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  • Cited by 14
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    This chapter has been cited by the following publications. This list is generated based on data provided by CrossRef.

    López-Benítez, Miguel 2018. Handbook of Cognitive Radio. p. 1.

    Al-Tahmeesschi, Ahmed Lopez-Benitez, Miguel Lehtomaki, Janne and Umebayashi, Kenta 2018. Accurate estimation of primary user traffic based on periodic spectrum sensing. p. 1.

    Al-Tahmeesschi, Ahmed Lopez-Benitez, Miguel Lehtomaki, Janne and Umebayashi, Kenta 2018. Improving primary statistics prediction under imperfect spectrum sensing. p. 1.

    Al-Tahmeesschi, Ahmed Lopez-Benitez, Miguel Umebayashi, Kenta and Lehtomaki, Janne 2017. Analytical study on the estimation of primary activity distribution based on spectrum sensing. p. 1.

    Al-Tahmeesschi, Ahmed Lopez-Benitez, Miguel Lehtomaki, Janne and Umebayashi, Kenta 2017. Investigating the Estimation of Primary Occupancy Patterns under Imperfect Spectrum Sensing. p. 1.

    Lopez-Benitez, Miguel Al-Tahmeesschi, Ahmed Umebayashi, Kenta and Lehtomaki, Janne 2017. PECAS: A Low-Cost Prototype for the Estimation of Channel Activity Statistics in Cognitive Radio. p. 1.

    Lopez-Benitez, Miguel and Lehtomaki, Janne 2016. Energy detection based estimation of primary channel occupancy rate in cognitive radio. p. 1.

    Lopez-Benitez, Miguel and Lehtomaki, Janne 2016. Energy detection based estimation of primary Channel Occupancy Rate in Cognitive Radio. p. 355.

    Lopez-Benitez, Miguel and Lehtomaki, Janne 2016. On the sensing sample size for the estimation of primary channel occupancy rate in cognitive radio. p. 1.

    Lopez-Benitez, Miguel and Casadevall, Fernando 2016. Space-dimension models of spectrum usage for cognitive radio networks. IEEE Transactions on Vehicular Technology, p. 1.

    Lopez-Benitez, Miguel and Casadevall, Fernando 2014. A framework for multidimensional modelling of spectrum occupancy in the simulation of cognitive radio systems. p. 453.

    Lopez-Benitez, Miguel 2014. Sensing-based spectrum awareness in Cognitive Radio: Challenges and open research problems. p. 459.

    Lopez-Benitez, M. and Casadevall, F. 2013. Signal Uncertainty in Spectrum Sensing for Cognitive Radio. IEEE Transactions on Communications, Vol. 61, Issue. 4, p. 1231.

    Lopez-Benitez, Miguel 2013. Can primary activity statistics in Cognitive Radio be estimated under imperfect spectrum sensing?. p. 750.

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  • Print publication year: 2013
  • Online publication date: June 2013

13 - Cognitive radio

Summary

Introduction

Cognitive radio (CR) has recently become one of the most intensively studied paradigms in wireless communications. In its broadest sense, a CR can be thought of as an enhanced smart software defined radio (SDR). The terms SDR and CRwere introduced by J. Mitola in 1992 [1] and 1999 [2], respectively. SDR, sometimes shortened to software radio, is generally a multi-band radio that supports multiple air interfaces and protocols, and is reconfigurable through software running on a digital signal processor (DSP), field-programmable gate array (FPGA), or general-purpose microprocessor [3]. CR, usually built upon an SDR platform, is a context-aware intelligent radio capable of autonomous reconfiguration by learning from and adapting to the surrounding communication environment [4]. CRs are capable of perceiving and sensing their radio frequency (RF) environment, learning about their radio resources, user equipment (UE), and application requirements, and adapting their configuration and behavior accordingly. From this definition, two main characteristics of CR can be identified: cognitive capability (ability to capture information and learn from the radio environment) and reconfigurability (which enables the transmitter parameters to be dynamically programmed and modified according to the radio environment).

An important specific application often associated with CR is dynamic spectrum access (DSA). DSA, despite being a broader concept [5–7], is commonly understood as the reutilization of licensed RF bands by unlicensed UEs provided that the legitimate licensed UEs are not using the reused frequencies at a given time or in a given region of space.

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Heterogeneous Cellular Networks
  • Online ISBN: 9781139149709
  • Book DOI: https://doi.org/10.1017/CBO9781139149709
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