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Spectrum Awareness in Cognitive Radio Systems based on Spectrum Sensing

Spectrum Awareness in Cognitive Radio Systems based on Spectrum Sensing. Miguel López-Benítez Department of Electrical Engineering and Electronics University of Liverpool, United Kingdom. Workshop on Wireless Networks University of Liverpool, U nited Kingdom, 25 June 2014. Introduction.

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Spectrum Awareness in Cognitive Radio Systems based on Spectrum Sensing

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  1. Spectrum Awareness in Cognitive Radio Systems based on Spectrum Sensing Miguel López-Benítez Department of Electrical Engineering and Electronics University of Liverpool, United Kingdom Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014

  2. Introduction Dynamic Spectrum Access (DSA) / Cognitive Radio (CR) Opportunistic spectrum access paradigm Behaviour and performance depends on primary spectrum activity Knowledge of spectrum activity statistics  spectrum decisions Prediction of future spectrum occupancy patterns Selection of channel / band of operation Spectrum / radio resource management decisions Obtaining spectrum information (research topics): 1) Spectrum sensing algorithms 2) Estimation of spectrum activity statistics 3) Modelling of spectrum occupancy patterns Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 2

  3. Spectrum awareness Beacon signals Perfect information Requires agreement primary-secondary Changes in legacy radio systems: economical / technical problems Databases  Perfect / accurate information  Relies on an external system (technical, administrative & legal problems)  Need for geolocation in DSA/CR terminals (cost, location accuracy, etc.)  Database updating rate  not suitable for dynamic bands Spectrum sensing  Does not rely on an external system  No changes needed to legacy (primary) system – simple and inexpensive  Suitable for dynamic spectrum bands  Inaccurate information (spectrum sensing errors) Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 3

  4. Spectrum sensing algorithms Wide range of spectrum sensing algorithms Trade-offs: detection performance, complexity, computational cost, applicability. Applicability depends on available information: Detailed knowledge  Matched filter Certain features  Feature detector (ciclostationarity, pilots, others…) Correlated signal (oversampling, multiple antennas)  Covariance detector No prior information  Energy detector Ideal sensor: Simple (low complexity and low computational cost) General applicability (ability to detect any signal format) High detection performance (high detection prob., low false alarm prob.) Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 4

  5. Spectrum sensing algorithms Common research trend: Maximise detection performance, At the expense of higher complexity / computational cost, limited applicability. Alternative approach: Improve detection performance. Without sacrificing complexity / computational cost and applicability. Why? (motivation): Meeting detection performance requirements by one single terminal may be unfeasible. Cooperative/collaborative sensing and network-aided approaches relax requirements. Even if feasible, too complex and expensive. Inexpensive terminals are key to the success of a new radio/mobile technology. How? (approach): Variations of the energy detection principle: simple, low complexity, applicability Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 5

  6. Spectrum sensing algorithms Example: Improved Energy Detection (IED) algorithm [*] Combination of spectrum sensing events based on energy detection. [*] M. López-Benítez, F. Casadevall, “Improved energy detection spectrum sensing for cognitive radio,” IET Communications, Special Issue on Cognitive Communications, vol. 6, no. 8, pp. 785-796, May 2012. Better performance Same/similar complexity Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 6

  7. Estimation of activity statistics Relevant for spectrum and radio resource decision-making processes Spectrum activity statistics can be estimated from sensing observations: Sensing observations  infer period durations  compute activity statistics Minimum period duration, mean/variance, underlying distribution, etc. Practical limitations (e.g., spectrum sensing is imperfect): Perfect Spectrum Sensing (PSS) (e.g., high SNR) Imperfect Spectrum Sensing (ISS) (e.g., low SNR) Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 7

  8. Estimation of activity statistics Relevant aspects: Activity statistics: Duration of idle/busy periods (minimum, average, variance, distribution, etc.) Other more sophisticated metrics (channel load/duty cycle, etc.) Practical limitations: Imperfect sensing performance Finite sensing period Limited number of observations Aspects to be analysed: What are the activity statistics actually estimated by DSA/CR terminals under real conditions? What is the difference (error) between the estimated and the real spectrum activity statistics? What can be done to minimise the estimation error? Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 8

  9. Modelling of spectrum occupancy Spectrum activity statistics can be used to parametrise spectrum occupancy models Application of spectrum occupancy models: Analytical studies Simulation tools Design/dimensioning of DSA/CR networks Design of new DSA/CR techniques Spectrum measurements: Models based on real spectrum data  Realism and accuracy Challenge: harmonisation of methodology (equipment, field measurements, data post-processing, etc.) Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 9

  10. Modelling of spectrum occupancy Relevant parameters to be modelled: Time-dimension parameters: Channel load (duty cycle) Period durations (minimum, average, variance, distribution) Correlation properties of period durations Frequency-dimension parameters: Statistical distribution of duty cycle Clustering of duty cycle Number of free channels at any time Space-dimension parameters: Perceived spectrum occupancy level Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 10

  11. Conclusions Opportunistic nature of DSA/CR systems Knowledge on spectrum occupancy is useful Spectrum awareness: Beacon signals Data bases Spectrum sensing Spectrum awareness based on spectrum sensing: 1) Spectrum sensing algorithms 2) Estimation of channel activity statistics 3) Modelling of spectrum occupancy Workshop on Wireless Networks University of Liverpool, United Kingdom, 25 June 2014 Slide 11

  12. for your attention ! Email: M.Lopez-Benitez@liverpool.ac.uk Website: www.lopezbenitez.es

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