cognitive radio realities

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. 2007; 7:1037–1048Published online 17 May 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/wcm.479

Cognitive radio realities

Bin Le∗,†, Thomas W. Rondeau and Charles W. BostianBradley Department of Electrical and Computer Engineering, Center for WirelessTelecommunications—Wireless@VT, Virginia Tech, Blacksburg, VA 24061-0111, U.S.A.

Summary

A cognitive radio (CR) is a transceiver that is aware, adaptive, and capable of learning. It may be visualized andrealized as an intelligent software package (cognitive engine) controlling a software defined radio platform. Thecognitive engine executes a set of nested loops constituting a cognition cycle, drawing on experience and storedknowledge to optimize a set of user-chosen quality of service measures. The process relies on the effectivenessof a set of tools that are individually well known but rarely found together: multi-objective genetic algorithms,case-based decision theory, and neural networks. Practical implementation problems include passing environmentalinformation from the radio to the cognitive engine, acting on that information, and performing real-time control ofthe radio platform by the cognitive engine. In this paper, we discuss our approach to developing and implementinga CR. Copyright © 2007 John Wiley & Sons, Ltd.

KEY WORDS: cognitive radio; cognitive engine; cognitive wireless network; software defined radio; wirelesscommunications; signal recognition; awareness; learning; multi-objective optimization

1. Introduction

This paper draws extensively on previous work pub-lished by the authors in conferences like the SoftwareDefined Radio Forum’s Technical Conference andsimilar sources. With permission of the copyrightholders, we repeat some of that text here to bring itbefore a larger audience.

1.1. Defining Cognitive Radio

Defining ‘cognitive radio’ is as difficult as defining‘artificial intelligence.’ The goals move with time,and behavior described as typifying ‘intelligent’ or‘cognitive’ before it has been demonstrated is oftencalled something else afterwards. For this paper,

*Correspondence to: Bin Le, Bradley Department of Electrical and Computer Engineering, Center for WirelessTelecommunications—Wireless@VT, Virginia Tech, 436 Whittemore Hall, Blacksburg, VA 24061, U.S.A.†E-mail: [email protected]

we define a cognitive radio (CR) as a transceiverthat is aware, adaptive, and capable of learningfrom experience. Each of these attributes can befurther qualified, and what is a CR to one personmay be an adaptive radio to another. Thus ‘aware’may include awareness of the RF environment,awareness of its own capabilities, awareness ofthe rules that govern its operation, awareness ofits user’s priorities and authorities, etc. ‘Adaptive’may range from simple transmitter power controlto selecting from a menu of standard waveforms tocreating new waveforms and protocols on the fly andnegotiating their use with another radio like itself.‘Learning’ may range from experience-weighted tablelookup to arbitrary combinations of machine learningalgorithms.

Copyright © 2007 John Wiley & Sons, Ltd.

1038 B. LE, T. W. RONDEAU AND C. W. BOSTIAN

Fig. 1. Timeline of cognitive radio research.

To avoid philosophical arguments about whether aparticular behavior or set of behaviors is cognitive, wewill focus on radios that solve one or more of a setof canonical problems, that improve their performancethrough time by learning from experience, and that arecapable of surprising their designers by finding newsolutions for unanticipated problems. The canonicalproblems include spectrum-oriented tasks like findingand using temporarily vacant spectrum (‘whitespace’), and service-oriented tasks like providingnear-universal interoperability by identifying andinteroperating with legacy waveforms and adjustingwaveform parameters to optimize figures of merit likethroughput, bit error rate, power consumption, spectralfootprint, etc.

1.2. A Brief Historical Overview of CognitiveRadio Research

In such a rapidly growing field, it is difficult toprovide a comprehensive history, and authors whowrite review papers like this run the risk of leavingout important work of which they were unaware. Fora fully comprehensive review of the history of CR andcurrent research, the reader should see Reference [1],and Haykin [2] provides a good overview of CR

research. The web sites of excellent university groupssuch as South Florida [3] and the Berkeley WirelessResearch Center [4] are also good sources for furtherinformation.

Figure 1 shows a timeline of the major milestonesin CR research activities globally and from theperspective of our own group, which ranges frompolicy/regulation reform to intelligent wireless com-munications. The timeline shows increasing density aswe try to represent the range of quality work beingpursued.

Joseph Mitola [5,6] invented the basic CR conceptin the late 1990s when he envisioned a CR asa universal and highly intelligent wireless personaldigital assistant operating primarily at the applicationlevel. Subsequently, the DARPA XG program extendedthe concept to allow the CR to operate as an intelligentagent [7]. In 2003, the Federal CommunicationsCommission (FCC) began to explore CR technologyto improve spectrum efficiency [8,9].

Our own work was sparked by Christian Rieser’seffort to develop a millimeter wave radio incorporatinga channel sounder that could find and use short-livedpaths of opportunity in a disaster communicationsscenario [10]. Rieser et al. [11,12] successfullyextended cognition to the medium access control

Copyright © 2007 John Wiley & Sons, Ltd. Wirel. Commun. Mob. Comput. 2007; 7:1037–1048

DOI: 10.1002/wcm

COGNITIVE RADIO REALITIES 1039

(MAC) and physical (PHY) layers using learningalgorithms and filed a patent application describingtheir work in 2004.

The number of conferences and conference sessionson CR is increasing rapidly. The Software DefinedRadio Forum’s Technical Conference has offeredmultiple CR tracks in the past few years [13,14].The IEEE in 2005 introduced the Dynamic SpectrumAccess Networks (DySPAN) conference, a meetingdedicated to both technical and regulatory challengesof spectrum sharing and allocation, much of which usedor suggested CR solutions [15]. The second DySPANmeeting will take place in Ireland in early 2007 [16]and will showcase advances in CR technology. In2006, the IEEE sponsored the first conference on CRs,CROWNCOM, bringing in new perspectives from adiverse set of researchers [17].

Recent advances include the use of the CWTcognitive engine on the GNU Radio [18] and theimplementing radio in software (IRIS) platformfrom the Center for Telecommunications Value-chainResearch (CTVR) of Trinity College, Dublin, Ireland[19,20]. Ireland has made a significant contribution toboth the SDR and CR research communities by openingtwo 25 MHz slices of spectrum for research in spectrumuse, and the CWT and CTVR are collaborating on aseries of projects to enable interoperability betweenSDRs and CRs in networks [21,22]. This work reflectsthe movement of CR research towards the idea ofcognitive networks [23]. Cognitive networks, likeCRs, use intelligent algorithms to improve end-to-endquality of service (QoS).

Fig. 2. Conceptual diagram of a cognitive radio.

2. Our Approach to Cognitive RadioDevelopment

2.1. The Cognitive Engine Concept:Intelligent Control of Radios

It is useful to visualize a CR as a SDR operating underthe control of an intelligent software package called acognitive engine (see Figure 2). The cognitive engineincludes a set of algorithms that perform the sensing,learning, optimization, and adaptation control of theCR. We think of the cognitive engine as continuallyadjusting the parameters of the software defined radio(‘turning the knobs’), observing the results (‘readingthe meters’), and taking actions to move the radiotoward some desired operational state while learningin the process.

2.2. The Cognition Cycle and the CognitiveEngine

The theoretical basis for our work is the cognition cycle[24] shown in Figure 3. It consists of an outer andinner loop. The full cycle (1) brings in information

Fig. 3. Cognition cycle.


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from the CR’s sensors, (2) synthesizes this informationinto simplified models for data representation, (3)remembers the model and compares it to past models,(4) decides whether to apply a previous action, developa new action, or optimize an old action, (5) estimatesthe action’s desired performance, (6) applies the actionto the radio, (7) and monitors and compares theactual performance to the desired performance andremembers the differences.

The outer loop provides the overall means to enableadaptation based on observations of the environment.The inner loop works in conjunction with the outerloop to improve the performance by learning from pastactions, decisions, and experience.

The cognition loop is put into practice based on thesoftware architecture of Figure 4, the cognitive engine.

There are three groups of modules in thecognitive engine: (1) a set of environment awarenessmodules including the wireless modeling system, radioperformance and resource monitors, and user andpolicy information modeler, (2) a wireless systemgenetic algorithm (WSGA) that optimizes radioadaptation, and (3) a reasoning and decision makingcore with a knowledge base that performs intelligentreasoning and decision making.

Much of the power of a CR comes from theability of a machine to learn about and understandits surroundings. Machine learning has been well-documented with both criticisms [25] as well as

successes [26], especially in narrowly defined, well-bounded applications. While machine learning inwireless communications is still a bounded problem,the technical demands for intelligence in a radio exceedthose normally associated with successful applicationsof classic computational intelligence and machinelearning algorithms like expert systems and artificialneural networks. Our work requires a machine tohave reasoning capabilities that create and test newsolutions, which are not provided by the standardmachine learning techniques. The structure of Figure 4combines AI techniques to extend machine learningand reasoning to wireless communications.

A CR becomes a learning machine through a tieredalgorithm structure based on environment modeling,knowledge representation and learning, adaptation,and feedback. These core functions are addressedthroughout this paper.

3. Detailed Development of CognitiveEngine

3.1. Environmental Awareness

Awareness is the first step toward cognition andencompasses two key areas: understanding the requiredenvironmental information and developing the abilityto sense the environment. A CR should be aware

Fig. 4. The Cognitive Engine.


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of its own capabilities and the world around it.This knowledge falls into the policy, radio, and userdomains.

The policy domain contains information regardingthe local policies that govern the radio’s operation,either stored in a database or downloaded in the field.The policies guarantee the security and legality of theCR’s operations. Regulatory concepts like frequencyplans, transmit power, and interference limits can beintegrated into a rule set in with rules that are tiered bypriority. Such a rule set can extend from PHY to MACand network layers to optimize network performance,leading to a cognitive wireless network [1].

The user domain defines both the operationpreferences and performance requirements of boththe service provider and end user. It encapsulatesperformance optimization objectives like accessavailability, service type, and service quality. A CRneeds to understand the QoS requirements and adaptto meet them.

In the radio domain, the CR requires awarenessof two classes of information. The first is thewireless air-interface information, which includes bothcommunication waveform features and propagationchannel characteristics. The second is radio hardwareinformation like power consumption, available signalprocessing resources (e.g., DSP clock rate, codecoptions), device operational status (e.g., filter setting,modem configuration), etc. They are combined inmachine reasoning in two ways, either to extendthe performance objective when some radio resourceadvantages can be utilized or to add operationconstraints when some hardware limitation becomesan issue. For example, the CR will prefer an energy-efficient waveform when running on battery powereven though the user may not specifically imposepower-saving requirements.

3.2. Case-Based Learning

In the cognition loop, the ‘Case-Based DecisionMaking,’ ‘Knowledge Base,’ and ‘Reasoning’ blocksare implemented using case-based decision theory[27] (CBDT), which is closely related to case-basedreasoning [28] (CBR). Because the two use similarconcepts to enable learning, we can just genericallyrefer to it as case-based learning.

Formally, case-based learning defines a set ofproblems q ∈ P , a set of actions a ∈ A, and a setof results r ∈ R. A case, c, is a tuple of a problem,an action, and a result such that c ∈ C where C =P × A × R . Furthermore, memory, M, is formally

defined as a set of cases c currently known such thatM ⊂ C.

When the sensor system observes a new problem, p,the cognitive engine must determine the action, a, totake in response. The problem input could be a changein channel condition, spectrum use (the presence of aprimary user), or a change in the desired QoS fromthe user/application domain. To determine the bestaction to take, the case-based system analyzes the newproblem against past cases in memory. The analysisdetermines how similar the new problem is to the pastcases, the similarity function in Equation 1, as well ashow useful the past actions were in solving the problem,the utility function in Equation 2. The action definedby the current cognitive engine is the radio’s waveformdefined in the PHY and MAC layers.

s : P × P → [0, 1] (1)

u : R → � (2)

Case analysis selects the case that is both mostsimilar to the new problem as well as how successfulthe action was in the past. We can look at this asa similarity-weighted utility function as shown inEquation 3. The resulting chosen case may not be themost similar case if the action of another, less similarcase, has performed better.

U(a) = s(p, q)u(r) where (q, a, r) ∈ M (3)

This equation is only one way of analyzing theresults of cases. The challenge of this technique is tocreate effective similarity and utility functions for thetypes of information received through the sensors. Ifthe cognitive engine has multiple domains of interest,such as the propagation, spectrum, and user/applicationdomains, then the case-base must both represent andquantify each of these.

3.3. Multi-Objective Optimization:Evolutionary Techniques

Optimization is a key piece of the full cognitiveengine’s capabilities, referred to as the ‘Link ConfigureOptimization’ in the cognition loop of Figure 3. Whileoptimization should occur across all layers and aspectsof the radio’s operation, this section presents our pastand current work dealing with optimization of the PHY


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and MAC layers. Future work will extend optimizationup to the network, transport, and even applicationlayers.

We have a case-base that understands the modelsdeveloped from the external environment and providesa method of looking at past behavior to determinefuture actions, but the case-base does not always have agood answer. The case-base may produce a suboptimalaction, or it may, through a lack of knowledge, takeno action at all. This latter problem might arise whenthe radio is first deployed or used, or if it suddenlyfinds itself in a completely new situation. When theCR needs a new or better waveform that is suitable forthe problem, then optimization must take place.

Optimization of the waveform involves developinga set of PHY and MAC layer settings that best providethe QoS for the given problem and user/applicationneeds. Unfortunately, QoS is not a single, simpleobjective function. Instead, the QoS depends on manydimensions or metrics of communication quality. Evenmore problematic is that many of the metrics aremutually exclusive.

A simple optimization scenario might call forminimizing BER and minimizing power consumption,which are competing objectives. As the radio’spower consumption is decreased by turning downthe transmitter power or using less optimal but morecomputationally efficient signal processing algorithms,the BER will increase. The only solution is to balancethe power and BER to create a waveform that satisfiesthe conditions as best as possible. This example hasonly two objectives, but many more objectives existwith dependent relationships like data rate, occupiedbandwidth, spectral efficiency, latency, etc. See ChapterSeven of Reference [1] for a detailed analysis ofthe multi-objective nature of radio reconfigurationand References [11] and [29] for more discussion.Figure 5 illustrates the objective functions used inthe cognitive engine. This figure also indicates whena direct relationship between objectives exists; thatis, when one objective directly impacts the value ofanother. The optimization process must balance theseobjective functions as they interact with the changes ofthe pieces of the waveform.

The analysis and optimization of multi-objectiveproblems is complex but has a rich history over the pastthree decades [30]. The general analysis comes downto finding the non-dominated solutions in the solutionspace, which is known as the Pareto front. Thesesolutions are called non-dominated when optimizationin any dimension negatively impacts other dimensions.Genetic algorithms (GA) [31] are well-known for

Fig. 5. Objectives currently used in the cognitive engine. Thearrows represent direct relationships between objectives.

successfully optimizing multi-objective problems, andit is fairly easy to produce the Pareto front, which is whythe core adaptation scheme in our cognitive engine isa GA. The real challenge is to find the proper solutionon the Pareto front that best satisfies the quality ofservice needs of the problem. Figure 6 shows a classicPareto front as a three-dimensional surface where theoptimization challenge is to maximize efficiency andperformance while minimizing cost. It is impossibleto find a solution that simultaneously gives the bestsystem performance at the lowest cost, so any solutionon this surface is a trade-off of the objectives.

When the Pareto front has been optimized, the finalchallenge in the algorithm’s performance is to makea decision about which waveform on the Pareto frontbest represents or satisfies the QoS needs. Some ofthe individuals will be better in certain dimensions

Fig. 6. A Pareto front showing the trade-off in the solutionspace between performance, efficiency, and cost.


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than others, as the quality of service measure mightvalue some objectives more than others such as reducedpower consumption over lower BER.

3.4. Hardware Portable Interface to theRadio Platform

3.4.1. Knobs and meters

The cognitive engine ‘reads the meters’ to obtainenvironment information and ‘turns the knobs’ tocontrol radio’s behavior. The key design challengehere is defining the knobs and meters to synthesizethe large amount of information collected directlyfrom radio and abstract it into a parametric form formachine reasoning. To represent appropriate knobsand meters, we use the extensible markup language(XML) as the interface between the cognitive engineand radio platform. The cognitive engine receives anXML profile of parametric information from the radioand delivers an XML profile of instructions to the radiofor adaptation [32]. The profiles are the abstractionsof the context information of target domain entitiessuch as radio device status and link performance.Profiles contain both static and dynamic informationfor different levels of awareness and adaptation, asshown in Figure 7.

3.4.2. Hardware portable cognitive engine toSDR API

The cognitive engine to SDR application pro-grammable interface (API) is the implementationof the interface described in previous section. Theradio platform API is divided into two parts: theperformance API and the radio API, as shown inFigure 8. The performance API transfers performance-related parameters, or meters, like signal strength,waveform features, data rate, etc., to the cognitiveengine to achieve waveform recognition. The radio APIdelivers both hardware status and control information.

Fig. 7. Profiles of knobs and meters for cognitive engine-SDRinterface.

Fig. 8. Hardware-portable cognitive engine to SDR API.

Hardware status parameters like modem configuration,power consumption, power amplifier (PA) mode,etc. are passed to the radio monitor modules,also representing meters. Hardware control APItransfers the cognitive engine’s control instructions,or knobs, to the radio platform to change itsoperations.

Through radio-specific implementation knowledge,the API supports the environment recognition and radioadaptation for the cognitive engine, thus the cognitiveengine does not need to take care of implementation-level considerations. Such a layered interface structurehas the following advantages:

(1) The cognitive engine is hardware independent.A radio domain-specific interface serves as themiddleware that interprets and transfers theenvironment and control information with acommon data-structure and a common profileformat. Figure 9 shows the flow of the informationusing XML to realize hardware portability.

(2) It supports modularized system design. Thecognitive engine’s AI systems can be reconfiguredto interface to different radio hardware platformswithout requiring changes to the intelligent core.

4. Radio Environment Recognition

4.1. Waveform Definition and SignalDefinition

The knowledge of the radio environment is generallycomprised of information extracted from the received


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Fig. 9. Hardware-independent radio behavior decisionmaking diagram.

waveform. In a practical radio network environment,the capability to detect the presence of existingwaveforms and recognize their characteristics providesthe CR with the necessary knowledge to makeappropriate decisions to either communicate with oravoid observed radio links.

Because the parametric representation is essential tomachine reasoning, the waveform is defined by a setof PHY, MAC, and encryption layer parameters. Weuse the term ‘signal’ for the PHY layer parameter set,which includes carrier frequency, channel bandwidth,symbol rate, pulse shape, modulation, error-correctioncoding, etc.

In order to understand a received waveform, thePHY layer characteristics (the signal) need to berecognized first so that the receiver can be configured toreceive and demodulate the signal successfully. Oncethe information bits are available, subsequent cognitivechallenges include frame and packet decompositionand higher layer data services. Many of these

challenges will require established protocols andunderstanding between cognitive nodes.

4.2. Signal Recognition and CognitiveReceivers

Signal recognition is a system-level design challengethat requires hierarchical signal processing from radiofrequency to baseband in order to obtain enoughknowledge of the waveform. Unlike conventionalradios, the CR approach requires the receiver to beaware of its radio environment so that the difficultylies in designing a cognitive receiver that can activelyrecognize incoming signals and adapt to receive it.Because signal recognition is inseparable from thesignal reception process, it bears all the challenges ofconventional receiver design [33] and adds a lot morebecause now the signal is to be received ‘cognitively’without adequate prior knowledge.

One major issue in conventional receiver design issynchronization, specifically carrier recovery, carrierphase lock, and symbol timing (for digital modulation).Although there are various synchronization algorithmsin literature [34], most of them rely on the priorknowledge of a given standard like modulation,filter characteristics, and symbol rate. Since the CRapproach does not assume that such key information isavailable, a standards-free method is required. Also, thedemodulator typically needs to know the input signal’smodulation format to work properly, which, in CR, isnot necessarily available. The CR therefore requires amodulation identification module.

To design such a cognitive receiver, as shownin Figure 10, there are at least two system-levelchallenges: the first is to design a bootstrap processto cycle and refine the knowledge of the input signal,and the second is to design a general-purpose receiverthat can provide synchronization and demodulation forall these signals. The recognition system also needs tobe adaptive to time varying input signal quality likeSNR.

Fig. 10. Multi-standard Cognitive Receiver.


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Signal recognition is often assumed or abstracted inlink or network level algorithm design and simulations.However, it is extremely important to recognize andunderstand all aspects of the input signal for successfuldemodulation. Key signal processing tasks includeenergy detection, signal classification, and generalcarrier recovery and symbol timing for any modulationscheme. They are not discrete issues. Due to the lack ofprior knowledge, joint recognition by multiple stagesthrough the receiver chain is needed, as shown inFigure 10.

Conventional signal detection techniques includematched filtering and energy detection [35]. However,matched filtering requires prior signal knowledge,and energy detection lacks spectral differentiation.Cyclostationarity detection is getting a lot of attentiondue to its noise suppression [34]; however, thecomputational cost of bi-frequency domain correlationis prohibitive of real time processing [36]. Given thecurrent improvements in processor technology, a goodbalance is to use wideband fast Fourier transform (FFT)for coarse energy detection and then run hierarchicalFFT with finer resolution in the band of interest.

Signal synchronization consists of carrier andsymbol timing recovery. The design philosophy ofcarrier recovery is based on the non-linear energyextraction for distinct frequency components, andsynchronization is maintained by feedback of phaseerror [33,37]. Without key PHY-layer parameterslike carrier frequency, baseband bandwidth, andmodulation, the signal cannot be synchronizedcorrectly. It is important to point out that in realreceivers, hardware issues like local oscillator (LO)drift, DC bias, and cross-talk all prevent the ‘accurate’carrier estimate that many simulation-based papersassume.

It is difficult and unnecessary to make a ‘universal’modulation classifier that can classify arbitrary signalsat the beginning. A practical modulation classifierdesign depends on the target modulations, receiverstructure, available processing resources, and signalquality. In Figure 11, the signal is frequency shifted tosome small IF, or complex quasi-baseband, due to realoscillator concerns such as unlocked phase and nominalversus real frequency values. From here, the cognitivereceiver can use modulation-dependent features ofthe signal to make a coarse classification betweendifferent modulation groups, such as real or quadraturemodulation, linear or non-linear modulation, etc. Sucha decision is adequate to guide a modulation-generalcarrier synchronization module toward phase lock. Aquadrature multiplication structure [38,39] is selected

because the in-phase and quadrature branches can bereconfigured for both real and quadrature modulations.With full digital implementation [40], the phase errordetector and loop filter are reconfigurable for bothlinear and non-linear modulations. The phase errordetectors can also be configured as an FM demodulator,and the structure can further be replaced by a low-passFIR filter for analog AM signals.

Symbol timing is also essential for demodulation. Tomaximize generality, an early-late gate symbol timingloop is incorporated in our cognitive receiver design[12].

4.3. Modulation Classification

There are two major design approaches in modulationclassification research: decision theoretic (such asmaximum likelihood) detection and feature patternclassification. Maximum Likelihood (ML) classifiers[41, 42] rely on certain prior signal knowledge andassume a coherent receiver. Methods based on higher-order non-linear statistics only classify frequencymodulated waveforms [43], and they produce largecomputational costs for non-coherent cases [44].Zero-crossing was found effective for non-coherentclassification, but requires high SNR [45]. A shortoverview of the pattern-classification modulationclassification approach is provided by Nagy [46].Feature-based approaches include using histograms ofthe phase, envelope, and instantaneous frequency ofthe analytic-signal representation of the input signal[47–49]. For the classifier design, an artificial neuralnetwork (ANN) structure is the most popular choicefor a pattern recognition approach [50–52].

Unfortunately, most previous work on modulationclassification has assumed knowledge of the carrierfrequency, the symbol rate, or the availability ofinfinite computational power, all of which are usuallyunavailable in any practical radio link implementation.Furthermore, the fundamental assumptions in buildingthe classifier system, like prior signal knowledge,synchronization condition, and timing logic, areusually not explained clearly. However, these are themost important parts of signal recognition design. Westress that the primary goal of modulation classificationis to enable synchronization so that complex basebandis available for demodulation.

The modulation classifier structure we developedin our lab [53,54] is shown in Figure 11. The signalobtained from the analog-to-digital converter (ADC)is complex quasi-baseband (centered near DC, notphase locked). The modulation classifier can use


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Fig. 11. Modulation classification block diagram.

the complex format of the signal to identify coursemodulation groups (whether linear or non-linear, realor quadrature) [54]. With adaptive block averaging tosuppress white noise, the envelope is not as noise-sensitive as phase information is, thus envelope-basedfeatures perform better than phase statistics, shownrelying on high SNR many times in literature, beforecarrier phase lock.

Although the envelope-based feature is robustand does not require carrier synchronization, it haslimits when trying to distinguish between higher-ordermodulations. Once carrier phase lock is achieved,the complex baseband signal information is available.From here, a refining modulation classification iscarried on at a level that fully utilizes the quadraturephase statistics [53], which show strong modulation-specific features.

Since there are two significantly different stages offeature extraction, the corresponding feature patternclassifiers are also designed differently. For thefirst modulation group classification with the quasi-baseband signal, the classifier is also simplified tofeature slicer, which is an adaptive threshold gridthat separates different groups of modulation. For therefined modulation classification at complex baseband,the clustering nature of each modulation is bestseparated by decision-based artificial neural network(DB-ANN) [55]. Fuzzy logic can be applied tocreate stochastic boundaries between clusters to furtherimprove performance stability.

5. Conclusions

In the few years since Mitola’s pioneering work, CR hasmoved from a science fiction-like dream to practicalreality. A software and hardware architecture isemerging for transceivers that are aware, adaptive, andcapable of learning. These offer exciting new solutionsto old problems of cross-layer optimization, spectrumaccess, and interoperability. Cognitive techniques

will extend upward to the network and higherlayers, providing powerful distributed intelligence andemergent behavior.

6. Acknowledgements

Our work was supported by the National Institute ofJustice, Office of Justice Programs, US Department ofJustice, under Award No. 2005-IJ-CX-K017, and by theNational Science Foundation under Grant No. CNS-0519959. The opinions, findings, and conclusions orrecommendations expressed are those of the authorsand do not necessarily reflect the views of our sponsors.

We gratefully acknowledge the contributions of ourcolleagues at Virginia Tech and in the wider cognitiveradio research community.

Some of the text and illustrations used appearedpreviously in References [1,11,12,53,54] and we repro-duce them here with permission of the SDR Forum.

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Authors’ Biographies

Bin Le is a Ph.D. student at VirginiaTech. He is a research assistant ofCenter for Wireless Telecommunica-tions (CWT). His research interestsinclude cognitive wireless communica-tions, software-defined radios, evolu-tionary algorithms, and neural networks.He is a student member of IEEECommunications Society.

Tom Rondeau is a Ph.D. studentin the Bradley Department of Electricaland Computer Engineering at VirginiaTech as an advisee of Dr CharlesBostian. He was formerly an IREANfellow, an NSF IGERT fellowship, and iscurrently a Bradley Fellow. He expectsto complete his degree in ElectricalEngineering in the spring of 2008 with


DOI: 10.1002/wcm


research in cognitive radios, distributed machine intelligence,and software defined radios. Tom received his B.S. degreein Electrical Engineering from Virginia Tech in May 2003,graduating Summa Cum Laude. As an undergraduate,Tom was awarded the Bradley Scholarship. Tom is astudent member of the IEEE, in the Microwave Theory andTechnique Society and in the Communications Society.

Charles W. Bostian joined VirginiaTech’s Bradley Department of Electricaland Computer Engineering in 1969,where he is now a VT Alumni Distin-guished Professor and director of theCenter of Wireless Telecommunications,which he co-founded in 1993. Hecurrently is also a key member ofthe newly formed umbrella wireless

organization, Wireless@Virginia Tech. Charles received hisPh.D. in Electrical Engineering from North Carolina StateUniversity and, prior to joining Virginia Tech, was aresearch engineer at Corning Glassworks and served for2 years as a U.S. Army officer. Bostian’s primary researchinterests are in cognitive electronics and radio system design.Currently, he directs National Science Foundation (NSF) andNational Institute of Justice (NIJ) projects on cognitive radio.

He has served on two international technology assessmentpanels sponsored by NSF and NASA. He has authored or co-authored 45 journal and magazine articles and approximately100 conference papers and presentations and contributedto the Wiley Encyclopedia of Electrical and ElectronicsEngineering. Elected a Fellow of the IEEE in 1992 forcontributions to and leadership in the understanding ofsatellite path radio wave propagation, Bostian is a formerchair of the IEEE-USA Engineering R&D Policy Committeeand served as associate editor for Propagation of IEEETransactions on Antennas and Propagation. On leave duringthe 1989 calendar year, he was as an IEEE CongressionalFellow on the staff of U.S. Representative Don Ritter,working on legislative issues related to the Americanelectronics industry and economic competitiveness. He isa fellow of the Radio Club of America. In his career atVirginia Tech, Bostian has taught more than 4,000 students.His teaching has been recognized by a number of awards,including ten certificates of teaching excellence, and heis an elected member of the Virginia Tech Academy ofTeaching Excellence. Bostian is the co-author of two widelyused textbooks, Solid State Radio Engineering and SatelliteCommunications, now in its second edition. John Wileypublishes both. His current teaching interests are in RF designand in undergraduate circuit analysis.


DOI: 10.1002/wcm

cognitive radio realities

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