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

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]

6. Motivation for Cognitive Radios Spectrum Concentration [2]

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. Overlay Cognitive Radios Time

11. Interference temperature TI is specified in Kelvin and is defined as where P I (f c, B) is the average interference power in Watts centered at f c, 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 Interference temperature model

12. Underlay Cognitive Radios Interference Temperature Model [10]

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

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]

18. Spectrum Sensing Section – II

19. Spectrum Sensing Techniques Spectrum Sensing Transmitter Detection Matched Filter Detection Energy Detection Cyclo-stationary Feature Detection Cooperative Detection Interference Based Detection

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

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

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

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

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

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

26. Interference Based Detection Interference Temperature Model [10]

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

28. Few Generalised Spectrum Sensing Challenges Multi user environment Interference temperature measurement Speed of detection etc.

29. Spectrum Allocation Section – III

30. Spectrum Allocation

31. Spectrum Analysis Channel capacity Primary user related information xG user information

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

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

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

35. Spectrum Decision Spectrum management Spectrum mobility Spectrum sharing User related info

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

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

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

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