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Spectrum Sensing and Allocation Techniques for Cognitive Radios

Explore cognitive radios, spectrum sensing & allocation, and the future of wireless communications. Understand the cognitive cycle, interference temperature models, and advanced spectrum analysis methods.

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Spectrum Sensing and Allocation Techniques for Cognitive Radios

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  1. Spectrum Sensing and Allocation Techniques for Cognitive Radios Farrukh Javed F-05-020/07-UET - PHD-CASE-CP-40

  2. Sequence of Presentation • Section I – Cognitive Radios • Introduction • Next generation networks • Cognitive radios • Section II – Spectrum Sensing • Transmitter detection • Cooperative detection • Interference based detection • Spectrum sensing challenges • Section III – Spectrum Allocation • Spectrum analysis • Spectrum decision • Section IV – Future of Cognitive Radios • Conclusion

  3. Cognitive Radios Section – I

  4. Motivation for Cognitive Radios Spectrum Scarcity [1]

  5. Motivation for Cognitive Radios Spectrum Utilisation [1] COGNITIVE RADIOS

  6. Motivation for Cognitive Radios Spectrum Concentration [2] COGNITIVE RADIOS

  7. Cognition • Oxford English Dictionary definition of “cognition” as “The action or faculty of knowing taken in its widest sense, including sensation, perception, conception, etc., as distinguished from feeling and volition” • Encyclopedia Encarta defines “cognition” as “To acquire knowledge by use of reasoning, intuition or perception” • Encyclopedia of computer Sciences gives a three point computational view of “cognition” as “1. Mental state and processes intervene between input stimuli and output responses 2. The mental state and processes are described by algorithms 3. The mental states and processes lend themselves to scientific investigations”

  8. Cognitive Radio • Joseph Mitola introduced the idea of Cognitive Radio in 2000 as “Situation in which wireless nodes and related networks are sufficiently computationally intelligent about radio resources and related computer to computer communication to detect the user communication needs as a function of user context and to provide the resources most required” • Simon Haykin explains the concept in six key words • Awareness • Intelligent • Learning • Adaptability • Reliability • Efficiency • An intelligent radio capable of adapting itself to best suit its surrounding radio environment

  9. Operating Principal of CR • Overlay CRs utilise the concept of spectrum holes • Underlay CRs use the concept of interference temperature

  10. Time Overlay Cognitive Radios COGNITIVE RADIOS

  11. Interference temperature model • Interference temperature TI is specified in Kelvin and is defined as where PI (fc , B) is the average interference power in Watts centered at fc, covering bandwidth B measured in Hertz. Boltzmann's constant k is 1.38 x 10-23 • Any Un-licensed transmission must not violate the interference temperature limit at the licensed receivers. Mi is a fractional value between 0 and 1, representing a multiplicative attenuation due to fading and path loss between the unlicensed transmitter and the licensed receiver. • The TL is to be decided by regulatory authority such as FCC or PTA

  12. Underlay Cognitive Radios Interference Temperature Model [10] SPECTRUM SENSING

  13. Interference Temperature Level • Interference temperature is the maximum RF interference acceptable at a receiving antenna

  14. Basic Characteristics of Cognitive Radios • Cognitive Capability • Re-configurability COGNITIVE RADIOS

  15. Cognitive Capability • Cognitive Cycle • Spectrum Sensing • Spectrum Allocation • Spectrum Analysis • Spectrum Decision Cognitive cycle [3]

  16. Re - Configurability • Operating Frequency • Modulation Scheme • Transmission Power • Communication Technology • Directivity of Transmission

  17. Next Generation Networks • Introduction • Protocol Layers and Cognitive Radio Functionalities xG Network Functionalities [3] COGNITIVE RADIOS

  18. Spectrum Sensing Section – II

  19. Spectrum Sensing Techniques SPECTRUM SENSING

  20. Transmitter Detection • Introduction • Techniques • Matched Filter Detection • Energy Detection • Cyclo – Stationary Feature Detection SPECTRUM SENSING

  21. Matched Filter Detection • Introduction • Opportunities • Commonly Used • High Processing Gain • Challenges • Matched Filter Bound • A priori knowledge of transmission is required Transmitter Detection SPECTRUM SENSING

  22. Energy Detection • Introduction • Opportunities • Easy implementation • Multi path and fading channel studies carried out • Challenges • Critical selection of threshold • Susceptible to noise power variations • Communication type identification not possible • Reduced flexibility Transmitter Detection SPECTRUM SENSING

  23. Cyclo – Stationary Feature Detection • Introduction • Opportunities • Robust against un-certain noise powers • Transmitter information is not required • Neural network application has been found very feasible • Challenges • Computationally complex • Transmission type identification is not possible • Reduced flexibility Transmitter Detection SPECTRUM SENSING

  24. Transmitter Detection Un – Certainties • Receiver Un-certainty • Shadowing Un-certainty Transmitter Detection (a) Receiver Uncertainty (b) Shadowing Uncertainty [3] SPECTRUM SENSING

  25. Cooperative Detection • Introduction • Centralised Detection • Distributed Detection • Cooperative Detection Opportunities • No receiver or shadowing un-certainties • Effects of degrading factors mitigated • Primary User’ interference reduced • Cooperative Detection Challenges • Implementation Complexity • Constrained Resources • Primary user un-certainty un-resolved SPECTRUM SENSING

  26. Interference Based Detection Interference Temperature Model [10] SPECTRUM SENSING

  27. Opportunities and Challenges of Interference Based Detection • Opportunities • Focus on primary receiver rather than primary transmitter • Frequency parameters of choice can be utilised • Challenge • Receiver temperature detection • Due to interference power constraints, the underlay techniques can only be employed for short range communications SPECTRUM SENSING

  28. Few GeneralisedSpectrum Sensing Challenges • Multi user environment • Interference temperature measurement • Speed of detection etc. SPECTRUM SENSING

  29. Spectrum Allocation Section – III

  30. Spectrum Allocation SPECTRUM ALLOCATION

  31. Spectrum Analysis • Channel capacity • Primary user related information • xG user information SPECTRUM ALLOCATION

  32. Channel Capacity • Path Loss • Wireless Link Layer • Link Layer Delay • Noise Info Spectrum Analysis

  33. User Related Information(Primary and xG Users) • Interference • Holding Time • User Transmission Parameters Spectrum Analysis

  34. Spectrum Analysis Challenges and Opportunities • Challenges • Heterogeneous Spectrum Sensing • Non Cooperative Primary and xG users • Varying Transmission Parameters • Real Time Analysis • Delays in Processing • Opportunities Spectrum Analysis

  35. Spectrum Decision • Spectrum management • Spectrum mobility • Spectrum sharing • User related info SPECTRUM ALLOCATION

  36. Spectrum Management • Decision Model • Multiple Spectrum decision • Reduced Transmission Power • Cooperation with reconfiguration • Heterogeneous Spectrum SPECTRUM ALLOCATION

  37. Spectrum Mobility • Introduction • Challenges • Latency • Suitable Algorithm • Appearance of a Primary User • Vertical and Inter-Cell Handoff Scheme • Suitable Threshold for Handoff • Spectrum Mobility in Time Domain • Spectrum Mobility in Space • Opportunities • Prioritised White Space • Soft and Hard Handoff SPECTRUM ALLOCATION

  38. Spectrum Sharing • Architecture Based Classification • Centralised or Distributed • Challenges and Opportunities • Access Behaviour Classification • Cooperative and Non-cooperative Sharing • Challenges and Opportunities • Access Technology Classification • Overlay and Underlay Techniques • Challenges and Opportunities • Generalised Spectrum Sharing Challenges • Common control Channel • Dynamic radio range • Spectrum Unit SPECTRUM ALLOCATION

  39. Future of Cognitive Radios Section IV

  40. Cognitive Radio Advantages • All of the benefits of software defined radio • Improved link performance • Adapt away from bad channels • Increase data rate on good channels • Improved spectrum utilization • Fill in unused spectrum • Move away from over occupied spectrum • New business propositions • High speed internet in rural areas • High data rate application networks (e.g., Video-conferencing) • Significant interest from FCC, DoD • Possible use in TV band refarming

  41. Cognitive Radio Drawbacks • All the software radio drawbacks • Significant research to realize • Information collection and modeling • Decision processes • Learning processes • Hardware support • Regulatory concerns • Loss of control • Fear of undesirable adaptations • Need some way to ensure that adaptations yield desirable networks

  42. How can CR improve spectrum utilization? • Allocate the frequency usage in a network • Assist secondary markets with frequency use, implemented by mutual agreements • Negotiate frequency use between users • Provide automated frequency coordination • Enable unlicensed users when spectrum not in use • Overcome incompatibilities among existing communication services

  43. Potential Applications of CR • Leased networks • Military usage • Emergency situations • Mesh networks • Licensed user may enhance its performance • Improving UWB transmission by avoiding NBI

  44. Jeffery H Reed and Wills G Worcester

  45. Conclusion Spectrum Sensing and Allocation Techniques for Cognitive Radios

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