Cognitive Radio - An Introduction R. David Koilpillai Department of Electrical Engineering Indian Institute of Technolo

1. Cognitive Radio - An IntroductionR. David KoilpillaiDepartment of Electrical EngineeringIndian Institute of Technology Madras IISc-DRDO Workshop on Cognitive Radio Bangalore – March 14, 2009

2. MIMO- Wave2 Evolution of Wireless … Rel. 7 Rel. 6 LTE-Adv GSM GPRS Rel. 5 (HSDPA) WCDMA LTE 1xEV-DV UMB cdma2000 cdmaOne 1xEV-DO IEEE 802.16 d/e IEEE 802.16 m Focus is on spectral efficiency – bits / sec / Hz

3. Radio Functionality Evolution Source: Prasad et al. IEEE Comm Magazine, April 2008

4. Software Defined Radio (SDR) • J. Mitola, “The software radio architecture” IEEE Communications Magazine, May 1995

5. Vanu SDR Architecture • Commercial product • Multistandard • GSM / GPRS / EDGE • Cdma / EV-DO • Flexibility • Scaleability • Cost-effectiveness Ref: www.vanu.com

6. Vanu SDR Architecture Ref: www.vanu.com

7. SDR Summary • Many technical challenges have been solved • SDR – now commercially viable and attractive • Drivers for SDR • Advances in processors, DSPs, FPGAs, … • High speed, high-resolution A/D, … • Multi-standard support, MIMO capability, … • Efficient software tools and structures • SDR: A flexible platform • New technology development • Technology migration • Focus on basestations and not user equipment • Numerous national and international initiatives • Multiple SDR test beds • Open-source material available • SDR Forum – an active group • The next step in SDR  Migration towards Cognitive Radio …

8. SDR  Cognitive Radio

9. Cognitive Radio (CR) Motivation for CR • Increasing demand for radio spectrum • Broadband wireless demand is rapidly growing • Current approach to spectrum allocation • Fixed allocation to licensed users • Existing scenario • Under-utilization of spectrum • Spatial and temporal “spectral holes” exist • Innovative approach to improve spectrum utilization • Cognitive Radio • Initiated by FCC – regarding secondary usage of spectrum

10. Utilization of Spectrum • Frequency range • 30 MHz – 2.9 GHz • Based on report by M.A. McHenry • Max. utilization ~ 25% • TV channels • Average usage ~ 5.2 % • New York City average ~ 13.1% • Significant # white spaces • Even in cellular bands Ref: M.A.McHenry, “NSF Spectrum Occupancy Measurements Project Summary,” August 2005 Ghasemi and Sousa, IEEE Communications Magazine, April 2008

11. CR Approach • Main steps in CR approach • Identify spectral bands not used by Primary User • Signal sensing (to detect Primary User’s signal) • Estimation of “Interference Temperature” • Localised around user • Spectral hole • A spectral band assigned to primary user • Currently unused at geographical location • Should be done reliably • Should be able to detect “low” level Primary User signals • Utilize spectrum as “Secondary User” • Increasing utilisation of radio spectrum • Without causing interference to Primary User • Primary user always has priority

12. Today’s CR Scenario • CR: Opportunistic Unlicensed Access • To temporarily unused frequency bands (across the entire licensed radio spectrum) • A means to increase efficiency of spectrum usage • Stringent safeguards required • On-going licensed operations should not be compromised • Spectrum sensing based access • Unlicensed user transmits if licensed band is sensed to be free • Main functionality of Cognitive Radios • Ability to identify unused frequency bands • Sensing must be reliable and autonomous • Conclusion • A perceived spectrum scarcity - due to inefficient, fixed spectrum allocation • Consider radically different paradigm • Secondary (unlicensed) users • Opportunistic use of unused primary (licensed) band(s)

13. IEEE 802.22 • Project started by IEEE in Nov 2004 • Charter: To develop a CR-based WRAN • PHY and MAC specifications • Transmission in unused TV and guard bands (54 MHz – 862 MHz) • Very favourable propagation characteristics • Channel BW 6 MHz (may be 7 MHz / 8 MHz in some countries) • Spectrum sensing for identifying white spaces • Distributed sensing • FCC maintained server – info about unused channels (by geographical location • Localised sensing • CPE’s perform periodic measurements and send measurements to BTS • BTS makes decision to use the current channel or any other alternatives • Application scenarios • Wireless broadband in rural / remote areas • Performance comparable to today’s DSL technology • Unlicensed devices  lower cost and increased affordability • Attractive for Wireless Internet Service Providers (WISP) • TV migration : moving from broadcast to cable and satellite •  Broadcast TV channels available

14. Comparison of Networks • WRAN Aspects • Large coverage footprint • Up to 100 Km • Larger cells than cellular • Leverage two factors • Higher EIRP • Attractive propgn characteristics • Ideal for rural /remote services • Broadband wireless access • Unlicensed devices Ref: Cordeiro et al., “IEEE 802.22: The First Worldwide Wireless Standard based on Cognitive Radio,” IEEE, 2005

15. IEEE 802.22 Specifications • Target specifications • Spectral efficiency – 0.5 b/s/Hz – 5 b/s/Hz • Average: 3 b/s/Hz  18 Mbps in 6 MHz • Assuming 12 simultaneous users – 1.5 Mbps (DL) and 384 Kbps (UL) • Range: 33 Km (extend to 100 Km) • CPE Tx power 4W EIRP @ CPE • Air interface • Requirements – Flexibility and quick adaptibility • Link adaptation based on SINR • Adapt modulation and Coding option • Frequency agility • OFDM(A) based UL and DL • Transmit Power Control : 30 dB withsteps of  1 dB • Channel Bonding – Utilizing more than one TV channel • System can use larger BW to support higher throughput

16. IEEE 802.22 MAC • Medium Access Control (MAC) • Design tailored for Cognitive Radio Technology • Key aspect – adaptability based on dynamic changes in environment • Spectrum sensing measurements • Two structures • Frame and Superframe • Superframe will have Superframe Control Header (SCH) and preamble • SCH sent by BS in every channel that is “available” • Two types of spectrum measurements • In-band measurements – in channel currently being used • Out-of-band measurements – Other channels • Two types of sensing • Fast sensing - < 1 msec per channel • Performed by CPE and BS - For quick information gathering • Fine sensing – up to 25 msec per channel • Verification / validation of measurements • Deal with large propagation delay (roundtrip delay up to 300 microsec) • MAC deals with a number of issues not addressed in traditional systems

17. Cognitive Radio = Sense + Learn + Adapt + Use

18. Spectrum Sensing

19. Methods of Spectrum Sensing • Energy Detector • Correlation-based detector • Cyclostationarity-based detector • Hybrid Detector • Performance of spectrum sensing • Sensing Criteria (Regulatory aspects) • Sensing Period • Detection Sensitivity

20. Spectrum Sensing • Optimum receiver • If structure of primary signal known • Optimum (in AWGN): Matched Filter (MF) followed by Threshold • Can be implemented for a few specific primary signals (selected bands) • Not practical for large # of primary users • Need for coherent detector for each transmitted signal • Alternative – Energy Detector • Measures energy of signal in primary band • Compare with properly set threshold • Declare presence of “white spaces”  primary user absent • Requires longer sensing time to achieve desired level of performanc e • Low computational complexity • Ease of implementation • ED - An attractive candidate for Cognitive Radio • Drawbacks of ED • Cannot discriminate between sources of input energy (signal vs. noise) • Uncertainty of noise floor will degrade performance • Especially at low SNR • ED can be effectively combined with more robust detectors – “Hybrid Detectors”

21. Spectral Sensing • Binary hypothesis testing problem • Decision statistic (Energy detector) • When signal absent, Δ is Central Chi-Square Variable with N degrees of freedom • When signal present, non-Central Chi-Square Variable

22. Energy Detector • Decision statistic • If N large, invoke CLT

23. Spectral Sensing Performance (1) • Performance of Energy Detector is validated against analytical performance • In AWGN, ED achieves good performance at very low SNRs ~ -8 dB • Achieves low probability of false alarm • Evaluated for frequency selective fading channels also

24. Spectral Sensing Performance (2) AWGN, Effect of sensing Period Performance in fading • Robustness of energy detector enhanced if longer sensing period is used • Performance in fading is poorer than in AWGN (as expected) • Noise uncertainty causes major degradation in performance • Energy detector not suited as a stand-alone detector

25. Spectrum Sensing Summary • Many methods available • Properties utilised: Energy, Correlation, Cyclostationarity • Computational complexity and estimation time are important factors • Searching over a vast frequency range • Focus on robustness (at low SNR) and reliability • Minimize probability of missed detection • To avoid interference to primary user • Uncertainties regarding measurement • Noise and interference environment • Strong motivation for Hybrid Detectors • Sensing Criteria (Regulatory aspects) • Sensing Period • Detection Sensitivity

26. Regulatory Constraints • Satisfactory protection of primary user from harmful interference • Essential for realization of opportunistic spectrum access • Regulatory constraints • Sensing Periodicity (Tp) • Period with which UL user must check for presence of primary user • Detection Sensitivity • Signal level at which the UL user must detect primary user reliably • Sensing Period (Tp) • Max. time (delay) UL user unaware of reappearance of primary user • Max. duration of harmful interference • Determines QoS degradation of primary user • Delay of primary user in accessing channel • Depends on type of primary user service – delay sensitivity • Must be set by regulator for each licensed band

27. Detection Sensitivity • Threshold to be satisfied even if PU Rx is at edge of coverage • Provided SU maintains distance D •  SU (CR) must be able to detect PU at distance (R+D) • Detection Sensitivity Ref: Ghasemi et al., IEEE Communications Mag, April 2008

28. Uncertainties in Sensing Channel Uncertainty • Due to fading / shadowing of PU signal Noise Uncertainty Aggregate Interference Uncertainty • PU may experience harmful interference • If multiple CR networks active • Requires more sensitive detectors • Detect PU at distance • Alternative – system level coordination among CR devices •  Cooperative sensing Ref: Ghasemi et al., IEEE Communications Mag, April 2008

29. Cooperative Sensing • Sensing of primary user difficult with multipath fading and shadowing • Significant fluctuation of signal level (worst case is very severe) • Need to maintain sensing performance •  CR requires higher detection sensitivity (lower ) • Requirement becomes very stringent • To alleviate the problem …  Cooperative Sensing • Independent measurements at different locations / CRs • Exchange of sensing information among CR nodes • Diversity gain achieved (with respect to fading and shadowing) • Improved probability of detecting PU • Without increasing sensitivity of each individual SU Rx • Introduces additional communications overhead • Requires functionality of “Band Manager” (Fusion Centre) • Collects information, makes decisions and shares information with all CR nodes • Shadowing is correlated over short distances •  Cooperation to be done over larger distances (few nodes) • Different from conventional view of Mesh / Ad Hoc networks (many nodes in close proximity)

30. Cooperative Sensing • Decision making options • Hard decision based • Soft decision based Hard Decision • Each SU makes indep decision • Reg presence of PU • One-bit decision • Band Manager gathers information • Shares decision with all CR nodes • Rule: If one of the SUs senses PU signal  Primary User present • ROC – Receiver Operating Characteristic to evaluate performance • Observation • HD based decision making – not beneficial if SU SNRs are vastly different

31. Multicarrier Techniques in CR

32. Code Frequency Time Multicarrier Techniques • Multicarrier techniques widely used in Cognitive Radio (PHY) • OFDM, Filterbank-based multicarrier, Multi-resolution filter banks • Spectrum sensing – determine spectral holes • Spectrum usage – communication • Transmit data w/o interfering with Primary user • In non-overlapping parts of spectrum • Multicarrier techniques – efficient and effective • To maximize efficiency • Sidelobes (frequency response) of the subcarriers must be minimized • CR transmission can be TDD or FDD • TDD has inherent advantages for CR • Tx and Rx in in same band  knowledge of channel • Implicit sensing of channel during Rx period (Tx OFF) • 802.22 WRAN standard focus on TDD • OFDM based

33. Multicarrier Techniques • OFDM • Widely studied and well-understood (based on IFFT / FFT) • Used for spectral sensing • Underlying filter is the Rectangular window • Poor side-lobe suppression • Significant interference between sub-carriers • Not suitable for spectral sensing / transmission (non-contiguous bands) • Acceptable for contiguous bands • Approaches to consider • Muti-Taper Method (MTM) for spectral estimation • Filterbank Multi-Carrier • Filterbank-based approaches can overcome spectral leakage problems • Less used than OFDM

34. Spectral Adaptation Waveforms T I M E Frequency OFDM Carriers in Available Spectrum Ref: B. Fette, “SDR Technology Implementation for the Cognitive Radio,” General Dynamics

35. Performance of FFT • Raised cosine filtering before FFT • Reduces side-lobes • Improved freq selectivity • At expense of lower time selectivity • Frequency response of “FFT filter” • Filtering at Rx end also possible • Similar tradeoff as at Tx Ref: Boroujeny et al., IEEE Communications Mag, April 2008

36. Multicarrier Techniques • Multitaper Method (MTM) • Advanced, non-parametric spectral estimation method • A set of filters (Slepian 1978, Bell Labs) • Discrete Prolate Spheroidal Sequences • Optimal trade-off between time selectivity and frequency selectivity • Combine the output of a family of filters • Near-optimal performance in spectral sensing (Haykin, 2005) • Example: A set of 5 DPSS based filters and their responses • Filterbank Method • Similar performance to MTM • Can be used for sensing and for transmission • Lower computational complexity than MTM • A rich area for further investigation for CR

37. Performance of Filterbank Ref: Boroujeny et al., IEEE Communications Mag, April 2008 • MTM – five filters of length 2048 • Three filters with attenuation more than -60 dB • Filterbank Multicarrier – Length 6x256=1536, 256-channel filterbank • Achieves comparable performance to MTM

38. UWB-based CR

39. UWB Overview • Cognitive network – an interconnection set of CR devices • Aware of radio channel characteristics • Interference temperature, spectrum availability, policies, … • Devices sharing of information to facilitate CR functions • Suitable wireless technology  facilitate collaboration between CR nodes • Ultra Wideband (UWB) • Bandwidth (BW) > 500 MHz or • Fractional BW • FCC permits unlicensed use of UWB (2002) • Proposed methods for UWB • OFDM-based UWB (UWB) – (OFDM-UWB) • Impulse radio based UWB (IR-UWB)

40. UWB Overview • UWB – an underlay system • Co-exist with other licensed (primary) / UL users • In same temporal, spatial, and spectral domain • Signal embedded in noise floor  secure transmission • UWB has multidimensional flexibility • Pulse shape, bandwidth (BW), data rate, power • UWB has inherent potential to meet CR requirements • IR-UWB – multiple attractive features • High multipath resolution • Ranging and positioning • UWB – unlicensed operation in 3.1-10.6 GHz • Tx power limit < -42 dBm/MHz • Ensures that UWB does not affect licensed operations

41. UWB-based CN • An interesting possibility … • UWB as a complement to other CR technologies • For sharing information via UWB • Locating other users • Information exchange in CN • CR nodes must have common understanding of spectrum to be used •  Sharing of sensing information • Possible options • Common control channel for CR nodes to share information • A centralized controller that gathers info and decides spectrum availability • Allocates distinct bands to each CR user • Alternative: Low-power UWB signaling to share information • Leverage all the advantages of UWB • Low-throughput needed • Low-complexity (OOK, with non-coherent detection) • Issue: range of UWB

42. Cognitive Networks • Network of nodes with CR functionality • Cognitive networks is attractive for Dynamic Spectrum Access • Sharing via UWB is attractive • Point-to-point model • Centralised model • Draw from research results in UWB-based sensor networks Source: Arslan et al., Cognitive Wireless Communication Networks, Springer

43. Security in Distributed Sensing • Reliable spectrum sensing is key in CR networks • Shadowing and multipath fading  challenges in sensing • Shadowing leads to “hidden node” problem • Sensing challenges alleviated by “Cooperative Sensing” • Using multiple distributed CR nodes • Two major security issues • Incumbent emulation • Caused by a malicious secondary • Gains priority over channel by emulating PU characteristics • Falsification of spectrum sensing data • False data to mislead band manager • Both are important issuesthat need to be addressed • Potential countermeasures • Authentication of the data and the sender • Robust data fusion methods

44. Information Theoretic Aspects - Capacity of CR Channel

45. Information Theoretic Aspects in CR • Current CR scenario • Device X1 transmits only when channel is free • Device X2 transmits after X1 • Or uses different freq band • X2 need not wait until X1 is done Ref: Devroye et al., “Limits on Communications in a Cognitive Radio Channel,” IEEE Communications Mag, June, 2006 • Is simultaneous transmission more efficient than time sharing? • What are the achievable rates at which two users (CR capable) could transmit • What are the achievable rates if two users do not have CR capability?

46. Information Theoretic Aspects in CR Ref: Devroye et al., “Limits on Communications in a Cognitive Radio Channel,” IEEE Communications Mag, June, 2006 • Cognitive Radio Scenario • Simplified model : Two transmitters (X1 and X2)and two receivers, (Y1 and Y2) • Goal: Define and evaluate channel capacity for CR channel • Two links: (X1  Y1 ) and (X2  Y2 ) • Evaluate max. rate at which information sent over both links • Capacity will be a two-dimensional graph (R1 , R2 ) • Capacity regions – max. set of all reliable rates that can be simultaneously achieved • Obtain inner (achievable region) bounds and outer bounds • Usually based on random coding (w/o explicitly constructing codes

47. Information Theoretic Aspects in CR • Two links: • (X1  Y1 ) and (X2  Y2 ) • X2 is a CR device • (X1  X2 ) exists • X2 knows message of X1 • Genie aided • X1does notknow message of X2 • An asymmetric problem • An idealized situation • Will provide an upper bound on rates achievable in practice • An open problem • Achievable region – combination of • Han-Kobyashi interference region • Dirty paper coding • Relaying Ref: Devroye et al., “Limits on Communications in a Cognitive Radio Channel,” IEEE Communications Mag, June, 2006

48. Capacity • Computing capacity regions uses three techniques • Han-Kobyashi interference region • Dirty paper coding • Relaying • Two links: (X1  Y1 ) and (X2  Y2 ) and X2 knows message of X1 • Two possible actions of X2 • Selfish Approach • Try to mitigate own interference  Dirty Paper coding • Achieves region where R2 >R1 • Selfless Approach • X2 acts a relay for X1 • X2 does not transmit own information • Region where R1 is higher thanR2 • Region 1 – Time sharing by X1 and X2 • Region 2 – Interference region – both do not know other’s information • Region 3 – Cognitive region • Region 4 – MIMO region – Both X1 , X2 andY1 , Y2 cooperate • This is the region that gives maximum capacity

49. CR – A Practical Implementation

50. Cor - DECT CPE Cor - DECT CPE CorDECT Rural WLL Deployment CorDECT Network Fixed Wireless Link Up to 240 Kbps per village 15 Km range (up to 25 km with repeater) PSTN SS7/ R2MF Village B V5.2 CorDECT CorDECT Internet xDSL/E1 Base Station CO Access Center Village A corDECT is deployed in > 15 countries