Advertisement

Conclusions and Discussions

Chapter
  • 203 Downloads

Abstract

This chapter concludes the monograph. A summary is first provided for the main results of each chapter, and two reference tables are provided that contain the main analytical results and algorithms. Furthermore, we provide discussions on the future research directions of low-overhead communications and the corresponding structured signal processing approaches.

References

  1. 1.
    Ahmed, A., Demanet, L.: Leveraging diversity and sparsity in blind deconvolution. IEEE Trans. Inf. Theory 64(6), 3975–4000 (2018)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Asim, M., Shamshad, F., Ahmed, A.: Blind image deconvolution using deep generative priors. arXiv preprint:1802.04073 (2018)Google Scholar
  3. 3.
    Bora, A., Jalal, A., Price, E., Dimakis, A.G.: Compressed sensing using generative models. In: Proceedings of the 34th International Conference on Machine Learning (ICML), vol. 70, pp. 537–546 (2017). http://JMLR.org
  4. 4.
    Borgerding, M., Schniter, P., Rangan, S.: AMP-inspired deep networks for sparse linear inverse problems. IEEE Trans. Signal Process. 65(16), 4293–4308 (2017)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Flinth, A.: Sparse blind deconvolution and demixing through 1,2-minimization. Adv. Comput. Math. 44(1), 1–21 (2018)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information Science and TechnologyShanghai Tech UniversityShanghaiChina
  2. 2.School of Information Science and TechnologyShanghaiTech UniversityShanghaiChina
  3. 3.Department of Electronic & Information EngineeringHong Kong Polytechnic UniversityKowloonHong Kong

Personalised recommendations