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



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





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





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.





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.”





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





The Basic Ingredients

Source: S. Haykin, “Cognitive radio: brain-empowered wireless communications”, IEEE JSAC, Feb. 2005





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)





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)





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





Companies Interested/Working on CR





Source: N. Holmes and J.H. Snider, “The cartoon guide to federal spectrum policy”, available online.




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





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!





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





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





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.





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





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 ))





Simulation Result





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





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





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





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





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





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





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




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





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





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





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





Capacity Limits of CR

Source: N. Devroye, P. Mitran and V. Tarokh, IEEE Comm. Mag. June 2006





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





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





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





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.




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




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




The End

Thank you very much!


introduction selected topics recent phy research conclusions · 2013. 1. 17. · selected topics...

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