Overview of Cognitive Radio Basics and Spectrum Sensing CN-S2013

1. Overview of Cognitive Radio Basics and Spectrum SensingCN-S2013 Jan.29, 2013 Suzan Bayhan Faculty of Science Department of Computer Science

2. Summary of Today’s Class • Cognitive radio: What, why, and how • SpectrumSensing: Basics and challenges Faculty of Science Department of Computer Science

3. Cognitive Radio: Definition and History • Joseph Mitola III and Gerald Q. Maguire, Jr. (KTH, Sweden), Aug.1999 IEEE Personal Communications, Cognitive Radio: Making Software Radios More Personal • Simon Haykin, Feb. 2005, IEEE Journal on Selected Areas in Communications, Cognitive Radio: Brain-Empowered Wireless Communications • “an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: (1) highly reliable communication whenever and wherever needed; (2) efficient utilization of the radio spectrum” Faculty of Science Department of Computer Science

4. Wireless data consumption increases (from Cisco’s report) By 2012, the number of mobile-connected devices will exceed the world's population. • Cisco Report: http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.html

5. How is the wireless spectrum is managed? • Radio spectrum: 3kHz to 300 GHz • The use of radio spectrum for communication dates back to • 1895: Guglielmo Marconi, radio signal transmission using telegraph codes over 1,25 mile distance • Static Spectrum Access Image from http://kids.britannica.com/elementary/art-87886/Guglielmo-Marconi-is-pictured-with-his-telegraph-equipment Faculty of Science Department of Computer Science

6. Use of Radio Frequencies in Finland (www.ficora.fi) Faculty of Science Department of Computer Science

7. Shortcomings of current spectrum management • License for a large region, usually country-wide • Large chunk of licensed spectrum (expensive licenses) • Barriers to new ideas • Prohibited spectrum access by unlicensed users • ISM bands are unlicensed  WLAN bands at 2.4 GHz, 5 GHz • Temporary short range licenses Faculty of Science Department of Computer Science

8. Radio Spectrum Use in Finland • The Finnish Communications Regulatory Authority (FICORA) • International Telecommunication Union (ITU) • European Telecommunications Standards Institute (ETSI) Faculty of Science Department of Computer Science

9. Ficora allocates spectrum in Finland • How much is this frequency? Calculate the fee for frequency! • http://www.ficora.fi/en/index/luvat/taajuusmaksut/laskentakaavatjakertoimet.html • You can check from this document: • http://www.ficora.fi/attachments/englantiav/673vb43bJ/TJTen_20042012.pdf • You can find radio spectrum regulations in Finland here: • http://www.ficora.fi/en/index/palvelut/palvelutaiheittain/radiotaajuudet.html Faculty of Science Department of Computer Science

10. Spectrum Measurements Image from http://www.cmpe.boun.edu.tr/WiCo/doku.php?id=research#cognitive_radio • Measurement campaigns have shown that there is plenty of unused spectrum! • Working time vs. night time usage • City-center to suburb usage Image from RWTH http://www.inets.rwth-aachen.de/static-spectrum.html Faculty of Science Department of Computer Science

11. Cognitive Radio (CR) • There is a huge demand for spectrum, but there is unused spectrum  Radio spectrum is inefficiently used. • Change in ownership; a resource is owned by the one who uses it. Sharing for sustainability. • Static spectrum management since 1900s. • Imagine a world with no-lane-changing. • Smarter schemes: Dynamic spectrum access (DSA) Faculty of Science Department of Computer Science

12. Primary User, Secondary User • Licensed, primary, incumbent, higher-priority user: PU • Secondary, cognitive, unlicensed user: SU, CR • Spectrum hole, white space, white spectrum, idle frequency/channel/band Faculty of Science Department of Computer Science

13. Software Defined Radio (SDR) • Hardware: Static, once designed at the factory, never changed • SDR: Reconfigurable radio (e.g. operation frequency, modulation type) • Multiple standards • Multiple bands SDR is the building block of the CR. Faculty of Science Department of Computer Science

14. How does cognitive radio work? • Cognitive Cycle SPECTRUM SENSING Image from http://pgcoaching.nl Faculty of Science Department of Computer Science

15. Spectrum Sensing Reading Material • Reading Material: • - T. Yucek and H. Arslan A survey of spectrum sensing algorithms for cognitive radio applications, IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 116-130, 2009. - Ghasemi, Amir, and Elvino S. Sousa. Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Communications Magazine, 46.4 (2008): 32-39. Faculty of Science Department of Computer Science

16. What is spectrum sensing? Time PU collision: Interference or harmful interference Time 1- Sense: There is PU 2- Sense: IDLE 3- Sense: PU Faculty of Science Department of Computer Science

17. Spectrum Sensing • 1- Sense for vacating the band if PU arrives. CR must not harm PUs • 2- Sense for finding unused spectrum • How to measure quality of sensing? • Probability of detection (Pd)  Higher is better • Probability of false alarm (Pf)  Lower is better Faculty of Science Department of Computer Science

18. Various aspects of spectrum sensing Faculty of Science Department of Computer Science

19. Sensing: PHY and MAC Layer Issues MAC Sensing Sensing and access strategy PHY Sensing Spectrum Sensor at PHY CR SENSING DESIGN = SENSOR + SENSING STRATEGY + ACCESS Faculty of Science Department of Computer Science

20. PHY Sensing • Energy Detector: Measures the energy received on a primary band during an observation interval and declares a white space if the measured energy is less than a properly set threshold. (2) Do not differentiate PU and CR signals (3) Low complexity • Waveform-based Sensing: (1) Preambles, midambles can be used to detect PU signals. (2) Short measurement time; Susceptible to synchronization errors • Match Filtering MF: (1) If transmitted signal is known, test using filters. (2) Dedicated circuitry for each primary licensee • Radio Identification: Identifying the transmission technologies used by PUs, channel bandwidth, coverage etc. • Cyclostationary: PU signal differentiated from noise Faculty of Science Department of Computer Science

21. Energy Detector:Binary Hypothesis Test • H0: The frequency is idle, there is no PU signal • H1: The frequency is occupied, there is PU signal • w(n): Noise, s(n): PU signal, y(n): Measured signal, N number of samples H0 or H1? Faculty of Science Department of Computer Science

22. Effect of Signal to Noise Ratio (SNR) Decibel: 10log10(P2/P1) Generally, sensing performance increases under increasing SNR. Faculty of Science Department of Computer Science

23. Comparison of Sensing Schemes Energy Detector Waveform-based Sensing Match Filtering Radio Identification Cyclostationary Faculty of Science Department of Computer Science

24. Types of Spectrum Sensing Parallel Sequential Synchronious Proactive Asynchronious Reactive SPECTRUM SENSING Out-of-band Local Cooperative In-band Distributed Centralized Faculty of Science Department of Computer Science

25. Parallel vs. Sequential Sensing Parallel Ifthereare N frequencychannels Sequential Proactive Reactive Sense channels 1 to N at the same time (parallel) requires N sensing device! Local Cooperative Centralized Distributed Sequential: Sense channels one by one. Which order? May take too long to find an empty channel. Synchronous Asynchron. In-band Out-of-band

26. Proactive vs. Reactive Sensing Parallel Sequential • Proactive Sensing: • CR senses even if it will not transmit immediately, e.g. periodic sensing. • Trade-off • collected information about the channels vs. sensing cost • Reactive Sensing: • CR senses only if it will transmit or receive • Energy-efficient, time to find an idle channel may be longer than Proactive Sensing. Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band

27. Cooperative vs. Non-cooperative Sensing Parallel Sequential • Local Sensing: • Each CR senses itself and uses its sensing data to give a decision on channel state, i.e. idle or busy • What if hidden node or bad channel conditions? • Cooperative Sensing: • CR shares its sensing data with others and utilize the sensing outcomes of others to give a decision • Robust to sensing errors due to hidden node or fading channels. • Cost of cooperation? Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band

28. Cooperative Sensing • More robust to sensing errors. • Hidden node problem Cooperate with this user! PU is hidden to the CR. CR’s transmission will result in interference at the PU receiver. Faculty of Science Department of Computer Science

29. Centralized vs. Distributed Sensing Parallel Sequential • Centralized • A Central Manager (BS or AP) collects CR sensing data and makes a decision on channel state, i.e. idle or busy • Cost of transmission sensing data? • What if the Central Manager fails? Single Point of Failure. • Distributed (Decentralized) • Each CR makes decision itself. Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band

30. Centralized/Distributed Cooperative Sensing Decision Fusion Center Increased sensing reliability at the expense of increased communication overhead How to communicate: Common control channels (CCC) Faculty of Science Department of Computer Science

31. Decision Fusion: How to decide? Yes, there is PU No, it is IDLE No Yes Yes • How to decide? (DECISION FUSION LOGIC) • AND • OR • MAJORITY • K-of-N • Soft or Hard Decision Combining: Yes or No answers (0-1), or Received Signal Strength Faculty of Science Department of Computer Science

32. Number of Cooperating Users vs. Sensing Time • Cooperation overhead generally increases with the number of cooperating • Optimal number of cooperating users Single CR or 5 CRs Amir Ghasemi and Elvino S. Sousa, Spectrum Sensing in Cognitive Radio Networks: Requirements,Challenges and Design Trade-offs Faculty of Science Department of Computer Science

33. Synchronous vs. Asynchronous Sensing Parallel Sequential • Synchronous • All CRs have the same sensing schedule to sense a channel. • How to synchronize? • Stop transmission and sense the medium. • Asynchronous • Each CR has its own schedule to sense a channel. • If other CRs are transmitting while this CR is sensing, how to distinguish between SU and PU signal. Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band

34. In-band vs. Out-of-band Sensing Parallel Sequential • In-band • CR sensesthechannelthat it is alreadytransmitting • Todetectif a PU appears • Out-of-band • CR senseschannelsotherthanthechannel it is in • Tofindotherspectrumholes • Tofindanotherchanneltoswitch since a PU has alreadyappeared. Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band

35. Challenges of Spectrum Sensing • Hardware requirements: • High speed processing units (DSPs or FPGAs) performing computationally demanding signal processing tasks with relatively low delay. • Operation in a wide spectrum range • Sensing-Transmission Tradeoff • Security: a selfish or malicious user can modify its air interface to mimic a primary user. Faculty of Science Department of Computer Science

36. Summary • Staticspectrumaccess is cumbersome! • CR facilitatesunusedspectrum to beusedopportunistically. • Spectrumsensingfacilitatesdiscovery of unoccupiedspectrum. • The spectrumsensingcanbedesignedconsideringvariouscriteria at MAC and PHY layer. • The longer is the sensingduration, generally the higher is the sensingreliability. • Cooperationincreasessensingperformancebuthashigheroverhead. Faculty of Science Department of Computer Science

37. References • T. Yucek and H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications, IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 116-130, 2009. • Ghasemi, Amir, and Elvino S. Sousa. Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Communications Magazine, 46.4 (2008): 32-39. Faculty of Science Department of Computer Science

38. Questions? Faculty of Science Department of Computer Science

39. Self-Study: Make sure you know all the terms below • Primary User • Secondary User • Cognitive Radio • Spectrum Hole • Spectrum Sensing • Harmful Interference • SNR • Cooperative Sensing • Dynamic Spectrum Access • Static Spectrum Access • Spectrum Underutilization • Sensing-transmission trade-off • Decision fusion logic Faculty of Science Department of Computer Science

40. Presentation Schedule Faculty of Science Department of Computer Science

41. Next week • 2-Minute Madness Session: In two minutes present your topic’s basic idea, questions, etc! Only 2 minutes. Faculty of Science Department of Computer Science