i n v i extending the reach of cognitive radio

INV ITEDP A P E R

Extending the Reach ofCognitive RadioResearch into cognitive radio systems, aware of their environment and capable of

self-adjustment, has shown promising results; programs for large-scale cognitive

wireless systems are being explored.

By Preston F. Marshall, Member IEEE

ABSTRACT | There has been significant research progress

reported over the last few years in the development of

cognitive radio technologies. This paper reviews some of these

results, describes several critical issues to integrate these

results in usable products, and describes the large-scale

Defense Advanced Research Projects Agency efforts to further

develop and exploit dynamic spectrum access, and initiate

development and operational use of cognitive networking and

a new generation of affordable wireless technology. This paper

also describes future research needs to fully exploit cognitive

radio technology and addresses the challenges that will arise

with its large-scale deployment.

KEYWORDS | Cognitive networks; cognitive radio; Defense

Advanced Research Projects Agency (DARPA) XG program;

delay-tolerant networking; mobile ad-hoc ontologies; network-

ing; policy reasoning; spectrum measurements; spectrum

policy

I . INTRODUCTION

The recent upsurge in interest in cognitive radio technol-

ogy attests to the rapid growth in understanding and

appreciation of its potential to revolutionize the interac-

tion between a radio, its environment, and its user. While

it has been known that the environment has strong (andusually deleterious) effects on the operation of a radio,

only recently have we become able to exploit awareness of

this environment. Traditionally, we have considered how a

fixed mode of operation is degraded in a variety of less than

ideal situations. In cognitive radio, there is now the

opportunity to investigate how the use of adaptive modes

maintains and optimizes performance across a range of

actual environments.

Previously, design studies stated an assumed environ-

ment for which performance was optimized; a cognitive

radio design instead can argue for a range of environments,

each of which will have individually and continually

optimized performance. Radio operation thus transitionsfrom being designed by the radio implementer based on a

projected environment to being dynamically determined

based on the radio’s continually updated perception of its

environment. We can approach this as an opportunity to

achieve incremental gains in radio performance or as an

opportunity to fundamentally change the relationship of

radios, their environment, and the networks and applica-

tions that run over them.

II . RECENT PROGRESS

The transition of cognitive radio from concept to reality

has made considerable progress in the last several years,

driven down two fundamental paths.

The first is the drive to mature, demonstrate, and

ultimately deploy dynamic spectrum access (DSA) systems.A number of commercial and governmental organizations

have completed prototype implementations and submitted

them for varying degrees of technical or operational

scrutiny. This effort broadly clusters into two applications,

with much of industry focused on television white space,

such as contemplated by the IEEE 802.22 Wireless Regional

Area Networks [1], and research into more broad-based

spectrum sharing, generally focused on military or publicsafety as the initial applications.

Progress specific to 802.22 applications has been

driven by the opportunities provided by technical achieve-

ments and potential regulatory acceptance of television

Bwhite space[ utilization [2]. Proposed approaches vary

from access to central databases to fully distributed and

fused sensing. Of particular interest is the reliance on

Manuscript received November 11, 2008. Current version published April 15, 2009.

The author is with the Defense Advanced Research Projects Agency, Arlington, VA

22203 USA and the Centre for Telecommunications Value-Chain Research, Trinity

College, Dublin, Ireland (e-mail: [email protected]; [email protected]).

Digital Object Identifier: 10.1109/JPROC.2009.2013008

612 Proceedings of the IEEE | Vol. 97, No. 4, April 2009 0018-9219/$25.00 �2009 IEEE

multiple resolutions of spectrum sensing to control theoperation of the device, with both fast (1 ms) and slow

sensing cycles (25 ms) driving the adaptation of the access

point and customer equipment. Implementations of

receivers supporting this architecture have been reported

[3], supporting the technical feasibility of this approach.

One approach relies upon analog spectral analysis and

temporal correlation, with only the correlation difference

requiring digitization at a very low rate, a departure fromthe assumption of all digital sensing [4]. Increased

processing in the analog domain and processing of

temporal signatures promises much lower energy con-

sumption and potentially lower cost than high-speed

digital approaches. Scheduling of cognitive radio sensing

within and across communities has been explored and

shown to be a feasible approach to the implementation of

multidomain cognitive radio operation [5].While much research and discussion has focused on

the Bwhite space[ arguments, important and relatively

neglected effects of adjacent channel energy are begin-

ning to be discussed within the framework of cognitive

radio. A simple Boccupied/not-occupied[ categorization

has been the basis of much of the discussion of spectrum

availability. Consideration of adjacent band effects [6]

showed that secondary use of television frequencies inadjacent bands has minimal effect on television recep-

tion and that reasonable performance is achievable at

relatively close distance to an adjacent channel television

transmitter.

Perhaps in recognition of both the likely deployment of

sensing-based systems and the history of malicious

applications of technology, methods of detection and

mitigation of falsified incumbent signals and trust ofdistributed cognitive radios sensing data have now been

proposed. These certainly need to be on the technology

agenda if the sensing scope of cognitive radios is to

transcend individual trust domains [7]. Without these

features, it is hard to imagine how any user or operator

would be willing to trust the operation of a network to a

technology so vulnerable to denial of service.

The second major research topic is the increasedunderstanding and initial prototyping of learning-based

cognitive radio algorithms. Reported work by a number of

researchers [8], [9] shows levels of performance that

should be sufficient to make the argument for incorpora-

tion into operational radios in the near future.

Early work on the representation of knowledge within

a cognitive radio has also been extended. Yarkin and

Arslan [10] described a radio knowledge representationlanguage that provides a mechanism to organize multiple

sources of environmental and location awareness into an

integrated representation. Of particular importance, this

work provided a canonical method to organize the channel

awareness into a structure upon which a cognitive radio

can make operating decisions. In a similar vein, other

researchers have extended knowledge representation and

encapsulation to describe the state of knowledge of theradio components of the transceiver [11].

Not only has research in DSA advanced the technology

over the last several years; the regulatory and spectrum

community also has become increasingly involved in the

process, evidenced by the success of the policy program

at the First, Second, and Third IEEE Conferences on

New Frontiers in Dynamic Spectrum Access Networks

(DYSPAN). Corresponding interest in industry andregulatory standards is reflected in transition of the

IEEE P1900 to a Standards Coordinating Committee

(SCC41) for Dynamic Spectrum Access Networks [12].

The breadth and international scope of membership offer

the opportunity to advance DSA as an integrated package

of technologies rather than as individual and standalone

efforts addressed on a country-by-country basis.

Several complete purpose-built cognitive radio exper-imental platforms have been demonstrated or are under

development. The Kansas University Agile Radio spon-

sored by National Science Foundation appeared as one of

the platform alternatives on which cognitive radio wave-

forms and algorithms can be constructed and tested

without being constrained to the characteristics of the

current commercial waveforms [13].

The Defense Advanced Research Projects Agency(DARPA) has initiated a Bsecond generation[ purpose-

built cognitive radio program [14]. This program, Wireless

Network after Next (WNaN), uses DSA as its fundamental

operating principle and trades high-performance individ-

ual transceivers for replicated but lower performance

transceivers. An example of this philosophy would be

adapting around situations that would otherwise cause

front-end overload, as will be discussed later in this paper.One of the stated objectives of this program is to

demonstrate that cognitive radio can produce at least the

performance of a conventional radio but accomplish this

with reduced performance components through use of

adaptation to mitigate the performance stressing environ-

ments that would otherwise drive energy consumption and

cost. The WNaN radio has four independent transceivers,

covers 900 MHz to 6 GHz, has high-quality front-endfilters to enable the DSA functions to identify and utilize

low-energy pre-selector bands, and uses commercial

quality radio-frequency integrated circuits for the trans-

ceiver functionality. This platform is intended to provide

not only the DSA functionality but cognitive topology

management and content management as well.

III . THE NEED FOR MULTIPLECOGNITIVE RADIO REASONINGTECHNOLOGIES

It is important to recognize that the various focus areas in

cognitive radio research implicitly have specific assump-

tions about the definition. There appear to be two that we

should consider. When Mitola [15] first referred to the

Marshall: Extending the Reach of Cognitive Radio

Vol. 97, No. 4, April 2009 | Proceedings of the IEEE 613

term, he was clearly referring to a device that had thecharacteristics of a conscious intellectual processVa

learning system, such as envisioned by decades of artificial

intelligence research. A (perhaps oversimplified) litmus

test of such systems might be that they derive their

behaviors from experiential learning. Consistent with that

definition of cognitive radio, researchers have reported

progress in applying learning engines to the selection of

channels and waveforms [16].In usage, the term cognitive radio has taken on a

significantly broader connotation and less technologically

challenging definition. The DARPA Next Generation

Communications (XG) program reported success in DSA

using fixed algorithms and policies [17]. The U.S. Federal

Communications Commission undertook a cognitive radio

access proceeding [18] focusing specifically on dynamic

spectrum behavior and was silent on how such behaviorwould be implemented. Several industry organizations

have announced progress on cognitive radios, although

there is little published information on their designs,

algorithms, behaviors, and, most important, performance.

Although the strict definition of cognitive might not

seem to include these technologies, if we wish to assert

that cognitive radio implements Bcognition,[ a fundamen-

tal distinction between a cognitive radio and a non–cognitive radio would be the degree to which the device

makes decisions based on awareness of the environment.

Such a definition has advantages since it is focused on

Bwhat[ the device does, not Bhow[ it does it.

The necessity to be open to both definitions is apparent

in the area of frequency selection, where two different and

competing concerns are being addressed through cognitive

radio: noninterfering and optimized operation. An insis-tence that a cognitive radio must derive its algorithms

through learned behavior provides no mechanism to

address the inherent constraint that the effect of some

decisions is only known external to the learning system

itself. Here the necessary feedback to reinforce behavior is

not available. The impressive results reported for physical

layer learning are achievable because the two link partners

constitute a closed system that can use itself to providereinforcement to the learning. In contrast, when we

consider a system comprising many heterogeneous users of

spectrum, with incompatible waveforms, protocols, and

even operational concepts (a broadcast, receive-only node

has no ability to report interference events), then it is

much harder to apply feedback as the sole mechanism to

discover and correct Baberrant[ behavior.

Some other practical issues also arise. Given the poorstate of information assurance technology, few system

owners or implementers would be willing to have their

system exchange information with any external party. The

growth of Internet malware demonstrates that it is

unrealistic to require participants to cooperate and

exchange information that will impact behavior and

performance. Engineers might argue that the exchanges

were harmless, but the seemingly endless discovery ofexploits and unintentional behaviors in commercial soft-

ware products would undoubtedly hinder any solution that

required nodes to Btrust[ actions or information provided

by other nodes external to the nodes own trust domain.

Regulators tightly control spectrum, due to the extreme

social and economic consequences of interference to key

infrastructure and services. It is unlikely that they would

accept the principle that, although a node’s behavior willinitially be very poor, it will eventually learn to Bplay nice[in the spectrum (after causing any number of interference

events while learning). Similarly, they might not be

pleased to learn a device tested and accepted as behaving

well and not interfering would have the capability to later

learn Bbad habits[ that would have the very consequences

the technology was intended to avoid.

There is a strong argument that when improperbehavior has negative impacts on other, external spectrum

users, than the use of declarative techniques is advanta-

geous. These techniques offer an approach that can be

analytically proven, experimented with to any desired level

of confidence, and then Bfrozen.[ Admittedly, this is not as

intellectually pleasing as a radio that Bprograms itself,[ but

it is testable, verifiable, predictable, and perhaps even

provable.This provides a clear partitioning of the cognitive radio

reasoning infrastructure. On one side, there are functional

behaviors such as sharing and etiquette that focus on the

device’s impacts on other users in the environment.

Presumably, this behavior is externally mandated, as the

radio does not benefit from the constraints and might have

its performance reduced. On the other side, one or more

adaptation mechanisms can optimize performance whileconstrained by external policies. Not surprisingly, these

two research areas have unique approaches to exploiting

environmental awareness.

One of the DARPA XG program’s objectives is

developing a declarative language and processing tool set

for external policy control, such as required by spectrum

sharing. The program has developed two radio-borne

reasoning engines that provide inferential processing ofpredicate calculus policies based on a spectrum manage-

ment ontology and rule set [19], [20].

IV. AN INCLUSIVE MODEL OFCOGNITIVE RADIO

It is appropriate to partition the intelligence model of a

cognitive radio into endogenous and exogenous compo-nents. Previously, the author had proposed the partition-

ing of DSA aspects of cognitive radio into two essential

elements: a system strategy reasoner (SSR) that optimizes

the spectrum decisions and performance of the device

and a policy conformance reasoner (PCR) that executes

and enforces externally provided policies, as shown in

Fig. 1 [21].


614 Proceedings of the IEEE | Vol. 97, No. 4, April 2009

The PCR provides the external (exogenous) policy

component(s) that address the device’s impact on theexternal environmentVprimarily, avoidance of spectrum

interference. The SSR provides the internal optimizing

behaviors (endogenous) to maximize performance through

selection of operating mode and other parameters. This

partition reflects the fact that the external effects of the

device are difficult and often impossible to detect from the

reference frame of the individual radio. It is therefore

difficult to argue for an exclusively learning model of acognitive radio or one based solely on performance

optimizing algorithms.

An example of this partitioning is apparent in the three

principles of cognitive radio, which the author proposed as

the standard for evaluating the performance of the XG

DSA technology [22]. These were:

1) do no harm (to other users of the spectrum);

2) add value (to the user, operator, or owner whoinvested in the technology);

3) perform (robustly and reliably in a range of

environments and user mission needs).

These principles reflect the role of the cognitive radio

intelligence mechanisms in protecting other users from

the effect of device’s behavior and providing a measurable

return on the recurring and nonrecurring investment in its

creation and manufacture. These first two principlesaddress unique and nonoverlapping objectives and respon-

sibilities. They would appear to be difficult to accommo-

date in any single abstraction (learning versus declarative

knowledge.

Looking beyond cognitive spectrum management,

there is a need to extend the concepts of cognitive radio

from a spectrum and link focus to an integrated abstraction

that is inclusive of aspects of wireless device decisionmaking. This includes multiple decision domains, such as

network routing, topology, user interaction, security, and

content management, in a structure capable of expressing

both constraining and optimizing policies, such as thoseshown in Fig. 2.

Two issues must be addressed to make this vision a

reality: 1) an abstraction is needed of how these different

cognitive adaptations interact and 2) a computer science

implementation. Without these, the cognitive radio

community will be building hundreds of stand-alone

experiments but few usable products that can benefit

from more than one breakthrough due to lack of anintegrated mechanism that can unify multiple technologies

for use in constructing cognitive radios. As a user of

artificial intelligence technology, the cognitive radio

community may be one of the first communities able to

articulate the need for a unified theory of cognitive

automata that can integrate learning and declarative

knowledge into a single logical framework rather than

distributed across two disparate mechanisms.As an example, consider spectrum-related decision

making. Traditionally, other users of the spectrum are

represented by national regulatory administrations. In the

cognitive radio model, these same protective interests are

represented by the constraining exogenous policy compo-

nent. The internal optimization of performance is provided

by the endogenous component. The exogenous compo-

nent(s) ensures that the decisions of the endogenouscomponent(s) do not have unacceptably detrimental

effects on other users of the environment with which the

cognitive device interacts, such as the spectrum, network,

or device users. This partitioning allows the endogenous

component(s) to operate as an unconstrained Bgreedy[algorithm, since the Bsocial[ values of the community

(other spectrum users, network users, and device manu-

facturers) are protected by the exogenous component(s).A reasonable assumption would be that the permissive

decisions of the exogenous component must be made

acceptable to the interests of the other environment users,

Fig. 2. Candidate scope of cognitive radio policies.

Fig. 1. PCR and SSR model of a DSA cognitive radio.



typically through a certification or licensing process. This

implies its behavior should be demonstrably deterministic.

It would be hard to obtain approval for a device that could

later learn bad behaviors.

This discussion points to the necessity that the advance-

ment and deployment of cognitive radio not be viewed assolely a technical challenge. Almost all descriptions of

cognitive radio include some degree of spectrum selection

and control; environmentally aware selection among

spectral and frequency options is clearly one of the defining

aspects of the technology. To achieve this, it is desirable

(inevitable) that the regulatory community be highly

involved in the development of the technology and that it

include features that address their considerations to at leastthe rigor of current technical and regulatory methods.

The operation of the exogenous processes can be

grouped into one or more policy conformance reasoners,

and the operation of the endogenous ones can be grouped

into a system strategy reasoner, as depicted in Fig. 3.

Potentially, the actual logic of these reasoners could be

provided by an external party, such as a spectrum

regulator, network owner, or hardware developer, througha common policy language. In fact, in a heterogeneous

network, a desirable attribute of the policy process is that it

specifically enable operation using policies derived from

multiple sources. Equally important, these sources of

policy should be able to develop policies that areindependent of the policy context (i.e., other policies a

user may have loaded) within the device. A spectrum

owner might provide policies for sharing a band, but these

policies would have to coexist harmoniously with policies

independently provided by national regulators, and

perhaps device limitations provided by the manufacturer.

Similarly, a device manufacturer might have products that

could be running a variety of cognitive radio software, andwould provide policies to ensure that the operating limits

of its specific device were not exceeded. Fig. 2 illustrates

the necessity of fitting policies together dynamically into

the architecture depicted in Fig. 3.

This figure depicts the control cycle of a cognitive radio:

sensing the environment(s); creating responsive strategies;

ensuring their compliance with exogenous constraints; and

(implicitly) implementing them within the device. Someauthors have treated the spectrum environment as unique

from the user behavior and device status; however, a more

general framework treats these as symmetric domains that

coexist and are integrated into the behavior of the cognitive

device through mechanisms within the system strategy

reasoner. Intuitively, it is reasonable that the more these

domains can be integrated within the SSR, the more

globally optimal the decisions should be. Although thefigure shows policy conformance reasoners for each of the

environmental domains, this one-to-one relationship is not

necessary if the policy language is general enough. The list

of environmental domains is not intended to be exhaustive

but to show the scope of a symmetric solution to cognitive

radio conformance policies.

Some illustrative spectrum domain considerations are

provided in Table 1 and are partitioned between theexogenous and endogenous reasoning processes. In

general, it is apparent that the exogenous concerns are

those typically associated with communications device

regulation, while those of the endogenous component are

typically associated with conventional communications

link design. This reflects that cognitive radio strategy

development is really an analog of the manual design

process, different in that it is interactive with itsenvironment, based on measured rather than predicted

data, and continually adjusted. However, the considera-

tions are the same as for a noncognitive device.

DSA may be the first domain in which we argue for

adaptation but clearly is not the only one and perhaps, in

retrospect, may not even be the most important.

Table 1 Considerations for Endogenous and Exogenous Spectrum Reasoning

Fig. 3. Relationship of endogenous and exogenous reasoning in

cognitive radio.



Most consideration of the exogenous PCR has been in

the spectrum domain and has been focused on the issues

associated with spectrum access [19]. The concept is

equally applicable to the other environments of the cog-

nitive radio. For example, a network owner might requirethe device to validate its admission and packet traffic to

ensure that the actions of the device were consistent with

the network’s operating rules, or a device manufacturer

might include a validation engine at the hardware interface

to ensure that the operation of cognitive radio algorithms

did not violate any of the operating limitations of the

device (e.g., thermal, duty cycle, amplifier bias, etc.). A

conventional network firewall is an obvious specializedimplementation of this structure’s PCR construct. Simi-

larly, the V-chip (television programming content control)

provides rudimentary video policy enforcement; Internet

content filters are also examples of PCRs that operate at

the user interface to ensure that information over this

interface complies with a externally provided policy. The

PCR is thus not really a new concept; it is a generalization

of functionality that experience has shown it is needed tobuffer the actions of a technology and the environments on

which it has an impact.

Another consideration is the location of these compo-

nents. Although there is a tendency to think of cognitive

radios as peers and self-contained; there is no necessity

that implementation of these components be symmetric or

local. Spectrum selection and conformance checking can

certainly be performed at a central node that isadvantaged; network policies can be provided at boundary

devices, etc. An illustrative list of these environments, the

characteristics that are dependent on them, and their

significance is provided in Table 2.

The partitioning of the reasoning into these two

categories provides a simplifying abstraction. At best, the

policy conformance process cannot improve performance;

its effect is only to preclude certain actions. Only the

endogenous component can contribute to performance,

and therefore it is the primary contributor to the

performance benefits achieved from cognitive radio.

The line between cognitive and noncognitive radio isnot absolute. Many low-cost consumer products have

adaptive behaviors, such as selecting the type of cellular

service to connect to, adapting WiFi modes, selecting ISM

channels for cordless phones, and selecting between WiFi

and cellular connectivity. These clearly meet at least the

letter if not the spirit of the cognitive radio definition.

The intuitive distinction is that a cognitive radio must

adapt a significant number of these parameters andmodes, and do so in a way that recognizes the interaction

of these parameters across multiple domains, as depicted

in Figs. 2 and 3.

V. A NEW STRUCTURE OF THEARGUMENT FOR COGNITIVE RADIO

The tendency has been to view cognitive radio as a newer,and ultimately more complex, device than the radios that

have preceded it in the evolution of wireless devices. This

relegates the use of cognitive radio to relatively cost-

insensitive applications. An alternative view is that

cognitive radio provides the opportunity to mitigate

shortfalls in hardware performance and, by doing so,

make less capable hardware perform reliably and effec-

tively in environments in which a conventional devicewould fail.

This argument would lead to the conclusion that the

community can present the benefits from cognitive radio

operation in two ways: improve the performance of similar

equipment or reduce component performance needs of the

implementation while not sacrificing performance. In

practice, an application might elect a mix of both of these.

Table 2 Illustrative Cognitive Radio Operating Environments



Table 3 illustrates a summary of how cognitive radio

benefits can be reflected.

The first measure (row 1) is the effect of cognitive radio

on the performance and reliability of the radio and

network. There is a growing literature providing both

analysis and methodology addressing this metric. The

second row of this table has received scant attention. Asmentioned, the DARPA WNaN program has the explicit

goal of demonstrating that a reduced performance

cognitive radio can achieve a high level of performance

due to the effectiveness of the adaptation methodology.

Fig. 4 illustrates two perspectives on cognitive radio

performance.

Line A illustrates the improvement in performance

achieved by implementing cognitive radio technology witha fixed device performance level (row 1 of Table 3).

Achieving this increased performance has been one of the

motivators for cognitive radio research.

What should be of equal interest to the cognitive radio

community is the opportunity permitted by line B (row 2

of Table 3). Line B represents the reduction in component-

level performance that can be tolerated (through cognitive

radio technology) while maintaining a consistent and/oradequate level of network performance from the device.

The reduction in component performance provides the

opportunity to either greatly decrease the cost of the

networks or greatly increase the density of the network.

An example of this argument is the mitigation of front-

end overload effects caused by high levels of energy within

the receiver preselector passband. A difficult challenge for

spectrum management is to ensure that radios do not

attempt to operate simultaneously on the same frequency.

However, it is even more difficult to also ensure that a

strong signal is not present near the assigned channel. This

is particularly true in peer-to-peer applications, such as

military or public safety operations. A noncognitive radiomust have sufficient front-end linearity to operate

effectively in any environment to which it might be

assigned a frequency. A cognitive radio, on the other hand,

need only have sufficient front-end linearity to ensure that

at least one of its front-end preselector bands (covering

candidate frequencies) has less than the overload thresh-

old energy. By applying DSA and operating band selection,

the radio’s front-end linearity performance requirements[such as third-order intercept point (IIP3)] can be reduced

from a level that can operate in all of the filter selections

that might contain an assignment to a level that is

sufficient to ensure reliable operation in at least one of the

filter settings. Since front-end energy consumption is

typically proportional to front-end dynamic range, a

corresponding savings in energy consumption is obtained.

Work by the author has shown that there are strongarguments that orders of magnitude reduced IIP3 levels, or

similar increases in reliability, can be achieved through

such adaptations [23].

This argument reverses some perceptions of cognitive

radio cost. One of the objectives of the WNaN program is

to demonstrate that a highly reliable, military mission

capable radio can be significantly reduced in cost (to $500

in quantity) through cognitive radio technology whileproviding more flexibility at physical and network layers.

WiFi, cellular, and MP3 players all demonstrate that

significant reductions in cost can create uses for technol-

ogy that would have been unimaginable at higher cost

points. Cognitive radio research should strive to enable

affordability as well as to enhance performance.

VI. THE NETWORK AND UPPER LAYER’SBEHAVIORS IN COGNITIVE RADIO

While much of the argument for cognitive radio has

focused on the characteristics of physical layer adaptation;

it can be argued that the network layer, leveraging the

flexibility enabled by the DSA and cognitive radio model,

Table 3 Metrics of Cognitive Radio Performance Benefits

Fig. 4. Benefits of cognitive radio operation.



provides equally significant benefits. DSA, by eliminating

the specific assignment of individual links to specific

frequencies and bandwidths, enables adaptation not only

of the physical layer but also in the organization of the

network itself. Some of the adaptations that WNaN will

perform to address and mitigate the effects of a range of

environments, device limitations, and network delivery

requirements are shown in Fig. 5. These adaptations have aprerequisite that the network be able to unilaterally select

frequency and bandwidth for each link.

If bandwidth in one region of the network is

inadequate, the network can Bmove[ spectrum through

locating or reassigning spectrum to supplement the

throughput. If a link is interfered with by other signals

or overloaded by adjacent channel energy, the cognitive

radio locates spectrum more suitable to its operation and isfree to implement that decision locally and unilaterally. If

there is no way to locate enough spectrum in a congested

region of the network, the network changes its topology to

route out of and around the congested region.

The DSA community has advocated the acceptance of

DSA technology to address many of the shortfalls in

manual frequency planning and management. The WNaN

approach additionally argues to adopt DSA for theflexibility it affords:

• to the device for self-management of the environ-

ment and the resulting reduction in component

performance requirements, and thus cost;

• to the spectrum manager to not have to separate

receivers from strong sources of adjacent band

energy;

• to the network and applications to reconfigure andreprovision wireless bandwidth dynamically

Simply put, it argues that a DSA cognitive radio has

advantages, even if the access to spectrum is neutral.

In initiating the DARPA Wireless Network after Next

WNaN cognitive radio program, the author postulated

11 theses for the program; seven of which are shown in

Table 4 [14]. Although many of these theses relate to

the network layer operation, they are enabled solely bythe adaptation that is provided by the DSA mechanism

at the physical layer.

For cognitive radio to fully achieve its maximum bene-

fit, the networking community needs to reframe the ques-

tion from Bhow do we route over a topology?[ to Bwhat is

the right topology to form in order to perform this

mission?[ This is a fundamental change in the direction of

networking and is an opportunity that is not present in theconventional terrestrial or more static wireless technolo-

gies. For the core network, it is inconceivable that new

fiber-optic lines could be determined, buried, terminated,

and provisioned to a network in a subsecond timeline, but

that is exactly the opportunity that cognitive radio pro-

vides. Similarly, the connectivity of new satellite trans-

ponders or cellular towers can ot be created by a network

Bon demand,[ such as a DSA architecture can inherentlyprovide.

For 30 years, topology has been treated as a given, and

the routing as the consequence. With cognitive radio, this

relationship can be reversed. It determines the routing that

is needed, then uses DSA to create the right topology to

implement it. The cognitive radio and network community

must accelerate the research to develop these mechanisms.

Table 4 Selected WNaN Cognitive Radio Theses

Fig. 5. Upper layer exploitation of DSA flexibility.



One of the initial investigations being performed by theWNaN program is the integration of content management

with the operation of the cognitive radio algorithms. The

Delay Tolerant Networking bundle protocol agent [24] is

the underlying protocol for this experimentation, which is

being performed as an element of the Disruption Tolerant

Networking program. Unlike a packet, a bundle is a unitary

set of payload and metadata information that can be

cached, routed, or stored by intermediate nodes. Itprovides the cognitive network the facility to organize

and optimize the placement of information within the

network. Integrated with the DSA facility, it now enables

the network to balance the constraints on topology

(derived from spectrum awareness and link path char-

acteristics) and the user behaviors in accessing content.

Content can be located as dictated by actual needs and

real-time assessment of physical constraints rather thanstatically preplanned. The cognitive radio network can

consider descriptions of information content as an address

rather than descriptions of fixed server node locations. In

fact, if we look at the process of Internet access today, the

search engines create essentially a content-based network,

just overlaid on a packet and address-based network.

A use case for this technology is instructive. Although

backhaul bandwidth in much of the world is plentiful, inother regions backhaul is not available, or highly con-

strained. A cognitive radio network would manage the

location of content distributed across end-user devices,

without discrete servers or any fixed address structure.

Many areas of the world would benefit from even localized

services that could provide information on locally critical

topics, such as medical assistance, food and water

availability, and education. Without the requirement forbackhaul to fixed services, a content-based cognitive radio

network is a feasible and highly desirable outcome of the

current research. Although wireless may not be the ideal

backhaul to distant services, it is an effective means to set

up networks whose users are concerned with resources

within walking distance more than those on worldwide

social networking sites.

An additional consideration is that the cognitivenetwork does not make demands on what is perhaps the

scarcest resource in many environments: the intellectual

infrastructure to assign frequencies and addresses, define

servers, manage indexes, caches and backhaul networks,

etc. An information network that operates Bout of the box[certainly has appeal and unimagined utility.

VII. WHAT IF WE SUCCEED? WHAT NEWRESEARCH IS NEEDED?

Much of the cognitive radio research is focused on current

problems in wireless technology, and clearly it is essential

that cognitive radio offer useful solutions to these

problems. However, we should also be cognizant that

when cognitive radio deployment occurs, it will introduce

new consequences, opportunities, and challenges of itsown. The deployment of tens of cognitive radios will have

little impact; the deployment of hundreds of thousands

will change the nature of the spectrum environment,

create new issues in network operation, and require policy

control solutions that are beyond those envisioned today.

In particular, the inclusion of dynamic and opportunistic

spectrum within the cognitive radio will fundamentally

change the nature of a radio deployment, due throughsignificant reduction in the constraints of spectrum avail-

ability and management. It is important that a portion of

our research look ahead to this epoch. It might not be that

far away.

A. Density and ScalingA fundamental change that cognitive radio will induce

is in network density. Prior work in self-forming networksand related technologies focused on discovering and

exploiting all possible connectivity. At some level of

network density, the situation reverses; the network needs

to organize its topology to reduce the complexity and

interactivity of the connectivity. Fig. 6 illustrates the

conventional view of mobile ad-hoc networks.

In this model, the network’s strategy is to maximize

connectivity by rendezvousing all of the nodes onto asingle frequency [14]. Unfortunately, this architecture has

been shown to have severe scaling limitations [25] due to

(among other reasons) mutual interference between the

nodes. Even nodes that cannot demodulate a signal may be

interfered with by its presence. The interference range is

well in excess of the communications range. A simple

measure of this network is to note that only one node can

transmit at any one time, despite the number oftransceivers that are present. This is a strong intuitive

argument against its scalability.

In WNaN, the author proposed [14] that providing

multiple separate transceivers, each forming small local

networks, would be one mechanism by which the network

could avoid performance constrained by interference.

Small, local networks on separate frequencies (made

possible by DSA) would decouple the effect of changes in

Fig. 6. Typical MANET network.



one portion of the network to reduce thrashing. Fig. 7

illustrates the network being developed by the WNaN

program. In this architecture, each node connects to a

different set of neighboring nodes through multiple

transceivers, frequencies, and autonomously managedsubnetworks.

Actual experimentation with this architecture will not

begin until later this year, and dense quantities of devices

will not be available for another year after that, so the

experimental results of this approach will not be available

for several years.

This architecture is enabled by DSA and cognitive radio

technologies. The management of this many frequencies ina complex and constantly changing network would be

impractical for any static spectrum assignment strategy

and is probably only practical with automated management

and interference control.

The second enabling consideration is the use of the

adaptation of a cognitive radio to use lower cost and

performance transceiver components in each of the nodes,

making the multitransceiver configuration affordable at alower cost than if a single, high-quality transceiver was

utilized. Not only does the cognitive radio functionality

improve the link-level performance; it is the key factor in

replication in the physical layer.

This approach may provide a mechanism to address

the scaling limits imposed by node-to-node interference

that are otherwise inherent in the MANET network struc-

ture. The issue of how to route and determine appropriatetopologies with this many degrees of freedom (frequency,

interference environment, waveform, power, connectivi-

ty, routing, etc.) is a fundamental research question that

arises with the integration of DSA and advanced

networking.

B. Expressions of Cognitive Radio Algorithmsand Reasoning

Cognitive radio research is being undertaken by a

significant number of researchers, attacking a large number

of problems and opportunities in the field. The resulting

concern is that as individual techniques are developed andvalidated to meet cognitive radio challenges, such as

channel selection, waveform selection, power control, and

filter settings, there is no common or community accepted

framework to integrate these technologies together into

products. A potential result of this will be islands of tech-

nology capabilities, each targeting specific problems but

not readily integrated with other solutions to other

problems. Even if the range of policies and controls shownin Fig. 2 are developed, how will their operation be in-

tegrated onto a single device, or within a single networking

product?

There has been progress reported in this area. Among

other initiatives, SCC41 has established a policy language

as one of its goals. The DARPA XG program has reported

development of two policy reasoners for spectrum policy

conformance reasoning [19], [20]. This is a reasonablestart, but a broader base of the community needs to be

involved in contributing their research insight into to these

ontologies, semantics, and necessary syntactic structures;

and a more extensive set of reasoning domains needs to be

considered during their formulation.

Additionally, the reasoning engine is only one-half of

the tool set that must integrate into a cognitive radio.

There is an equal need for learning-based engines thatoffer so much promise for the endogenous portion of the

decision process. Even with these two components, there

is still the challenge of integrating these two very different

structures into a cohesive theory and implementation for

network devices. There are a large number of promising

results being reported but little in the way of an abstract or

conceptual structure which they can populate.

C. StabilityThe logistics of acquiring, programming, and operating

wireless nodes has generally limited the size and extent of

the networks that have been used to prototype cognitive

and ad-hoc wireless technology. When the node count is in

the hundreds, we can certainly treat each node as an

individual, and the dynamics of the system is sufficiently

limited that it can be analyzed by discrete tools. As wecontemplate networks that have large membershipVnot

in the hundreds, but in the hundreds of thousands of

nodesVthen explicit understanding of the behavior of

individual nodes becomes unachievable and a transition to

a more systemic treatment of network behavior is

mandatory.

Large-scale simulation tools have given us the ability to

achieve insight into the operation of individual nodeswithin routing networks. What will make cognitive radio

even more challenging is that the complex networks

envisioned not only interact through the network mechan-

isms but also interact through the spectrum they share at

the physical layer and have the necessity and capability to

continually morph the connectivity and content locations.

Conventional radios assume (and generally receive)

Fig. 7. Cognitive radio–enabled WNaN network.



spectrum that is intended to make node operationorthogonal; a cognitive radio will share spectrum, so deci-

sions that are made on one node will ripple through

neighboring nodes and, through them, potentially of all the

nodes within a network. A cognitive radio network will

consist of at least the physical, media access, and network

layer processes, each making decisions that ripple across

and between layers.

These stimuli transit the network at different rates, aredamped or initiate cascading changes at different points,

and have different responses at each layer. The problem

becomes a system exhibiting characteristics of transient

response, coupling, damping, and oscillation.

An example of such behavior could be as simple as just

one radio starting to transmit on a frequency, forcing ten

radios to move to new frequencies, each of which in turn

force ten more to do the same. At the same time as thisripple of frequency changes moves through the network,

the network layer would be attempting to update routing,

even as the network connectivity was changing. Some of

these effects are shown in Fig. 8, providing change stimuli,

coupling, and damping effects on a potentially oscillatory

system. Stability analysis of layer 3 has become an accepted

technique, but a system that has this scale of interaction

with and through the environment, users, devices, andapplications of a cognitive radio network is inherently

much more complex.

Neel [26] provides an argument that the behavior of

spectrum interaction can become stable, even given locally

optimizing behaviors, but this analysis must be extended to

include the interaction of these processes with the other

layers and the effects between them.

The change in focus from node to system behavior isimperative to adoption of cognitive radio technology. One

appeal of cognitive radio is that it can manage dense

environments; the problem is that it may become so

complex we cannot prove it will have an acceptable range

of behavior and performance.

The critical consideration of density is inherent in any

of the concepts of spectrum sharing. One effect of current

spectrum management has been to protect most radiosfrom detrimental effects of their environments, including

adjacent band signals. Where these protections have failed,

the effects have been significant, as demonstrated in the

case of interference between adjacent cellular services and

public safety radio systems in the United States [27]. Since

one assumption of cognitive radio is that it will share

spectrum, it is reasonable to assume that it will both be

required to locate in the proximity of strong signals, suchas television broadcasts, and will increase the density of

band usage where it is permitted.

A quantitative sense of one of these effects is

considering that if a radio had just enough IIP3 perfor-

mance to not have its noise floor impacted in a given

environment, and DSA enables ten times the emitter

density (a typical claim for increased spectral usage) [17],

then energy provided to the front end would be expectedto increase proportionately by ten and the third-order

intermodulation products to correspondingly increase by

20–30 dB. Density creates its own issues at both the

network and the physical layers of the device!

To some extent, the dense cognitive radio network will

function like an ecosystem, similar to a population model

that makes no assertions about the future state of

individual organisms but can predict the aggregatedynamics and end state of the population as a whole.

The need for showing the stability of an extremely large

number of nodes would argue for a transition from a

discrete simulation to a formal analytic model of the

dynamics of a system of nodes. This transition might have

significant implications for how the design of such large-

scale and highly coupled systems is approached. Proof of a

formal stability may have to become a fundamental driverin media access control, network, and applications layers.

Proof that they have appropriate damping of impulsive

modes, resist excitation, randomize responses, vary

resonant modes, and the kind of design decisions normally

associated with a control process may need to become

integral in the design of these layers, and of equal or

greater importance than today’s concerns with throughput

and latency. The understanding of these complex interac-tions needs to move from Bif, then, else[ to differential

equations, modes, and generalized expressions of stability

and equilibrium conditions that can be proven for all cases

rather than shown for a single or set of discrete cases.

D. Cognitive Radio Environmental AbstractionsThe environment of a cognitive radio is complex and

unique to each location on the earth. It is significant thatthere are now a number of concerted efforts to capture and

disseminate the specifics of representative environments.

However, this leaves the researcher with the profound

problem of how to show that a given cognitive radio

technique is effective in a range of environments All

researchers cannot be expected to show that all techniques

work against all of the (increasing number of) collectionsFig. 8. Illustrative stimuli, coupling, and damping modes.



of spectrum and network data. An automated threshold

determination algorithm has been presented by research-

ers at the University of Kansas [28], which adds rigor and

quantitative confidence to what other researchers have

assumed from Bvisual[ inspection and points to the need

for a universal and community accepted way to both

exchange and characterize spectrum measurements. Thequestion of spectrum characterization (beyond occupancy

metrics) has not been approached generally.

There are a number of techniques that have been

developed to characterize spectrum usage on a frequency

by frequency basis, such as the Spectrum Utilization Index,

which provide a focus on frequencies usage characteristics

[29] and effective use of the spectrum. Measures such as

Bpercentage spectrum occupied[ are useful for illustratingthe opportunity for DSA systems and the effectiveness of

spectrum management but are not insightful into the

dynamics of how devices will operate in the spectrum. For

the community to be able to compare results of alternative

algorithms and technologies, a much more subtle set of

effects must be considered. Table 5 illustrates some of the

considerations that any environmental abstraction should

address, ideally as closed-form expressions.To compare alternative implementations of cognitive

radio, it is necessary to be able to characterize the

environments in ways that are repeatable within a line of

research, and across researchers. Additionally, it is

preferable to be able to characterize these environments

in ways that enable the community to apply the generality

of formal methods rather than rely solely on empirical data

whose general applicability is often unknowable, andcertainly not provable. These closed-form arguments could

become powerful tools for understanding and advocating

cognitive radio technology without sole reliance on

specific sets of empirical data and limited scope of proof.

The author has proposed a structure to describe the

distribution of narrow- and wide-band energy throughout a

spectral environment [30], but the purpose of this

discussion is not to advocate for any specific representa-tion of spectrum characteristics. This is itself a subject for

continued research. Would the community rather argue

that a given technique appears to work well in the Chicago

spectrum collection but seems to fail in the New York one;

or would it prefer to argue that this technique has provably

positive performance gains for any environment in which

some agreed-to parameter �spectrum > 3:0.

E. Decision Theory in Cognitive RadioThere is a considerable body of literature that details the

optimizations that can be performed on individual chan-

nels, and we are seeing the beginning of a similar literaturein the area of more complex adaptations. In the imple-

mentation of DSA functionality, the information required

to make spectrum decisions can be obtained at relatively

low cost in energy and channel time. This class of sensing

can generally be accomplished as unilateral operations

performed by the nodes, without explicit coordination. On

the other hand, channel measures such as delay spread can

only be obtained when the channel is established and ableto exchange information in sufficient quantity to satisfy the

decision needs of the adaptation algorithms. The actual

amount of channel state information may not be excessive,

but the time to perform rendezvous, synchronization, and

measurements may be significant, particularly if the time is

significant compared to the correlation time of the channel

information that is being acquired.

Although it has been shown that the use of this in-formation can improve the performance of devices, prog-

ression of the field requires we now consider the benefits

we obtain from additional channel information compared

to the cost of obtaining it. Designers generally have at least

statistical knowledge of the environment in which their

devices will operate, so the problem is one of decision

theory: how much is a decision improved if uncertainly is

reduced compared to the cost of obtaining additional in-formation. For example, a radio might have both a constant

envelope and high peak average ratio waveform, and would

choose based on the measured delay spread and range of

intersymbol interference that would occur. The question

that the research community must start to address is how

much is a better decision between these waveforms worth,

compared with the cost of initially and periodically mea-

suring the channel characteristics. One can imagine thatsome decisions would be better left with some uncertainly,

but today there is no formal mechanism to investigate these

trades available. The first algorithm needed is to decide

whether to invest in acquiring channel state at all, not the

decision of how to use it.

Table 5 Examples of Desirable Spectrum Distribution Characteristics



F. Relationship of Content Flow and Cognitive RadioMost research in cognitive radio has followed the con-

ventional abstraction of the application layer operating

independently of the network layer. As previously men-

tioned, current research is just starting to investigate ex-

panding the cognitive management domain to include

determining the location of content in the network. Just as

reaching down to the physical layer greatly increased the

complexity and interaction of the networking dynamics,introduction of dynamic content location does the same for

the upper layer functions of the network. It is certainly too

soon to argue for the benefits of this expansive vision of

cognitive adaptation, but there is a strong intuitive argu-

ment that the control over content location provides radio

and network optimizing options that are not available when

content location is fixed, and managed as an independent

domain. A decade ago, self-forming packet wireless net-works were a research community priority; perhaps self-

forming wireless information networks should be the next.

G. The Practice of Interference Tolerant OperationMuch of the dynamic spectrum access research has

focused on the challenge of assuring noninterfering

operation with respect to incumbent users. A much more

aggressive role for cognitive radio is to ensure that devicescan meet stated reliability, even in the presence of

increasing levels of interference. In this regime, devices

need not ensure noninterfering operation to the levels of

assurance otherwise required to protect interference

intolerant devices. Fig. 9 illustrates the differences from

current DSA to range of technologies that are needed to

provide interference-tolerant operation. Note that each of

these is a very active research area in its own right; thedistinction is integrating their individual contributions to

significantly increase the density of devices. In this model,

the current evolution of DSA is a waypoint to a much more

significant change in communications practice: complete

delegation of both interference avoidance and mitigation

to the devices that populate the spectrum.

VIII. CONCLUSION

Cognitive radio research has now advanced to the point

where a number of techniques and technologies have

shown desirable analytic and experimental results. Some

of the technology has advanced to the point of reporting

meaningful field test results that strongly argue that

cognitive radio can begin to fulfill its promise to

revolutionize wireless communications.

There would appear to be sufficient technology andexperimental platform development under way to enable

early deployments of cognitive radio platforms, probably

focusing on DSA as the initial argument for deployment.

However, to complete this evolution, the research

community needs to focus on establishing unified abstrac-

tions, research taxonomies, and tool sets that can span the

scope of individual research projects. This will enable

results to be aggregated and create the ability to integrateand synthesize products that draw from technology

developed by independent research thrusts.

Additionally, there is a transition from envisioning

cognitive radio as an adjunct to current wireless architec-

tures and services to new environments, opportunities, and

capabilities that can be created uniquely through cognitive

radio. The opportunity for cognitive radio may lie not in

displacing existing wireless network infrastructure but increating wireless information networks that provide new

services and capabilities that current wireless service

architectures are incapable of providing. h

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ABOUT TH E AUTHOR

Preston F. Marshall (Member, IEEE) has an almost 30 year

background in communications systems and software in both

research and commercial environments. Currently he is with the

Defense Advanced Research Projects Agency (DARPA) Strategic

Technology Office (STO), and serves a Program Manager for many

of the DARPA Wireless and Cognitive Radio and Network

programs. These programs include development of dynamic

spectrum access technology, Wireless Networking after Next

(WNAN) program for low cost wireless networking and the

Disruption and Delay Tolerant Networking Program (DTN). These

programs collectively provide the technology base for high performance and affordable

infrastructureless wireless networking. Mr. Marshall has been Technical Program Chair

for all of the IEEE DYSPAN conferences. Mr. Marshall holds a B.S.E.E and M.S. Information

Science from Lehigh University, and is a Graduate student at Trinity College, Dublin.



i n v i extending the reach of cognitive radio

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