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Cognitive radio networks: realistic or not?

Published: 29 April 2013 Publication History

Abstract

A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important aspects of commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. While on the one hand it is necessary to detect the existence of spectrum holes, on the other hand, simply sensing (cooperatively or not) the energy emitted from a primary transmitter cannot enable correct dynamic spectrum access. For example, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing solutions to the DSA problem no robust, implementable scheme has emerged. The set of assumptions that these schemes are built upon do not always hold in realistic, wireless environments. Specific settings are assumed, which differ significantly from how existing telecommunication networks work. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, namely TV and cellular, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments. The proposed study extends beyond cognitive radio networks, and further highlights the often existing gap between research and commercialization, paving the way to new thinking about how to accelerate commercialization and adoption of new networking technologies and services.

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Cited By

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  • (2022)Multiscale Discrete Wavelet Transform based Efficient Energy Detection for Wideband Spectrum Sensing2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP53593.2022.9760625(1-5)Online publication date: 12-Feb-2022
  • (2021)Polarization Reconfigurable Antenna for 5G Wireless Communication Systems On 3.5 GHz Frequency2021 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)10.1109/ICE3IS54102.2021.9649680(60-64)Online publication date: 15-Oct-2021
  • (2017)Performance Comparison of Cognitive Radio Sensor Networks for Industrial IoT With Different Deployment PatternsIEEE Systems Journal10.1109/JSYST.2015.250051811:3(1456-1466)Online publication date: Sep-2017
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Published In

cover image ACM SIGCOMM Computer Communication Review
ACM SIGCOMM Computer Communication Review  Volume 43, Issue 2
April 2013
72 pages
ISSN:0146-4833
DOI:10.1145/2479957
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 April 2013
Published in SIGCOMM-CCR Volume 43, Issue 2

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Author Tags

  1. cellular networks
  2. cognitive radio networks
  3. spectrum sensing
  4. tv bands

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Cited By

View all
  • (2022)Multiscale Discrete Wavelet Transform based Efficient Energy Detection for Wideband Spectrum Sensing2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP53593.2022.9760625(1-5)Online publication date: 12-Feb-2022
  • (2021)Polarization Reconfigurable Antenna for 5G Wireless Communication Systems On 3.5 GHz Frequency2021 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)10.1109/ICE3IS54102.2021.9649680(60-64)Online publication date: 15-Oct-2021
  • (2017)Performance Comparison of Cognitive Radio Sensor Networks for Industrial IoT With Different Deployment PatternsIEEE Systems Journal10.1109/JSYST.2015.250051811:3(1456-1466)Online publication date: Sep-2017
  • (2017)Local and Low-Cost White Space Detection2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2017.292(503-516)Online publication date: Jun-2017
  • (2017)Dynamic interference-limited relay sharing in cognitive radio networks by using hierarchical modulationIET Communications10.1049/iet-com.2016.081611:12(1903-1912)Online publication date: 24-Aug-2017
  • (2017)Comprehensive Survey on Quality of Service Provisioning Approaches in Cognitive Radio Networks: Part OneInternational Journal of Wireless Information Networks10.1007/s10776-017-0352-524:4(356-388)Online publication date: 5-Apr-2017
  • (2016)Synergistic spectrum sharing in 5G HetNets: A harmonized SDN-enabled approachIEEE Communications Magazine10.1109/MCOM.2016.737842454:1(40-47)Online publication date: 1-Jan-2016
  • (2016)Distributed power control with received power constraints for time-area-spectrum licensesSignal Processing10.1016/j.sigpro.2015.09.009120:C(141-155)Online publication date: 1-Mar-2016
  • (2015)Interference-aware spectrum sensing mechanisms in cognitive radio networksComputers and Electrical Engineering10.1016/j.compeleceng.2014.10.01142:C(193-206)Online publication date: 1-Feb-2015
  • (2015)Joint channel and sink assignment for data collection in cognitive wireless sensor networksInternational Journal of Communication Systems10.1002/dac.304730:5Online publication date: 10-Sep-2015
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