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Low-overhead Communications in IoT Networks pp 1–11Cite as

Introduction

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Abstract

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.

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References

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  3. Arunabha, G., Zhang, J., Andrews, J.G., Muhamed, R.: Fundamentals of LTE. Prentice-Hall, Englewood Cliffs (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  5. Baraniuk, R.G.: Compressive sensing. IEEE Signal Process. Mag. 24(4), 118–121 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  8. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  11. Dong, J., Shi, Y.: Nonconvex demixing from bilinear measurements. IEEE Trans. Signal Process. 66(19), 5152–5166 (2018)

    Article  MathSciNet  Google Scholar 

  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. 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. Dong, J., Yang, K., Shi, Y.: Blind demixing for low-latency communication. IEEE Trans. Wirel. Commun. 18(2), 897–911 (2019)

    Article  Google Scholar 

  15. Donoho, D.L., Maleki, A., Montanari, A.: Message-passing algorithms for compressed sensing. Proc. Natl. Acad. Sci. 106(45), 18914–18919 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  24. Ling, S., Strohmer, T.: Blind deconvolution meets blind demixing: algorithms and performance bounds. IEEE Trans. Inf. Theory 63(7), 4497–4520 (2017)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  28. Muthukrishnan, S., et al.: Data streams: algorithms and applications. Found. Trends Theor. Comput. Sci. 1(2), 117–236 (2005)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Tsakiris, M.C., Peng, L.: Homomorphic sensing. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 6335–6344 (2019)

    Google Scholar 

  35. Tsakiris, M.C., Peng, L., Conca, A., Kneip, L., Shi, Y., Choi, H.: An algebraic-geometric approach to shuffled linear regression (2018). arXiv:1810.05440

    Google Scholar 

  36. Wunder, G., Boche, H., Strohmer, T., Jung, P.: Sparse signal processing concepts for efficient 5G system design. IEEE Access 3, 195–208 (2015)

    Article  Google Scholar 

  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. 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. Zhu, H., Giannakis, G.B.: Exploiting sparse user activity in multiuser detection. IEEE Trans. Commun. 59(2), 454–465 (2010)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, Shanghai Tech University, Shanghai, China

    Yuanming Shi

  2. School of Information Science and Technology, ShanghaiTech University, Shanghai, China

    Jialin Dong

  3. Department of Electronic & Information Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Jun Zhang

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  1. Yuanming Shi
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  2. Jialin Dong
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  3. Jun Zhang
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Shi, Y., Dong, J., Zhang, J. (2020). Introduction. In: Low-overhead Communications in IoT Networks. Springer, Singapore. https://doi.org/10.1007/978-981-15-3870-4_1

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  • DOI: https://doi.org/10.1007/978-981-15-3870-4_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3869-8

  • Online ISBN: 978-981-15-3870-4

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