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Introduction

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Data-Driven Wireless Networks

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

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Abstract

Radio frequency (RF) spectrum is a valuable but tightly regulated resource due to its unique and important role in wireless communications. The demand for RF spectrum is increasing due to a rapidly expanding market of multimedia wireless services, while the usable spectrum is becoming scarce due to current rigid spectrum allocation policies.

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References

  • Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Network, 50, 2127–2159.

    Article  Google Scholar 

  • Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4, 40–62.

    Article  Google Scholar 

  • Candes, E. (2006). Compressive sampling. In Proceedings of the International Congress of Mathematicians, Madrid, Spain (vol. 3, pp. 1433–1452)

    Google Scholar 

  • Farhang-Boroujeny, B. (2008). Filter bank spectrum sensing for cognitive radios. IEEE Transactions on Signal Processing, 56, 1801–1811.

    Article  MathSciNet  Google Scholar 

  • Federal Communications Commission (FCC). (2008). Second report and order and memorandum opinion and order in matter of unlicensed operation in the TV broadcast bands, additional spectrum for unlicensed devices below 900 MHz and in the 3 GHz band, Document 08-260.

    Google Scholar 

  • Gao, Y., Qin, Z., Feng, Z., Zhang, Q., Holland, O., & Dohler, M. (2016). Scalable and reliable IoT enabled by dynamic spectrum management for M2M in LTE-A. IEEE Internet of Things Journal, 3, 1135–1145.

    Article  Google Scholar 

  • Ghasemi, A., & Sousa, E. (2005). Collaborative spectrum sensing for opportunistic access in fading environments. In Proceedings of the IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN), Baltimore, MD (pp. 131–136)

    Google Scholar 

  • Kolodzy, P., & Avoidance, I. (2002). Spectrum policy task force. Federal Communications Commission, Washington, DC, Rep. ET Docket.

    Google Scholar 

  • Landau, H. (1967). Necessary density conditions for sampling and interpolation of certain entire functions. Acta Mathematica, 117, 37–52.

    Article  MathSciNet  Google Scholar 

  • Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6, 13–18.

    Article  Google Scholar 

  • Qin, Z., Gao, Y., & Parini, C. G. (2016a). Data-assisted low complexity compressive spectrum sensing on real-time signals under sub-Nyquist rate. IEEE Transactions on Wireless Communications, 15, 1174–1185.

    Article  Google Scholar 

  • Qin, Z., Gao, Y., Plumbley, M., & Parini, C. (2014). Efficient compressive spectrum sensing algorithm for M2M devices. In IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA (pp. 1170–1174).

    Google Scholar 

  • Qin, Z., Gao, Y., & Plumbley, M. D. (2018). Malicious user detection based on low-rank matrix completion in wideband spectrum sensing. IEEE Transactions on Signal Processing, 66, 5–17.

    Article  MathSciNet  Google Scholar 

  • Qin, Z., Gao, Y., Plumbley, M. D., & Parini, C. G. (2016b). Wideband spectrum sensing on real-time signals at sub-Nyquist sampling rates in single and cooperative multiple nodes. IEEE Transactions on Signal Processing, 64, 3106–3117.

    Article  MathSciNet  Google Scholar 

  • Qin, Z., Liu, Y., Gao, Y., Elkashlan, M., & Nallanathan, A. (2017). Wireless powered cognitive radio networks with compressive sensing and matrix completion. IEEE Transactions on Communications, 65, 1464–1476.

    Article  Google Scholar 

  • Qin, Z., Wei, L., Gao, Y., & Parini, C. (2015). Compressive spectrum sensing augmented by geo-location database. In Proceedings of the International Workshop on Smart Spectrum at IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA (pp. 170–175).

    Google Scholar 

  • Quan, Z., Cui, S., Sayed, A. H., & Poor, H. V. (2009). Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Transactions on Signal Processing, 57, 1128–1140.

    Article  MathSciNet  Google Scholar 

  • Sun, H., Laurenson, D. I., & Wang, C. X. (2010). Computationally tractable model of energy detection performance over slow fading channels. IEEE Communications Letters, 14, 924–926.

    Article  Google Scholar 

  • Sun, H., Nallanathan, A., Wang, C.-X., & Chen, Y. (2013). Wideband spectrum sensing for cognitive radio networks: A survey. IEEE Wireless Communications, 20, 74–81.

    Google Scholar 

  • Tian, Z., & Giannakis, G. (2007). Compressed sensing for wideband cognitive radios. In IEEE International Conference on Acoustics, Speech, and Signal Processing, Honolulu, HI (ICASSP) (pp. 1357–1360).

    Google Scholar 

  • Treichler, J., Davenport, M., & Baraniuk, R. (2009). Application of compressive sensing to the design of wideband signal acquisition receivers. In US/Australia Joint Work. Defense Apps of Signal Processing (DASP) (vol. 5).

    Google Scholar 

  • UK Office of Communications (Ofcom). (2009). Statement on cognitive access to interleaved spectrum.

    Google Scholar 

  • UK Office of Communications (Ofcom). (2015). Decision to make the wireless telegraphy (White Space Devices).

    Google Scholar 

  • Wang, Y., Tian, Z., & Feng, C. (2012). Collecting detection diversity and complexity gains in cooperative spectrum sensing. IEEE Wireless Communications, 11, 2876–2883.

    Google Scholar 

  • Zhang, X., Ma, Y., Gao, Y., & Zhang, W. (2018). Autonomous compressive sensing augmented spectrum sensing. IEEE Transactions on Vehicular Technology, 67, 6970–6980.

    Article  Google Scholar 

  • Zhang, X., Qin, Z., & Gao, Y. (2014). Dynamic adjustment of sparsity upper bound in wideband compressive spectrum sensing. In Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA (pp. 1214–1218).

    Google Scholar 

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Authors and Affiliations

  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Yue Gao & Zhijin Qin

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  1. Yue Gao
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  2. Zhijin Qin
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Gao, Y., Qin, Z. (2019). Introduction. In: Data-Driven Wireless Networks. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-00290-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-00290-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00289-3

  • Online ISBN: 978-3-030-00290-9

  • eBook Packages: EngineeringEngineering (R0)

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