Topics 1 Global Perspective 2 China Situation 3 United States 4 California 5 Conclusions
Introduction Selected Topics Recent PHY Research Conclusions · 2013. 1. 17. · Selected Topics...
Transcript of Introduction Selected Topics Recent PHY Research Conclusions · 2013. 1. 17. · Selected Topics...
IntroductionSelected Topics
Recent PHY ResearchConclusions
Cognitive Radio: a (biased) overview
Chandra [email protected]
Dept. of ECE, IISc
Apr. 10th, 2008
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Outline
1 Introduction to Cognitive RadioWhat is CR?The Cognition CycleOn a Lighter Note
2 Selected TopicsSpectrum SensingCognitive Radar
3 Recent PHY ResearchSpectrum SensingInfo. Theoretic AspectsSystem Design
4 Conclusions and Future Directions
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
A Simple Introduction
Radio-frequency spectrum, like the acoustic spectrum, is anatural resource, but its use is regulated by governmentsvia licensing agreementsHowever:
Some frequency bands are unoccupied most of the timeSome are only partially occupiedOthers are very heavily used
If a frequency band is unused now, it is gone forever - sothink in terms of detecting and utilizing spectral “holes”Cognitive Radios attempt to improve spectral utilization by
Radio scene analysisDynamic spectrum managementTransmit power control
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
The Case for Cognitive Radio
“If you look at the entire RF frequency up to 100GHz,and take a snapshot at any given time, you’ll see thatonly 5 to 10 percent of it is being used. So there’s90GHz of bandwidth available.”
- Ed Thomas, former chief engineer at the FCC.So a Cognitive Radio that can intelligently use spectrumlicensed to other users when they aren’t using it offers asignificant benefit.
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Some Definitions of a Cognitive Radio
Wikipedia: “Cognitive radio is a paradigm for wirelesscommunication in which either a network or a wirelessnode changes its transmission or reception parameters tocommunicate efficiently avoiding interference with licensedor unlicensed users. This alteration of parameters is basedon the active monitoring of several factors in the externaland internal radio environment, such as radio frequencyspectrum, user behavior and network state.”
FCC: “A Cognitive Radio is a radio that can change itstransmitter parameters based on interaction with theenvironment in which it operates.”
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Cognitive Radio Tasks/Features
Spectrum Sensing: detect unused spectrum and use itwithout causing interference to other usersSpectrum Management: matching the available spectrumto user requirementsSpectrum Mobility: maintaining seamless connection whilefrequently(!) changing over to better frequency(!) bandsSpectrum Sharing: fair spectrum scheduling methods
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
The Basic Ingredients
Source: S. Haykin, “Cognitive radio: brain-empowered wireless communications”, IEEE JSAC, Feb. 2005
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Terminology
Two basic kinds:The Mitola CR: every possible parameter is taken intoaccount and chosen in a context-aware mannerSpectrum-sensing CR: only the radio frequency spectrum isconsideredSome say that a lot of CR is already implemented, e.g.,power control, adaptive modulation and coding!
In terms of their spectrum use:Licensed Band CR: IEEE 802.22 Work Group (WRANusing licensed TV bands)Unlicensed Band CR: IEEE 802.15 Task Group 2(coexistence of IEEE 802.11 and bluetooth)
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Terminology
Two basic kinds:The Mitola CR: every possible parameter is taken intoaccount and chosen in a context-aware mannerSpectrum-sensing CR: only the radio frequency spectrum isconsideredSome say that a lot of CR is already implemented, e.g.,power control, adaptive modulation and coding!
In terms of their spectrum use:Licensed Band CR: IEEE 802.22 Work Group (WRANusing licensed TV bands)Unlicensed Band CR: IEEE 802.15 Task Group 2(coexistence of IEEE 802.11 and bluetooth)
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Standardization Efforts
Dynamic Frequency Selection (DFS) in IEEE 802.11aNow DFS refers more generally to automatically selecting afrequency band to minimize or avoid interference to aprimary transmitter-receiver.
Transmit Power Control (TPC) in IEEE 802.11aNow TPC refers more generally to automatically setting thetransmit power based on the spectrum used and theregulatory requirement in the current environment(interference temperature)
Also in IEEE 802.11h (DFS and TPC for WLAN sharing),IEEE P1900 (standards for advanced spectrummanagement), IEEE 802.22 (WRANs in unused TV bands)
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Spotlight on CR
CR workshop/tutorial in every major communications andsignal processing conferenceIEEE JSAC special issue: April 2007CR Workshop here in IISc: Apr. 19, 2007!IEEE Comm. Mag.: May 2007IEEE Wireless Comm. Mag.: June 2007SpringerLink special issue on Cognitive Radio: May 2008IEEE JSAC, JSTSP 2008Conferences: CrownCom, CogNet, CWNet, DySPAN
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Companies Interested/Working on CR
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Source: N. Holmes and J.H. Snider, “The cartoon guide to federal spectrum policy”, available online.
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Source: N. Holmes and J.H. Snider, “The cartoon guide to federal spectrum policy”, available online.
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
DefinitionFeatures & ClassificationSome Fun
Source: N. Holmes and J.H. Snider, “The cartoon guide to federal spectrum policy”, available online.
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
1 Introduction to Cognitive RadioWhat is CR?The Cognition CycleOn a Lighter Note
2 Selected TopicsSpectrum SensingCognitive Radar
3 Recent PHY ResearchSpectrum SensingInfo. Theoretic AspectsSystem Design
4 Conclusions and Future Directions
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Spectrum Sensing
Objective: Designing high quality spectrum sensingdevices and algorithms for exchanging spectrum sensingdata between nodes to
Reliably detect spectral holes for use by the CRReliably detect when the primary transmitter comes onTwo challenges: hidden transmitter, silent receiver
Possible approaches:Matched filterEnergy detectorFeature detector
Yet another classification:Centralized (or local) sensingDecentralized (or cooperative) sensing
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Matched Filter-Based Spectrum Sensing
Optimal for signal detection (even demodulation)Requires carrier and timing synchronization with primaryPilots, preambles, spreading codes, etc can be usedExamples:
TV signals have narrowband pilotsCDMA systems have pilot and paging channelsOFDM systems have preamble words for timing acquisition
Drawback: Need special receiver for each primarytransmitter!
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Energy Detector-Based Spectrum Sensing
Sub-optimal non-coherent detectorDetect primary if measured energy in a band > thresholdMore general and versatile than the matched filterDrawbacks:
Susceptible to changing noise levels and fadingDoes not work well for wideband frequency hopping ordirect-sequence spread signalsVery limited discriminatory ability between signals, noiseand interference
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Feature Detection-Based Spectrum Sensing
Modulated signals are always coupled with sine-wavecarriers, pilot/preamble sequences, cyclic prefixes, etc withbuilt-in periodicityThis periodicity implies the process is cyclostationary,which results in spectral correlationThe spectral correlation is used for feature detectionAssumption: stationary noise and interference exhibit nospectral correlationDifferent types of modulated signals could have
Identical power spectral density, butVery different spectral correlation functions
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
An Example: Distributed Spectrum Sensing
Work by J. Unnikrishnan and V. V. Veeravalli, UIUCDecentralized detection with identical sensors, butcorrelated measurements
Source: J. Unnikrishnan and V. V. Veeravalli, IEEE J. Sel. Topics in SP, pp. 18–27, Feb.2008.
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Decentralized Detection Problem
Let Y = vector of energy measurements from all sensorsWish to distinguish the two hypotheses
H0 : Y ∼ N (0, σ20I)
H1 : Y ∼ N (µ11,Σ)
µ1,Σ known at the fusion centerIndividual sensors perform a Likelihood Ratio Test to meetthe global false alarm probability on their own
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Two Possible Approaches
Counting Rule:Optimal for conditionally i.i.d. observationsSimply compare the number of sensors that decide in favorof H1 to a threshold
Linear Quadratic Detector:Uses a decision metric
T (X ) = hT X + X T MX
where X is the vector of LLRs of received bits (with meansunder H0 subtracted)Optimize h and M to maximize the deflection criterion
DT =[E1(T (X ))− E0(T (X ))]2
Var0(T (X ))
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Simulation Result
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Cognitive Radar
Learning from Bats:The bat uses sonar to figure out the location, size, velocity,etc of the target with an accuracy that would be the envy ofany radar engineer
Source: S. Haykin, “Cognitive Radar: A Way to the Future”, IEEE SP Mag, Jan. 2006
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Cognitive Radar Requirements
A cognitive radar should be able to:1 Build on learning through interactions of the radar with the
surrounding environment2 Use feedback from the receiver to the transmitter to
facilitate acquisition of intelligence3 Learn from past information by recursively updating
state-related information
All of the above features are currently implemented in batsDifference from bats: while bats track one target at a time,radar systems have to deal with multiple targets
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Basic Ingredients of a Cognitive Radar
Information preservation: through “soft” signal processing.Must leave hard decisions till the point where a finaldecision is madeAdaptive tracking of multiple targets: define the state of areceiver as the a posteriori probability that a target exists.Then, should adaptively track the receiver stateFeedback: from the receiver to the transmitter based onthe inputs received from the environment to facilitateefficient tracking of the state
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Example of Adaptive Tracking: Radar SceneAnalysis
Ocean environment under surveillance by a coherent radarThe information from radar returns (after processing by apeak filter) is modeled as
Clutter statistics: F -distribution w/ (2, 2k) degrees offreedom, denoted F2,2k (z) where z is the power spectrummeasurement, k is the number of neighboring doppler binsused in averaging (for the peak filter)Target + clutter statistics: F -dist., (1/γ)F2,2k (z/γ), where γis the target-to-clutter power ratio
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Bayesian Filtering System
Closed loop feedback system: propagates the state vector ofprobabilities from one iteration to the next
Source: S. Haykin, “Cognitive Radar: A Way to the Future”, IEEE SP Mag, Jan. 2006
Important note: need to establish the right relation between radar measurements and statisticalcharacteristics of clutter and target-plus-clutter for filter stability
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Cognitive Radar Networks
Several radars work together in a cooperative manner totrack multiple targets. Employs a central base station tofuse information from different radars.Two options:
Distributed cognition: all radars including the central basestation are cognitiveCentralized cognition: the radars are “dumb” and all the“intelligence” is confined to the central base station
Cognitive radar networks offer a remote-sensing capabilityfar in excess of what the radar components are capable ofachieving individually
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingCognitive Radar
Challenges in Cognitive Radar Networks
Development of statistical models to describe theinformation content of radar returnsMulti-sensor information fusion (limited computingresources at the base station)Defining success metrics for cognitive radar networks
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
1 Introduction to Cognitive RadioWhat is CR?The Cognition CycleOn a Lighter Note
2 Selected TopicsSpectrum SensingCognitive Radar
3 Recent PHY ResearchSpectrum SensingInfo. Theoretic AspectsSystem Design
4 Conclusions and Future Directions
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Soft-Sensing and Optimal Power Control for CR
Work by S. Srinivasa and S.A. Jafar, UCILook at transmit power control at the secondary transmitterTry to maximize the average received SNR (or capacity) atthe secondary receiver, subject to
A peak power constraint on the secondary txAn average interference constraint on the primary tx
Show that a binary power control strategy maximizes theaverage received SNR at the secondary receiverDerive the power control strategy to maximize the averagecapacity to the secondary receiver
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Cooperative Spectrum Sensing
Work by Z. Quan, S. Cui (Texas A&M) and A. H. Sayed(UCLA)Due to fading, the individual CRs may not be able toreliably detect the existence of the primary userPropose spectrum sensing based on linearly combininglocal test statistics from individual secondary usersTwo optimization schemes are proposed to control thecombining weights:
Optimize the CDF of the test statistic at the fusion centerMaximize the global detection probability under theconstraints on false alarm probability
Significant cooperative gains are demonstrated
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Spectrum Sensing using CyclostationaryProperties
Work by H-S. Chen, W. Gao (Thomson Research) and D.G. Daut (Rutgers)Spectrum sensing in a very low SNR environment (-20dB)Sensing algorithms based on the measurement of thecyclic spectrum, or the spectrum correlation density (SCD)function of received signalsPresent three different SCD measurement methods andanalyze the statistics of the SCD of stationary whiteGaussian noisePresent simulation results of the receiver operatingcharacteristics of the three SCD methods
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Capacity Limits of CR
Related work by A. Jovicic and P. Vishwanath and N.Devroye, P. Mitran and V. TarokhSystem Model:
Source: N. Devroye, P. Mitran and V. Tarokh, IEEE Comm. Mag. June 2006
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Capacity Limits of CR
Source: N. Devroye, P. Mitran and V. Tarokh, IEEE Comm. Mag. June 2006
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Spectral Efficiency of CRs
Work by M. Haddad, A. M. Hayar (Eurecom) and M.Dabbah (Supelec)Primary and cognitive users communicate with differentreceivers, with perfect spectrum sensingCR only transmits when the channel is idleUsers successively transmit over available bands throughwater-fillingDerive the total spectral efficiency of the CR system
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
How Much Spectrum Sharing is Optimal?
Work by S. Srinivasa and S.A. Jafar, UCILook at the tradeoff between opportunistic access andlicensed access in multi-user CR networksGoal: find the optimum number of secondary users tomaximize the total throughput of the systemTwo cases:
Perfect SS at secondary and zero interference tolerance atthe primary and secondary receiver(s)Imperfect SS at secondary and non-zero interferencetolerance at the primary receiver(s)Show that the optimal fraction of users lies b/w theextremes of fully opportunistic and fully licensed operation
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Cognitive Medium Access
Work by S. Geirhofer and L. Tong, Cornell, and B. Sadler(Army research lab)Propose cognitive medium access (CMA) as a protocol forcoexistence within independently evolving WLAN bandsFormulate the problem of maximizing the throughput of thecognitive radio (subject to interference constraints) withinthe constrained Markov decision process frameworkOptimal control policy obtained via linear programmingAlso find structured solutions that provide more insight
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
Spectrum SensingInfo. Theoretic AspectsSystem Design
Can CR Work in a Frequency PlannedEnvironment?
Work by E. G. Larsson and M. J. Skoglund, KTHFirst order analysis of the impact on the SINR in a wirelessnetwork of CR users starting to transmitIf cognitive devices are to be introduced, they need to:
Be few in numbers (aggregate power scales with thenumber of CR users)Transmit with extremely low power (30dB below primary)Have very sensitive receivers (about 20dB more sensitivethan the primary receivers in terms of C/(C + I))
Conclusion: introducing CR transmitters in a frequencyplanned cellular network without causing substantialinterference is very challenging.
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
R & D Opportunities
Fundamental research: better CR enabling algorithms,performance limits
Cooperative & decentralized spectrum sensing algorithmsAchievable rates/performance limits of cognitive radioNetwork capacity and scaling laws for CR networksCognitive MAC protocols for secondary networks
Implementation: building software-defined radios with CRcapabilitiesGovernment: policy and regulation related research andrecommendationStandardization activities
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
CR Related ResourcesWikipedia entry:http://en.wikipedia.org/wiki/Cognitive radio
Joseph Mitola III’s Thesis:http://www.it.kth.se/∼jmitola/Mitola Dissertation8 Integrated.pdf
FCC:http://www.fcc.gov/oet/cognitiveradio
DARPA XG program:http://www.darpa.mil/sto/smallunitops/xg.html
Europe’s E2R project:http://e2r2.motlabs.com
CRT Wireless blog:http://www.crtwireless.com/blog
Last, but not least, there’s always:http://www.google.com
Chandra Murthy Cognitive Radio: a (biased) overview
IntroductionSelected Topics
Recent PHY ResearchConclusions
The End
Thank you very much!
Chandra Murthy Cognitive Radio: a (biased) overview