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ACRA: An Autonomic and Expandable Architecturefor Cognitive Radio Nodes

Shixian Wang, Lunguo Xie, Hengzhu Liu, Botao Zhang, and Heng Zhao

School of Computer,National University of Defense Technology,Changsha, Hunan, China

{sxwang, lgxie, hengzzhuliu, botaozhang, zhaoheng }@nudt.edu.cn

AbstractCognitive radio has been a research hotspot because of

its promise to improve the utilization of the assigned but unused

radio spectrum. The complexity of current networks has been a

main drawback of network development, and so will the problem

of cognitive radio networks. To solve the complexity problem,

autonomic computing has been proposed to enable the network

self-management. This paper puts emphasis on the autonomic

cognitive radio nodes architecture for the purpose of establishing

a cognitive radio network with autonomic computing propertyfrom bottom up. We model the cognitive cycle using autonomic

computing principles and introduce an autonomic cognitive

radio conceptual model. And then, an autonomic cognitive radio

nodes architecture (ACRA) based on the proposed conceptual

model and a realization method are proposed. ACRA can realize

the cognitive radio function with autonomic computing property,

which makes the collaboration and management in a network

formed by this kind of node easier.

Keywords-cognitive radio; autonomic computing; cognitive

radio node architecture; MPSoC; ASSL

I. INTRODUCTION

With the development of computer and VLSI technology,wireless communication has rapidly developed in the pastseveral years. At present, we are moving from 2G and 3Gcommunication to the next generation communication. But, theupgrading demand is contradictory to the limited usable radioresource. Cognitive Radio (CR) or Dynamic Spectrum Access(DSA) has been a research hotspot because of its promise toimprove the utilization of the assigned but unused radiospectrum [1, 2].

The key idea of CR is using unused primary user (PU)radio spectrum by successfully sensing it from spectrum space.To accomplish this task, the CR must have the following twocapabilities. First, it has the ability to sense the unused radio

spectrum, or spectrum holes, which is the sensing ability.Second, once it knows the PU began to use its own spectrum,the CR has to vacant the using band timely to avoidinterference with the PU, and switch to another availablespectrum hole to continue communication, more precisely, itshould have the ability of reconfiguration [3].

The complexity of current networks has been a maindrawback of network development. To reduce the dramaticcomplexity of network, autonomic computing [4], based onstimulation from biological systems, may provide a way tosolve the problem of unmanageability of wire or wireless

networks. The autonomic computing paradigm enables self-management, which is composed of self-protecting, self-healing, self-configuring and self-optimizing components.Research has proved the effectiveness of autonomic computingin addressing the complexity of many kinds of networks, e.g.hybrid wireless networks, Beyond 3rd Generation (B3G)networks [5, 6], etc.

This paper puts emphasis on the autonomic cognitive radionodes architecture for the purpose of establishing a cognitiveradio network with autonomic computing property from

bottom up. We analyze cognitive cycle and current cognitiveradio models, and model the cognitive cycle using autonomiccomputing principles. Then, a cognitive radio conceptualmodel with autonomic property and its formal definition isintroduced. We propose an autonomic cognitive radio nodesarchitecture (ACRA) based on the conceptual model. ACRAhas autonomic computing property and can realize cognitiveradio function. In a network formed by this kind of nodes, thecollaboration and management in it will be easier.

The rest of this paper is organized as follows. Related work

is analyzed in section II. Section III presents a novel CRconceptual model and its formal definition based on autonomiccomputing principles. In section IV, this paper introduces a CRnodes architecture based on the novel CR conceptual model

proposed in section III, and then, a realization method basedon multiprocessor system-on-chip (MPSoC) platform and anautonomic computing model language is presented. The lastsection is the conclusion of this paper.

II. R ELATED WORK

With the upgrading complexity of network, the manage-ment and collaboration in it is more difficult than ever. Toreduce complexity and management costs in network, the

autonomic computing (AC) has been proposed by IBM [4, 7].The autonomic computing based computer system can governits operations automatically when facing the changes ofelements, commands, loads and environment. On therealization of autonomic computer system, J. Kephart and D.Chess proposed the autonomic element (AE) architecture in [4].AE is the basic element of autonomic computer system, it ismade up of an autonomic manager (AM) and one or manymanaged resources (or managed autonomic elements). AM ismade up of monitor, analyze, plan, execute and knowledge

parts.

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Autonomic computing principles have been used toimprove the performance and self-management ability oftelecommunication networks. Work in [5] proposes to applyAC concept to the hybrid networks. In [6], the B3G networksDistributed, Cross-Layer Reconfigurations (DCLR) problemis analyzed, and an AC based method to solve this problem isintroduced. However, to our knowledge, there is no researchon autonomic property of network nodes in the literature.

Networks consist of nodes with expandable autonomic elementarchitecture will have autonomic computing property to solvecurrent networks problems.

CR nodes architecture is the basis of cognitive radiorealization. Current CR architecture is mainly composed of theSoftware Defined Radio (SDR) platform and some cognitive

parts. F. Jondral has proposed a CR model with the view ofcommunications engineering in [8]. In [9], A Context DrivenArchitecture for CR nodes is analyzed, with an emphasis onthe improvement of CRs cognitive capability. On the researchof CR architecture realization, the WiNC2R CR prototype isdeveloped by the WINLAB in Rutgers University [10]. TheAAF project has realized a CR system on their MPSoC

platform [11].In [12], I. F. Akyildiz et al. put emphasis on the spectrum

management problem of cognitive radio ad hoc networks(CRAHNs), and pointed out the research challenges in thisarea based on current CR nodes architecture and CR networkstructure. We think a novel CR nodes architecture withautonomic property will provide a new approach to solveCRAHNS problems.

III. COGNITIVE RADIONODES CONCEPTUAL MODEL

The basic element of cognitive radio network is theCognitive Radio Node (CRN). Under the direction of upperlevel policy, these CRNs sensing and using spectrum holes,establishing collaboration among them to accomplish thewhole CR systems self-adaptation.

According to [1, 2 and 3], the cognitive ability of CRenables it communicate with the environment in realtime. Theappropriate communication parameters according to thedynamic changing spectrum environment are executed by thereconfiguration unit. These adaptive operations are containedin the cognitive cycle. The cognitive cycle defines the trigger

procedure of different actions, which correspond to theoperative sequences of CRs different parts. In sum, thecognitive cycle start from monitor operations to the outsideenvironment and stop at the adaptive actions to the changes,and include some analysis and plan procedures.

We propose to use the monitor-analysis-plan-executecontrol loop of autonomic element to model CRs function.And more precisely, for the purpose of realizing autonomicmanagement in cognitive cycle, the CRN is modeled as thesum of Cognitive Radio Manager (CRM) and a collection ofidle radio spectrum. The CRM is made up of main unit,spectrum sensing unit, experiential database and SDR [8]. Theidle radio spectrum is sensed by CRM. For CRN, it senses thespectrum condition by the spectrum sensing unit of CRM and

provides standard interface to accept upper layer policy or

communicates with other nodes (like AE, this interface can beseen as Sensor/Effector).

The novel autonomic computing based CRM modelprovides a system level control mechanism. CRN can realizefunctions included in the cognitive cycle. These functionsinclude many management works, such as spectrum sensing orsystem configuration, optimization and protection, etc.

A. Autonomic Cognitive Radio Node

The CRN with autonomic computing attributes like an AEwill degrade the complexity of management processes. Weintroduce an autonomic CRN conceptual model in Fig. 1. Thismodel references autonomic element architecture in [4, 13].However, we put emphasis on autonomic operations in CRNsmanagement processes. Moreover, for the radio spectrum,there is no Sensor/Effector to work on it. Once the CRN senseschanges of spectrum space, the analysis-plan-execute loopwill work, and the CRN will adapt to these changes by its ownreconfiguration. This is the difference to original AE concept,and also the reason for a different realization mechanism forCR.

Figure 1. Autonomic Cognitive Radio Node Conceptual Model

Definition 1 (Autonomic Cognitive Radio Node) cognitiveradio node can be defined as tetrad: CRN= (CRM, FUN, MR,S/E), in which the CRM is the CR manager; FUN stands forthe function parameters defined by the upper layer; MR is theset of managed resources, according to the FUN, MR can bethe idle radio spectrum set and/or other CRNs; S/E stands forSensor/Effector.

As Fig. 1 illustrated, CRM can realize the observe-analysis-plan-execute loop by the observer, analyze, plan,execute and experiential database. FUN is defined by the upperlayers (such as network layer in ISO model). For example,under collaborative spectrum sensing condition, a CRN may

be a spectrum information collection node, or just a sensingand sharing node [14]. For simplicity, we assume the MR iscomposed of idle radio spectrum in this paper. S/E is used tocommunicate within the same group or execute upper layersQoS policy.

The observer of CRM provides the ability to sense thespectrum environment, and generates the usable spectrum

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information for autonomic decision with the help of analyzer.The plan part is ready for the plan realization of systems newstate objective. And the execute fulfills the actions defined bythe plan part. These four parts cooperate with experientialdatabase in the process of executing their own function.

The experiential database of CRM includes experientialknowledge K to set the transceivers parameters according tothe dynamic spectrum environment and different functionassignment. It accepts the feedback information from observerand executer to form new knowledge. The knowledge Kinclude managed resource knowledge KI (spectrum spaceknowledge and/or managed CRN knowledge), to realize thecognition to outside environment; system policy knowledge KP,to define the mapping from states to actions; problem solvingknowledge KS, to solve the problem of systems state deviation.In a word, the experiential knowledge of CRM is K= KI+ KP+KS.

B. Autonomic Cognitive Cycle

The autonomic cognitive cycle of CRM can be depicted byFig. 2, which based on the AEs process mechanisms (PA, PG,

and PU means action policy, objective policy, and effectivefunction policy) [13]. However, in Fig. 2, we express Mitolascognitive cycle in [1] and Jondrals CR model in [8] based onthe autonomic computing.

Figure 2. CRM autonomic cognitive cycle

Definition 2 (Autonomic Cognitive Cycle) the cognitive procedures of CRM have five main steps: 1) Spectrumsensing/Environment awareness; 2) Decision based on policy;3) Plan under the objectives direction; 4) Action/Plan execute;5) Learn and Update. According to the changing spectrumstate, environment information and different function, therealization mechanism can be different.

In Fig. 2, Observer process stands for spectrumsensing/environment awareness, which is the base of CRMdecision. Decide process stands for the decision for different

policies, when the spectrum state and environment information

are different, the policies will change accordingly. We use Planto express plan part of CRM, which will start a plan processfor a sequence of action based on the decision result andcurrent state. Act process stands for action/plan execute. Learn

process stands for learn and update, which can be seen as adifferent aspect of database and plan parts in Fig. 1. Its incharge of the intelligence update of CRM. Decide, Plan andLearn procedures are all similar to the corresponding part ofautonomic computing research, so we will not give their detaildefinitions here. But the observer and act process will beexplained in detail in this paper.

1) Spectrum Sensing/Environment Awareness: Spectrum

sensing and environment awareness are the basis of CRM

dynamic spectrum access and cognitive behavior, it will map

the system to a certain state according to the outside

environment. Spectrum sensing gets the information of

spectrum space, and environment awareness is CRMs

cognitive behavior to the outside world.

For example, in a CRNs idle spectrum set IS, its usingspectrum Us in this set. KI is the new knowledge of spectrumsensing, when Us becomes BUSY in KI, system state SIshould be set to Immediately. CRMs decision based on thesystem state should vacate the using spectrum band. Themapping function is,

for (i=0; i

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Definition 4 (Action/Plan Execute) suppose Q= {q0, , qn}stands for the parametric set of execute part, A is theexecutable action set, q0 is initial state, F= {q0,, qn} is the

parametric set of execute part after reconfiguration. In above

statements, qQ and FQ Act procedure can be defined

by quintuple: , in which, frQAQ standsfor reconfiguration function.

Reconfiguration is the key ability of cognitive radio nodes.The execute part in AE is also important for system statechanging. But CRN will adapt to outside environmentvariation by system reconfiguration, which makes the execute

part of CRN more important for its autonomic operation.

IV. AUTONOMIC COGNITIVE RADIONODES ARCHITECTURE

Because of the intrinsic autonomic property in cognitivecycle, the autonomic computing principles can be easily usedfor generating autonomic cognitive radio conceptual model.This model reduces the complexity in one node because of itsautonomic or self-management property.

A. Architecture Model

Based on the mainstream architecture of cognitive radionode in [8, 9] and proposed CRN conceptual model, weintroduce autonomic CRN architecture model, as illustrated inFig. 3. This model is mainly composed of two parts: CRM forspectrum sensing, decision and reconfiguration ability of CRN;Sensor/Effector provide the interface among cognitive radionodes for cooperation and communication.

Figure 3. Autonomic Computing based CRN Architecture Model

CRM is composed of spectrum observer, autonomic unit,experiential database and reconfigurable SDR. Spectrumobserver is responsible for spectrum sensing and spectruminformation analysis. It senses the changes of spectrum spacein realtime and sends the process result to autonomic unit forsystem level decision. Experiential database stores idlespectrum information and upper layer policy requiredsend/receive parameters. The knowledge of experientialdatabase will be updated after the interaction with other three

parts. Reconfigurable SDR fulfills CRNs communicationsignal processing tasks and reconfiguration requirement.

Autonomic unit makes decision based on experientialdatabases information, analysis of spectrum information fromspectrum observer, and QoS requirement from effector.Autonomic unit also directs the changes of spectrum observerand reconfigurable SDR with the help of experiential database.

Different parts of CRM can be connected tightly based onautonomic computing model, and information exchangeamong them will be easier. The complexity in a node isdegrading and CRNs autonomic ability is upgrading.Furthermore, changes in spectrum space and upper layer

policy can be easily monitored and adapted by CRNs.

B. Realization Method

1) ACRAThe autonomic CRN architecture model in Fig. 3 provides

a blueprint to realize the autonomic cognitive radio nodesarchitecture. SDR has been thought of the basis of CRarchitecture in the research community. The trend in theimplementation of SDR is moving towards MPSoC platformswhich combine flexibility, performance and energy efficiency[11].

MPSoC uses multiple CPUs along with other hardwaresubsystems to implement a system [16]. Heterogeneousreconfigurable MPSoC platforms provide general purposecores to process control oriented tasks, coarse or fine-grainedreconfigurable architectures for other signal processingcomputation, which are good candidates to support cognitiveradio nodes implementation [11].

The MPSoC platform based ACRA is illustrated in Fig. 4.ACRA is composed of RISC processor for autonomic unit andexperiential database realization, spectrum observercoprocessor for spectrum sensing, reconfigurable SDR

platform for communication signal processing and external bus

interface for sensor and effector implementation.

Figure 4. Autonomic Cognitive Radio nodes Architecture

Autonomic unit is the main control component of ACRA,whose realization method will be discussed later. Manyreconfigurable SDR processors have been studied in theliterature [11, 16]. The coarse-grained reconfigurablearchitecture is adopted in ACRA. Spectrum observer is acoprocessor in ACRA to fulfill the accurately and timelyspectrum sensing function.

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2) Autonomic Unit RealizationThe realization challenge of autonomic computing has

been a research hotspot because of its meaningfulness anddifficulty. Among many realization methods [17, 18],autonomic system specification language (ASSL) proposed byE. Vassev has obtained much attention. A certain systemdescribed by ASSL formal definition can be used to produceautonomic computing system [18, 19]. ASSL describes an

autonomic system through formal hierarchy. It can be seen as aframework used for defining and generating autonomiccomputing system. It also can be seen as a kind of formallanguage and model language to describe the autonomiccomputing system.

For the purpose of autonomic unit realization of ACRA, weuse ASSL to describe CRNs autonomic management property.This method provides a way to realize an autonomic unit ingeneral purpose processor. Fig. 5 shows CRNs selfconfiguration policy of a sharing node for spectrum sensingexpressed by ASSL.

CRN_SELF_MANAGEMENT{

int crnState;

SELF_CONFIGURING{SWITCH:ON;

PRIORITY:1;

EVENT Immediately

EVENT Urgent

EVENT Normal

FLUENT inImmediately{

INITIATES:Immediately

TERMINATES Normal

}

MAPPING{

CONDITION: inImmediately;

ACTION {

CRM.ACTIONS.SDRReconf

}

...

}}

SELF_OPTIMIZING{}

SELF_PROTECTING{}

OTHER_POLICIES{}

}

Figure 5. The Self Configuration Policy of CRN Described by ASSL

V. CONCLUSION

Autonomic computing provides a remedy to thecomplexity problem which will emerge in the cognitive radionetworks. For the purpose of establishing a cognitive radio

network with autonomic computing property from bottom up,a cognitive radio node conceptual model and correspondingarchitecture based on autonomic computing principles is

presented in this research.

The ACRA can accomplish CRNs function. Furthermore,the control complexity of CRN is degrading because of itsautonomic computing property. A cognitive radio networkwith autonomic property is easy formed by this kind of CRNs.On the realization of ACRA, the autonomic unit is modeled byASSL and the reconfigurable SDR is implemented by SDR

processor.

ACKNOWLEDGMENT

This work was partly supported by the MicroprocessorInnovation Team of China (IRT0416), CAST (20080302) and

NSFC (60970037).

REFERENCES

[1] J. Mitola. Cognitive Radio: An Integrated Agent Architecture for

Software Defined Radio. PhD Thesis, Royal Institute of Technology,Sweden, May 2000.

[2] S. Haykin, Cognitive Radio: brain-empowered wirelesscommunication, IEEE J. Select. Areas Commun., vol. 23, no. 2, pp.201-220, Feb. 2005.

[3] Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran and S. Mohanty,NeXt generation/dynamic spectrum access/cognitive radio wirelessnetworks: A survey, Computer Networks, vol.50, no. 13, pp. 2127-2159, May 2006.

[4] J. Kephart, D. Chess, The vision of autonomic computing, IEEEComputer, vol. 36, no. 1, pp. 41-50, 2003.

[5] Chong Shen, Dirk Pesch and James Irvine, A Framework for Self-Management of Hybrid Wireless Networks Using AutonomicComputing Principles, Proceedings of the 3rd Annual CommunicationNetworks and Services Research Conference, 2005.

[6] Demestichas, K., Adamopoulou, E., Theologou, M., Demestichas, P.,Boscovic, D., and Bourse, D., Towards Cognitive B3G Networks:Autonomic Management of Access Points, 16th IST Mobile andWireless Communications Summit, 2007.

[7] IBM. An architectural blueprint for autonomic computing. 2006.http://www-03.ibm.com/autonomic/pdfs/AC_Blueprint_White_Paper_4th.pdf

[8] Friedrich K. Jondral, Cognitive Radio: a Communications EngineeringView, IEEE Wireless Communications, vol. 14, no. 3, pp. 28-33,August 2007.

[9] Balamuralidhar P. and Ramjee Prasad, A Context Driven Architecturefor Cognitive Radio Nodes, Wireless Personal Communications, vol.45, no. 3, pp. 423434, 2008.

[10] Z. Miljanic, I. Seskar, K. Le, and D. Raychaudhuri, The WINLAB Network Centric Cognitive Radio Hardware Platform-WiNC2R, In2nd International Conference on Cognitive Radio Oriented Wireless

Networks and Communications, 2007.[11] Q. Zhang. Cognitive Radio on a Reconfigurable MPSoC Platform. PhD

thesis, University of Twente, Netherlands, Feb. 2009.

[12] Ian F. Akyildiz, Won-Yeol Lee, and Kaushik R. Chowdhury, SpectrumManagement in Cognitive Radio Ad Hoc Networks, IEEE Network,vol. 23, no. 4, pp. 6-12, 2009.

[13] Liao BS, Li SJ, Yao Y, Gao J. Conceptual model and realizationmethods of autonomic computing. Journal of Software,2008,19(4):779802. http://www.jos.org.cn/1000-9825/19/779.htm

[14] Z. Quan, S. Cui, H. V. Poor, and Ali H. Sayed, CollaborativeWideband Sensing for Cognitive Radios, IEEE Signal ProcessingMagazine, vol. 25, no. 6, pp. 60-73, November 2008.

[15] D. Cabric, A. Tkachenko, and R. W. Brodersen, Spectrum SensingMeasurements of Pilot, Energy and Collaborative Detection, IEEEMilitary Communications Conference, 2006.

[16] W. Wolf, A. A. Jerraya, and G. Martin, Multiprocessor System-on-Chip (MPSoC) Technology, IEEE Transactions on Computer-aidedDesign of Integrated Circuits and Systems, vol. 27, no. 10, pp. 1701-1713, 2008.

[17] D. Agrawal, J. Giles, K.-W. Lee, and J. Lobo, Autonomic ComputingExpressing Language, IBM developerWorks tutorial, March 2005.

[18] E. Vassev and J. Paquet, ASSL - Autonomic System SpecificationLanguage, In Proc. of the 31st Annual IEEE/NASA SoftwareEngineering Workshop (SEW-31), pp. 300-309, March 2007.

[19] E. Vassev and M. Hinchey, ASSL: A Software Engineering Approachto Autonomic Computing, Computer, vol. 42, no. 6, pp. 90-93, 2009.