Skip to main content

Advertisement

Springer Nature Link
Log in

An Introduction to Neural Networks in SCMA

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Sparse Code Multiple Access (SCMA) has proved to be a fascinating research in order to curtail the complications faced by the wireless communication networks. SCMA being a Non-Orthogonal Multiple Access technique evinces to be an outstanding candidate, to cater the complications faced by 5G communication networks to improve the bit error rate and reduce the complexity of decoding the transmitted signal from received signal. This paper explains the concept of SCMA by explaining the basic structure of encoder, decoder and codebook design with the help of neural networks. It explains the concept of reducing the complexity of the traditional decoder of the SCMA by implementing Neural Networks. Further sections explain the use of Convolutional Neural Networks for blind decoding, that outperforms the complexity of decoding carried by conventional SCMA using Message Passing Algorithm. This further explains the use of Deep Neural Networks for designing the codebook and decoding it, by adopting an autoencoder structure.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Saito, Y., Benjebbour, A., Kishiyama, Y., & Nakamura, T. (2013). System level performance evaluation of downlink non-orthogonal multiple access (NOMA), In: Proc. IEEE Annu. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC), London, U.K., pp. 611–615.

  2. Dai, L., Wang, B., Yuan, Y., Han, S., Chih-Lin, I., & Wang, Z. (2015). Non-orthogonal multiple access for 5G: Solutions challenges opportunities and future research trends. IEEE Communication Maganize, 53(9), 74–81.

    Article  Google Scholar 

  3. Gui, G., Huang, H., Song, Y., & Sari, H. (2018). Deep learning for an effective nonorthogonal multiple access scheme. IEEE Transactions on Vehicular Technology, 67(9), 8440–8450. https://doi.org/10.1109/TVT.2018.2848294.

    Article  Google Scholar 

  4. Simeone, O. (2018). A very brief introduction to machine learning with applications to communication systems. IEEE Transactions on Cognitive Communications and Networking, 4(4), 648–664. https://doi.org/10.1109/TCCN.2018.2881442.

    Article  Google Scholar 

  5. Morocho-Cayamcela, M. E., & Njoku, J. N., Park, J. & Lim. W. (2020). Learning to Communicate with Autoencoders: Rethinking Wireless Systems with Deep Learning, 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, pp. 308 311, https://doi.org/10.1109/ICAIIC48513.2020.9065246.

  6. O’Shea, T., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563–575.

    Article  Google Scholar 

  7. Morocho-Cayamcela M. E., & Lim, W. (2019). Proposed cost function using wireless propagation for self-organizing networks, In: The Korean Institute of Communications and Information Sciences Fall Conference 2019 (KICS 2019), Seoul, South Korea, 11, pp. 172–174.

  8. Qin, Z., Ye, H., Li, G. Y., & Juang, B. H. F. (2018). Deep learning in physical layer communications.

  9. Wang et al. Q. (2018). Supervised and semi-supervised deep neural networks for CSI-based authentication.

  10. Hastie, T., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning, in the elements of statistical learning (pp. 485–585). New York: Springer.

    Book  Google Scholar 

  11. Dörner, S., Cammerer, S., Hoydis, J., & Brink, S. T. (2018). Deep learning based communication over the air. IEEE Journal of Selected Topics in Signal Processing, 12(1), 132–143.

    Article  Google Scholar 

  12. Xu, T., Xu, T. & Darwazeh, I. (2018). Deep learning for interference cancellation in non-orthogonal signal based optical communication systems, In: 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama), Toyama, 2018, pp. 241-248, doi: https://doi.org/10.23919/PIERS.2018.8597902

  13. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT Press.

    MATH  Google Scholar 

  14. Zhang, C., Patras, P. & Haddadi, H. Deep Learning in Mobile and Wireless Networking: A Survey, In: IEEE Communications Surveys IEEE 18th Int. Workshop Signal Process. Advance Wireless Communication & Tutorials, vol. 21, no. 3, pp. 2224–2287, thirdquarter 2019, doi: https://doi.org/10.1109/COMST.2019.2904897.

  15. Nachmani, E., Be’ery, Y., & Burshtein, D. Learning to decode linear codes using deep learning, In: Proceedings 54th Annual Allerton Conference on Communication, Control, and Computing, 2016, pp. 341–346.

  16. Nachmani, E., Marciano, E., Burshtein, D., & Be’ery, Y. (2017). RNN decoding of linear block codes.

  17. Gruber, T., Cammerer, S., Hoydis, J., & Ten Brink, J. (2017). On deep learningbased channel decoding, In: Proc. 51st Annual Conference on Information Sciences and Systems. pp. 1–6, https://doi.org/10.1109/CISS.2017.7926071.

  18. Samuel, N., Diskin, T., & Wiesel, A. (2017). Deep MIMO detection, In: proceedings, 2017, pp. 690–694.

  19. Jeon, Y. S., Hong, S. N., & Lee, N. (2016). Blind detection for MIMO systems with low-resolution ADCs using supervised learning,” IEEE International Conference on Communications, pp. 1–6, https://doi.org/10.1109/ICC.2017.7997434.

  20. Kim, J., Lee, B., Lee, H., Kim, Y., & Lee, J. (2018). Deep learning-assisted multi-dimensional modulation and resource mapping for advanced OFDM systems. In: IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates, pp. 1-6, https://doi.org/10.1109/GLOCOMW.2018.8644281

  21. Zhang, W., Liu, K., Zhang, W., Zhang, Y., & Jason, Gu. (2016). Deep neural networks for wireless localization in indoor and outdoor environment. Neurocomputing, 194, 279–287.

    Article  Google Scholar 

  22. Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019). Machine learning for 5G/B5G mobile and wireless communications: potential, limitations, and future directions. IEEE Access, 7, 137184–137.

    Article  Google Scholar 

  23. Lawrence, S., Giles, C. L., Tsoi, Ah Chung, & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98–113. https://doi.org/10.1109/72.554195.

    Article  Google Scholar 

  24. He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp. 1026–1034.

  25. Zheng, S., Qi, P., Chen, S., & Yang, X. (2019). Fusion methods for CNN-based automatic modulation classification. IEEE Access, 7, 66496–66504. https://doi.org/10.1109/ACCESS.2019.2918136.

    Article  Google Scholar 

  26. Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., & Pollin, S. (2018). Deep learning models for wireless signal classification with distributed low-cost spectrum sensors. IEEE Transactions on Cognitive Communications and Networking, 4(3), 433–445.

    Article  Google Scholar 

  27. Peng, S., Jiang, H., Wang, H., Alwageed, H., Zhou, Y., Sebdani, M. M., & Yao, Y.-D. (2019). Modulation classification based on signal constellation diagrams and deep learning. IEEE Transactions on Neural Networks and Learning Systems, 30(3), 718–727.

    Article  Google Scholar 

  28. Wang, Y., Liu, M., Yang, J., & Gui, G. (2019). Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Transactions on Vehicular Technology, 68(4), 4074–4077.

    Article  Google Scholar 

  29. O’Shea, T. J., Roy, T., & Clancy, T. C. (2018). Over-the-air deep learning based radio signal classification. IEEE Journal of Selected Topics in Signal Processing, 12(1), 168–179.

    Article  Google Scholar 

  30. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 119–130.

    Article  Google Scholar 

  31. Vieira, J., Leitinger, E., Sarajlic, M., Li, X., & Tufvesson, F. (2017). Deep convolutional neural networks for massive MIMO fingerprint-based positioning. In: Proceedings 28th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications.

  32. Neumann, D., Utschick, W., & Wiese, T. (2017). Deep Channel Estimation, In: Proceedings 21th International ITG Workshop on Smart Antennas, pages 1–6. VDE.

  33. Wang, B., Wang, K., Lu, Z., Xie, T., & Quan, J. (2015). Comparison study of non-orthogonal multiple access schemes for 5g. In: Broadband Multimedia Systems and Broadcasting (BMSB), 2015 IEEE International Symposium on, pp. 1–5.

  34. Saito Y., Kishiyama, Y., Benjebbour, A., Nakamura, T., Li, A., & Higuchi, K. (2013). Non-orthogonal multiple access (NOMA) for cellular future radio access. In: Vehicular Technology Conference (VTC Spring), 2013 IEEE 77th, pp. 1–5.

  35. Nikopour, H., & Baligh, H. (2013). Sparse Code Multiple Access. In: IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications:Fundamentals and PHY Track.

  36. Wu, Y., Wang, C., Chen, Y., & Bayesteh, A. (2017). Sparse Code Multiple Access for 5g Radio Transmission. In: Vehicular Technology Conference (VTC-Fall), 2017 IEEE 86th, pp. 1–6.

  37. Nikopour, H. & Baligh, M. (2016). Systems and Methods for Sparse Code Multiple Access. January 19 2016. US Patent 9,240,853.

  38. Xiao, B., Xiao, K., Zhang, S., Chen, Z., Xia, B., & Liu, H. (2015). Iterative detection and decoding for SCMA systems with LDPC codes. In: Wireless Communications & Signal Processing (WCSP), 2015 International Conference on, pp. 1–5.

  39. Liu, J., Wu, G., Li, S., & Tirkkonen, O. (2016). On fixed point implementation of Log-MPA for SCMA Signal. IEEE Wireless Communications Letters, 5(3), 324–327.

    Article  Google Scholar 

  40. Alam, M., & Zhang, Q. (2017). Performance study of SCMA codebook design. In: Wireless Communications and Networking Conference (WCNC), 2017 IEEE, pp. 1–5.

  41. Madhura, K., Raut, R., & Rukmini, M. S. S. (2019). A study on codebook design techniques in SCMA. International Journal of Control and Automation, 12, 31–40.

    Google Scholar 

  42. Madhura, K., & Raut, R. (2017). SCMA: A survey on advancement towards 5G Communications. pp. 487–498. https://doi.org/10.14257/astl.2017.14

  43. Liu, S., Wang, J., Bao, J., & Liu, C. (2018). Optimized SCMA codebook design by QAM constellation segmentation with maximized MED. IEEE Access, 6, 63232–63242.

    Google Scholar 

  44. Simonyan, K. & Zisserm, A. (2014). Very deep convolutional networks for large-scale image recognition.

  45. Murphy, K. P. (2012). Machine learning: A probabilistic perspective. Cambridge: MIT Press.

    MATH  Google Scholar 

  46. O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016). Convolutional radio modulation recognition networks, In: Proceedings of EANN, Aberdeen, UK, pp213–26.

  47. O’Shea, T. J., Karra K., & Clancy T. C. (2016). Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention. In: Proceedings of ISSPIT, Limassol, Cyprus, pp. 223–228.

  48. Hoshyar, R., Razavi, R. & Al-Imari, M. (2010). LDS-OFDM an Efficient Multiple Access Technique. In: 2010 IEEE 71st Vehicular Technology Conference, Taipei, 2010, pp. 1–5.

  49. Hoshyar, R., Wathan, F. P., & Tafazolli, R. (2008). Novel low-density signature for synchronous CDMA systems over AWGN channel. IEEE Transactions on Signal Processing, 56(4), 1616–1626.

    Article  MathSciNet  Google Scholar 

  50. Abidi, I., Hizem, M., Ahriz, I., Cherif, M. & Bouallegue, R. (2019). Convolutional neural networks for blind decoding in sparse code multiple access, In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco, pp. 2007-2012.

  51. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems, 6, 1097–1105.

    Google Scholar 

  52. Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A Convolutional Neural-Network approach. IEEE Transactions on Neural Networks, 8(1), 98–113.

    Article  Google Scholar 

  53. Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019). Machine learning for 5G/B5G mobile and wireless communications: Potential limitations, and future directions. IEEE Access, 7, 137184–137206.

    Article  Google Scholar 

  54. Simonyan, K., Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition.

  55. Kim, M., Kim, N., Lee, W., & Cho, D. (2018). Deep learning-aided SCMA. IEEE Communications Letters, 22(4), 720–723.

    Article  Google Scholar 

  56. Sheng, G., Min, M., & Xiao, L. (2018). Reinforcement learning-based control for unmanned aerial vehicles. Journal of Communications and Information Networks, 3, 39–48. https://doi.org/10.1007/s41650-018-0029-y.

    Article  Google Scholar 

  57. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371–3408.

    MathSciNet  MATH  Google Scholar 

  58. Lin, Jinzhi, Feng, Shengzhong, Zhang, Yun, Yang, Zhile, & Zhang, Yong. (2020). A novel deep neural network based approach for sparse code multiple access. Neurocomputing, 382, 52–63.

    Article  Google Scholar 

  59. Cao, W., Wang, X., Ming, Z., & Gao, J. (2018). A review on neural networks with random weights. Neurocomputing, 275, 278–287.

    Article  Google Scholar 

  60. Aldossari, Saud, & Chen, Kwang-Cheng. (2019). Machine learning for wireless communication channel modelling: An overview. Wireless Personal Communications. https://doi.org/10.1007/s11277-019-06275-4.

    Article  Google Scholar 

  61. Sheth, K., Patel, K., Shah, H., Tanwar, S., Gupta, R., & Kumar, N. (2020). A taxonomy of AI techniques for 6G communication networks. Computer Communications, 161, 279–303.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Vignan’s Foundation for Science, Technology, and Research, Guntur, India

    Madhura Kanzarkar & M. S. S. Rukmini

  2. Department of ETC, Government College of Engineering, Nagpur, India

    Rajeshree Raut

Authors
  1. Madhura Kanzarkar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. M. S. S. Rukmini
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Rajeshree Raut
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Madhura Kanzarkar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kanzarkar, M., Rukmini, M.S.S. & Raut, R. An Introduction to Neural Networks in SCMA. Wireless Pers Commun 119, 509–525 (2021). https://doi.org/10.1007/s11277-021-08222-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08222-8

Keywords