Skip to main content
SpringerLink
Log in

Big Data and Deep Learning for Stochastic Wireless Channel

  • Chapter
  • First Online:
Computational Intelligence in Sensor Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 776))

Abstract

Continuous advances in wireless communication technology and the proliferation of hand held multimedia devices have been instrumental in the enormous expansion in the data-driven environments. A significant impact of a symbiotic linkage of analytical tool and learning based approaches is to be observed in increasing link reliability of mobile devices due to the application of big data and learning aided mechanisms. In this paper, we analyze the trends of big data and deep learning techniques to handle large data volumes and explore the ways and means for their application while handling the stochastic wireless channel. We formulate certain learning based approach which is expected to contribute towards spectrum conservation and achieve better link reliability. This work focuses on some of the emerging issues involving big data and the roles played by the capabilities of 5G and the advantages that could be achieved due to the use of deep learning.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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. 5G Research lab, available: http://5gmobile.fel.cvut.cz/about/5Gmobile_overview_20151016.pdf

  2. Agarwalla, S., Sarma, K.K.: Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech. Neural Netw. 78, 97–111 (2016)

    Article  Google Scholar 

  3. Akyildiz, I.F., Lee, W.Y., Vuran, M.C., Mohanty, S.: Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Netw. 50(13), 2127–2159 (2006)

    Article  Google Scholar 

  4. Alsheikh, MA., Lin S., Niyato, D., Tan H.P.: Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications. pp. 1–22 (2015)

    Google Scholar 

  5. Arel, I., Rose, D., Karnowski, T.: Deep machine learning—a new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)

    Article  Google Scholar 

  6. Bengio, Y., Courville, A.C., Vincent, P.: Unsupervised feature learning and deep learning: a review and new perspectives. CoRR, abs/1206.5538, 1 (2012)

    Google Scholar 

  7. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 3–130 (2009)

    Article  Google Scholar 

  8. Bhuyan, M., Sarma, K.K.: MIMO-OFDM channel tracking using a dynami-cANN topology. World Acad. Sci. Eng. Technol. Online Int. J. 71, 1321–1327 (2012)

    Google Scholar 

  9. Chavez-Santiago, R., Szydelko, M., Kliks, A., Foukalas, F., Haddad, Y., Nolan, K.E., Kelly, M.Y., Masonta M.T., Balasingham, I.: 5G: the convergence of wireless communications. Wireless Pers Commun 83:1617–1642 (2015)

    Article  Google Scholar 

  10. Chen, C.S.: Artificial neural network for location estimation in wireless com-munication systems. Sensors 12(3), 2798–2817 (2012)

    Article  Google Scholar 

  11. Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mobile Netw. Appl. 19(2), 171–209 (2014)

    Article  Google Scholar 

  12. Chen X.C, Lin, X.: Big Data Deep Learning: Challenges and Perspectives. pp. 514–525 (2014)

    Google Scholar 

  13. Chinchali, S., Tandon, S.: Deep Learning for Wireless Interference Segmentation and Prediction, https://www.semanticscholar.org/author/Sameep-Tandon/3330228

  14. Cui, L., Yu, F.R., Yan, Q.: When big data meets software-defined networking: SDN for big data and big data for SDN. IEEE Netw. 30(1), 58–65 (2016)

    Article  Google Scholar 

  15. Deng L., Yu D., Platt J.: Scalable stacking and learning for building deep architectures. In: Proceedings of IEEE ICASSP, pp. 2133–2136 (2012)

    Google Scholar 

  16. EMPhAtiC Deliverable 4.1. http://www.ictem-phatic.eu/images/deliverables/deliverabled4.1final.pdf

  17. Erman, M., Mohammed, A., Rakus-Andersson, E.: Fuzzy logic applications in wireless communications. In: Proceedings of IFSA-EUSFLAT. Lisbon, Portugal, pp. 763–767 (2009)

    Google Scholar 

  18. Fang, Y., Chow, T.W.S.: Blind equalization of a noisy channel by linear neural network. IEEE Trans. Neural Netw. 10(4), 918–924 (1999)

    Article  Google Scholar 

  19. Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manag. 35, 137–144 (2015)

    Article  Google Scholar 

  20. Gowrishankar, P.S., Satyanarayana: Recurrent neural network based BER pre-diction for NLOS channels. In: Proceedings of 4th International Conference on Mobile Technology. Applications and Systems, Singapore, pp. 410–416 (2007)

    Google Scholar 

  21. Heath, R.W., Paulraj, A.J.: Switching between diversity and multiplexing in MIMO systems. IEEE Trans. Commun. 53(6), 962–968 (2005)

    Article  Google Scholar 

  22. Heskes, T.M., Kappen, B.: On-line learning processes in artificial neural networks. North-Holland Math. Libr. 51, 199–233 (1993)

    Article  Google Scholar 

  23. http://5g.ecs.umass.edu/ downloaded on 5/10/2016

  24. http://bwn.ece.gatech.edu/5G_systems/ downloaded on 6/10/2016

  25. http://wireless.engineering.nyu.edu/presentations/NYSWAX.pdf, downloaded on 6/10/2016

  26. http://www.ee.iitm.ac.in/~giri/pdfs/EE5141/Qualcomm-5g-Waveforms.pdf, downloaded on 20/09/16

  27. http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation

  28. http://www.nokia.com/en-int/news/releases/2015/07/01/, telecom industry and European academia join forces to develop a multiservice mobile network architecture for the 5G er

  29. http://www.sra.samsung.com/research/standards-and-5g-mobility

  30. https://5g-ppp.eu/5g-ppp-work-groups/

  31. https://doi.org/10.1007/s11107-015-0558

  32. https://www.ericsson.com/en/press-releases/2015/10/1961609-ericsson-drives-5gex-project-to-unify-5g-infrastructure-service-market

  33. https://www.etri.re.kr/eng/sub6/sub6_0101.etridepart-Code=6/ downloaded on 6/10/2016

  34. Hua, C., Xiao-Hui, Z.: MIMO-OFDM channel estimation based on neural net-work. In: Proceedings of WiCOM-2010. Chengdu, China, vol. 6, pp. 1–4 (2010)

    Google Scholar 

  35. Hutchinson, B., Deng, L., Yu, D.: Tensor deep stacking networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1944–1957 (2008)

    Article  Google Scholar 

  36. Internet resource: http://www.cisco.com/c/dam/en/us/solutions/collateral/service-provider/visual-networking-indexvni/complete-white-paper-c11-481360.pdf, downloaded on 20/09/16

  37. Internet resource: http://www.keysight.com/ain/editorial.jspx.ckey=2311424id=2311424nid=-34869.0lc=engc=US, downloaded on 30/09/16

  38. Internet resource: http://www.prnewswire.com/news-releases/bell-labs-and-technische-universitat-dresden-sign-agreement-to-collaborate-on-advancing-the-development-of-5g-networks-300098830.html downloaded on 6/10/2016

  39. Internet resource: https://www.itu.int/dms-pubrec/itu-r/rec/m/R-REC-M.2083-0-201509-IPDF-E.pdf

  40. Internet resource: https://www.qualcomm.com/news/onq/2015/11/11/designing5gunifiedairinterface

  41. Larsson, E.G., Edfors, O., Tufvesson, F., Marzetta, T.L.: Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)

    Article  Google Scholar 

  42. Letaief, K.B., Zhang, W.: Cooperative communications for cognitive radio networks. Proc. IEEE 97(5), 878–893 (2009)

    Article  Google Scholar 

  43. Li, M., Zhang, T., Chen, Y., Smola, A.J.: Efficient mini-batch training for stochastic optimization. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 661–670 (2014)

    Google Scholar 

  44. Ling, Z., Xianda, Z.: MIMO channel estimation and equalization using three-layer neural networks with feedback. Tsinghua Sci. Technol. J. 12(6), 658–661 (2007)

    Article  Google Scholar 

  45. Liu, J., Deng, R., Zhou, S., Niu, Z.: Seeing the unobservable: channel learning for wireless communication networks. In: 2015 IEEE Global Communications Conference (GLOBECOM), IEEE. 62. pp. 1–6 (2015)

    Google Scholar 

  46. Lngkvist, Martin, Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recog. Lett. 42, 11–24 (2014)

    Article  Google Scholar 

  47. Mane, D., Salian, S.: Big data, cognitive computing and big data testing to deduce optimized result based decisions. Int. J. Eng. Res. Gen. Sci. 3, 351–356 (2016)

    Google Scholar 

  48. Manyika, J., et al.: Big data: the next frontier for innovation, competition and productivity, pp. 1–137. McKinsey Global Institute, San Francisco (2011)

    Google Scholar 

  49. Michailow, N., Matth, M., Gaspar, I.S., Caldevilla, A.N., Mendes, L.L., Festag, A., Fettweis, G.: Generalized frequency division multiplexing for 5th generation cel-lular networks. IEEE Trans. Commun. 62(9), 3045–3061 (2014)

    Article  Google Scholar 

  50. Mitra, R.N., Agrawal, D.P.: 5G mobile technology: a survey. ICT Express, pp. 132–137 (2015)

    Article  Google Scholar 

  51. Mohamed, M.F., Oweisb, N.E., Gaber, T., Ahmedd, M., Snasel, V.: Data mining and fusion techniques for WSNs as a source of the big data. International Conference on Communication, Management and Information Technology. pp. 778–786 (2015)

    Google Scholar 

  52. Mohammad, M.M., Kumar, S.: Evolution of mobile wireless technology from 0G to 5G. Int. J. Comput. Sci. Inf. Technol. 6(3), 2545–2551 (2015)

    Google Scholar 

  53. Monserrat, J.F., Mange, G., Braun, V., Tullberg, H., Zimmermann, G.: Bulakci (2015) METIS research advances towards the 5G mobile and wireless system definition. EURASIP J. Wireless Commun. Netw. 1, 1 (2015)

    Google Scholar 

  54. Moreno, R., Mayer, R.E.: Cognitive principles of multimedia learning: the role of modality and contiguity. J. Educ. Psychol. 91(2), 358–368 (1999)

    Article  Google Scholar 

  55. Mousa, A.M.: Prospective of fifth generation mobile communications. Int. J. Next-Gener. Netw. (IJNGN) 4(3) (2012)

    Article  Google Scholar 

  56. NEWCOM Deliverables 23.3: http://www.newcom-project.eu/images/Delivarables/D23.3Secondreportontoolsandtheirintegrationontheexperimentalsetups.pdf

  57. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11). pp. 689–696 (2011)

    Google Scholar 

  58. Niemi, A., Joutsensalo, J., Ristaniemi, T.: Fuzzy channel estimation in multipath fading CDMA channel. In: Proceedings of 11th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 2. London, UK, pp. 1131–1135 (2000)

    Google Scholar 

  59. Osseiran, A., et al.: Scenarios for 5G mobile and wireless communications: the vision of the METIS project. IEEE Commun. Mag. 52(5), 26–35 (2014)

    Article  Google Scholar 

  60. Paul, J.R., Vladimirova, T.: Blind equalization with recurrent neural networks using natural gradient. In: Proceedings of ISCCSP, Malta, pp. 178–183 (2008)

    Google Scholar 

  61. Saikia, S.J., Sarma, K.K.: ANN based STBC-MIMO set-up for Wireless Communication. Book: Recent Trends in Intelligent and Emerging Systems, Signals and Communication Technology. pp. 19–28 (2015)

    Chapter  Google Scholar 

  62. Saito, Y., Kishiyama, Y., Benjebbour, A., Nakamura, T., Li, A., Higuchi, K.: Non-orthogonal multiple access (NOMA) for cellular future radio access. In: Vehicular Technology Conference (VTC Spring). pp. 1–5 (2013)

    Google Scholar 

  63. Salakhutdinov, R.R., Hinton, G.E.: Deep Boltzmann machines. In: Proceedings of the International Conference on Artificial Intelligence and Statistics. pp. 448–458 (2009)

    Google Scholar 

  64. Samimi, M.K., Sun, S., Rappaport, T.S.: MIMO channel modeling and capacity analysis for 5G millimeter-wave wireless systems. In: 2016 10th European Conference on Antennas and Propagation (EuCAP), IEEE. pp. 1–5 (2016)

    Google Scholar 

  65. Samimi, M.K, et al.: 28 GHz millimeter-wave ultra wideband small-scale fading models in wireless channels. Vehicular Technology Conference (VTC Spring), IEEE 83rd. IEEE, pp 1–6 (2016)

    Google Scholar 

  66. Sarma, K.K., Mitra, A.: Stochastic MIMO channel modelling using FMLP-based inference engine. Int. J. Inf. Commun. Technol. 5(2), 122–136 (2013)

    Google Scholar 

  67. Shannon, C, E.: A mathematical theory of communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 5(1), 3–55 (2001)

    Article  MathSciNet  Google Scholar 

  68. Shao, L., Wu, D., Li, X.: Learning deep and wide: a spectral method for learning deep networks. IEEE Trans. Neural Netw. Learn. Syst. 25(12), 2023–2028 (2014)

    Article  Google Scholar 

  69. Srivastava, N., Salakhutdinov, R.R.: Multimodal learning with deep Boltzmann machines. In: Advances in Neural Information Processing Systems. pp. 2222–2230 (2012)

    Google Scholar 

  70. Sun, S., Rappaport, T.S., et al.: Propagation path loss models for 5G urban micro-and macro-cellular scenarios. Vehicular Technology Conference (VTC Spring), IEEE 83rd. IEEE, pp 1–6 (2016)

    Google Scholar 

  71. Sun, S., Rappaport, T.S., et al.: Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5G wireless communication. IEEE Trans. Veh. Technol. 65(5), 2843–2860 (2016)

    Article  Google Scholar 

  72. Thomas, T.A., Rybakowski, M., Sun, S., Rappaport, T.S., Nguyen, H., Kovacs, I.Z., Rodriguez, I.: A prediction study of path loss models from 2-73.5 GHz in an urban-macro environment. In: Vehicular Technology Conference (VTC Spring), IEEE 83rd, pp. 1–5 (2016)

    Google Scholar 

  73. Wunder, Gerhard, et al.: 5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications. IEEE Commun. Mag. 52(2), 97–105 (2014)

    Article  Google Scholar 

  74. Yang, H.: A road to future broadband wireless access: MIMO-OFDM-based air interface. IEEE Commun. Mag. 43(1), 53–60 (2005)

    Article  MathSciNet  Google Scholar 

  75. Yu, D., Deng, L.: Deep learning and its applications to signal and information processing. IEEE Sig. Process. Mag. 28(1), 145–154 (2011)

    Article  MathSciNet  Google Scholar 

  76. Ze, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE. pp. 7962–7966 (2013)

    Google Scholar 

Download references

Acknowledgements

The authors express their thanks and gratitude to the Ministry of Communication and Information Technology (MCIT), Govt. of India for their support in executing the work.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Technology, Gauhati University, Guwahati, 781014, Assam, India

    Ankumoni Bora & Kandarpa Kumar Sarma

Authors
  1. Ankumoni Bora
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kandarpa Kumar Sarma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankumoni Bora .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Silicon Institute of Technology, Bhubaneswar, India

    Bijan Bihari Mishra

  2. Department of Information and Communication, Fakir Mohan University, Balasore, Odisha, India

    Satchidanand Dehuri

  3. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  4. Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India

    Ajit Kumar Nayak

  5. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Bhabani Shankar Prasad Mishra

  6. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Himansu Das

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bora, A., Sarma, K.K. (2019). Big Data and Deep Learning for Stochastic Wireless Channel. In: Mishra, B., Dehuri, S., Panigrahi, B., Nayak, A., Mishra, B., Das, H. (eds) Computational Intelligence in Sensor Networks. Studies in Computational Intelligence, vol 776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57277-1_13

Download citation

Publish with us

Policies and ethics

Search

Navigation