 Volume 1

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 6))

  • 1273 Accesses

Abstract

With multiple antennas’ transmission, multiple-input multiple-output (MIMO) technique is able to utilize the space diversity to obtain high spectrum efficiency. In this study, we propose a single hidden layer feedforward network trained with extreme learning machine (ELM) to estimate channel performances in MIMO system. Bit error rate (BER) and signal-to-noise ratio (SNR) performance of back-propagation (BP) algorithm are also compared with our proposed neural network. The simulation results show that ELM has got much better time efficiency than BP in MIMO modeling. Furthermore, MIMO modeling based on ELM doesn’t need to send pilot, which reduce the waste of spectrum resources.

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

Access this chapter

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Deek, L., Garcia-Villegas, E., Belding, E., Lee, S., Almeroth, K.: A practical framework for 802.11 MIMO rate adaptation. Comput. Netw. 83, 332–348 (2015)

    Article  Google Scholar 

  2. Li, Q., Lin, X., Zhang, J., Roh, W.: Advancement of MIMO technology in WiMAX: from IEEE 802.16d/e/j to 802.16m. IEEE Commun. Mag. 47, 100–107 (2009)

    Google Scholar 

  3. Liu, L., Chen, R., Geirhofer, S., Sayana, K., Shi, Z., Zhou, Y.: Downlink MIMO in LTE-advanced: SU-MIMO vs. MU-MIMO. IEEE Commun. Mag. 50, 140–147 (2012)

    Article  Google Scholar 

  4. Zheng, K., Taleb, T., Ksentini, A., Magedanz, T., Ulema, M.: Research & standards: advanced cloud & virtualization techniques for 5G networks. IEEE Commun. Mag. 53, 16–17 (2015)

    Article  Google Scholar 

  5. Akyildiz, I., Wang, P., Lin, S.: SoftAir: a software defined networking architecture for 5G wireless systems. Comput. Netw. 85, 1–18 (2015)

    Article  Google Scholar 

  6. Foschini, J.: Layered space-time architecture for wireless communications in a fading environment when using multiple antennas. Bell Labs Tech. J. 1, 41–59 (1996)

    Article  Google Scholar 

  7. Zheng, L., Tse, D.: Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels. IEEE Trans. Inf. Theory. 49, 1073–1096 (2003)

    Article  MATH  Google Scholar 

  8. Sarma, K., Mitra, A.: Modeling MIMO channels using a class of complex recurrent neural network architectures. Int. J. Electron. Commun. 66, 322–331 (2012)

    Article  Google Scholar 

  9. Sarma, K., Mitra, A.: MIMO channel modelling using multi-layer perceptron with finite impulse response and infinite impulse response synapses. IET Commun. 7, 1540–1549 (2013)

    Article  Google Scholar 

  10. Seyman, M., Taspinar, N.: Channel estimation based on neural network in space time block coded MIMOCOFDM system. Digit. Signal Process. 23, 275–280 (2013)

    Article  MathSciNet  Google Scholar 

  11. Sit Y., Agatonovic, M., Zwick T.: Neural network based direction of arrival estimation for a MIMO OFDM radar. In: 9th European Radar Conference, pp. 298–301. IEEE Press, New York (2012)

    Google Scholar 

  12. George, M., Francis, A., Mathew, B.: Neural network estimation for MIMO-OFDM receivers. In: International Conference on Circuit, Power and Computing Technologies, 2014, pp. 1329-1333. IEEE Press, New York (2014)

    Google Scholar 

  13. Belkacem O., Zayani R., Ammari M., Bouallegue R., Roviras, D.: Neural network equalization for frequency selective nonlinear MIMO channels. In: IEEE Symposium on Computers and Communications, 2012, pp. 18–23. IEEE Press, New York (2012)

    Google Scholar 

  14. Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: theory and applications. Neuro comput. 70, 489–501 (2006)

    Google Scholar 

  15. Cao, J., Huang, G., Liu, N.: Voting based extreme learning machine. Inf. Sci. 185, 66–77 (2012)

    Article  MathSciNet  Google Scholar 

  16. Fang, J., Tan, Z., Tan, K.: Soft MIMO: a software radio implementation of 802.11n based on sora platform. In: 4th IET International Conference Wireless, Mobile & Multimedia Networks, pp. 165–168. IEEE Press, New York (2011)

    Google Scholar 

  17. Zhang, P., Chen, S., Hanzo, L.: Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection. IEEE Trans. Wirel. Commun. 14, 122–137 (2015)

    Article  Google Scholar 

  18. Berber, S.: An automated method for BER characteristics measurement. IEEE Trans. Instrum. Meas. 53, 575–580 (2004)

    Article  Google Scholar 

  19. Sharma S., Chatzinotas S., Ottersten B.: Eigenvalue based SNR estimation for cognitive radio in presence of channel correlation. In: 56th IEEE Global Communications Conference, pp. 1107–1121. IEEE Press, New York (2013)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Natural Science Foundation of Zhejiang province (China under Grants LY15F030017 and Q13G010016), National Natural Science Foundation of China (China under Grants 61403224).

Author information

Authors and Affiliations

  1. Institution of Information Science and Electrical Engineering, Zhejiang University, Yugu Street 38, Hangzhou, China

    Junbiao Liu & Xinyu Jin

  2. School of Information and Electrical Engineering, Zhejiang University City College, HuZhou Street 50, Hangzhou, China

    Fang Dong

  3. Institute of Information and Control, Hangzhou Dianzi University, Erhao Street 1158, Hangzhou, China

    Jiuwen Cao

Authors
  1. Junbiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fang Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiuwen Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinyu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junbiao Liu .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Liu, J., Dong, F., Cao, J., Jin, X. (2016). MIMO Modeling Based on Extreme Learning Machine. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28397-5_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28396-8

  • Online ISBN: 978-3-319-28397-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation