Cognitive Radio Resource Management Using Multi-Agent Systems
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1 Cogntve Rado Resource Management Usng Mult- Systems Jang Xe, Ivan Howtt, and Anta Raja Department of Electrcal and Computer Engneerng Department of Software and Informaton Systems The Unversty of North Carolna at Charlotte Emal: {jxe, lhowtt, Abstract Ths paper nvestgates cooperatve rado resource management for multple cogntve rado networks n nterference envronments. The objectve of ths research s to manage shared rado resources farly among multple noncooperatve cogntve rado networks to optmze the overall performance. We emphasze the underlyng predctablty of network condtons and promote management solutons talored to dfferent nterference envronments. A mult-agent-systembased approach s proposed to acheve nformaton sharng and decson dstrbuton among multple cogntve rado networks n a dstrbuted manner. We address the dstrbuted constrant optmzaton problem (DCOP) n cogntve rado networks and study the effectveness of DCOP algorthms to fnd the optmal rado resource assgnment through communcatons between dstrbuted agents. Keywords Spectrum management, dstrbuted constrant optmzaton problem (DCOP), predctve models, thrd-party-based archtecture I. INTRODUCTION Cogntve rados [] provde a potental soluton for more effcent spectrum utlzaton. To acheve effcent spectrum utlzaton, a balanced and ntegrated communcaton system s requred []. One soluton s to ncorporate spectrum management functonalty wth the software-defned rados attrbutes n communcaton systems. Ths paper provdes an ntal nvestgaton nto cooperatve resource management for multple cogntve rado networks. Interference from colocated, co-band, and non-coorporatve wreless technologes s antcpated and s a component of the study presented. The objectve of ths research s to manage shared rado resources farly among multple non-cooperatve cogntve rado networks to optmze the overall performance. We emphasze the underlyng predctablty of network condtons and promote management solutons talored to dfferent nterference envronments. A mult-agent-system-based approach s proposed to acheve nformaton sharng and decson dstrbuton among multple cogntve rado networks n a dstrbuted manner. Cogntve rado resource management requres a tght couplng between the spectrum management functonalty and the software-defned rados attrbutes,.e., modes of operaton supported by the physcal layer. Wreless local area networks (s) provde essental components for projected cogntve rado platforms. Snce predctve models can be readly Ths work s supported n part by the US Natonal Scence Foundaton (NSF) under Grant No. CNS developed for current s, they make an deal hardware platform for developng our resource management strategy. The rest of ths paper s organzed as follows. The proposed archtecture for the dstrbuted cogntve rado resource management s present n Secton II. A centralzed mplement s presented n Secton III and s used to llustrate the concept and provde a benchmark for the performance. A dstrbuted mplementaton based on mult-agent-systems s outlned n Secton IV, followed by the conclusons and future work n Secton V. II. ARCHITECTURE OF COGNITIVE RADIO RESOURCE MANAGEMENT USING MULTI-AGENT SYSTE One applcaton of cogntve rado resource management s the mult-doman envronment. In recent years, many hot-spots are emergng and multple s are beng deployed wthn small geographc vcnty. Dfferent s n a partcular area may be deployed by dfferent operators. In such a mult-doman envronment, there s a growng nterest n provders settng up recprocal agreements so that moble users may share the usage of multple s. A mult-agent system-based approach s proposed to acheve nformaton sharng and decson dstrbuton among multple s n a dstrbuted manner. provders may set up servce-level agreements among themselves on how much data can be exchanged among agents. Compared to usng a centralzed controller, a mult-agent system-based approach s more scalable. A. Mult--Based Archtecture We propose a resource management archtecture for multple s usng mult-agent systems, as shown n Fg.. Multple s are co-located wthn a partcular geographc area. Communcatons nsde the surroundng wreless personal area networks (WPANs) such as Bluetooth networks and wreless sensor networks (WSNs) generate nterference to actvtes. s are located nsde each access pont () and nteract wth other agents wthn ts neghborhood. An agent's neghborhood conssts of those agents wth whom t has frequent nteractons. These nteractons nclude sharng of data and negotatng about resource assgnments. Indvdual agents act as rado resource coordnators and cooperate wth agents n ther neghborhood to take care of resource management across multple s.
2 Interacton Neghborhood Mult - Meta Level B s Sphere of Influence C Physcal Operatonal Level C WPAN WSN A Fg.. Archtecture of resource management usng mult-agent systems. Through agent coordnaton, provders may offer nter- roamng servces to ther subscrbers as a value-added servce feature. They can also support communcatons wth betterqualty sgnals snce the mpact of nteractve nterference can be globally balanced through mult-agent control. The functons related to user authentcaton, bllng, securty and prvacy, and moblty management can also be mplemented n agents. Wthn the mult-agent system, the agents are leveraged to farly balance system-wde resources n order to accommodate more users wth the least amount of cost. The agent at each collects the statstcs from the measured operatonal envronment as well as ts neghborhood and estmates the requred parameters for optmzng system performance based on predctve models. The IEEE 80.k task group [3] s developng a rado resource measurement extenson to the IEEE 80. standard. As suggested by the IEEE 80.k task group, the sgnal characterstcs are obtaned drectly from s. The data can be augmented by an addtonal sensng component to provde addtonal data specfcally assocated wth WPAN nterference sources n the envronment. The agents use the measured data to generate local control decsons and try to optmze the performance of the entre system n a dstrbuted fashon through agent nteracton and coordnaton. nteracton s an essental aspect of ths archtecture. nteracton occurs on the backbone network connectng all the s. Therefore, the bandwdth requrement for agent nteracton s not a crtcal ssue. However, snce multple agents contrbute to the control of optmal resource allocaton across s, they need to decde what nformaton should be exchanged among neghbors, how often to exchange ths nformaton, and whch neghbors should act as relay nodes for the data. When a control decson s made, an agent also needs to decde what actons ts effector should take and how the control decson should be dstrbuted to the desred area. B. Mult--Based Archtecture Fg. presents a block dagram of a general framework for physcal envronment predcton and resource management usng agent technologes. The major functonal blocks are: and WPAN cluster, RF envronment sensng (RES), and agent operatons whch nclude predctve parameter estmaton (PPE) and resource management optmzaton. They are explaned n detals as follows. and WPAN Cluster: Each moble staton () n s operates wthn a dynamc RF envronment comprsng tme-varyng co-channel nterference sources and tme-varyng nterference sources from co-located WPANs. The agents nsde each perodcally collect measured statstcs from the dynamc RF envronment requred for resource management. RF Envronment Sensng (RES): Ths block s used to provde estmates of the sgnal characterstcs from both s wthn the cluster as well as potental nterference sources wthn the operatonal envronment. Part of the functons defned n ths block can be provded by the specfcatons of IEEE 80.k rado resource measurement. Statstcs related to WPAN envronmental nterference levels should be provded from an addtonal sensng component nsde each. It s mportant to remark that t does not mply measurng nstantaneous small-scale multpath sgnal characterstcs whch are very tme-senstve. Instead, measurements would be targeted at capturng large-scale changes n sgnal characterstcs due to varatons n shadowng, moblty, nterference sources, and nterference locatons. In other words, the RES needs to measure the factors whch nfluence the resource management of the performance. Operaton-Predctve Models for Parameter Estmaton (PPE): Estmates of sgnal characterstcs are nput to the agent nsde each. An agent also receves data from ts neghborhood through agent nteracton and coordnaton. The general concept for the PPE block s to use predctve models to generate parameter estmates requred by the resource management optmzaton. The parameters to be estmated nclude: Lnk Qualty: lnk qualty between each and ts. Moblty Rate: rate of changes n the expected lnk qualty between each and ts. Energy Expendture: energy requred to successfully transmt a packet between each and ts. Throughput: throughput for each cell based on Operaton Operaton Operaton Predctve Parameter Estmaton (PPE) Predctve Parameter Estmaton (PPE) Predctve Parameter Estmaton (PPE) RF Envronment Sensng (RES) WPAN WSN Dynamc RF Envronment C Resource Management Optmzaton Resource Management Optmzaton TxOptmzaton Resource Management Utlzaton Modelng Tx Modelng Utlzaton Assgnment Optmzaton Tx Optmzaton Utlzaton Assgnment LoadModelng Balance/Hand -Off Assgnment Optmzaton Load Balance/Hand -Off Strategy to Effect Load Balance/Hand -Off StrategyUtlzaton to Effect (EOU) Optmal Optmal StrategyUtlzaton to Effect (EOU) Optmal Utlzaton (EOU) Tx Tx Assgnment Assgnment -Off Tx Load Balance/Hand Operaton Load Balance/Hand Assgnment -Off Operaton Load Balance/Hand -Off Operaton Interacton Coordnaton Between Neghborhoods A Fg.. Block dagram of physcal envronment predcton and agent operatons.
3 the operatonal envronment characterstcs, current offered traffc, and projected offered traffc. Latency: expected tme delay and the varance n the tme delay between each and ts. Operaton-Resource Management Optmzaton: Ths block analyzes the parameter estmatons and makes nstructonal decsons to optmze the overall performance based on desgned optmzaton models. Instructonal decsons nclude the optmal transmt power at s, the optmal channel s should operate n order to mnmze nterference levels and make the best use of overall resources, whether or not to accept assocaton requests from specfc s, whether to drect specfc s to be assocated to another for load balancng, and so on. These decsons are updated perodcally n order to address changes n the traffc load and nterference envronment. They should target long-term performance mprovement. The operatonal changes are downloaded to the cluster wth the help of agent effectors and dstrbuted to the neghborhood of agents through agent nteracton and coordnaton. The resource management optmzaton block ncludes two components: Utlzaton Modelng and Optmzaton : Ths block fnds the optmal utlzaton,.e., the maxmum allowable throughput, of each based on the envronmental nformaton agents possess. The decson of the optmal utlzaton s used by the EOU block (whch s explaned n the followng) to generate specfc strateges to acheve the optmal utlzaton at each. Strategy to Effect Optmal Utlzaton (EOU): Gven the optmal utlzaton of each, nstructonal decsons are generated to acheve the optmal utlzaton whle mnmzng nterference to the envronment. Operatonal changes are negotated wthn the agent's neghborhood and appled to the cluster. They are also fed back to the UMO block to update the optmal utlzaton decson. III. ILLUSTRATIVE EXAMPLE: LOAD BALANCING IN AN INTERFERENCE CONSTRAINED In ths secton, we explan how to manage rado resource of mult-doman s usng a centralzed approach. In the next secton, a decentralzed approach s adopted based on mult-agent systems. The goal of our work s to adaptvely manage shared system-wde rado resources under tmevaryng network condtons among multple s. Ths rado resource management should ncorporate the mpact from the nterference envronment. Due to the co-locaton of s such as the IEEE 80.b and WPANs such as Bluetooth or the IEEE low-rate WPAN (LR-WPAN) whch operate n a shared spectrum, ther communcaton actvtes nterfere each other because of spectral overlap. Interference sources wll mpact moble statons dfferently due to varatons n RF path loss. These varatons make t dffcult and costly, n terms of network resources, to mantan performance requrements. Hence, t s mperatve that the dynamc effects of nterference be ncorporated nto network management and control decson-makng. Although a consderable amount of research on rado resource management n a sngle s proposed [4]-[7], cooperatve resource management for mult-doman s remans largely unexplored. Resource management schemes desgned for a sngle cannot be drectly appled to mult-doman s because the nteractve effect of nterdoman co-channel nterference s not taken nto consderaton. A. Thrd-Party-Based Archtecture We propose a thrd-party-based resource management archtecture to facltate the cooperatve mult-doman resource management. A trusted thrd-party agent s needed who s ndependent from each network provder's fnancal nterests. When a new s deployed, the provder does not need to set up drect servce level agreements wth all the other provders of the exstng s n the area. It only regsters to the thrd-party agent. The thrd-party controller can collect nformaton across multple domans and send control sgnals back to each doman, thereby makng rado resource management and other features possble []. A new entty, local network controller (LNC), s connected to all the s of multple s, as shown n Fg. 3. s under the control of an LNC form a cluster. The LNC acts as a rado resource coordnator across domans and takes care of ssues related to nter-doman roamng and resource sharng wthn a cluster. As the number of domans n a cluster ncreases, the LNC can be bult n a herarchcal structure to make t more scalable. As shown n Fg. 3, a global network controller (GNC) s connected to all LNCs supportng nter--cluster roamng and resource sharng. The LNC gathers the measured resource usage statstcs from all the s va Smple Network Management Protocol (SNMP) [8]. s collect sgnal characterstcs from clent statons n each doman based on IEEE 80.k specfcatons [3]. The measured data can then be used by the LNC to generate control decsons to optmze the performance of the entre cluster. B. Proposed Resource Management Scheme The goal of the proposed scheme s to mnmze the total system cost by adjustng resource allocaton n each doman. The cost s what the system needs to pay to support all the s to acheve performance requrements. It s related to the avalable rado resources for supportng the offered load n LNC Cluster GNC LNC Cluster Fg. 3. Thrd-party-based mult-doman resource management archtecture for s.
4 each doman and mtgatng nterference from the operatonal envronment. The LNC manages resource sharng across domans by controllng the maxmum allowable throughput of each. When the maxmum allowable throughput at an changes, the avalable rado resources of the cell s lmted. Consequently, the cell utlzaton changes whch leads to a dfferent system cost. Therefore, mnmzng the overall system cost s equvalent to fndng the optmal allowable throughput at each. In addton, WPAN nterference can adversely affect the performance by changng ts resource utlzaton requrements and therefore needs to be consdered. Moreover, due to the dynamcs n the RF envronment, sgnal characterstcs, traffc load, and nterference ntensty are tme-varant. As a result, the optmal resource allocaton decson should be dynamcally adjusted to reflect the nfluences of the tme-varyng envronment. The proposed resource management scheme ncludes three steps. Frst, based on the overall traffc load dstrbuton at all the s n a cluster, the mpact of co-channel nterference at each cell can be calculated. Then, by ncorporatng the mpact of nterference from other sources n the operatonal envronment, the communcaton cost of the overall system can be derved whch s a functon of cell load, co-channel nterference, and nterference from other wreless servces. Second, the LNC fnds the optmal pattern of maxmum allowable throughput at each n multple domans. In other words, the LNC decdes whch should provde how much capacty to ts users. Ths optmal throughput pattern results n the mnmum system cost. Fnally, the LNC sends control sgnals to s to nstruct them on how to update ther allowable resources for users based on the calculated optmal throughput. The mult-doman resource management ssue can be formulated as an optmzaton problem. The LNC perodcally optmzes the resource usage n each doman by mnmzng the overall system cost functon. The LNC not only fnds the optmal throughput pattern for all the s, but also determnes the optmal capacty for each doman. After the LNC fnds the optmal resource allocaton, resources at each doman should be updated. Both the co-channel nterference from other s and the nterference from co-located WPANs are consdered durng the optmzaton process. Therefore, the proposed mult-doman resource management scheme s able to mnmze the co-channel nterference across domans and mtgate other nterference from the operatonal envronment through far resource allocaton. Under the proposed scheme, resource utlzaton and co-channel nterference can be adaptvely balanced across the entre ntegrated system. C. Performance Evaluaton Usng Smulatons We smulate a two-doman envronment wth IEEE 80.b A and B co-located. Multple Bluetooth nodes are also co-located wth the two s. Ther communcatons nterfere wth each other. A two-state Markov traffc model s used for our smulaton. There are two Pareto dstrbutons nvolved n the model: one for the traffc load wth a cutoff value at 6Mbps and the other for the HIGH/LOW state duraton. The traffc s generated at both states wth a burst threshold 00kbps, whch means, when the generated traffc load s less than 00kbps, we assume the s at the LOW state. The Bluetooth traffc model s based on an ON- OFF Markov model and the traffc swtches from an ON to an OFF state wth probablty 0.6. Smulaton results demonstrated that the proposed multdoman cooperatve resource management scheme s more cost-effcent for a /WPAN nterference envronment. The proposed scheme can save up to 99.8% and 47.3% cost compared to the scheme that each doman optmzes resource usage ndependently wthout the consderaton of potental nterference from co-located WPANs and the scheme that LNC s nvolved to help control the resource allocaton n each doman but wthout the consderaton of potental nterference from co-located WPANs, respectvely. IV. MULTI-AGENTS PROVIDING A DISTRIBUTED IMPLEMENTATION We are nterested n solvng the resource allocaton problem nvolvng resource management n a decentralzed fashon usng mult-agent systems (MAS). As presented n the prevous secton, central to ths process s balancng the traffc load between dsparate s. A mechansm for mplementng load balancng s the handoff process of transferrng the resource usage of an from one to another. Ths process can be trggered by two events: Type : An requests a handoff from one due to moblty requrements; Type : An, sheds or acqures addtonal load n order to balance the traffc load wthn ts neghborhood set. We present a model based on mult-agent constrant optmzaton problem (MCOP) to optmze Type handoffs n ths paper n order to llustrate the approach. A dscrete mult-agent constrant optmzaton problem (MCOP) [9] s a tuple A, X, D, R, where A { A,, K A n s the set of agents nterested n the soluton, X X, K, X m s the set of varables; usually each agent A s assgned one varable, D d, K, d m s a set of domans of the varables, where each doman s a fnte dscrete set of possble values, and R r, K, r p s a set of relatons where a relaton r s a utlty functon whch provdes a measure of the value assocated wth a gven combnaton of varables. { { { * The objectve of the MCOP s to fnd an assgnment X for the varables X that maxmzes the sum of utltes of the mult-agent system. DPOP [0], a dstrbuted constrant optmzaton algorthm for general networks, uses dynamc programmng for ts utlty propagaton. DPOP has three phases: n phase, the algorthm performs a dstrbuted depth frst traversal of the general network to establsh a pseudotree [] structure; n phase, the algorthm propagates utlty A pseudo-tree of a graph G s a rooted tree wth the same vertces as G and has the property that adjacent vertces from the orgnal graph fall n the same
5 messages whch contan utlty vectors bottom-up along the pseudo-tree; n phase 3, the optmal value assgnments are propagated top-down from the root node. We map the resource allocaton problem to a mult-agent dstrbuted constrant optmzaton problem. Each s assgned an agent. However, at any pont n tme, only a subset of these agents wll be nvolved n the resourceallocaton process, whch means that the mult-agent system s constructed dynamcally. Perodcally, each agent lstens for event trggers. The frequency of event trggers can be an ssue. If they occur too often, then the envronment s too dynamc and a greedy reactve control would be preferable to planned delberaton,.e., DPOP, as the latter uses up tme only to have ts results become obsolete pror to beng appled to the ntended envronment, thus leadng to nstablty. Each event trgger wakes up the correspondng agent and the mult-agent system ntates the resource-allocaton process along wth every other agent that has been awakened. The varables belongng to each agent are recpent ds wth assocated handoff tmes. The doman for the varables s the set of s n s neghborhood set that are potental X recpents of s handoff. The recpent agents could nclude agents n s mmedate neghborhood as well as n ts nterference neghborhood. Consder the followng smple scenaro wth Type event trggers. Suppose at tme t 0, 3 s are trggered by 4 s. Each trgger s represented as where : ( t ) ( ) { x y t, s the requestng a handoff; ( ) current handler of x s the and t s the estmate of the tme by y s the set of { whch the handoff has to occur; and ( ) destnaton s n be handed off to and each estmate of the earlest tme X s neghborhood set that t t could t, the y has an assocated tme can be handed off to y. The handoff duraton tme could also be ncorporated as an addtonal parameter n each event trgger to represent the mnmum requrement of the requested handoff. As depcted n Fg., the utlty functon for the decsons s provded by the Utlzaton Modelng and Optmzaton block. The followng example gves an llustraton of the even trgger explaned above. ( t 0) { ( t 8) ( t 6) { 3 ( t 7) ( t 3) { ( t 5 ), 3 ( t 30) ( t 4) { ( t 7) : : 3 :. 4 : 3 branch of the tree. Pseudo-trees are used n search due to the relatve ndependence of nodes lyng n ts dfferent branches. If an has multple event trggers at the same tme, the correspondng agent wll assgn each a varable and solve the cumulatve resourceallocaton problem. The vertces of the pseudo-tree constructed n the DPOP algorthm are, and 3. The utlty vectors of the leaves are determned by the utlty functon from the UMO and the optmal assgnment of handoff destnatons and tmngs s computed usng the DPOP algorthm descrbed above. The resource allocaton process s trggered every tme a new set of event trggers occurs. The DPOP algorthm provdes the optmal soluton wthn a bounded tme,.e., the algorthm s guaranteed to converge to the optmal soluton. V. CONCLUSION AND FUTURE WORK In ths paper, we nvestgated how to dynamcally manage shared rado resources farly among multple non-cooperatve cogntve rado networks usng mult-agent systems. We explaned the components n our proposed archtecture for the dstrbuted cogntve rado resource management. We presented a centralzed mplement for mult-doman s. We then outlned a decentralzed mplementaton based on mult-agent systems and explaned how to map resource allocaton problem nto a DCOP usng mult-agent systems. We are currently studyng the effectveness of usng DCOP algorthms to fnd the optmal rado resource management and comparng the performance of ths dstrbuted approach to that of the centralzed approach. REFERENCES [] S. Haykn, Cogntve rado: bran-empowered wreless communcatons, IEEE Journal of Selected Areas n Communcaton, vol.3, no., pp. 0-0, February 005. [] I. F. Akyldz, W. Lee, M. C. Vuran, and S. Mohanty, NeXt generaton/dynamc spectrum access/cogntve rado wreless networks: A survey, Computer Networks (Elsever), vol. 50, no. 3, pp. 7-59, September 006. [3] IEEE 80. WG draft supplement - specfcaton for rado resource measurement, IEEE 80.k/D0.7. [4] Hlls and B. Frday, Rado resource management n wreless LANs, IEEE Communcatons Magazne, vol. 4, no., pp. S9--S4, December 004. [5] Y. Wang, L. G. Cuthbert, and J. Bgham, Intellgent rado resource management for IEEE 80., n Proc. IEEE Wreless Communcatons and Networkng Conference (WCNC), 004, vol. 3, pp [6] Y. Bejerano, S.-J. Han, and L. L, Farness and load balancng n wreless LANs usng assocaton control, n Proc. ACM MOBICOM, 004, pp [7] A. Balachandran, P. Bahl, and G. M. Voelker, Hot-spot congeston relef n publc-area wreless networks, n Proc. IEEE Workshop on Moble Computng Systems and Applcatons (WMCSA), 00, pp [8] D. Harrngton, R. Presuhn, and B. Wjnen, An archtecture for descrbng smple network management protocol (SNMP) management frameworks, Request for Comments (RFC) 34, IETF, December 00. [9] A. Petcu and B. Faltngs, A dstrbuted, complete method for multagent constrant optmzaton, n CP 004 Ffth Internatonal Workshop on Dstrbuted Constrant Reasonng (DCR 004), Toronto, Canada, September 004. [0] A. Petcu, B. Faltngs, A Scalable Method for Multagent Constrant Optmzaton, IJCAI 005: [] R. Dechter, Constrant Processng, Morgan Kaufmann, 003.
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