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Www.cs.helsinki.fi Overview of Cognitive Radio Basics and Spectrum Sensing CN-S2013 Faculty of Science Department of Computer Science1 Jan.29, 2013 Suzan.

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Presentation on theme: "Www.cs.helsinki.fi Overview of Cognitive Radio Basics and Spectrum Sensing CN-S2013 Faculty of Science Department of Computer Science1 Jan.29, 2013 Suzan."— Presentation transcript:

1 Overview of Cognitive Radio Basics and Spectrum Sensing CN-S2013 Faculty of Science Department of Computer Science1 Jan.29, 2013 Suzan Bayhan

2 CN-S2013 Cognitive radio: What, why, and how Spectrum Sensing: Basics and challenges Summary of Todays Class 2 Faculty of Science Department of Computer Science

3 CN-S2013 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 Cognitive Radio: Definition and History Faculty of Science Department of Computer Science3

4 CN-S2013 Cisco Report: c html Wireless data consumption increases (from Ciscos report) 4 By 2012, the number of mobile-connected devices will exceed the world's population.

5 CN-S2013 Radio spectrum: 3kHz to 300 GHz The use of radio spectrum for communication dates back to How is the wireless spectrum is managed? Faculty of Science Department of Computer Science5 Image from /Guglielmo-Marconi-is-pictured-with-his- telegraph-equipment 1895: Guglielmo Marconi, radio signal transmission using telegraph codes over 1,25 mile distance Static Spectrum Access

6 CN-S2013 Faculty of Science Department of Computer Science6 Use of Radio Frequencies in Finland (www.ficora.fi)

7 CN-S2013 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 Shortcomings of current spectrum management Faculty of Science Department of Computer Science7

8 CN-S2013 The Finnish Communications Regulatory Authority (FICORA) International Telecommunication Union (ITU) European Telecommunications Standards Institute (ETSI) Radio Spectrum Use in Finland Faculty of Science Department of Computer Science8

9 CN-S2013 Ficora allocates spectrum in Finland Faculty of Science Department of Computer Science9 How much is this frequency? Calculate the fee for frequency! You can check from this document: You can find radio spectrum regulations in Finland here:

10 CN-S2013 Spectrum Measurements Faculty of Science Department of Computer Science10 Image from RWTH aachen.de/static-spectrum.html Image from tive_radio Measurement campaigns have shown that there is plenty of unused spectrum! Working time vs. night time usage City-center to suburb usage

11 CN-S2013 Cognitive Radio (CR) 11 Faculty of Science Department of Computer Science 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)

12 CN-S2013 Faculty of Science Department of Computer Science12 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

13 CN-S2013 Hardware: Static, once designed at the factory, never changed SDR: Reconfigurable radio (e.g. operation frequency, modulation type) Multiple standards Multiple bands Software Defined Radio (SDR) Faculty of Science Department of Computer Science13 SDR is the building block of the CR.

14 CN-S2013 How does cognitive radio work? Faculty of Science Department of Computer Science14 SPECTRUM SENSING Cognitive Cycle Image from

15 CN-S2013 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 , Ghasemi, Amir, and Elvino S. Sousa. Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Communications Magazine, 46.4 (2008): survey of spectrum sensing algorithms for cognitive radio applications,Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs Spectrum Sensing Reading Material Faculty of Science Department of Computer Science15

16 CN-S2013 What is spectrum sensing? Faculty of Science Department of Computer Science16 Time 1- Sense: There is PU 2- Sense: IDLE 3- Sense: PU PU collision: Interference or harmful interference

17 CN-S 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 (P d ) Higher is better Probability of false alarm (P f ) Lower is better Spectrum Sensing Faculty of Science Department of Computer Science17

18 CN-S2013 Various aspects of spectrum sensing Faculty of Science Department of Computer Science18

19 CN-S April 2012 Faculty of Science Department of Computer Science19 Sensing: PHY and MAC Layer Issues PHY Sensing Spectrum Sensor at PHY MAC Sensing Sensing and access strategy CR SENSING DESIGN = SENSOR + SENSING STRATEGY + ACCESS

20 CN-S2013 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 PHY Sensing Faculty of Science Department of Computer Science20

21 CN-S2013 Energy Detector: Binary Hypothesis Test Faculty of Science Department of Computer Science21 H 0 : The frequency is idle, there is no PU signal H 1 : The frequency is occupied, there is PU signal w(n): Noise, s(n): PU signal, y(n): Measured signal, N number of samples H 0 or H 1 ?

22 CN-S2013 Effect of Signal to Noise Ratio (SNR) Faculty of Science Department of Computer Science22 Decibel: 10log10(P2/P1) Generally, sensing performance increases under increasing SNR.

23 CN-S2013 Comparison of Sensing Schemes Faculty of Science Department of Computer Science23 1.Energy Detector 2.Waveform-based Sensing 3.Match Filtering 4.Radio Identification 5.Cyclostationary

24 CN-S2013 Types of Spectrum Sensing Faculty of Science Department of Computer Science24 Proactive Reactive Local Cooperative Distributed Centralized In-band Out-of- band Synchroni ous Asynchro nious SequentialParallel SPECTRUM SENSING

25 CN-S2013 Parallel Sequential Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band Sense channels 1 to N at the same time (parallel) requires N sensing device! If there are N frequency channels Sequential: Sense channels one by one. Which order? May take too long to find an empty channel. Parallel vs. Sequential Sensing

26 CN-S2013 Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band 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 vs. Reactive Sensing

27 CN-S2013 Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band 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? Cooperative vs. Non- cooperative Sensing

28 CN-S2013 Cooperative Sensing Faculty of Science Department of Computer Science28 More robust to sensing errors. Hidden node problem PU is hidden to the CR. CRs transmission will result in interference at the PU receiver. Cooperate with this user!

29 CN-S2013 Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band 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. Centralized vs. Distributed Sensing

30 CN-S2013 Centralized/Distributed Cooperative Sensing Faculty of Science Department of Computer Science 30 Decision Fusion Center Increased sensing reliability at the expense of increased communication overhead How to communicate: Common control channels (CCC)

31 CN-S2013 Decision Fusion: How to decide? Faculty of Science Department of Computer Science31 Yes, there is PU No, it is IDLE Yes No 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

32 CN-S2013 Number of Cooperating Users vs. Sensing Time 11 April 2012 Faculty of Science Department of Computer Science32 Amir Ghasemi and Elvino S. Sousa, Spectrum Sensing in Cognitive Radio Networks: Requirements,Challenges and Design Trade-offs Cooperation overhead generally increases with the number of cooperating Optimal number of cooperating users Single CR or 5 CRs

33 CN-S2013 Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band 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. Synchronous vs. Asynchronous Sensing

34 CN-S2013 Proactive Reactive Local Cooperative Centralized Distributed Synchronous Asynchron. In-band Out-of-band Parallel Sequential In-band CR senses the channel that it is already transmitting -To detect if a PU appears Out-of-band CR senses channels other than the channel it is in To find other spectrum holes To find another channel to switch since a PU has already appeared. In-band vs. Out-of-band Sensing

35 CN-S2013 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. Challenges of Spectrum Sensing Faculty of Science Department of Computer Science35

36 CN-S2013 Static spectrum access is cumbersome! CR facilitates unused spectrum to be used opportunistically. Spectrum sensing facilitates discovery of unoccupied spectrum. The spectrum sensing can be designed considering various criteria at MAC and PHY layer. The longer is the sensing duration, generally the higher is the sensing reliability. Cooperation increases sensing performance but has higher overhead. Summary 36 Faculty of Science Department of Computer Science

37 CN-S2013 References Faculty of Science Department of Computer Science37 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 , Ghasemi, Amir, and Elvino S. Sousa. Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Communications Magazine, 46.4 (2008):

38 CN-S2013 Questions? Faculty of Science Department of Computer Science38

39 CN-S2013 Self-Study: Make sure you know all the terms below 11 April 2012 Faculty of Science Department of Computer Science39 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

40 CN-S2013 Presentation Schedule Faculty of Science Department of Computer Science40

41 CN-S2013 Next week 2-Minute Madness Session: In two minutes present your topics basic idea, questions, etc! Only 2 minutes. Faculty of Science Department of Computer Science41


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