enabling location and environment awareness in cognitive radios

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Computer Communications 31 (2008) 1114–1125

Enabling location and environment awareness in cognitive radios

Hasari Celebi *, Huseyin Arslan

Electrical Engineering Department, University of South Florida, 4202 E. Fowler Avenue, ENB-118, Tampa, FL 33620, USA

Available online 18 January 2008

Abstract

Location and environment awareness are two prominent features of cognitive radios and networks enabling them to interact with andlearn the operating environment. A cognitive radio architecture with location and environment awareness engines is introduced in thispaper. Architectural framework of both engines along with their components are presented. The proposed architecture is a promisingmodel to support advanced and autonomous location and environment-aware applications (e.g. advanced location-based services(LBS)). Implementation options, design challenges, issues, and potential solutions for the realization of both engines are discussed.An underlying method for both engines, which is range accuracy adaptation, is presented. Finally, concluding remarks with futureresearch directions are provided.� 2008 Elsevier B.V. All rights reserved.

Keywords: Cognitive radio; Location awareness; Environment awareness; Location-based services

1. Introduction

Advances in mobile computing and enabling technolo-gies along with user demands for new and improved appli-cations are the main driving forces for the evolution ofconcepts of wireless systems. For instance, location aware-ness concept for wireless systems has been used tradition-ally to imply positioning, tracking, and location-basedservices (LBS). Relative to location awareness, environ-ment awareness is a new concept and it has not been inves-tigated as much as location awareness. Relying on therecent advances in mobile computing and enabling technol-ogies such as introduction of sophisticated processors (e.g.microprocessors and FPGAs) and software defined radio(SDR) technology, it is time for paradigm shift in locationand environment awareness systems. In this study, we con-sider the location and environment awareness capabilitiesof human beings and bats as models for realization ofadvanced and autonomous location and environmentawareness features in cognitive radios and networks.

0140-3664/$ - see front matter � 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.comcom.2008.01.006

* Corresponding author. Tel.: +1 8139744783.E-mail addresses: [email protected] (H. Celebi), [email protected].

edu (H. Arslan).

Cognitive radio, invented by Mitola [1], is one of themost promising technologies to realize such advancedand autonomous location and environment awarenesscapabilities in wireless systems [2–4]. Haykin introducedthe idea of cognitive radar along with cognition cycle forenvironment awareness, which is a physical realization ofbat echolocation system [5]. Although cognitive radar isconsidered as a standalone device in [5], it can be consid-ered as a subsystem of cognitive radio and one of the mostpromising methods for the realization of environmentawareness in cognitive radios. Afterwards, radio map envi-ronment method for cognitive radio networks is introduced[6]. Furthermore, a location awareness engine for cognitiveradios and networks is proposed in [2,4]. This is followedby the introduction of cognitive positioning systems alongwith the concept of range accuracy adaptation [3].

In this study, a cognitive radio architecture with locationand environment awareness cycles is introduced. An over-view of location awareness engine is provided and a concep-tual model for environment awareness engine is proposed.Architectural framework of both engines along with theircomponents are presented. Furthermore, design consider-ations and potential solutions for embodying both capabil-ities to cognitive radios are discussed. An underlying

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Fig. 1. Illustration of location and environment awareness in humanbeing using eyes and ears (Human head image by courtesy of [11]).

Fig. 2. Simplified conceptual model of location and environment aware-ness cycles for the creatures (e.g. human, bat).

H. Celebi, H. Arslan / Computer Communications 31 (2008) 1114–1125 1115

technique for both engines, which is range accuracy adapta-tion, is presented.

The remaining part of the paper is organized as follows.The concepts of location and environment awareness inboth nature and wireless systems are discussed in Section2. In Section 3, proposed cognitive radio structure alongwith the components are introduced. Design considerationsof the proposed model and range accuracy adaptation tech-niques are presented in Section 4. Finally, conclusions andfuture research directions are provided in Section 5.

2. Location and environment awareness concepts

Before proceeding to discuss location and environmentawareness concepts, it is worth to provide some clarifica-tions in the terminology. Although there are some signifi-cant efforts such as IEEE SCC41 standard [7] totechnically define cognitive radio and related terminologiessuch as SDR, a globally recognized definition of cognitiveradio has not existed yet [3]. In this article, we adopt thefollowing definition that includes all the features of cogni-tive radio transceiver reported in the literature [8]: sensing,awareness, learning, decision, adaptation, reconfigurability,goal driven autonomous operation. Note that learning, mem-ory, judgement, and decision mechanisms are folded intoawareness term in this study. Moreover, although locationand associated environment are tightly coupled concepts,we treat them separately throughout the paper unlessotherwise stated. The physical place occupied by an object(e.g. designated user) is referred as location. Furthermore,position term is defined as the coordinates of a single pointin space that represents the location of an object [4]. On theother hand, environment is briefly defined as the volumeoriented at a specific location. Detailed definition of envi-ronment is provided in a later section.

2.1. Location and environment awareness in the nature

As the name implies ‘‘Location and EnvironmentAwareness” concept can be defined as being cognizant oflocation and associated environment. The creatures in thenature have been considered as models for most of theinnovations in science history. Similarly, most of the crea-tures in the nature have already location and environmentawareness capabilities to some extent and they have beenconsidered as models for incorporating such capabilitiesto electronic devices [9]. For instance, bat has locationand environment awareness capability, which is known asecholocation, for the navigation and prey capturing [10].The bats emit high frequency ultrasonic signals (20–200 KHz) from their mouths (transmitter) and listen tothe echoes from the environment using their ears (receiv-ers). The received echoes are processed by these animalsfor different purposes such as navigation, object recogni-tion, and ranging. More intricate example is the humanbeing that is equipped with sophisticated location and envi-ronment awareness capabilities. The human beings have

multiple sensors such as ears, eyes, and skin that can be uti-lized for being aware of their locations and correspondingenvironments as illustrated in Fig. 1. Moreover, the col-lected signals through these sensors (e.g. optic and acousticsignals) are converted into electrical signals that the braincan interpret. The human being can be aware of its locationand surrounding environment by processing the sensed sig-nals in the brain. Consequently, the human being can adapthimself/herself to the environment accordingly. As a result,location and environment awareness mechanisms in thehuman being mainly consist of sensing, awareness, andadaptation processes, which is illustrated in Fig. 2.

2.2. Location and environment awareness in wireless systems

Location and environment awareness features can beintroduced to electronic systems and such approaches havebeen investigated extensively for biologically inspiredrobotics [12]. However, this is not the case for wireless sys-tems. Utilization of location and environment informationin wireless systems have been limited to positioning systemsand LBS [4]. Nevertheless, the aforementioned advancedlocation and environment awareness capabilities of humanbeing or bat can be introduced to wireless systems as well[2,4]. This can be accomplished by using cognitive radio

1116 H. Celebi, H. Arslan / Computer Communications 31 (2008) 1114–1125

technology, which is defined previously [1,13]. Accordingto the definition, cognitive radio has sensing, awareness,and adaptation features, which are the main ingredientsof location and environment awareness conceptual modelshown in Fig. 2. Hence, the consequent conclusion is thatcognitive radio is one of the most promising technologiestowards realization of these two capabilities in wireless sys-tems [2–4]. The following natural question that arise is:how to realize such capabilities in cognitive radios?, whichis the main focus of this article.

3. Proposed architecture

A conceptual model for cognitive radios including loca-tion and environment awareness cycles shown in Fig. 3 isproposed in order to support autonomous location andenvironment-aware systems. Due to the relevancy, spec-trum awareness engine is included to the model withoutdetails. We refer to [14] for details on spectrum awareness.However, cognitive radio is not limited to these threeengines. According to the model in Fig. 3, location andenvironment awareness engines consist of sensing, aware-ness core, and adaptation systems, respectively, similar tothe location and environment awareness cycles of creaturesin the nature. In this model, location and environment

Fig. 3. Conceptual model for cognitive radios with location and environ-ment awareness cycles.

awareness engines receive tasks from cognitive engine andthey report back the results to the cognitive engine forachieving autonomous location and environment-awareapplication at the hand. Furthermore, both engines can uti-lize various sensors to interact with and learn the radioenvironments. Additionally, there are direct or indirect(through cognitive engine) collaborations between bothengines. For instance, environment awareness enginesenses the environmental parameters [15] and providesthese parameters (e.g. frequency dependency constant ofchannel environment [16]) to location awareness engine.Similarly, spectrum awareness engine senses the spectrum[17] and provides the spectrum information (e.g. availablebandwidth) to the location awareness engine. The compo-nents of the proposed model are presented in the followingsections.

3.1. Sensing interface

Sensing process is composed of mainly two components,which are sensors and associated data post-processingmethods. Similar to the creatures in the nature, differentsensors have been used in wireless systems for sensing. Sen-sors are utilized to convert the signals acquired from envi-ronment to electrical signals so that cognitive radios caninterpret. The acquired signals can be in different formatsuch as electromagnetic, optic, and sound. Therefore, sen-sors can be categorized under three types; electromagnetic,image, and acoustic sensors. Note that the correspondingdata post-processing algorithm for each sensing techniqueis different. Inspiring from the sensing features of the crea-tures, we classify the sensing mechanisms in cognitiveradios under three main categories based on the type ofsensors used; radiosensing, radiovision, and radiohearing.Radiosensing is a sensing technique utilizing electromag-netic sensors and the associated post-processing schemes.Similarly, radiovision is a sensing approach using imagesensors and the corresponding post-processing schemes.Finally, radiohearing is a sensing method employing acous-tic sensors and the associated post-processing schemes.Although sensing interface is a common component in cog-nitive radios to interact with environment and other users,we focus on the sensing methods for location and environ-ment awareness systems. As a result, the details of sensinginterface are discussed in the context of location and envi-ronment awareness in this section.

3.1.1. Radiosensing sensors

Although light can be considered as an electromagneticwave, we study the image sensors in a separate section dueto widely usage of image sensors in the literature. The mostwidely used radiosensing (electromagnetic) sensor in wire-less systems is antenna, which is the focus of this section.Antenna is a transducer that converts electromagnetic sig-nal into electrical signals and vice versa. For instance, inantenna-based wireless positioning systems, location infor-mation is estimated from the received signal statistics such


as time-of-arrival (TOA), receive signal strength (RSS), andangle-of-arrival (AOA) [3]. It is envisioned that cognitiveradios will have advanced location awareness capabilitiesusing antenna-based algorithms. In fact, there are someefforts in this line such as proposed cognitive positioningsystems in [3]. Since weather-induced impairments canaffect the performance of wireless systems, performanceof cognitive radios and networks can be improved as wellby having meteorological information of the operatingenvironment. Such information can be acquired by cogni-tive radios either from a central server or embedded auxil-iary sensors such as thermometer and barometer.

3.1.2. Radiovision sensors

Radiovision sensor such as image sensor is a device thatcaptures optic signals from the environment and convertsthem to electrical signals in order to construct the corre-sponding image. These sensors have been already used indifferent areas such as digital cameras and computer visionsystems. Computer vision is a branch of artificial intelli-gence aiming to provide vision systems functioning likehuman vision in computers. The recent advances in visionsystems such as cognitive vision systems [18] and sceneanalysis [4,19] show the feasibility of designing cognitiveradios with vision capabilities. Cognitive radio with cogni-tive vision systems can have a capability to convert theacquired scene state into text, image or voice formatsdepending on the applications. Consequently, numerousimage-based location and environment-aware applications[20,21] can be developed. However, it is a challenging taskto embody such advanced cognitive vision systems due tolow power, cost, and size limitations. Assuming that cogni-tive radio has cognitive vision system capabilities, anotherchallenge is the placement of image sensors since it isrequired to point such sensors towards the target directionor object. However, this is not a problem in human beingsince the eyes are located in the most strategical positionof human body. Different solutions can be developed toaddress this issue in cognitive radios, especially when thecontinuous scene acquisition is required. For instance,image sensors (e.g. video camera) along with Ultrawide-band (UWB) transceiver can be mounted to the user’ hat,which is known as wearable computing devices in the liter-ature [22]. In such solution, digital camera acquires theimages and transmits them to the cognitive radio locatedin a part of the body (e.g. pocket) for the data post-process-ing using UWB transceiver.

3.1.3. Radiohearing sensors

One of the radiohearing sensors is acoustic sensor,which is a transducer that converts acoustic signals intoelectrical signals and vice versa. This type of sensor hasalready been used in different wireless systems. The mainidea behind acoustic technique is utilizing sound propaga-tion to navigate, detect objects, and communicate. Never-theless, our concern here is the utilization of acousticsignals for cognitive location and environment awareness

systems. For instance, acoustic location estimation tech-niques (e.g. sonar [23]) can be utilized for cognitive loca-tion-aware applications. Furthermore, bat echolocation isa perfect example for the active sonar, which can beemployed for developing numerous environment-awaresystems. Ideally, cognitive radio with passive sonar func-tioning like human ear or active sonar functioning likebat echolocation are envisioned. There are some effortstowards achieving these goals such as Cricket indoor loca-tion system [24]. Another potential utilization of acousticsensor in cognitive radios is extracting environmental fea-tures from the sensed sound signal similar to human beingsand bats. For instance, a blind or closed-eye person caninfer to his/her location from the sounds that he/she hears.More specifically, a blind or closed-eye person can deter-mine whether he/she is in a forest or zoo if he/she hearsthe sounds of various animals. Similarly, cognitive radiocan utilize its microphone, which is an integral part ofthe most of wireless devices, for location-aware applica-tions. One potential approach is to capture sound signalas a fingerprint and then compare it to predefined finger-prints in database for extraction of certain environmentalfeatures.

Image sensors are utilized mainly in passive manner(only receiver) whereas acoustic and electromagnetic sen-sors are used in active manner (both transmitter and recei-ver) in wireless systems. Furthermore, image sensorsmainly require to point the cognitive radios towards thetarget direction whereas antennas do not require pointing.In other words, cognitive radio with antennas can continu-ously interact with channel environment even if it is locatedin a pocket or bag. Note that several antenna-based loca-tion-aware algorithms for cognitive radios and networksare proposed in [2,3]. Furthermore, since cognitive radiohas a common sensing interface including different sensors,it can utilize one or combination of the sensors dependingon the autonomous task at the hand. For instance, cogni-tive radio can use both image and acoustic sensors for sup-porting autonomous location and environment-awareapplications similar to utilization of both eyes and earsby human being.

3.2. Location awareness engine

In this section, the details of location awareness enginecomponents are discussed. A conceptual model for locationawareness engine in cognitive radios is introduced in [2,4].The proposed model consists of the following subsystems:location sensing, location awareness core, and location-

aware algorithm adaptation. In what follows, we describeeach of these subsystems.

3.2.1. Location sensing methods

One of the fundamental features of location awarenessengine is to estimate the location information of targetobject in a given format. The format of location informa-tion (e.g. datum and dimension) that needs to be sensed


can have significant effects on the complexity of location-aware algorithms [4]. Therefore, a comprehensive taxon-omy of location information for location awareness in cog-nitive radios is introduced in [4]. In this section, locationsensing techniques are presented.

Radiosensing methods: Antenna-based location sensingalgorithms have been studied extensively for wireless posi-tioning systems and they can be categorized under threegroups: range-based schemes, range-free schemes, and pat-

tern matching-based schemes. Evaluation of these methodsin the context of cognitive radio can be found in [4]. Thelegacy antenna-based location estimation methods do nothave cognition capabilities that cognitive radios require.However, there are some efforts towards realization ofantenna-based location sensing technique with cognitioncapabilities (e.g. range accuracy adaptation) such as cogni-tive positioning systems introduced in [3]. The details ofcognitive positioning systems can be found in [3].

Radiovision methods: Image sensors are used for visuallocation sensing methods [22]. In this approach, the loca-tion of observer is estimated solely based on the imagesacquired from the image sensors. The relationship betweenvideo camera mounted to the user hat and cognitive enginein cognitive radios resembles to the relationship betweeneye and brain in the human body. By using wearable com-puting devices such as video camera, signals (e.g. video)from the scene are acquired and sent to cognitive radio.The acquired images can be processed using advanced dig-ital image and signal processing techniques (e.g. patternanalysis and machine intelligence algorithms [19]) to con-struct the scene state in the desired formats; text, image,video, and voice. One of the well-known visual locationsensing techniques is scene analysis [19–21]. Scene analysissimply is a pattern matching based location sensing tech-nique similar to RF pattern matching based methods(e.g. RF fingerprinting) [25]. Acquired images are used aspatterns in the scene analysis, whereas channel statistics(e.g. TOA) are utilized as patterns in the RF patternmatching based methods. One of the well-known conse-quent steps is comparison of the acquired pattern to thepatterns in a pre-built database.

Note that the location accuracy of radiovision basedlocation sensing methods is pretty rough compared toother location sensing methods such as radiosensing basedschemes. Therefore, radiovision based sensing methods arepreferable for object and environment recognition ratherthan location sensing. Two of the main drawbacks ofradiovision based sensing techniques are the requirementof image database and extensive image processing power.Compared to robotics and computer systems, implement-ing radiovision techniques such as cognitive vision systemsin cognitive radios is a challenging task due to low power,cost, and size constraints.

Radiohearing methods: Radiohearing based locationsensing methods utilize acoustic sensors for interactingwith environments. Similar to radiosensing based locationsensing techniques, radiohearing based location sensing

methods can be implemented using three group of schemes;range-based, range-free, and pattern matching based tech-niques. The majority of the studies in the literature focuseson the first two methods and these studies are mostly forlegacy location estimation techniques to the best ofauthors’ knowledge. Cognitive radio is a promising tech-nology to realize advanced radiohearing based locationsensing techniques functioning similar to bat echolocationsystems. For instance, cognitive radio can acquire soundsignal and use it as a pattern. In addition, it can look atthe spectrum of the captured sound pattern and compareit with spectrum patterns stored in the database in orderto infer to the location. Different radiohearing based loca-tion sensing methods using the aforementioned threeapproaches can be developed for the realization of locationawareness in cognitive radios.

3.2.2. Location awareness core

In this section, fundamental components of locationawareness core are presented. The main objective of thiscore is to perform critical tasks related to location informa-tion such as learning, reasoning, and making decisions. Thecore is composed of seamless positioning and interoperabil-ity, security and privacy, statistical learning and tracking,mobility management, and location-aware applications.The details of statistical learning and tracking, and mobil-ity management can be found in [4]. Therefore, we empha-size on the seamless positioning and interoperability,security and privacy, and location-aware applications inthis section.

Seamless positioning and interoperability: Seamless posi-tioning is defined as a system that can keep the positionaccuracy at a predefined level regardless of the changes inchannel environment. There are mainly two approachesfor achieving seamless positioning; waveform-based meth-

ods and environment sensing-based methods. The firstapproach is based on utilization of appropriate waveformor technology depending on the environment [26]. Thisrequires supporting all or predefined waveforms of theexisting and future positioning systems and waveformswitching mechanism. An example for the first approachis the European SPACE project [26]. The main objectiveof this project is to build a prototype positioning systemthat can provide centimeter level positioning accuracy any-where and at all times. The SPACE prototype consists ofthe existing positioning waveforms, algorithms and sensorssuch as GPS, Galileo, 3G, UWB, WLAN, and Bluetooth.Depending on the user requirements and environments,the most appropriate positioning system is selected toachieve seamless positioning. Moreover, the prototypehas plug and play integrated positioning system capabilityfor supporting the existing and future positioning tech-niques. We refer to [26] for further details on the SPACEproject.

The second approach, which does not require multiplewaveforms, is based on sensing channel environmentparameters (e.g. path loss coefficient [27]) and adapt the

Table 1Some representative location-aware applications for cognitive radios andcognitive wireless networks

Application type Applications

Location-based services (LBS) –Public safety (e.g. police, fire,ambulance)–E911, E112–Package tracking–Child finder–Patient tracking–Real-time traffic services–Roadside assistance

Location-assisted networkoptimization

–Dynamic spectrum management–Network plan and upgrade–Handover–Dynamic channel allocation–Routing–Power management–Internetworking–Adaptive coverage system

Location-assisted transceiveroptimization

–Adaptive beamforming–Interference avoidance–Link adaptation

Location-assisted environmentidentification

–Channel environmentcharacterization–LOS/NLOS identification


positioning algorithm accordingly in real-time. The pro-posed RSS based location estimation algorithm forunknown channel environment in [27] is a good examplefor this approach. In the proposed algorithm, the locationof target wireless device and path loss coefficient of thechannel environment are jointly estimated to keep the pre-defined accuracy at constant level. Note that the path losscoefficient is not the only parameter to detect the changesin channel environments. For instance, frequency-depen-dent coefficient is a recently discovered channel parameterthat can be used for monitoring the environments [16]. As aresult, multiple distinguished parameters of channel envi-ronment can be monitored to achieve seamless positioning,which is handled by environment awareness engine in cog-nitive radios. Compared to waveform-based methods, envi-ronment-sensing methods have lower complexity. As aresult, cognitive radio is envisioned to have capability ofsupporting both type of methods.

The IEEE defines interoperability as the ability of twoor more systems or components to exchange informationand to use the information that has been exchanged [28].The interoperability issues can be grouped under twomain categories; cognitive radio-cognitive radio interoper-ability and cognitive radio-legacy radio interoperability.For the first issue, both cognitive radios can have thesame or different waveforms. In the former case, theycan exchange the information directly. However, in thelatter case, both needs to agree on one of the waveformsin order to communicate, which is a current researchtopic. As a straightforward solution to the second issue,cognitive radio can switch its waveform to the waveformof legacy radio, since the latter radio does not have recon-figurability features. In addition, the type of sensed loca-tion information exposes another issue due to thediversity in location information format. For instance,the location information of a device can be in the formatof WGS84 (used by GPS) and this information can beconverted to Tokyo Datum (TD) format by using refer-ence datum conversion capability of cognitive radios [4].As a result, cognitive radio is a promising technology torealize advanced seamless positioning and interoperabilityalgorithms.

Security and privacy: The prospect extensive utilizationof location information in cognitive radios and networksbrings two issues on the surface; security and privacy.The majority of the proposed location estimation and posi-tioning techniques in the literature assume the absence ofadversarial attacks. Nevertheless, positioning techniquesare highly vulnerable to such attacks [29]. Of the manypotential threats, tracking the position of a cognitive radiouser without authorization and adversarial attacks are thetwo main ones. The first threat can violate the user privacyand the second one can result in catastrophic scenariossince LBSs highly depend on the location information. Itis crucial to develop effective solutions to address theseissues. For instance, local or global geolocation privacyprotection methods can be developed to address privacy

issue. Indeed, there is an effort in this line for mainly wirednetworks (e.g. world wide web), which is the formation ofgeographic location/privacy (Geopriv) working groupunder The Internet engineering task force (IETF) [30].The primary task of this working group is to assess autho-rization, security, integrity, and privacy requirements thatmust be met in order to transfer such information, orauthorize the release or representation of such informationthrough an agent. Similar geographic privacy methods canbe developed for cognitive wireless networks. To addresssecurity issues in location-aware applications, secure posi-tioning systems that are robust to adversarial attacks(e.g. spoofing and cheating) can be developed. Forinstance, the proposed verifiable multilateration in [29] isa good example for secure positioning technique. Suchsecure positioning techniques can be developed for cogni-tive radios as well. In summary, cognitive radios have acapability to support advanced geographic privacy andsecure positioning methods.

Location-aware applications: Various location-awareapplications can be developed using the location informa-tion. However, we categorized such applications under fourcategories: LBS, location-assisted network optimization,location-assisted transceiver optimization, and location-

assisted environment identification [2,4]. Some representativelocation-aware applications for each group are tabulated inTable 1, yet we refer to [2,4,15] for further details.

3.2.3. Adaptation of location-aware algorithms

The main objective of the adaptation block is to supportlocation awareness engine in terms of adaptation of algo-rithms and parameters for the satisfaction of the user, con-

Fig. 4. A conceptual model for environment awareness engine.


sequently, cognitive engine requirements. These require-ments stem from the autonomous location-aware applica-tions that are supported. The reported performanceparameter or requirement of location-aware applicationsin the literature are accuracy, integrity, continuity, andavailability [26]. Nevertheless, range accuracy is one ofthe most important performance parameters or require-ments of location-aware applications, which is consideredin this section. We refer to [26] for the details on the integ-rity, continuity, and availability requirements of position-ing systems.

Autonomous location-aware applications (e.g.advanced LBS) can require different level of accuracy.For instance, indoor positioning systems demand higherprecision accuracy compared to outdoor positioning sys-tems. More specifically, asset management in industrialareas, which is a local positioning application, can requiretypically 0.05–30 m accuracy. On the other hand, E911

services require 50–300 m accuracy in the most cases [3].In order to address this issue, accuracy adaptation meth-ods can provide arbitrary accuracy to support autono-mous location-aware applications. Moreover, as mobilecognitive radio moves, the channel environment canchange and it is known that change of environment affectsthe range accuracy [16]. In order to keep the accuracy atdesired level in spite of change of channel environment,the operational environment can be monitored by usingenvironment awareness engine. The range accuracy canbe adapted based on the input from the environmentawareness engine regarding the environmental changes.In summary, supporting autonomous location-awareapplications requires having range accuracy adaptationmethods that can provide arbitrary accuracy anywhereand anytime. The details of this method are provided laterin this paper.

3.3. Environment awareness engine

Environment awareness is one of the most substantialand complicated task in cognitive radios since channelenvironment is the bottleneck of wireless systems. Crea-tures with environment awareness capabilities such ashuman being and bats can be considered as models forthe realization of environment awareness in cognitiveradios. For instance, human being has different sophisti-cated senses such as observing and learning thesurrounding environment and bats utilize their echoloca-tion systems for object and environment identification,and target detection and tracking. As a result, similarenvironment awareness techniques can be developed forcognitive radios. The consequent essential questions thatarise are:

– What type of information to acquire from environment?– How to acquire such information?– How to utilize the acquired environmental knowledge in

cognitive radios and networks?

In this section, we address to these questions briefly. Inorder to achieve this goal, a conceptual model for environ-ment awareness engine is introduced, which is shown inFig. 4. The model consists of environment awareness core,topographical information, object recognition and tracking,propagation characteristics, meteorological information,environment sensing, and environment adaptation. Thesesubsystems are described in the order to answer the afore-mentioned three questions as follows.

The answer to the first question is hidden in the defini-tion of ‘‘environment”. From the wireless systems pointof view, an environment mainly consists of the followingentities: topographical information, objects, propagationcharacteristics, and meteorological information. Althoughsome sensing techniques for the aforementioned environ-mental information exist in the literature, there is not anystructured foundation for environment sensing in cognitiveradios to the best of authors’ knowledge. Therefore, devel-opment of environment sensing techniques is a currentresearch topic as well. The same discussion is also validfor environment adaptation techniques. In what follows,we describe each entity along with some correspondingrepresentative sensing, adaptation techniques, andapplications.

3.3.1. Topographical information

According to Oxford English Dictionary, topography isdefined as ‘‘the science or practice of describing a particularplace, city, town, manor, parish, or tract of land; the accu-rate and detailed delineation and description of any local-ity”. In other words, topography of a local regionprovides information about not only the relief (Earth’s sur-face features), but also vegetation, human-made structures,history and culture of that particular area. Assuming thatcentral environment awareness engine has topographicalmap including the aforementioned information, numerousadvanced LBS can be developed. There are some effortstowards the realization of topographical map such as theGoogle MapsTM. Another example is the proposed vision


enhanced object and location awareness method for mobileservices in [21]. In the proposed method, a mobile user (e.g.tourist) points the embedded camera towards to the objectof interest (e.g. historical structure) and captures the image.Consequently, the captured image is transmitted to a serverto extract the information related to the image and thensend this information to the mobile user.

3.3.2. Object recognition and tracking

Objects are defined as the human-made entities presentin the target local environment temporarily or permanentlyin this study. The large and permanent human-made struc-tures such as buildings and bridges are considered as partof topography of environment, hence, such human-madestructures are included in the topographical information.On the other hand, relatively small and movable human-made entities such as vehicles, home and office appliancesare considered as objects. Object detection, identificationand tracking are important features of environment aware-ness engine since they can affect the dynamic of environ-ment. For instance, cognitive radar and cognitive sonarare two promising technologies to embody such capabili-ties, which are the features of bat echolocation, in cognitiveradios [5]. In [5], cognitive radar is introduced with thecapability of target detection and tracking using Bayesianapproach. The proposed cognitive radar architecture isbased on dynamic closed-loop feedback system (e.g. a cog-nition cycle) encompassing the transmitter, environment,and receiver. We refer to [5] for further details.

3.3.3. Propagation characteristicsThis entity provides information on the characteristics

of signal progression through a medium (channel environ-ment). Basically, propagation characteristics of channelenvironment shows that how the channel affects the trans-mitted signal. The statistical characteristics of wirelesschannel are described mainly with two group of statistics:(1) large-scale, (2) small-scale. Large-scale statistics provideinformation on path loss behavior of channel environment.On the other hand, small-scale statistics determine thedrastic variations of received signal in time and frequencydue to short displacements. In addition, the selectivity ofthe channel provides important statistics related to multi-path radio channel. Some representative channel statisticsare delay spread, doppler spread, and angular spread[15]. Traditionally, these statistical parameters are obtainedafter performing extensive measurements and data post-processing, which is known as propagation channel model-

ing process [31]. Alternatively, the propagation statisticsof local environment can be obtained in different waysusing cognitive radios such as the proposed location-awareness based performance improvement of wireless sys-tems in [15]. The proposed method consists of the followingthree main steps: environment recognition and classification,statistical propagation model parameters extraction, andchannel environment adaptation. Various propagation char-acteristics acquisition methods can be developed and such

information can be utilized for different applications bycognitive radios.

3.3.4. Meteorological information

This entity provides information on the weather of tar-get local region, which can affect the signal propagation.The current and future weather parameters such as rain,snow, temperature, humidity, and pressure can be acquiredeither using radio auxiliary sensors or from central cogni-tive base station. By having current and forecasted meteo-rological information, cognitive radio can adapt itselfaccordingly. For instance, rain can have significant affectson the performance of broadband fixed wireless accesslinks (e.g. Fixed WiMAX) [32], especially operating athigher carrier frequencies. One of the performance param-eters that can be affected from rain is the carrier-to-interfer-ence ratio (C/I) and this performance metric depends onthe rain intensity of the location of desired signal pathand interferer signal paths. Some of the representative sce-narios showing the rain effects on C/I performance of thebroadband fixed wireless access links are given as follows[32]: rain-induced C/I degradation, rain-induced C/I improve-

ment, and no C/I change. The details of these scenarios canbe found in [32]. If cognitive radio or network has a capa-bility to acquire rain intensity of local regions from a cen-tral meteorological server or Internet, then, C/I adaptationcan be performed accordingly.

Inspiring from the definition of environment term, themain task of environment awareness engine in cognitiveradios can be summarized as of acquiring the informationon topography, objects, propagation channel, and meteo-rology of the target local region and provides these infor-mation to other components of cognitive radios to beused for different applications. For instance, object andenvironment identification, seamless positioning, andLOS–NLOS identification are three potential environ-ment-aware applications that can be developed. In sum-mary, cognitive radio is envisioned to have environmentawareness capability, which can lead to the developmentof advanced LBSs.

4. Design considerations

4.1. Implementation options

Learning through interactions of cognitive radio withthe surrounding environment can be accomplished mainlyin one of three ways; cooperative, self, and composite. With-out loss of generality, the details of each implementationoption are provided in the context of location awarenessin the sequel. Nevertheless, environment awareness canbe achieved using same methods.

In cooperative location awareness approach, (at least)two cognitive radios collaborate on learning the distancebetween them by estimating the ranging information. Inthis approach, one of the cognitive radios transmits signal(e.g. RF or acoustic) through the channel environment and


the other cognitive radio extracts the ranging informationfrom the received signal as illustrated in Fig. 5. In otherwords, the transmitter and receiver are not co-located.Cooperative location awareness can be accomplished byusing cognitive positioning systems [3]. Both cognitiveradios negotiate on the ranging parameters that achievesgiven accuracy through cognitive ranging protocol illus-trated in Fig. 6. This protocol consists of three stages; rang-

ing parameter setup, two-way TOA ranging, time-stamp

report. Both cognitive radios negotiate or provide feed-backs to each other during the ranging parameter setupstage (loop). Once both agree on the parameters, the nexttwo stages are initiated. The last two stages are parts ofconventional two-way TOA ranging protocol, which thedetails of these steps can be found in [33].

Unlike to cooperative location awareness approach,self-location awareness method enables a cognitive radioto perform location awareness without the need of another

Fig. 5. A conceptual model for cooperative location awareness.

Fig. 6. Illustration of cognitive ranging protocol.

cognitive radio or infrastructure. The self-location aware-ness can be achieved in one of two ways, which are activeand passive manners as illustrated in Fig. 7. In activeself-location awareness methods, simply, both transmitterand receiver are utilized and they are co-located. A perfectexample for this approach in the nature is bat echolocationsystem. Such system can be accomplished in cognitiveradios by using cognitive radar [5] or cognitive sonar tech-niques. On the other hand, passive self-location awarenessmethods observe and acquire the signals (e.g. optic oracoustic) from the environment without transmitting anysignal. Basically, this approach requires only receptor suchas image sensors. Human vision and hearing are two natu-ral examples of passive self-location awareness technique.Such capabilities can be embodied into cognitive radiosusing aforementioned location sensing methods.

Both cooperative and self-location awareness methodshave some strengths and weaknesses. For instance, coopera-tive methods have capability to provide absolute and relativeranging and positioning information whereas the self-techniques can provide only relative ranging information.Furthermore, the implementation of closed-loop feedback(receiver–transmitter–environment) in self-location aware-ness architectures is less complex than that in cooperativelocation awareness architectures since the transmitter andreceiver are co-located in the former architectures. Cognitiveradio can leverage the strengths of both cooperative and self-location awareness methods, which is referred as composite

location awareness for supporting advanced autonomouslocation-aware applications.

4.2. A case study: range accuracy adaptation

Range accuracy is one of the most essential perfor-mance metrics of cognitive location and environment-aware systems. In order to support such systems, rangeaccuracy adaptation is inevitable. Furthermore, rangeaccuracy adaptation is one of the main adaptive behav-iors of bat echolocation system [5]. The bats using fre-quency modulation (FM-bats) during emission makeadjustments on the emitted-sound duration, bandwidth,and repetition rate during the target (e.g. insect)approach. For instance, as the FM-bat gets closer toits target, it decreases the transmitted signal duration

Fig. 7. A Conceptual model for self-location awareness, (a) active; (b)passive.


and increases the burst repetition rate. This is accom-plished by using the feedback information provided bythe receiver, which is the distance to the target. In [3],range accuracy adaptation concept for cognitive position-ing system is introduced. Nevertheless, the concept canbe applied to cognitive radar to accomplish bat echoloca-tion system with range accuracy adaptation capability incognitive radios. The proposed range accuracy adapta-tion utilizes Cramer-Rao lower bound (CRLB) in thetransmitter as an optimization criterion. This criterionshows the relationship between range accuracy and trans-ceiver and environment parameters. In the proposedmethod, only bandwidth parameter is adapted to achievearbitrarily given range accuracy. However, this approachcan be extended by adapting the remaining parameters inthe CRLB, which is discussed in the following sections.

Before proceeding to the details of range accuracy adap-tation algorithm, it is worth to mention the challengesrelated designing an ideal cognitive radio transceiver dueto its dynamic nature. Since the spectrum utilization isdynamic and even random, the available spectrum param-eters such as carrier frequencies and corresponding band-widths and transmit power levels can be random.Consequently, the observed channel statistics betweentwo cognitive radios can be random, which is referred ascognitive radio X-channel [34]. In such scenarios, radiochannel needs to monitored and modeled. As a result, thereceiver algorithms and parameters will be dynamic as well.It is a challenging task to design such dynamic cognitiveradio transceivers, which is a current research topic.

4.2.1. Transmitter for range accuracy adaptation

Cognitive engine sends the desired range accuracy to thelocation awareness engine. This engine determines thetransmitter parameter values for the corresponding rangeaccuracy dictated by CRLB. Once the transmitter parame-ters are determined, both cognitive radios handshake onthe range parameter via cognitive ranging protocol inFig. 6. After the range handshaking, originator cognitiveradio (e.g. CR device A as shown in Fig. 6) generates thecorresponding signal waveform using its adaptive wave-form generation capability. In the sequel, we provide theCRLB that is used for range accuracy adaptation. Thestandard deviation of time-of-arrival (TOA) based distanceestimation error (square root of CRLB) r½d̂� for additivewhite Gaussian noise (AWGN) channel is given by [3],

r½d̂� ¼ffiffiffi

3p

vffiffiffiffiffiffiffiffi

Kcs

ppaB

; ð1Þ

where v is the velocity of signal wave used (e.g. speed oflight or sound), K is the number of observation symbol,cs is the symbol signal-to-noise ratio (SNR), a is the chan-nel path coefficient, and B is the absolute bandwidth of thetransmitted signal. In order to accomplish arbitrarily givenrange accuracy, the aforementioned parameters areadapted jointly.

4.2.2. Receiver for range accuracy adaptation

Since the proposed range accuracy adaptation methodutilizes CRLB in the transmitter, there is a need to developa location estimator that can achieve the performance ofCRLB in the receiver side. Maximum a posteriori (MAP)estimator using both line of sight (LOS) and non-line ofsight (NLOS) signals and maximum likelihood (ML) esti-mator using only LOS signals achieve the CRLB [35]. Inthe context of range accuracy adaptation for cognitivepositioning systems, the r½d̂� of ML estimator under theassumptions of AWGN and LOS environment is derived.The resultant equation agrees with the CRLB equationin(1).

4.2.3. Main error sourcesAny arbitrarily range accuracy is accomplished precisely

using (1) if the parameters have infinite values. However, inpractice, the value of the parameters and the correspondingresources such as bandwidth and power are limited, whichcan affect the performance of range accuracy adaptationtechnique. In what follows, we emphasize the effects ofthe parameters and resources on the performance of rangeaccuracy adaptation for the practical cases.

Dynamic spectrum effects: Bandwidth is one of the mostimportant parameters that affects the performance ofTOA-based range accuracy adaptation due to the random-ness in the availability of bandwidth in the spectrum [16].In other words, the distribution of available bandwidthaffects the performance of range accuracy adaptationdepending on the optimization method used to select theparameter values in (1) jointly. Furthermore, the resolutionof available bandwidth also can affect the performancerange accuracy adaptation. For instance, if the optimiza-tion criterion estimates the required bandwidth to be10.335 MHz and the bandwidth resolution of cognitiveradio transceiver is 1 MHz, such residue in bandwidth res-olution results in an additional range accuracy error.

The location of the selected available bandwidth in thespectrum, which is the carrier frequency, also can affectthe performance range accuracy adaptation. For instance,operating at higher carrier frequencies has some disadvan-tages such as higher propagation loss and lower range andpenetration compared to low frequency bands [36]. In addi-tion, weather-induced impairments and attenuation haveimpacts on the high frequency propagation as well [37],which can affect the performance of range accuracy adap-tation. A mitigation technique to combat the aforemen-tioned losses is to use appropriate antennas (e.g.directional antennas) and multiple antenna systems (e.g.MIMO, beamforming) [38–40].

Another issue that can have impact on the performanceof range accuracy adaptation is the form of available band-width. The available bandwidth in the spectrum can be inthe form of either whole or dispersed manner. For instance,if the required bandwidth is 10 MHz, this bandwidth can beavailable mainly in two forms. The first form is 10 MHzbandwidth as a chunk and the second one is the combina-


tion of multiple dispersed bandwidths that sums up10 MHz. An example to latter case is 10 MHz = 3.3 MHzat fc ¼ 1900 MHzþ 2:7 MHz at fc ¼ 2450 MHzþ 4 MHzat fc ¼ 3550 MHz. The effects these two forms of band-width availability on the performance range accuracy adap-tation is a current research topic.

Transceiver impairments effects: One of the transceiverparameters that affect the performance of range accuracyadaptation technique is the support of arbitrary band-width and its resolution, which can be realized by theSDR capability of cognitive radio. Another parameteris the arbitrary transmit power level affecting cs, whichcan be realized by adaptive transmit power level controlfeature of SDR. However, it is worth note that the trans-mit power level across the spectrum is regulated by thelocal agencies. The last transceiver parameter in (1) isK, which is the length of observation symbols. Thisparameter can easily be adapted by cognitive radio trans-ceiver. However, increasing value of K improve the rangeaccuracy with the cost of additional complexity such asoverhead.

Environmental effects: The environmental parameter in(1) affecting the range accuracy is the channel tap coeffi-cient a. This parameter represents the effect of channel dis-tortion on the propagated signal and (1) includes this effectin order to estimate the resultant range accuracy at thereceiver end. This parameter is usually estimated by theprocess known as channel estimation in the literature. Since(1) is derived for AWGN channel, the only channel propa-gation related parameter in the equation is a. Nevertheless,in realistic multipath channel, there are more channel envi-ronment related parameters that can affect the range accu-racy such as frequency-dependency coefficient of channel[16], path loss coefficient [27], and LOS/NLOS condition[41]. For instance, the effects of frequency-dependencycoefficient of channel on the range accuracy can be signif-icant depending on the frequency-dependent feature ofthe radio channel [16]. There is a need to re-think theseenvironmental problems from the cognitive radio perspec-tive and develop effective solutions.

Interference effects: Interference sources are mainlydivided into two categories based on the origin of theinterferers; external and internal. Multiuser interferenceand clutter are two examples for the external interfer-ence, and intersymbol interference and interframe inter-ference are two examples for the internal interference.Moreover, external interference can be further split intwo groups; object-oriented and device-oriented. As thenames indicate that the object-oriented interference isthe interference type that is originated from the objectsin the surrounding environment. For instance, interfer-ence signal from the undesired objects (clutter) in thesurroundings for the case of cognitive radar is an exam-ple for the object-oriented interference. Similarly, theinterference originated from the undesired cognitive andnon-cognitive device is referred as device-oriented inter-ference. The aforementioned interference sources can

affect the range accuracy [42]. Hence, interference avoid-ance, cancelation, or reduction techniques are needed tocombat the interference sources.

5. Conclusions and future research

A conceptual model for cognitive radios with locationand environment awareness cycles is presented. An over-view of location awareness engine is provided and a con-ceptual model for environment awareness engine isproposed. The detailed descriptions of the components ofthe proposed cognitive radio model are presented. Designconsiderations and implementation options are discussed.Range accuracy adaptation that is an underlying methodfor cognitive location and environment aware systems ispresented. The proposed model is a promising underlyingarchitecture for developing advanced and autonomouslocation and environment-aware systems, especiallyadvanced LBS. Finally, each component of the modelneeds to be developed, which can be considered as futureresearch directions.

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enabling location and environment awareness in cognitive radios

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