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NeXt generation/dynamic spectrum access/cognitiveradio wireless networks: A survey

Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran *, Shantidev Mohanty

Broadband and Wireless Networking Laboratory, School of Electrical and Computer Engineering,

Georgia Institute of Technology, Atlanta, GA 30332, United States

Received 2 January 2006; accepted 2 May 2006Available online 17 May 2006

Responsible Editor: I.F. Akyildiz

Abstract

Todays wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of theassigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage neces-sitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networkingparadigm is referred to as NeXt Generation (xG) Networks as well as Dynamic Spectrum Access (DSA) and cognitiveradio networks. The term xG networks is used throughout the paper. The novel functionalities and current research chal-lenges of the xG networks are explained in detail. More specifically, a brief overview of the cognitive radio technology isprovided and the xG network architecture is introduced. Moreover, the xG network functions such as spectrum manage-ment, spectrum mobility and spectrum sharing are explained in detail. The influence of these functions on the performanceof the upper layer protocols such as routing and transport are investigated and open research issues in these areas are alsooutlined. Finally, the cross-layer design challenges in xG networks are discussed. 2006 Elsevier B.V. All rights reserved.

Keywords: Next generation networks; Dynamic spectrum access networks; Cognitive radio networks; Spectrum sensing; Spectrummanagement; Spectrum mobility; Spectrum sharing

1. Introduction

Todays wireless networks are regulated by afixed spectrum assignment policy, i.e. the spectrum

is regulated by governmental agencies and isassigned to license holders or services on a long termbasis for large geographical regions. In addition, alarge portion of the assigned spectrum is used spo-radically as illustrated in Fig. 1, where the signalstrength distribution over a large portion of thewireless spectrum is shown. The spectrum usage isconcentrated on certain portions of the spectrumwhile a significant amount of the spectrum remainsunutilized. According to Federal Communications

1389-1286/$ - see front matter 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.comnet.2006.05.001

* Corresponding author. Tel.: +1 404 894 5141; fax: +1 404 8947883.

E-mail addresses: [email protected] (I.F. Akyildiz), [email protected] (W.-Y. Lee), [email protected] (M.C.Vuran), [email protected] (S. Mohanty).

Computer Networks 50 (2006) 21272159

www.elsevier.com/locate/comnet
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Commission (FCC) [20], temporal and geographicalvariations in the utilization of the assigned spectrum

range from 15% to 85%. Although the fixed spec-trum assignment policy generally served well in thepast, there is a dramatic increase in the access tothe limited spectrum for mobile services in therecent years. This increase is straining the effective-ness of the traditional spectrum policies.

The limited available spectrum and the inefficiencyin the spectrum usage necessitate a new communi-cation paradigm to exploit the existing wireless spec-trum opportunistically [3]. Dynamic spectrum accessis proposed to solve these current spectrum ineffi-

ciency problems. DARPAs approach on DynamicSpectrum Access network, the so-called NeXt Gener-ation (xG) program aims to implement the policybased intelligent radios known as cognitive radios[67,68].

NeXt Generation (xG) communication net-works, also known as Dynamic Spectrum AccessNetworks (DSANs) as well as cognitive radio net-works, will provide high bandwidth to mobile usersvia heterogeneous wireless architectures anddynamic spectrum access techniques. The inefficientusage of the existing spectrum can be improvedthrough opportunistic access to the licensed bandswithout interfering with the existing users. xG net-works, however, impose several research challengesdue to the broad range of available spectrum as wellas diverse Quality-of-Service (QoS) requirements ofapplications. These heterogeneities must be cap-tured and handled dynamically as mobile terminalsroam between wireless architectures and along theavailable spectrum pool.

The key enabling technology of xG networks isthe cognitive radio. Cognitive radio techniques pro-

vide the capability to use or share the spectrum in

an opportunistic manner. Dynamic spectrum accesstechniques allow the cognitive radio to operate inthe best available channel. More specifically, the cog-nitive radio technology will enable the users to (1)determine which portions of the spectrum is avail-

able and detect the presence of licensed users whena user operates in a licensed band (spectrum sens-ing), (2) select the best available channel (spectrummanagement), (3) coordinate access to this channelwith other users (spectrum sharing), and (4) vacatethe channel when a licensed user is detected (spec-trum mobility).

Once a cognitive radio supports the capability toselect the best available channel, the next challengeis to make the network protocols adaptive to theavailable spectrum. Hence, new functionalities arerequired in an xG network to support this adaptivity.

In summary, the main functions for cognitive radiosin xG networks can be summarized as follows:

Spectrum sensing: Detecting unused spectrumand sharing the spectrum without harmful inter-ference with other users.

Spectrum management: Capturing the best avail-able spectrum to meet user communicationrequirements.

Spectrum mobility: Maintaining seamless com-munication requirements during the transition

to better spectrum. Spectrum sharing: Providing the fair spectrum

scheduling method among coexisting xG users.

These functionalities of xG networks enable spec-trum-aware communication protocols. However,the dynamic use of the spectrum causes adverseeffects on the performance of conventional commu-nication protocols, which were developed consider-ing a fixed frequency band for communication. Sofar, networking in xG networks is an unexploredtopic. In this paper, we also capture the intrinsicchallenges for networking in xG networks and layout guidelines for further research in this area. Morespecifically, we overview the recent proposals forspectrum sharing and routing in xG networks aswell as the challenges for transport protocols. More-over, the effect of cross-layer design is addressed forcommunication in xG networks.

The xG network communication componentsand their interactions are illustrated in Fig. 2. It isevident from the significant number of interactionsthat the xG network functionalities necessitate a

cross-layer design approach. More specifically, spec-

Fig. 1. Spectrum utilization.

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trum sensing and spectrum sharing cooperate witheach other to enhance spectrum efficiency. In spec-trum management and spectrum mobility functions,application, transport, routing, medium access andphysical layer functionalities are carried out in acooperative way, considering the dynamic nature

of the underlying spectrum.This paper presents a definition, functions and

current research challenges of the xG networks. InSection 2, we provide a brief overview of the cognitiveradio technology. The xG network architectures onlicensed band and on unlicensed band are presentedin Section 3. In Section 4, we explain the existingwork and challenges in spectrum sensing. Then, wedescribe the xG network functionalities: spectrummanagement, spectrum mobility and spectrum shar-ing in Sections 5, 6, and 7, respectively. In Section 8,

we investigate how xG features influence the perfor-mance of the upper layer protocols, i.e., routingand transport. Finally, we explain how xG functionscan be implemented in a cross-layer approach in Sec-tion 9 and conclude the paper in Section 10.

2. Cognitive radio

Cognitive radio technology is the key technologythat enables an xG network to use spectrum in adynamic manner. The term, cognitive radio, can

formally be defined as follows [20]:

A Cognitive Radio is a radio that can change its

transmitter parameters based on interaction with

the environment in which it operates.

From this definition, two main characteristics ofthe cognitive radio can be defined [27,58]:

Cognitive capability: Cognitive capability refersto the ability of the radio technology to captureor sense the information from its radio environ-ment. This capability cannot simply be realizedby monitoring the power in some frequency bandof interest but more sophisticated techniques arerequired in order to capture the temporal andspatial variations in the radio environment andavoid interference to other users. Through thiscapability, the portions of the spectrum thatare unused at a specific time or location can beidentified. Consequently, the best spectrumand appropriate operating parameters can beselected.

Reconfigurability: The cognitive capability pro-vides spectrum awareness whereas reconfigu-rability enables the radio to be dynamicallyprogrammed according to the radio environ-ment. More specifically, the cognitive radio canbe programmed to transmit and receive on avariety of frequencies and to use different trans-mission access technologies supported by its

hardware design [34].

Fig. 2. xG network communication functionalities.

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The cognitive radio concept was first introducedin [45,46], where the main focus was on the radioknowledge representation language (RKRL) andhow the cognitive radio can enhance the flexibilityof personal wireless services. The cognitive radio is

regarded as a small part of the physical world touse and provide information from environment.The ultimate objective of the cognitive radio is to

obtain the best available spectrum through cogni-tive capability and reconfigurability as described

before. Since most of the spectrum is alreadyassigned, the most important challenge is to sharethe licensed spectrum without interfering with thetransmission of other licensed users as illustratedin Fig. 3. The cognitive radio enables the usage of

temporally unused spectrum, which is referred toas spectrum hole or white space [27]. If this band isfurther used by a licensed user, the cognitive radiomoves to another spectrum hole or stays in the sameband, altering its transmission power level or mod-ulation scheme to avoid interference as shown inFig. 3.

In the following subsections, we describe thephysical architecture, cognitive functions and recon-figurability capabilities of the cognitive radiotechnology.

2.1. Physical architecture of the cognitive radio

A generic architecture of a cognitive radio trans-ceiver is shown in Fig. 4(a) [34]. The main compo-nents of a cognitive radio transceiver are the radiofront-end and the baseband processing unit. Eachcomponent can be reconfigured via a control busFig. 3. Spectrum hole concept.

Fig. 4. Physical architecture of the cognitive radio [12,34]: (a) Cognitive radio transceiver and (b) wideband RF/analog front-end

architecture.



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to adapt to the time-varying RF environment. Inthe RF front-end, the received signal is amplified,mixed and A/D converted. In the baseband process-ing unit, the signal is modulated/demodulated andencoded/decoded. The baseband processing unit of

a cognitive radio is essentially similar to existingtransceivers. However, the novelty of the cognitiveradio is the RF front-end. Hence, next, we focuson the RF front-end of the cognitive radios.

The novel characteristic of cognitive radio trans-ceiver is a wideband sensing capability of the RFfront-end. This function is mainly related to RFhardware technologies such as wideband antenna,power amplifier, and adaptive filter. RF hardwarefor the cognitive radio should be capable of tuningto any part of a large range of frequency spectrum.Also such spectrum sensing enables real-time

measurements of spectrum information from radioenvironment. Generally, a wideband front-end archi-tecture for the cognitive radio has the following struc-ture as shown in Fig. 4(b) [12]. The components of acognitive radio RF front-end are as follows:

RF filter: The RF filter selects the desired bandby bandpass filtering the received RF signal.

Low noise amplifier (LNA): The LNA amplifiesthe desired signal while simultaneously minimiz-ing noise component.

Mixer: In the mixer, the received signal is mixedwith locally generated RF frequency and con-verted to the baseband or the intermediate fre-quency (IF).

Voltage-controlled oscillator (VCO): The VCOgenerates a signal at a specific frequency for agiven voltage to mix with the incoming signal.This procedure converts the incoming signal tobaseband or an intermediate frequency.

Phase locked loop (PLL): The PLL ensures thata signal is locked on a specific frequency and canalso be used to generate precise frequencies withfine resolution.

Channel selection filter: The channel selection fil-ter is used to select the desired channel and toreject the adjacent channels. There are two typesof channel selection filters [52]. The direct conver-sion receiver uses a low-pass filter for the channelselection. On the other hand, the superheterodynereceiver adopts a bandpass filter.

Automatic gain control (AGC): The AGC main-tains the gain or output power level of an ampli-fier constant over a wide range of input signal

levels.

In this architecture, a wideband signal is receivedthrough the RF front-end, sampled by the highspeed analog-to-digital (A/D) converter, and mea-surements are performed for the detection of thelicensed user signal. However, there exist some limi-

tations on developing the cognitive radio front-end.The wideband RF antenna receives signals from var-ious transmitters operating at different power levels,bandwidths, and locations. As a result, the RF front-end should have the capability to detect a weak sig-nal in a large dynamic range. However, this capabil-ity requires a multi-GHz speed A/D converter withhigh resolution, which might be infeasible [12,13].

The requirement of a multi-GHz speed A/D con-verter necessitates the dynamic range of the signal tobe reduced before A/D conversion. This reductioncan be achieved by filtering strong signals. Since

strong signals can be located anywhere in the widespectrum range, tunable notch filters are requiredfor the reduction [12]. Another approach is to usemultiple antennas such that signal filtering is per-formed in the spatial domain rather than in the fre-quency domain. Multiple antennas can receivesignals selectively using beamforming techniques[13].

As explained previously, the key challenge of thephysical architecture of the cognitive radio is anaccurate detection of weak signals of licensed users

over a wide spectrum range. Hence, the implementa-tion of RF wideband front-end and A/D converterare critical issues in xG networks.

2.2. Cognitive capability

The cognitive capability of a cognitive radioenables real time interaction with its environmentto determine appropriate communication parame-ters and adapt to the dynamic radio environment.The tasks required for adaptive operation in openspectrum are shown in Fig. 5 [27,46,58], which isreferred to as the cognitive cycle. In this section,we provide an overview of the three main steps ofthe cognitive cycle: spectrum sensing, spectrum anal-ysis, and spectrum decision. The details and therelated work of these functions are described in Sec-tions 4 and 5.

The steps of the cognitive cycle as shown in Fig. 5are as follows:

1. Spectrum sensing: A cognitive radio monitors theavailable spectrum bands, captures their infor-

mation, and then detects the spectrum holes.



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2. Spectrum analysis: The characteristics of the

spectrum holes that are detected through spec-trum sensing are estimated.

3. Spectrum decision: A cognitive radio determinesthe data rate, the transmission mode, and thebandwidth of the transmission. Then, the appro-priate spectrum band is chosen according to thespectrum characteristics and user requirements.

Once the operating spectrum band is determined,the communication can be performed over this spec-trum band. However, since the radio environment

changes over time and space, the cognitive radioshould keep track of the changes of the radio envi-ronment. If the current spectrum band in usebecomes unavailable, the spectrum mobility functionthat will be explained in Section 6, is performed toprovide a seamless transmission. Any environmen-tal change during the transmission such as primaryuser appearance, user movement, or traffic variationcan trigger this adjustment.

2.3. Reconfigurability

Reconfigurability is the capability of adjustingoperating parameters for the transmission on thefly without any modifications on the hardware com-ponents. This capability enables the cognitive radioto adapt easily to the dynamic radio environment.There are several reconfigurable parameters thatcan be incorporated into the cognitive radio [20]as explained below:

Operating frequency: A cognitive radio is capableof changing the operating frequency. Based on

the information about the radio environment,

the most suitable operating frequency can bedetermined and the communication can bedynamically performed on this appropriate oper-ating frequency.

Modulation: A cognitive radio should reconfigure

the modulation scheme adaptive to the userrequirements and channel conditions. For exam-ple, in the case of delay sensitive applications, thedata rate is more important than the error rate.Thus, the modulation scheme that enables thehigher spectral efficiency should be selected. Con-versely, the loss-sensitive applications focus onthe error rate, which necessitate modulationschemes with low bit error rate.

Transmission power: Transmission power can bereconfigured within the power constraints. Powercontrol enables dynamic transmission power con-

figuration within the permissible power limit. Ifhigher power operation is not necessary, the cog-nitive radio reduces the transmitter power to alower level to allow more users to share the spec-trum and to decrease the interference.

Communication technology: A cognitive radio canalso be used to provide interoperability amongdifferent communication systems.

The transmission parameters of a cognitive radiocan be reconfigured not only at the beginning of a

transmission but also during the transmission.According to the spectrum characteristics, theseparameters can be reconfigured such that thecognitive radio is switched to a different spectrumband, the transmitter and receiver parameters arereconfigured and the appropriate communicationprotocol parameters and modulation schemes areused.

3. The xG network architecture

Existing wireless network architectures employheterogeneity in terms of both spectrum policiesand communication technologies [3]. Moreover,some portion of the wireless spectrum is alreadylicensed to different purposes while some bandsremain unlicensed. For the development of commu-nication protocols, a clear description of the xG net-work architecture is essential. In this section, the xGnetwork architecture is presented such that all pos-sible scenarios are considered.

The components of the xG network architecture,as shown in Fig. 6, can be classified in two groups as

the primary network and the xG network. The basic

Fig. 5. Cognitive cycle.



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elements of the primary and the xG network aredefined as follows:

Primary network: An existing network infrastruc-ture is generally referred to as the primary net-work, which has an exclusive right to a certainspectrum band. Examples include the commoncellular and TV broadcast networks. The compo-nents of the primary network are as follows:

Primary user: Primary user (or licensed user)has a license to operate in a certain spectrumband. This access can only be controlled bythe primary base-station and should not beaffected by the operations of any other unli-censed users. Primary users do not need anymodification or additional functions for coex-istence with xG base-stations and xG users.

Primary base-station: Primary base-station (orlicensed base-station) is a fixed infrastructurenetwork component which has a spectrumlicense such as base-station transceiver system(BTS) in a cellular system. In principle, theprimary base-station does not have any xGcapability for sharing spectrum with xG users.However, the primary base-station may be

requested to have both legacy and xG proto-

cols for the primary network access of xGusers, which is explained below.

xG network: xG network (or cognitive radio net-work, Dynamic Spectrum Access network, sec-ondary network, unlicensed network) does nothave license to operate in a desired band. Hence,the spectrum access is allowed only in an oppor-tunistic manner. xG networks can be deployedboth as an infrastructure network and an adhoc network as shown in Fig. 6. The componentsof an xG network are as follows:

xG user: xG user (or unlicensed user, cognitiveradio user, secondary user) has no spectrumlicense. Hence, additional functionalities arerequired to share the licensed spectrum band.

xG base-station: xG base-station (or unli-censed base-station, secondary base-station)is a fixed infrastructure component with xGcapabilities. xG base-station provides singlehop connection to xG users without spectrumaccess license. Through this connection, an xGuser can access other networks.

Spectrum broker: Spectrum broker (or sched-uling server) is a central network entity thatplays a role in sharing the spectrum resources

among different xG networks. Spectrum

Fig. 6. xG network architecture.



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broker can be connected to each network andcan serve as a spectrum information managerto enable coexistence of multiple xG networks[10,32,70].

The reference xG network architecture is shownin Fig. 6, which consists of different types of net-works: a primary network, an infrastructure basedxG network, and an ad-hoc xG network. xG net-works are operated under the mixed spectrum envi-ronment that consists of both licensed andunlicensed bands. Also, xG users can either commu-nicate with each other in a multihop manner oraccess the base-station. Thus, in xG networks, thereare three different access types as explained next:

xG network access: xG users can access their own

xG base-station both on licensed and unlicensedspectrum bands.

xG ad hoc access: xG users can communicatewith other xG users through ad hoc connectionon both licensed and unlicensed spectrum bands.

Primary network access: The xG users can alsoaccess the primary base-station through thelicensed band.

According to the reference architecture shown inFig. 6, various functionalities are required to sup-

port the heterogeneity in xG networks. In Section3.1, we describe the xG network functions to sup-port the heterogeneity of the network environment.Moreover, in Sections 3.2 and 3.3, we overview xGnetwork applications and existing architectures.

3.1. xG network functions

As explained before, xG network can operate inboth licensed and unlicensed bands. Hence, the func-tionalities required for xG networks vary accordingto whether the spectrum is licensed or unlicensed.Accordingly, in this section, we classify the xG net-work operations as xG network on licensed bandand xG network on unlicensed band. The xG networkfunctions are explained in the following sectionsaccording to this classification.

3.1.1. xG network on licensed band

As shown in Fig. 1, there exist temporally unusedspectrum holes in the licensed spectrum band.Hence, xG networks can be deployed to exploitthese spectrum holes through cognitive communi-

cation techniques. This architecture is depicted in

Fig. 7, where the xG network coexists with the pri-mary network at the same location and on the samespectrum band.

There are various challenges for xG networks on

licensed band due to the existence of the primaryusers. Although the main purpose of the xG net-work is to determine the best available spectrum,xG functions in the licensed band are mainly aimedat the detection of the presence of primary users.The channel capacity of the spectrum holes dependson the interference at the nearby primary users.Thus, the interference avoidance with primary usersis the most important issue in this architecture.Furthermore, if primary users appear in the spec-trum band occupied by xG users, xG users should

vacate the current spectrum band and move to thenew available spectrum immediately, called spec-trum handoff.

3.1.2. xG network on unlicensed band

Open spectrum policy that began in the industrialscientific and medical (ISM) band has caused animpressive variety of important technologies andinnovative uses. However, due to the interferenceamong multiple heterogeneous networks, the spec-trum efficiency of ISM band is decreasing. Ulti-mately, the capacity of open spectrum access, andthe quality of service they can offer, depend on thedegree to which a radio can be designed to allocatespectrum efficiently.

xG networks can be designed for operation onunlicensed bands such that the efficiency isimproved in this portion of the spectrum. The xGnetwork on unlicensed band architecture is illustratedin Fig. 8. Since there are no license holders, allnetwork entities have the same right to access thespectrum bands. Multiple xG networks coexist inthe same area and communicate using the same por-

tion of the spectrum. Intelligent spectrum sharing

Fig. 7. xG network on licensed band.



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algorithms can improve the efficiency of spectrumusage and support high QoS.

In this architecture, xG users focus on detecting

the transmissions of other xG users. Unlike thelicensed band operations, the spectrum handoff isnot triggered by the appearance of other primaryusers. However, since all xG users have the same rightto access the spectrum, xG users should compete witheach other for the same unlicensed band. Thus,sophisticated spectrum sharing methods among xGusers are required in this architecture. If multiplexG network operators reside in the same unlicensedband, fair spectrum sharing among these networksis also required.

3.2. xG network applications

xG networks can be applied to the followingcases:

Leased network: The primary network can pro-vide a leased network by allowing opportunisticaccess to its licensed spectrum with the agreementwith a third party without sacrificing the servicequality of the primary user [56]. For example, theprimary network can lease its spectrum access rightto a mobile virtual network operator (MVNO).Also the primary network can provide its spectrumaccess rights to a regional community for the pur-pose of broadband access.

Cognitive mesh network: Wireless mesh networksare emerging as a cost-effective technology for pro-viding broadband connectivity [4]. However, as thenetwork density increases and the applicationsrequire higher throughput, mesh networks requirehigher capacity to meet the requirements of theapplications. Since the cognitive radio technologyenables the access to larger amount of spectrum,

xG networks can be used for mesh networks that

will be deployed in dense urban areas with the pos-sibility of significant contention [38]. For example,the coverage area of xG networks can be increasedwhen a meshed wireless backbone network of infra-structure links is established based on cognitive

access points (CAPs) and fixed cognitive relay nodes(CRNs) [6]. The capacity of a CAP, connected via awired broadband access to the Internet, is distrib-uted into a large area with the help of a fixedCRN. xG networks have the ability to add tempo-rary or permanent spectrum to the infrastructurelinks used for relaying in case of high traffic load.

Emergency network: Public safety and emergencynetworks are another area in which xG networks canbe implemented [41]. In the case of natural disasters,which may temporarily disable or destroy existingcommunication infrastructure, emergency personnel

working in the disaster areas need to establish emer-gency networks. Since emergency networks deal withthe critical information, reliable communicationshould be guaranteed with minimum latency. Inaddition, emergency communication requires a sig-nificant amount of radio spectrum for handling hugevolume of traffic including voice, video and data. xGnetworks can enable the usage of the existing spec-trum without the need for an infrastructure and bymaintaining communication priority and responsetime.

Military network: One of the most interestingpotential applications of an xG network is in a mil-itary radio environment [47]. xG networks canenable the military radios choose arbitrary, interme-diate frequency (IF) bandwidth, modulationschemes, and coding schemes, adapting to the vari-able radio environment of battlefield. Also militarynetworks have a strong need for security and pro-tection of the communication in hostile environ-ment. xG networks could allow military personnelto perform spectrum handoff to find secure spec-trum band for themselves and their allies.

3.3. Existing architectures

The main representative examples of the xG net-work architectures are described in this section.

Spectrum pooling: In [61,62], a centralizedspectrum pooling architecture is proposed basedon orthogonal frequency division multiplexing(OFDM). This architecture consists of an xGbase-station and mobile xG users. OFDM has theadvantage of feeding certain sub-carriers with zeros

resulting in no emission of radio power on the

Fig. 8. xG network on unlicensed band.



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carriers that are occupied by the licensed users. Thelicensed user detection is performed through detec-tion frames that are periodically broadcast by thebase-station. During the detection frames, themobile users perform spectrum sensing. The sensing

information is then gathered at the base-station.Mobile terminals modulate a complex symbol atmaximum power in the sub-carriers where a licenseduser appears. Through this operation, the base-station receives an amplified signal on all sub-carri-ers with new licensed users. Physical and MAC layerissues, such as detection of spectral access, schedul-ing, and handoff are ongoing investigations in thisarchitecture.

CORVUS: In [8,14], a cognitive radio approachfor usage of virtual unlicensed spectrum (CORVUS)system is presented to exploit unoccupied licensed

bands. In CORVUS, based on the local spectrumsensing, the primary user detection and the spec-trum allocation are performed in a coordinatedmanner. This cooperative effort greatly increasesthe systems ability in identifying and avoiding pri-mary users. In CORVUS, a group of users form asecondary user group (SUG) to coordinate theircommunication. Each member of this group sensesthe spectrum pool, which is divided into sub-chan-nels. A universal control channel is used by allgroups for coordination and separate group control

channels are used by the members of a group toexchange sensing information and establish second-ary user links. The performance of the physical andlink layers are evaluated through the CORVUS test-bed [44]. Moreover, recently, a reliable link mainte-nance protocol is proposed within CORVUS tomaintain the quality of secondary user communica-tion [64].

IEEE 802.22: IEEE 802.22 is the first worldwidestandard based on the cognitive radio technology[16,31] and is now in the process of standardization.This project, formally called the standard for wire-less regional area networks (WRAN), focuses onconstructing fixed point-to-multipoint WRAN thatwill utilize UHF/VHF TV bands between 54 and862 MHz. Specific TV channels as well as guardbands will be used for communication in IEEE802.22. The IEEE 802.22 system specifies a fixedpoint-to-multipoint wireless air interface wherebya base-station manages its own cell and all associ-ated users, which are denoted as consumer premiseequipments (CPEs). IEEE 802.22 base-station man-ages a unique feature of distributed sensing. This is

needed to ensure proper incumbent protection and

is managed by the base-station, which instructs thevarious CPEs to perform distributed measurementactivities. The IEEE 802.22 system specifies spectralefficiencies in the range of 0.55 bit/s/Hz. A distinc-tive feature of IEEE 802.22 WRAN as compared to

the existing IEEE 802 standards is the base-stationcoverage range, which can go up to 100 km if thepower is not an issue. Current specified coveragerange is 33 km at 4 W CPE effective isotropic radi-ated power (EIRP) [66]. Table 1 depicts the capacityand coverage of IEEE 802.22 WRAN system. IEEE802.22 working group was formed in 2004 and hasfinalized the specification of technical requirements.The first draft of IEEE 802.22 standard will beready around mid 2006.

DIMSUMnet: The dynamic intelligent manage-ment of spectrum for ubiquitous mobile network

(DIMSUMnet) [10] implements statistically multi-

Table 1IEEE 802.22 WRAN system capacity and coverage [65]

RF channel bandwidth 6 MHzAverage spectrum efficiency 3 bit/s/HzChannel capacity 18 Mbit/sSystem capacity per subscriber

(forward)1.5 Mbit/s

System capacity per subscriber(return)

384 kbit/s

Forward/return ratio 3.9Over-subscription ratio 50Number of subscribers per

forward channel600

(FDD operation is assumed tomaximize system capacity for largecoverage distances, TDD would reducecapacity to 75% per TV channel)

Minimum number of subscribers 90 subs.Assumed early take-up rate 3 bit/s/HzPotential number of subscribers 1800 subs.Assumed number of persons

per household2.5 persons

Total number of personsper coverage area

4500 persons

WRAN base station EIRP 98.3 WRadius of coverage for WRAN system 30.7 kmMinimum population density covered 1.5 person/km2

Assuming 1.5 Mbit/s forward and 384 kbit/s return in a 6 MHzchannel with 3 bit/s/Hz and 50:1 over-subscription, each TVchannel can provide service to up to 600 subscribers. The areathat the Wireless Regional Area network (WRAN) operatorneeds to cover should include enough subscribers to make iteconomically viable early in the process when the take-up rate islow. This will define the potential subscriber base and the pop-

ulation density for a sustainable business case.



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plexed access (SMA) to spectrum in the coordinatedaccess band (CAB). While the CAB improves thespectrum access efficiency and fairness, the SMAis focused on improving the spectrum utilization.CAB is a contiguous chunk of spectrum reserved

by regulating authorities. A spectrum brokerpermanently owns the CAB and leases it accordingto requests. DIMSUMnet uses a centralized, regio-nal network level brokering mechanism that aimsto significantly improve spectrum utilization whilereducing the complexity and the agility require-ments of the deployed system. The base-station reg-isters with its designated radio access networkmanager (RANMAN), which negotiates a leasewith a spectrum information and management(SPIM) broker for an appropriate portion of thespectrum. If the lease is successfully obtained, the

RANMAN configures the leased spectrum inthe base-station. The base-station sends the spec-trum information received from the RANMAN toits users for the configuration of client. The spec-trum utilization of DIMSUMnet is currently mea-sured in existing Code Division Multiple Access(CDMA) and Global System for Mobile communi-cation (GSM) cellular networks, aimed at character-izing feasibility of CAB and SMA [35]. Recent workfocuses on the spectrum pricing and allocation func-tions for spectrum brokers [11].

DRiVE/OverDRiVE project: The EuropeanDynamic Radio for IP Services in Vehicular Envi-ronments (DRiVE) project focuses on dynamicspectrum allocation in heterogeneous networks byassuming a common coordinated channel [75]. Thefollow-up project, Spectrum Efficient Uni- and Mul-ticast Over Dynamic Radio Networks in VehicularEnvironments (OverDRiVE) aims at UMTSenhancements and coordination of existing radionetworks into a hybrid network to ensure spectrumefficient provision of mobile multimedia services[26]. Two aspects of dynamic spectrum allocationwere investigated in DRiVE/OverDRiVE, i.e.,temporal dynamic spectrum allocation (DSA), andspatial DSA [39]. In the case of the temporalDSA, a radio access network (RAN) can use thespectrum that is not currently being used by otherRANs, at that time. On the other hand, spatialDSA allows spectrum allocations to adapt to regio-nal fluctuations in traffic demands. The efficiency ofthese DSA schemes depends on the ability to predictthe traffic load. Although these projects have shownsignificant potential for increasing spectral effi-

ciency, the reconfigurable system implementation

for temporal and spatial DSA is still a majorchallenge.

Nautilus: Nautilus project is designed to empha-size distributed coordination enabled spectrum shar-ing, without relying on centralized control [48]. In

the Nautilus project, a distributed, scalable and effi-cient coordination framework for open spectrum adhoc networks is proposed, which accounts for spec-trum heterogeneity while not relying on the existenceof a pre-defined common channel for control traffic[73,74]. Based on this framework, three different col-laborative spectrum access schemes are presented. In[73], a graph coloring based collaborative spectrumaccess scheme is proposed, where a topology-opti-mized allocation algorithm is used for the fixedtopology. In mobile networks, however, the networktopology changes due to the node mobility. Using

this global optimization approach, the networkneeds to completely recompute spectrum assign-ments for all users after each change, resulting inhigh computational and communication overhead.Thus, distributed spectrum allocation based on localbargaining is proposed in [15], where mobile usersnegotiate spectrum assignment within local self-organized groups. For the resource constrained net-works such as sensor and ad hoc network, rule-baseddevice centric spectrum management is proposed,where unlicensed users access the spectrum indepen-

dently according to both local observation and pre-determined rules. Currently, this project focuses onselecting the best channel for data transmissionusing proposed distributed coordination framework.

OCRA network: In [5], an OFDM-based cogni-tive radio (OCRA) network is proposed. OCRAnetwork considers all possible deployment scenariosover the heterogeneous xG network environmentand develops cross-layer operations for the OFDMbased dynamic spectrum access. OCRA networkarchitecture and its components are shown inFig. 6. For the spectrum decision and the spectrumhandoff, OCRA network provides a novel conceptof an OFDM-based spectrum management overthe heterogeneous spectrum environment. Basedon this physical layer (PHY) structure, a dual-modespectrum sharing framework is proposed, whichenables access to existing networks as well as coor-dination between xG users. Furthermore, a newrouting paradigm that considers joint re-routingand spectrum handoff is proposed. Moreover,OCRA network introduces multi-spectrum trans-port techniques to exploit the available but

non-contiguous wireless spectrum for high quality



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communication. In [5], the testbed design for evalu-ation and integration of the OCRA network is pro-posed. The OCRA testbed is based on IEEE802.11a/g technology, which exploits the OFDMtechnology. Moreover, a standalone cognitive sens-

ing unit is developed to emulate the spectrum sens-ing capabilities of the cognitive radio.

4. Spectrum sensing

An important requirement of the xG network isto sense the spectrum holes. As explained in Section2, a cognitive radio is designed to be aware of andsensitive to the changes in its surrounding. The spec-trum sensing function enables the cognitive radio toadapt to its environment by detecting spectrumholes.

The most efficient way to detect spectrum holes isto detect the primary users that are receiving datawithin the communication range of an xG user. Inreality, however, it is difficult for a cognitive radioto have a direct measurement of a channel betweena primary receiver and a transmitter. Thus, the mostrecent work focuses on primary transmitter detec-tion based on local observations of xG users.

Generally, the spectrum sensing techniques canbe classified as transmitter detection, coopera-tive detection, and interference-based detection, as

shown in Fig. 9. In the following sections, wedescribe these spectrum sensing methods for xG net-works and discuss the open research topics in thisarea.

4.1. Transmitter detection (non-cooperative

detection)

The cognitive radio should distinguish betweenused and unused spectrum bands. Thus, the cognitiveradio should have capability to determine if a signal

from primary transmitter is locally present in a cer-tain spectrum. Transmitter detection approach isbased on the detection of the weak signal from a pri-mary transmitter through the local observations ofxG users. Basic hypothesis model for transmitter

detection can be defined as follows [25]:

xt nt H0;hst nt H1;

(1

where x(t) is the signal received by the xG user, s(t)is the transmitted signal of the primary user, n(t) isthe AWGN and h is the amplitude gain of the chan-nel. H0 is a null hypothesis, which states that there isno licensed user signal in a certain spectrum band.On the other hand, H1 is an alternative hypothesis,which indicates that there exist some licensed user

signal.Three schemes are generally used for the trans-

mitter detection according to the hypothesis model[53]. In the following subsections, we investigatematched filter detection, energy detection and cyclo-stationary feature detection techniques proposedfor transmitter detection in xG networks.

4.1.1. Matched filter detection

When the information of the primary user signalis known to the xG user, the optimal detector in sta-

tionary Gaussian noise is the matched filter since itmaximizes the received signal-to-noise ratio (SNR)[53]. While the main advantage of the matched filteris that it requires less time to achieve high process-ing gain due to coherency, it requires a priori know-ledge of the primary user signal such as themodulation type and order, the pulse shape, andthe packet format. Hence, if this information isnot accurate, then the matched filter performspoorly. However, since most wireless network sys-tems have pilot, preambles, synchronization wordor spreading codes, these can be used for the coher-ent detection.

4.1.2. Energy detection

If the receiver cannot gather sufficient informa-tion about the primary user signal, for example, ifthe power of the random Gaussian noise is onlyknown to the receiver, the optimal detector is anenergy detector [53]. In order to measure the energyof the received signal, the output signal of bandpassfilter with bandwidth W is squared and integratedover the observation interval T. Finally, the output

of the integrator, Y, is compared with a threshold, k,Fig. 9. Classification of spectrum sensing techniques.



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to decide whether a licensed user is present or not[17].

If the energy detection can be applied in a non-fading environment where h is the amplitude gainof the channel as shown in (1), the probability of

detectionPd

and false alarmPf

are given as follows[17]:

Pd PfY > kjH1g Qmffiffiffiffiffi

2cp

;ffiffiffik

p ; 2

Pf PfY > kjHog Cm; k=2Cm ; 3

where c is the SNR, u = TW is the time bandwidthproduct, C() and C(, ) are complete and incompletegamma functions and Q

m() is the generalized Mar-

cum Q-function. From the above functions, whilea low Pd would result in missing the presence of

the primary user with high probability which in turnincreases the interference to the primary user, a highPf would result in low spectrum utilization sincefalse alarms increase the number of missed opportu-nities. Since it is easy to implement, the recent workon detection of the primary user has generallyadopted the energy detector [23,53].

In [25], the shadowing and the multi-path fadingfactors are considered for the energy detector. Inthis case, while Pf is independent of C, when theamplitude gain of the channel, h, varies due to

the shadowing/fading, Pd gives the probability ofthe detection conditioned on instantaneous SNRas follows:

Pd Zx

Qm

ffiffiffiffiffi2c

p;ffiffiffik

p fcxdx; 4

where fc(x) is the probability distribution functionof SNR under fading.

However, the performance of energy detector issusceptible to uncertainty in noise power. In orderto solve this problem, a pilot tone from the pri-mary transmitter is used to help improve theaccuracy of the energy detector in [53]. Anothershortcoming is that the energy detector cannot dif-ferentiate signal types but can only determine thepresence of the signal. Thus, the energy detector isprone to the false detection triggered by the unin-tended signals.

4.1.3. Cyclostationary feature detection

An alternative detection method is the cyclosta-tionary feature detection [12,22,57]. Modulated sig-nals are in general coupled with sine wave carriers,

pulse trains, repeating spreading, hopping sequen-

ces, or cyclic prefixes, which result in built-in period-icity. These modulated signals are characterized ascyclostationarity since their mean and autocorrela-tion exhibit periodicity. These features are detectedby analyzing a spectral correlation function. The

main advantage of the spectral correlation func-tion is that it differentiates the noise energy frommodulated signal energy, which is a result of the factthat the noise is a wide-sense stationary signal withno correlation, while modulated signals are cyclosta-tionary with spectral correlation due to the embed-ded redundancy of signal periodicity. Therefore, acyclostationary feature detector can perform betterthan the energy detector in discriminating againstnoise due to its robustness to the uncertainty in noisepower [57]. However, it is computationally complexand requires significantly long observation time.

For more efficient and reliable performance, theenhanced feature detection scheme combining cyclicspectral analysis with pattern recognition based onneural networks is proposed in [22]. Distinct fea-tures of the received signal are extracted using cyclicspectral analysis and represented by both spectralcoherent function and spectral correlation densityfunction. The neural network, then, classifies signalsinto different modulation types.

4.2. Cooperative detection

The assumption of the primary transmitter detec-tion is that the locations of the primary receivers areunknown due to the absence of signalling betweenprimary users and the xG users. Therefore, the cog-nitive radio should rely on only weak primary trans-mitter signals based on the local observation of thexG user [72,73]. However, in most cases, an xGnetwork is physically separated from the primarynetwork so there is no interaction between them.Thus, with the transmitter detection, the xG usercannot avoid the interference due to the lack ofthe primary receivers information as depicted inFig. 10(a). Moreover, the transmitter detectionmodel cannot prevent the hidden terminal problem.An xG transmitter can have a good line-of-sight to areceiver, but may not be able to detect the transmit-ter due to the shadowing as shown in Fig. 10(b).Consequently, the sensing information from otherusers is required for more accurate detection.

In the case of non-cooperative detection ex-plained in Section 4.1, the xG users detect the pri-mary transmitter signal independently through their

local observations. Cooperative detection refers to



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spectrum sensing methods where information from

multiple xG users are incorporated for primary userdetection. Cooperative detection can be imple-mented either in a centralized or in a distributedmanner [23,71]. In the centralized method, the xGbase-station plays a role to gather all sensing infor-mation from the xG users and detect the spectrumholes. On the other hand, distributed solutionsrequire exchange of observations among xG users.

Cooperative detection among unlicensed users istheoretically more accurate since the uncertainty ina single users detection can be minimized [25].

Moreover, the multi-path fading and shadowingeffect are the main factors that degrade the perfor-mance of primary user detection methods [44].However, cooperative detection schemes allow tomitigate the multi-path fading and shadowingeffects, which improves the detection probability ina heavily shadowed environment [25].

In [55], the limitation of non-cooperative spec-trum sensing approaches is investigated. Generally,the data transmission and sensing function are co-located in a single xG user device. However, thisarchitecture can result in suboptimal spectrum deci-sion due to possible conflicts between data transmis-sion and sensing. In order to solve this problem, in[55], two distinct networks are deployed separately,i.e., the sensor network for cooperative spectrumsensing and the operational network for data trans-mission. The sensor network is deployed in thedesired target area and senses the spectrum. A cen-tral controller processes the spectrum informationcollected from sensors and makes the spectrumoccupancy map for the operational network. Theoperational network uses this information to deter-

mine the available spectrum.

While cooperative approaches provide more

accurate sensing performance, they cause adverseeffects on resource-constrained networks due tothe additional operations and overhead traffic.Furthermore, the primary receiver uncertaintyproblem caused by the lack of the primary receiverlocation knowledge is still unsolved in the coopera-tive sensing. In the following section, we explaininterference-based detection methods, which aimto address these problems.

4.3. Interference-based detection

Interference is typically regulated in a transmit-ter-centric way, which means interference can becontrolled at the transmitter through the radiatedpower, the out-of-band emissions and location ofindividual transmitters. However, interference actu-ally takes place at the receivers, as shown inFig. 10(a) and (b).

Therefore recently, a new model for measuringinterference, referred to as interference temperatureshown in Fig. 11 has been introduced by the FCC[21]. The model shows the signal of a radio stationdesigned to operate in a range at which the receivedpower approaches the level of the noise floor. Asadditional interfering signals appear, the noise floorincreases at various points within the service area, asindicated by the peaks above the original noisefloor. Unlike the traditional transmitter-centricapproach, the interference temperature modelmanages interference at the receiver through theinterference temperature limit, which is representedby the amount of new interference that the receivercould tolerate. In other words, the interference tem-

perature model accounts for the cumulative RF

Fig. 10. Transmitter detection problem: (a) Receiver uncertainty and (b) shadowing uncertainty.



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energy from multiple transmissions and sets a max-imum cap on their aggregate level. As long as xG

users do not exceed this limit by their transmissions,they can use this spectrum band.

However, there exist some limitations in measur-ing the interference temperature. In [9], the interfer-ence is defined as the expected fraction of primaryusers with service disrupted by the xG operations.This method considers factors such as the type ofunlicensed signal modulation, antennas, ability todetect active licensed channels, power control, andactivity levels of the licensed and unlicensed users.However, this model describes the interference dis-

rupted by a single xG user and does not considerthe effect of multiple xG users. In addition, if xGusers are unaware of the location of the nearby pri-mary users, the actual interference cannot bemeasured using this method.

In [63], a direct receiver detection method is pre-sented, where the local oscillator (LO) leakage poweremitted by the RF front-end of the primary receiveris exploited for the detection of primary receivers. Inorder to detect the LO leakage power, low-costsensor nodes can be mounted close to the primaryreceivers. The sensor nodes detect the leakage LOpower to determine the channel used by the primaryreceiver and this information is used by the unli-censed users to determine the operation spectrum.

4.4. Spectrum sensing challenges

There exist several open research challenges thatneed to be investigated for the development of thespectrum sensing function.

Interference temperature measurement: The diffi-

culty of this receiver detection model lies in effec-

tively measuring the interference temperature.An xG user is naturally aware of its transmit

power level and its precise location with the helpof a positioning system. With this ability, however,its transmission could cause significant interfer-ence at a neighboring receiver on the same fre-quency. However, currently, there exists nopractical way for a cognitive radio to measure orestimate the interference temperature at nearbyprimary receivers. Since primary receivers are usu-ally passive devices, an xG user cannot be aware ofthe precise locations of primary receivers. Further-more, if xG users cannot measure the effect of their

transmission on all possible receivers, a usefulinterference temperature measurement may notbe feasible.

Spectrum sensing in multi-user networks: Usually,xG networks reside in a multi-user environmentwhich consists of multiple xG users and primaryusers. Furthermore, the xG networks can alsobe co-located with other xG networks competingfor the same spectrum band. However, currentinterference models [9,21] do not consider theeffect of multiple xG users. Multi-user environ-ment makes it more difficult to sense the primaryusers and to estimate the actual interference.Hence, spectrum sensing functions should bedeveloped considering the possibility of multi-user/network environment. In order to solve themulti-user problem, the cooperative detectionschemes can be considered, which exploit the spa-tial diversity inherent in a multi-user network.

Detection capability: One of the main require-ments of xG networks is the detection of theprimary users in a very short time [24,53].OFDM-based xG networks are known to be

excellent fit for the physical architecture of xG

Power atReceiver

Original Noise Floor

InterferenceTemperature Limit

Licensed Signal

New Opportunities

for

Minimum Service

Range with

Interference Cap

Service Range at

Original Noise Floorriginal Noise FloorPower atReceiver

Original Noise Floor

InterferenceTemperature Limit

Licensed Signal

New Opportunities

for Spectrum Access

Minimum Service

Range with

Interference Cap

Service Range at

O

Fig. 11. Interference temperature model [21].



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networks [5,57,62]. Since multi-carrier sensingcan be exploited in OFDM-based xG networks,the overall sensing time can be reduced. Once aprimary user is detected in a single carrier, sens-ing in other carriers is not necessary. In [57], a

power-based sensing algorithm in OFDM net-works is proposed for detecting the presence ofa primary user. It is shown that the overall detec-tion time is reduced by collecting informationfrom each carrier. However, this necessitatesthe use of a large number of carriers, whichincreases the design complexity. Hence, novelspectrum sensing algorithms need to be devel-oped such that the number of samples neededto detect the primary user is minimized within agiven detection error probability.

5. Spectrum management

In xG networks, the unused spectrum bands willbe spread over wide frequency range including bothunlicensed and licensed bands. These unused spec-trum bands detected through spectrum sensingshow different characteristics according to not onlythe time varying radio environment but also thespectrum band information such as the operatingfrequency and the bandwidth.

Since xG networks should decide on the bestspectrum band to meet the QoS requirements overall available spectrum bands, new spectrum man-agement functions are required for xG networks,considering the dynamic spectrum characteristics.We classify these functions as spectrum sensing,spectrum analysis, and spectrum decision. Whilespectrum sensing, which is discussed in Section 4,is primarily a PHY layer issue, spectrum analysisand spectrum decision are closely related to theupper layers. In this section, spectrum analysis andspectrum decision are investigated.

5.1. Spectrum analysis

In xG networks, the available spectrum holesshow different characteristics which vary over time.Since the xG users are equipped with the cognitiveradio based physical layer, it is important to under-stand the characteristics of different spectrumbands. Spectrum analysis enables the characteriza-tion of different spectrum bands, which can beexploited to get the spectrum band appropriate to

the user requirements.

In order to describe the dynamic nature of xGnetworks, each spectrum hole should be character-ized considering not only the time-varying radioenvironment and but also the primary user activityand the spectrum band information such as operat-

ing frequency and bandwidth. Hence, it is essentialto define parameters such as interference level,channel error rate, path-loss, link layer delay, andholding time that can represent the quality of a par-ticular spectrum band as follows:

Interference: Some spectrum bands are morecrowded compared to others. Hence, the spectrumband in use determines the interference character-istics of the channel. From the amount of theinterference at the primary receiver, the permissi-ble power of an xG user can be derived, which is

used for the estimation of the channel capacity. Path loss: The path loss increases as the operating

frequency increases. Therefore, if the transmis-sion power of an xG user remains the same, thenits transmission range decreases at higher fre-quencies. Similarly, if transmission power isincreased to compensate for the increased pathloss, then this results in higher interference forother users.

Wireless link errors: Depending on the modula-tion scheme and the interference level of the spec-

trum band, the error rate of the channel changes. Link layer delay: To address different path loss,

wireless link error, and interference, differenttypes of link layer protocols are required at dif-ferent spectrum bands. This results in differentlink layer packet transmission delay.

Holding time: The activities of primary users canaffect the channel quality in xG networks. Hold-ing time refers to the expected time duration thatthe xG user can occupy a licensed band beforegetting interrupted. Obviously, the longer theholding time, the better the quality would be.Since frequent spectrum handoff can decreasethe holding time, previous statistical patterns ofhandoff should be considered while designingxG networks with large expected holding time.

Channel capacity, which can be derived from theparameters explained above, is the most importantfactor for spectrum characterization. Usually SNRat the receiver has been used for the capacity estima-tion. However, since SNR considers only localobservations of xG users, it is not enough to avoid

interference at the primary users. Thus, spectrum



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characterization is focused on the capacity estima-tion based on the interference at the licensed receiv-ers. The interference temperature model [21] givenin Section 4.3 can be exploited for this approach.The interference temperature limit indicates an

upper bound or cap on the potential RF energy thatcould be introduced into the band. Consequently,using the amount of permissible interference, themaximum permissible transmission power of anxG user can be determined.

In [63], a spectrum capacity estimation methodhas been proposed that considers the bandwidthand the permissible transmission power. Accord-ingly, the spectrum capacity, C, can be estimatedas follows:

C

B log 1

S

N I ; 5where B is the bandwidth, S is the received signalpower from the xG user, N is the xG receiver noisepower, and I is the interference power received atthe xG receiver due to the primary transmitter.

Estimating spectrum capacity has also beeninvestigated in the context of OFDM-based cogni-tive radio systems in [57]. Accordingly, the spectrumcapacity of the OFDM-based xG networks isdefined as follows [57]:

C ZX1

2 log2 1 G

f

S0

N0

df; 6where X is the collection of unused spectrum seg-ments, G(f) is the channel power gain at frequencyf, S0 and N0 are the signal and noise power per unitfrequency, respectively.

The recent work on spectrum analysis, as dis-cussed above, only focuses on spectrum capacityestimation. However, besides the capacity, otherfactors such as delay, link error rate, and holdingtime also have significant influence on the qualityof services. Moreover, the capacity is closely relatedto both interference level and path loss. However, acomplete analysis and modeling of spectrum in xGnetworks is yet to be developed. In order to decideon the appropriate spectrum for different types ofapplications, it is desirable and an open researchissue to identify the spectrum bands combining allcharacterization parameters described above.

5.2. Spectrum decision

Once all available spectrum bands are character-

ized, appropriate operating spectrum band should

be selected for the current transmission consideringthe QoS requirements and the spectrum characteris-tics. Thus, the spectrum management function mustbe aware of user QoS requirements.

Based on the user requirements, the data rate,

acceptable error rate, delay bound, the transmissionmode, and the bandwidth of the transmission can bedetermined. Then, according to the decision rule, theset of appropriate spectrum bands can be chosen. In[73], five spectrum decision rules are presented,which are focused on fairness and communicationcost. However, this method assumes that all chan-nels have similar throughput capacity. In [36], anopportunistic frequency channel skipping protocolis proposed for the search of better quality channel,where this channel decision is based on SNR. Inorder to consider the primary user activity, the num-

ber of spectrum handoff, which happens in a certainspectrum band, is used for spectrum decision [37].Spectrum decision constitutes rather important butyet unexplored issues in xG networks, which are pre-sented in the following subsection.

5.3. Spectrum management challenges

There exist several open research issues that needto be investigated for the development of spectrumdecision function.

Decision model: Signal to noise ratio (SNR) is notsufficient to characterize the spectrum band in xGnetworks. Besides the SNR, many spectrum char-acterization parameters would affect the quality,as investigated in Section 5.1. Thus, how tocombine these spectrum characterization para-meters for the spectrum decision model is stillan open issue. Moreover, in OFDM based xGnetworks, multiple spectrum bands can be simul-taneously used for the transmission. For thesereasons, a decision framework for the multiplespectrum bands should be provided.

Multiple spectrum band decision: In xG networks,multiple spectrum bands can be simultaneouslyused for the transmission. Moreover, the xG net-works do not require the selected multiple bandsto be contiguous. Thus, an xG user can sendpackets over non-contiguous spectrum bands.This multi-spectrum transmission shows lessquality degradation during the spectrum handoffcompared to the conventional transmission onsingle spectrum band [5]. For example, if a pri-

mary user appears in a particular spectrum band,



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the xG user has to vacate this band. However,since the rest of spectrum bands will maintainthe communication, abrupt service quality degra-dation can be mitigated. In addition, transmis-sion in multiple spectrum bands allows lower

power to be used in each spectrum band. As aresult, less interference with primary users isachieved, compared to the transmission on singlespectrum band [5]. For these reasons, a spectrummanagement framework should support multiplespectrum decision capabilities. For example, howto determine the number of spectrum bands andhow to select the set of appropriate bands are stillopen research issues in xG networks.

Cooperation with reconfiguration: The cognitiveradio technology enables the transmissionparameters of a radio to be reconfigured for opti-

mal operation in a certain spectrum band. Forexample, if SNR is fixed, the bit error rate(BER) can be adjusted to maintain the channelcapacity by exploiting adaptive modulation tech-niques, e.g., cdma2000 1x EVDO [1,18]. Hence, acooperative framework that considers both spec-trum decision and reconfiguration is required.

Spectrum decision over heterogenous spectrum

bands: Currently, certain spectrum bands arealready assigned to different purposes while somebands remain unlicensed. Thus, the spectrum

used by xG networks will most likely be a combi-nation of exclusively accessed spectrum and unli-censed spectrum. In case of licensed bands, the xGusers need to consider the activities of primaryusers in spectrum analysis and decision in ordernot to influence the primary user transmission.Conversely, in unlicensed bands, since all the xGusers have the same spectrum access rights,sophisticated spectrum sharing techniques arenecessary. In order to decide the best spectrumband over this heterogeneous environment, xGnetwork should support spectrum decisionoperations on both the licensed and the unli-censed bands considering these different charac-teristics.

6. Spectrum mobility

xG networks target to use the spectrum in adynamic manner by allowing the radio terminals,known as the cognitive radio, to operate in the bestavailable frequency band. This enables Get the

Best Available Channel concept for communication

purposes. To realize the Get the Best AvailableChannel concept, an xG radio has to capture thebest available spectrum. Spectrum mobility isdefined as the process when an xG user changesits frequency of operation. In the following sections,

we describe the spectrum handoff concept in xG net-works and discuss open research issues in this newarea.

6.1. Spectrum handoff

In xG networks, spectrum mobility arises whencurrent channel conditions become worse or a pri-mary user appears. Spectrum mobility gives rise toa new type of handoff in xG networks that we referto as spectrum handoff. The protocols for differentlayers of the network stack must adapt to the chan-

nel parameters of the operating frequency. More-over, they should be transparent to the spectrumhandoff and the associated latency.

As pointed out in earlier sections, a cognitiveradio can adapt to the frequency of operation.Therefore, each time an xG user changes its fre-quency of operation, the network protocols aregoing to shift from one mode of operation toanother. The purpose of spectrum mobility manage-ment in xG networks is to make sure that such tran-sitions are made smoothly and as soon as possible

such that the applications running on an xG userperceive minimum performance degradation duringa spectrum handoff. It is essential for the mobilitymanagement protocols to learn in advance aboutthe duration of a spectrum handoff. This informationshould be provided by the sensing algorithm. Oncethe mobility management protocols learn about thislatency, their job is to make sure that the ongoingcommunications of an xG user undergo only mini-mum performance degradation.

Consequently, multi-layer mobility managementprotocols are required to accomplish the spectrummobility functionalities. These protocols supportmobility management adaptive to different typesof applications. For example, a TCP connectioncan be put to a wait state until the spectrum handoffis over. Moreover, since the TCP parameters willchange after a spectrum handoff, it is essential tolearn the new parameters and ensure that the transi-tion from the old parameters to new parameters arecarried out rapidly. For a data communication e.g.,FTP, the mobility management protocols shouldimplement mechanisms to store the packets that

are transmitted during a spectrum handoff, whereas



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for a real-time application there is no need to storethe packets as the stored packets, if delivered later,will be stale packets and can not be used by the cor-responding application.

6.2. Spectrum mobility challenges in xG networks

The followings are the open research issues forefficient spectrum mobility in xG networks.

At a particular time, several frequency bandsmay be available for an xG user. Algorithmsare required to decide the best available spectrumbased on the channel characteristics of the avail-able spectrum and the requirements of the appli-cations that are being used by an xG user.

Once, the best available spectrum is selected, thenext challenge is to design new mobility and con-nection management approaches to reduce delayand loss during spectrum handoff.

When the current operational frequency becomesbusy (this may happen if a licensed user starts touse this frequency) in the middle of a communi-cation by an xG user, then applications runningon this node have to be transferred to anotheravailable frequency band. However, the selectionof new operational frequency may take time.

Novel algorithms are required to ensure thatapplications do not suffer from severe perfor-mance degradation during such transitions.

Spectrum handoff may occur due to reasonsother than the detection of the primary user.Thus, there exist various other spectrum handoffschemes in xG networks. If an xG user movesfrom one place to another, spectrum handoffmay occur just because the available spectrumbands change. Thus the desired spectrum handoffscheme should integrate inter-cell handoff. Apartfrom this, spectrum handoff between differentnetworks, referred to as vertical handoff is alsolikely to occur in xG networks. Under such adiverse environment, it is essential that spectrumhandoff scheme takes all the above mentionedpossibilities into consideration.

Spectrum mobility in time domain: xG networksadapt to the wireless spectrum based on availablebands on the spectrum. Since these availablechannels change over time, enabling QoS in thisenvironment is challenging. The physical radioshould move through the spectrum to meet

the QoS requirements.

Spectrum mobility in space: The available bandsalso change as a user moves from one place toanother. Hence, continuous allocation of spec-trum is a major challenge. in xG networks.

7. Spectrum sharing

In xG networks, one of the main challenges inopen spectrum usage is the spectrum sharing. Spec-trum sharing can be regarded to be similar to gen-eric medium access control (MAC) problems inexisting systems. However, as we will investigate inthis section, substantially different challenges existfor spectrum sharing in xG networks. The coexis-tence with licensed users and the wide range ofavailable spectrum are two of the main reasons for

these unique challenges. In this section, we delveinto the specific challenges for spectrum sharing inxG networks, overview the existing solutions anddiscuss open research areas.

In order to provide a directory for different chal-lenges during spectrum sharing, we first enumeratethe steps in spectrum sharing in xG networks. Thechallenges and the solutions proposed for thesesteps will then be explained in detail. The spectrumsharing process consists of five major steps.

1. Spectrum sensing: An xG user can only allocate aportion of the spectrum if that portion is not usedby an unlicensed user. In Section 4, the solutionsand the challenges for this problem, i.e., spec-trum sensing, are described. Accordingly, whenan xG node aims to transmit packets, it firstneeds to be aware of the spectrum usage aroundits vicinity.

2. Spectrum allocation: Based on the spectrumavailability, the node can then allocate a channel.This allocation not only depends on spectrumavailability, but it is also determined based oninternal (and possibly external) policies. Hence,the design of a spectrum allocation policy toimprove the performance of a node is an impor-tant research topic.

3. Spectrum access: In this step, another majorproblem of spectrum sharing comes into picture.Since there may be multiple xG nodes trying toaccess the spectrum, this access should also becoordinated in order to prevent multiple userscolliding in overlapping portions of the spectrum.

4. Transmitter-receiver handshake: Once a portion of

the spectrum is determined for communication,



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the receiver of this communication should also beindicated about the selected spectrum. Hence, atransmitter-receiver handshake protocol is essen-tial for efficient communication in xG networks.Note that the term handshake by no means

restricts this protocol between the transmitterand the receiver. A third party such as a central-ized station can also be involved.

5. Spectrum mobility: xG nodes are regarded asvisitors to the spectrum they allocate. Hence,if the specific portion of the spectrum in use isrequired by a licensed user, the communica-tion needs to be continued in another vacantportion. As a result, spectrum mobility is alsoimportant for successful communication betweenxG nodes.

The existing work in spectrum sharing in xG net-works aims to provide solutions for each stepexplained above. The existing solutions constitutea rich literature for spectrum sharing in xG net-works. In Section 7.1, we classify the spectrum shar-ing techniques and describe the fundamental resultsabout these techniques in xG networks. These workprovide insight about how a spectrum sharing pro-tocol can be designed. Accordingly, in Sections 7.2and 7.3, we overview the solutions for spectrumsharing among multiple coexisting xG networks

(inter-network spectrum sharing), and inside an xGnetwork (intra-network spectrum sharing), respec-tively. Finally, in Section 7.4, the open researchissues for spectrum sharing in xG networks arediscussed.

7.1. Overview of spectrum sharing techniques

The existing solutions for spectrum sharing in xGnetworks can be mainly classified in three aspects:i.e., according to their architecture assumption, spec-trum allocation behavior, and spectrum access tech-nique as shown in Fig. 12. In this section, wedescribe these three classifications and present thefundamental results that analyze these classifica-

tions. The analysis of xG spectrum sharing tech-niques has been investigated through two majortheoretical approaches. While some work uses opti-mization techniques to find the optimal strategiesfor spectrum sharing, game theoretical analysis

has also been used in this area.The first classification for spectrum sharing tech-niques in xG networks is based on the architecture,which can be described as follows:

Centralized spectrum sharing: In these solutions, acentralized entity controls the spectrum alloca-tion and access procedures [7,51,70]. With aidto these procedures, generally, a distributed sens-ing procedure is proposed such that each entity inthe xG network forward their measurementsabout the spectrum allocation to the central

entity and this entity constructs a spectrum allo-cation map.

Distributed spectrum sharing: Distributed solu-tions are mainly proposed for cases where theconstruction of an infrastructure is not preferable[15,29,40,54,7173]. Accordingly, each node isresponsible for the spectrum allocation andaccess is based on local (or possibly global)policies.

The second classification for spectrum sharing

techniques in xG networks is based on the accessbehavior. More specifically, the spectrum accesscan be cooperative or non-cooperative as explainedbelow:

Cooperative spectrum sharing: Cooperative (orcollaborative) solutions consider the effect ofthe nodes communication on other nodes[7,15,29,40,71]. In other words, the interferencemeasurements of each node are shared amongother nodes. Furthermore, the spectrum alloca-tion algorithms also consider this information.While all the centralized solutions can be regardedas cooperative, there also exist distributed cooper-ative solutions.

Fig. 12. Classification of spectrum sharing in xG networks based on architecture, spectrum allocation behavior, and spectrum access

technique.



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Non-cooperative spectrum sharing: Contrary tothe cooperative solutions, non-cooperative (ornon-collaborative, selfish) solutions consideronly the node at hand [54,72,73]. These solutionsare also referred to as selfish. While non-cooper-

ative solutions may result in reduced spectrumutilization, the minimal communication require-ments among other nodes introduce a tradeofffor practical solutions.

These two solutions have generally been com-pared through their spectrum utilization, fairness,and throughput. The utilization and fairness in spec-trum access has been investigated in [50], where thespectrum allocation problem is modeled as a graphcoloring problem and both centralized and distrib-uted approaches are investigated. Using this model,

an optimization framework is developed. In thisframework, secondary users allocate channelsaccording to the interference that will be caused bythe transmission. Both cooperative and non-cooper-ative approaches are considered such that coopera-tive approaches also consider the effect of thechannel allocation on the potential neighbors. Thesimulation results show that cooperative approachesoutperform non-cooperative approaches as well asclosely approximating the global optimum. More-over, the comparison of centralized and distributed

solutions reveals that distributed solution closely fol-lows the centralized solution. A similar analysis hasalso been provided in [74], where the effects of col-laboration in spectrum access is investigated. Animportant assumption in these work is that second-ary users know the location and transmit power ofprimary users so that the interference calculationscan be performed easily. However, such an assump-tion may not always be valid in xG networks.

Game theory has also been exploited for perfor-mance evaluation of xG spectrum access schemes.Especially, the comparison between cooperativeand non-cooperative approaches has been presentedin [49] through game theoretical analysis. In [49],game theory is exploited to analyze the behaviorof the cognitive radio for distributed adaptive chan-nel allocation. It is assumed that users deployCDMA and determine the operating channel andthe coding rate by keeping transmission power con-stant. It is shown that the cooperative case can bemodeled as an exact potential game, which con-verges to a pure strategy Nash equilibrium solution.However, this framework has been shown not to be

applicable for non-cooperative spectrum sharing

and a learning algorithm has been proposed. Theevaluations reveal that Nash equilibrium point forcooperative users is reached quickly and results ina certain degree of fairness as well as improvedthroughput. On the other hand, the learning

algorithm for non-cooperative users converge to amixed strategy allocation. Moreover, the fairnessis degraded when non-cooperative approach is used.While this approach results in slightly worse perfor-mance, the information exchange required by selfishusers is significantly low.

Finally, the third classification for spectrum shar-ing in xG networks is based on the access technol-ogy as explained below:

Overlay spectrum sharing: Overlay spectrum shar-ing refers to the spectrum access technique used.

More specifically, a node accesses the networkusing a portion of the spectrum that has not beenused by licensed users [7,15,40,54,7173]. A s aresult, interference to the primary system isminimized.

Underlay spectrum sharing: Underlay spectrumsharing exploits the spread spectrum techniquesdeveloped for cellular networks [29]. Once a spec-trum allocation map has been acquired, an xGnode begins transmission such that its transmitpower at a certain portion of the spectrum is

regarded as noise by the licensed users. This tech-nique requires sophisticated spread spectrumtechniques and can utilize increased bandwidthcompared to overlay techniques.

The effects of underlay and overlay approaches ina cooperative setting are investigated in [19], wherenon-cooperative users are analyzed using a gametheoretical framework. Using this framework, it isshown that frequency division multiplexing is opti-mal when interference among users is high. As aresult, the overlay approach becomes more efficientthan underlay when interference among users ishigh. The lack of cooperation among users,however, necessitates an overlay approach. Thecomparative evaluations show that the performanceloss due to the lack of cooperation is small, and van-ishes with increasing SNR. However, in this frame-work, the cost and inaccuracies of informationexchange between users are not considered.

Another comparison of underlay and overlayapproaches is provided in [42]. The comparison isbased on the influence of the secondary system on

the primary system in terms of outage probability



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and three spectrum sharing techniques have beenconsidered. The first technique (spreading basedunderlay) requires secondary users to spread theirtransmit power over the full spectrum such asCDMA or Ultra Wide Band (UWB). The second

technique (interference avoidance overlay) requiresnodes to choose a frequency band to transmit suchthat the interference at a primary user is minimized.Also an hybrid technique (spreading based underlaywith interference avoidance) is investigated where anode spreads its transmission over the entire spec-trum and also null or notch frequencies where a pri-mary user is transmitting. Consequently, first, theinterference statistics for each technique are deter-mined for outage probability analysis. Then, theoutage probability for each technique is derivedassuming no system knowledge, perfect system

knowledge, and limited system knowledge. Similarto other existing work, when perfect system knowl-edge is assumed, the overlay scheme outperformsthe underlay scheme in terms of outage probability.However, when interference avoidance is incorpo-rated into spectrum sharing, the underlay schemewith interference avoidance guarantees smaller out-age probability than the pure interference avoid-ance. In a more realistic case, when limited systemknowledge is considered, the importance of thehybrid technique is exacerbated. The overlay

schemes result in poor performance due imperfec-tions at spectrum sensing. More specifically, a nodecan transmit at a channel where a primary user istransmitting. However, when underlay with interfer-ence avoidance is used, the interference caused tothe primary user is minimized. Another importantresult is that a higher number of secondary users

can be accommodated by the hybrid scheme thanthe pure interference avoidance scheme.

The theoretical work on spectrum access in xGnetworks reveals important tradeoffs for the designof spectrum access protocols. As expected, it has

been shown that cooperative settings result in higherutilization of the spectrum as well as fairness. How-ever, this advantage may not be so high consideringthe cost of cooperation due to frequent informationexchange among users. On the other hand, the spec-trum access technique, i.e., whether it is overlay orunderlay, affects the performance in each setting.While an overlay technique focuses on the holes inthe spectrum, dynamic spreading techniques arerequired for underlay techniques for interference-free operation between primary and secondary sys-tems. Considering the tradeoff between system com-

plexity and performance, hybrid techniques may beconsidered for the spectrum technique. In the follow-ing two sections, we explain the existing spectrumsharing techniques that are combinations of thethree classifications we have discussed in this section.

7.2. Inter-network spectrum sharing

xG networks are envisioned to provide opportu-nistic access to the licensed spectrum using unli-censed users. This setting enables multiple systems

being deployed in overlapping locations and spec-trum as shown in Fig. 13. Hence, spectrum sharingamong these systems is an important research topicin xG networks. Up to date, inter-network spectrumsharing has been regulated via static frequencyassignment

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