Advertisement

Deep Learning-Based Space Shift Keying Systems

  • Yue Zhang
  • Xuesi Wang
  • Jintao WangEmail author
  • Yonglin Xue
  • Jian Song
Conference paper
  • 416 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 286)

Abstract

To handle the performance degradation of space shift keying (SSK) systems under practical non-Gaussian channels, we propose a deep neural network model in which an auto-encoder (AE) is developed to design proper constellations and corresponding demodulation. With full knowledge of channel statistics, the transmitter and receiver are jointly optimized in our scheme. By representing the SSK system as an AE, we consider the cross-entropy loss function for antenna index and formulate the overall pipeline using deep learning techniques. Moreover, our implementation can be adopted in several noise conditions successfully. Results confirm that our model outperforms the maximum likelihood (ML) detection scheme in terms of block error rates (BLER).

Keywords

Space shift keying (SSK) Deep learning Neural network 

References

  1. 1.
    Shafi, M., et al.: 5G: a tutorial overview of standards, trials, challenges, deployment, and practice. IEEE J. Sel. Areas Commun. 35(6), 1201–1221 (2017)MathSciNetCrossRefGoogle Scholar
  2. 2.
    He, L., Wang, J., Song, J.: Spatial modulation for more spatial multiplexing: RF-chain-limited generalized spatial modulation aided mmWave MIMO with hybrid pre-coding. IEEE Trans. Commun. 66(3), 986–998 (2018)CrossRefGoogle Scholar
  3. 3.
    Jaganathan, J., Ghrayeb, A., Szczecinski, L., Ceron, A.: Space shift keying modulation for MIMO channels. IEEE Trans. Wirel. Commun. 8(7), 3692–3703 (2009)CrossRefGoogle Scholar
  4. 4.
    Shahi, S., Tuninetti, D., Devroye, N.: On the capacity of the AWGN channel with additive radar interference. IEEE Trans. Commun. 66(2), 629–643 (2018)CrossRefGoogle Scholar
  5. 5.
    Ikki, S.-S., Mesleh, R.: A general framework for performance analysis of space shift keying (SSK) modulation in the presence of Gaussian imperfect estimation. IEEE Commun. Lett. 16(2), 228–230 (2012)CrossRefGoogle Scholar
  6. 6.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  7. 7.
    Horinik, K., Stinchcombe, M., White, H.: Multiplayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  8. 8.
    Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Li, J., Luong, M., Jurafsky, D.: A hierarchical neural autoencoder for paragraphs and documents. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 1106–1115 (2015)Google Scholar
  10. 10.
    O’Shea, T., Karra, K., Clancy, T.-C.: Learning to communicate: channel auto-encoders, domain specific regularizers, and attention. In: 2016 IEEE International Symposium on Signal Processing and Information Technology, pp. 223–228 (2016)Google Scholar
  11. 11.
    Alberge, F.: Deep learning constellation design for the AWGN channel with additive radar interference. IEEE Trans. Commun. 1 (2018)Google Scholar
  12. 12.
    He, H., Wen, C., Jin, S., Li, G.-Y.: Deep learning-based channel estimation for beamspace mmWave massive MIMO systems. IEEE Wirel. Commun. Lett. 7(5), 852–855 (2018)CrossRefGoogle Scholar
  13. 13.
    Gruber, T., Cammerer, S., Hoydis, J., Brink, S.-T.: On deep learning-based channel decoding. In: 2017 51st Annual Conference on Information Sciences and Systems, pp. 1–5 (2017)Google Scholar
  14. 14.
    Wen, C., Shih, W., Jin, S.: Deep learning for massive MIMO CSI feedback. IEEE Wirel. Commun. Lett. 7(5), 748–751 (2018)CrossRefGoogle Scholar
  15. 15.
    O’Shea, T., Hoydis, J.: An introduction to deep learning for the physical layer. IEEE Trans. Cogn. Commun. Netw. 3(4), 563–575 (2017)CrossRefGoogle Scholar
  16. 16.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv: 1412.6980 (2014)
  17. 17.
    Samuel, N., Diskin, T., Wiesel, A.: Deep MIMO detection. In: 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications, pp. 1–5 (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Yue Zhang
    • 1
  • Xuesi Wang
    • 1
  • Jintao Wang
    • 1
    Email author
  • Yonglin Xue
    • 1
  • Jian Song
    • 1
  1. 1.Tsinghua UniversityBeijingPeople’s Republic of China

Personalised recommendations