This chapter presents a background on low-overhead communications in IoT networks and structured signal processing. It starts with introducing three key techniques for low-overhead communications: grant-free random access, pilot-free communications, and identification-free communications. Then different models for structured signal processing to support low-overhead communications are presented, which form the main theme of this monograph. A classical exemplary of structure signal processing, i.e., compressed sensing, is provided to illustrate the main principles of algorithm design and theoretical analysis. Finally, the outline of the monograph is presented.


  1. 1.
    Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutorials 17(4), 2347–2376 (2015)CrossRefGoogle Scholar
  2. 2.
    Amelunxen, D., Lotz, M., McCoy, M.B., Tropp, J.A.: Living on the edge: phase transitions in convex programs with random data. Inf. Inference 3(3), 224–294 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Arunabha, G., Zhang, J., Andrews, J.G., Muhamed, R.: Fundamentals of LTE. Prentice-Hall, Englewood Cliffs (2010)Google Scholar
  4. 4.
    Bajwa, W.U., Haupt, J., Sayeed, A.M., Nowak, R.: Compressed channel sensing: a new approach to estimating sparse multipath channels. Proc. IEEE 98(6), 1058–1076 (2010)CrossRefGoogle Scholar
  5. 5.
    Baraniuk, R.G.: Compressive sensing. IEEE Signal Process. Mag. 24(4), 118–121 (2007)CrossRefGoogle Scholar
  6. 6.
    Björnson, E., De Carvalho, E., Sørensen, J.H., Larsson, E.G., Popovski, P.: A random access protocol for pilot allocation in crowded massive MIMO systems. IEEE Trans. Wirel. Commun. 16(4), 2220–2234 (2017)CrossRefGoogle Scholar
  7. 7.
    Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Choi, J.W., Shim, B., Ding, Y., Rao, B., Kim, D.I.: Compressed sensing for wireless communications: useful tips and tricks. IEEE Commun. Surv. Tutorials 19(3), 1527–1550 (2017)CrossRefGoogle Scholar
  10. 10.
    Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Dong, J., Shi, Y.: Nonconvex demixing from bilinear measurements. IEEE Trans. Signal Process. 66(19), 5152–5166 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Dong, J., Shi, Y., Ding, Z.: Sparse blind demixing for low-latency signal recovery in massive IoT connectivity. In: Proceedings of the IEEE International Conference on Acoustics Speech Signal Process (ICASSP), pp. 4764–4768. IEEE, Piscataway (2019)Google Scholar
  13. 13.
    Dong, J., Shi, Y., Ding, Z.: Sparse blind demixing for low-latency signal recovery in massive IoT connectivity. In: Proceedings of the IEEE International Conference on Acoustics Speech Signal Process (ICASSP), pp. 4764–4768 (2019)Google Scholar
  14. 14.
    Dong, J., Yang, K., Shi, Y.: Blind demixing for low-latency communication. IEEE Trans. Wirel. Commun. 18(2), 897–911 (2019)CrossRefGoogle Scholar
  15. 15.
    Donoho, D.L., Maleki, A., Montanari, A.: Message-passing algorithms for compressed sensing. Proc. Natl. Acad. Sci. 106(45), 18914–18919 (2009)CrossRefGoogle Scholar
  16. 16.
    Durisi, G., Koch, T., Popovski, P.: Toward massive, ultrareliable, and low-latency wireless communication with short packets. Proc. IEEE 104(9), 1711–1726 (2016)CrossRefGoogle Scholar
  17. 17.
    Figueiredo, M.A., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Sign. Proces. 1(4), 586–597 (2007)CrossRefGoogle Scholar
  18. 18.
    Fu, M., Dong, J., Shi, Y.: Sparse blind demixing for low-latency wireless random access with massive connectivity. In: Proceedings of the IEEE Vehicular Technology Conference (VTC), pp. 4764–4768. IEEE, Piscataway (2019)Google Scholar
  19. 19.
    Hasan, M., Hossain, E., Niyato, D.: Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches. IEEE Commun. Mag. 51(6), 86–93 (2013)CrossRefGoogle Scholar
  20. 20.
    Jiang, T., Shi, Y., Zhang, J., Letaief, K.B.: Joint activity detection and channel estimation for IoT networks: phase transition and computation-estimation tradeoff. IEEE Internet Things J. 6(4), 6212–6225 (2018)CrossRefGoogle Scholar
  21. 21.
    Keller, L., Siavoshani, M.J., Fragouli, C., Argyraki, K., Diggavi, S.: Identity aware sensor networks. In: IEEE INFOCOM, pp. 2177–2185. IEEE, Piscataway (2009)Google Scholar
  22. 22.
    Kong, L., Khan, M.K., Wu, F., Chen, G., Zeng, P.: Millimeter-wave wireless communications for IoT-cloud supported autonomous vehicles: overview, design, and challenges. IEEE Commun. Mag. 55(1), 62–68 (2017)CrossRefGoogle Scholar
  23. 23.
    Letaief, K.B., Chen, W., Shi, Y., Zhang, J., Zhang, Y.A.: The roadmap to 6G: AI empowered wireless networks. IEEE Commun. Mag. 57(8), 84–90 (2019)CrossRefGoogle Scholar
  24. 24.
    Ling, S., Strohmer, T.: Blind deconvolution meets blind demixing: algorithms and performance bounds. IEEE Trans. Inf. Theory 63(7), 4497–4520 (2017)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Ling, S., Strohmer, T.: Regularized gradient descent: a nonconvex recipe for fast joint blind deconvolution and demixing. Inf. Inference J. IMA 8(1), 1–49 (2019)CrossRefGoogle Scholar
  26. 26.
    Liu, L., Larsson, E.G., Yu, W., Popovski, P., Stefanovic, C., De Carvalho, E.: Sparse signal processing for grant-free massive connectivity: a future paradigm for random access protocols in the Internet of Things. IEEE Signal Process. Mag. 35(5), 88–99 (2018)CrossRefGoogle Scholar
  27. 27.
    Motlagh, N.H., Bagaa, M., Taleb, T.: UAV-based IoT platform: a crowd surveillance use case. IEEE Commun. Mag. 55(2), 128–134 (2017)CrossRefGoogle Scholar
  28. 28.
    Muthukrishnan, S., et al.: Data streams: algorithms and applications. Found. Trends Theor. Comput. Sci. 1(2), 117–236 (2005)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Nitsche, T., Cordeiro, C., Flores, A.B., Knightly, E.W., Perahia, E., Widmer, J.: IEEE 802.11 ad: directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi. IEEE Commun. Mag. 52(12), 132–141 (2014)Google Scholar
  30. 30.
    Pananjady, A., Wainwright, M.J., Courtade, T.A.: Linear regression with shuffled data: statistical and computational limits of permutation recovery. IEEE Trans. Inf. Theory 64(5), 3286–3300 (2018)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Peng, L., Song, X., Tsakiris, M.C., Choi, H., Kneip, L., Shi, Y.: Algebraically-initialized expectation maximization for header-free communication. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5182–5186. IEEE, Piscataway (2019)Google Scholar
  32. 32.
    Qin, Z., Fan, J., Liu, Y., Gao, Y., Li, G.Y.: Sparse representation for wireless communications: a compressive sensing approach. IEEE Signal Process. Mag. 35(3), 40–58 (2018)CrossRefGoogle Scholar
  33. 33.
    Schepker, H.F., Bockelmann, C., Dekorsy, A.: Exploiting sparsity in channel and data estimation for sporadic multi-user communication. In: Proceedings of the International Symposium on Wireless Communication Systems, pp. 1–5. VDE, Frankfurt (2013)Google Scholar
  34. 34.
    Tsakiris, M.C., Peng, L.: Homomorphic sensing. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 6335–6344 (2019)Google Scholar
  35. 35.
    Tsakiris, M.C., Peng, L., Conca, A., Kneip, L., Shi, Y., Choi, H.: An algebraic-geometric approach to shuffled linear regression (2018). arXiv:1810.05440Google Scholar
  36. 36.
    Wunder, G., Boche, H., Strohmer, T., Jung, P.: Sparse signal processing concepts for efficient 5G system design. IEEE Access 3, 195–208 (2015)CrossRefGoogle Scholar
  37. 37.
    Wunder, G., Jung, P., Wang, C.: Compressive random access for post-LTE systems. In: Proceedings of the IEEE International Conference on Communications Workshops (ICC), pp. 539–544. IEEE, Piscataway (2014)Google Scholar
  38. 38.
    Xu, X., Rao, X., Lau, V.K.: Active user detection and channel estimation in uplink CRAN systems. In: Proceedings of the IEEE International Conference on Communications (ICC), pp. 2727–2732. IEEE, Piscataway (2015)Google Scholar
  39. 39.
    Zhu, H., Giannakis, G.B.: Exploiting sparse user activity in multiuser detection. IEEE Trans. Commun. 59(2), 454–465 (2010)CrossRefGoogle 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