ROSA: Distributed Joint Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks

Transcription

1 ROSA: Dstrbuted Jont Routng and Dynamc Spectrum Allocaton n Cogntve Rado Ad Hoc Networks Le Dng Tommaso Meloda Stella Batalama Mchael J. Medley Department of Electrcal Engneerng, State Unversty of New York at Buffalo, Buffalo, NY 14260, USA Ar Force Research Laboratory, RIGE, Rome, NY 13441, USA {ledng,tmeloda,batalama}@eng.buffalo.edu mchael.medley@rl.af.ml ABSTRACT Throughput maxmzaton s one of the man challenges n cogntve rado ad hoc networks, where local spectrum resources may change from tme to tme and hop-by-hop. For ths reason, a crosslayer opportunstc spectrum access and dynamc routng algorthm for cogntve rado networks s proposed, called ROSA (ROutng and Spectrum Allocaton algorthm). Through local control actons, ROSA ams at maxmzng the network throughput by performng jont routng, dynamc spectrum allocaton, schedulng, and transmt power control. Specfcally, the algorthm dynamcally allocates spectrum resources to maxmze the capacty of lnks wthout generatng harmful nterference to other users whle guaranteeng bounded BER for the recever. In addton, the algorthm ams at maxmzng the weghted sum of dfferental backlogs to stablze the system by gvng prorty to hgher-capacty lnks wth hgh dfferental backlog. The proposed algorthm s dstrbuted, computatonally effcent, and wth bounded BER guarantees. ROSA s shown through dscrete-event packet-level smulatons to outperform baselne solutons leadng to a hgh throughput, low delay, and far bandwdth allocaton. Categores and Subject Descrptors C.2.1 [Computer-Communcaton Networks]: Network Archtecture and Desgn Wreless Communcaton General Terms Algorthms, Desgn Keywords Cogntve Rado Networks, routng, dynamc spectrum allocaton, cross-layer desgn 1. INTRODUCTION Cogntve 1 rado networks [2] [19] have recently emerged as a promsng technology to mprove the utlzaton effcency of the 1 Ths work was supported by the U.S. Ar Force Research Laboratory under Grant FA Approved for Publc Release; dstrbuton unlmted: 88ABW Aug Permsson to make dgtal or hard copes of all or part of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes bear ths notce and the full ctaton on the frst page. To copy otherwse, to republsh, to post on servers or to redstrbute to lsts, requres pror specfc permsson and/or a fee. MSWM 09, October 26 29, 2009, Tenerfe, Canary Islands, Span. Copyrght 2009 ACM /09/10...$ exstng rado spectrum. In a cogntve rado network, users access the exstng wreless spectrum opportunstcally, wthout nterferng wth exstng users. A key challenge n the desgn of cogntve rado networks s dynamc spectrum allocaton, whch enables wreless devces to opportunstcally access portons of the spectrum as they become avalable. Consequently, technques for dynamc spectrum access have receved sgnfcant attenton n the last two years, e.g., [5] [17] [27]. In addton to ths, n cogntve rado networks wth mult-hop communcaton requrements (e.g., cogntve rado ad hoc networks [1]), the dynamc nature of the rado spectrum calls for the development of novel spectrum-aware routng algorthms. In fact, spectrum occupancy s locaton-dependent, and therefore n a mult-hop path avalable spectrum bands may be dfferent at each relay node. Hence, n mult-hop cogntve rado networks controllng the nteracton between the routng and the spectrum management functonaltes s of fundamental mportance. Whle cross-layer desgn prncples have been extensvely studed by the wreless networkng research communty n the recent past, the avalablty of cogntve and frequency agle devces motvates research on new algorthms and models to study cross-layer nteractons that nvolve spectrum management-related functonaltes. For the reasons above, n ths paper we consder nteractons between spectrum management and dynamc routng functonaltes. Wth ths respect, we propose a dstrbuted algorthm that jontly solves the routng, dynamc spectrum assgnment, schedulng and power allocaton problems for mult-hop cogntve rado networks. The objectve of the proposed algorthm s to allocate resources effcently, dstrbutvely, and n a cross-layer fashon. For ths reason, we focus on real-tme and computatonally effcent spectrum allocaton and routng algorthms. We further show how our algorthm can be nterpreted as a dstrbuted soluton to a centralzed cross-layer optmzaton problem. Whle the optmzaton problem s centralzed and hard to solve, our algorthm s practcally and dstrbutvely mplementable and provdes performance guarantees. We show how a cross-layer soluton that solves routng and spectrum allocaton jontly at each hop outperforms approaches where routes are selected ndependently of the spectrum assgnment, wth moderate computatonal complexty. Our man contrbutons can be outlned as follows: We derve a dstrbuted and localzed algorthm for jont dynamc routng and spectrum allocaton for mult-hop cogntve rado networks. To the best of our knowledge, ths s the frst algorthm to jontly solve routng and spectrum assgnment wth power control under the so-called physcal nterference model. The proposed algorthm consders and leverages the unque characterstcs of cogntve rado ncludng the avalablty of spectrum holes at a partcular geographc locaton and ther possble varablty wth tme.

2 Our proposed soluton jontly consders routng, spectrum assgnment, power allocaton, and (potentally) congeston control n a dstrbuted way. The proposed soluton lets each cogntve rado make real-tme decsons on spectrum and power allocaton based on locally collected nformaton. Nodes can adjust transmsson power to maxmze lnk capacty based on the assgned spectrum porton. We assume a rcher physcal layer model than prevously consdered n the related lterature; we ntroduce a noton of spectrum hole that consders nterference from neghborng secondary as well as prmary users, and leverage t to optmze resource utlzaton at a low computatonal cost. We dscuss detals of a practcal mplementaton of the proposed algorthm that reles on a dual rado wth a common control channel and a frequency-agle data channel. We show how the proposed algorthm can be nterpreted as a dstrbuted and practcal soluton to a cross-layer optmal resource allocaton problem, whose performance s close to the optmum wth low computatonal complexty. The remander of ths paper s organzed as follows. In Secton 2, we revew related work on cogntve rado networks. In Secton 3, we ntroduce the system model. In Secton 4 we propose ROSA, our dstrbuted algorthm for jont routng and dynamc spectrum allocaton. Secton 5 addresses mplementaton detals. In Secton 6 we show how ROSA can be nterpreted as a dstrbuted soluton to a centralzed cross-layer network utlty maxmzaton problem for cogntve rado ad hoc networks. Secton 7 evaluates the performance of the algorthm. Fnally, Secton 8 concludes the paper. 2. RELATED WORK Recent work has nvestgated algorthms and protocols for dynamc spectrum allocaton n cogntve rado networks. Proposed approaches to assgn spectrum can be broadly classfed nto centralzed and dstrbuted schemes. For example, the Dynamc Spectrum Access Protocol (DSAP) [3] s centralzed, and thus requres a central controller to allocate spectrum. In contrast, we propose a dstrbuted soluton to let each cogntve rado make real-tme decson on spectrum based on locally collected nformaton. In [27], a dstrbuted spectrum assgnment algorthm s proposed, whch ams at solvng the spectrum allocaton problem: whch node should use how wde a spectrum-band at what center-frequency and for how long. Our work dffers sgnfcantly from [27], whch assumes mutually exclusve transmssons wth zero nterference tolerance. Spectrum band auctons [10][28] have been proposed to allocate wreless spectrum resources, n whch bdders obtan dfferent spectrum channels to mnmze the nterference. In contrast, our proposed soluton jontly consders spectrum allocaton and routng n a cross-layer desgn fashon, snce avalable spectrum bands may be dfferent at each hop. Some recent work has made ntal steps n the drecton of leveragng nteractons between routng and spectrum allocaton. In [24], each source node fnds canddate paths based on Dynamc Source Routng (DSR) [14] and collects nformaton on lnk connectvty and qualty. For each canddate route, the algorthm fnds all feasble spectrum assgnment combnatons and estmates the end-to-end throughput performance for each combnaton. Based on ths computaton, t selects the route and spectrum assgnment wth maxmal throughput and schedules a conflct-free channel for ths route. The authors n [25] propose a layered graph model, where each layer corresponds to a channel, and fnd shortest paths based on the layered graph. Both [24] and [25] are channel-based solutons,.e., the avalable spectrum s dvded nto predefned channels, and devces are assgned opportuntes to transmt on channels for a relatvely long tme scale. However, the tme-varyng nature of avalable lnks needs to be consdered n cogntve rado networks snce the avalable spectrum may change or vansh when lcensed users enter the network. In addton, the algorthms n [24] and [25] are based on the so-called protocol model [12] n whch two lnks ether nterfere destructvely or do not nterfere at all. Although smple, ths model fals to capture the cumulatve effect of nterference. Conversely, our work assumes a rcher nterference model, whch provdes a comprehensve representaton of rado nterference. For example, t accounts for the fact that advanced transmsson technques such as code-dvson multple access (CDMA) [21][20] allow concurrent co-located communcatons so that a message from node to node j can be correctly receved even f there s a concurrent transmsson close to j. Recent work has started nvestgatng cross-layer optmzatons for cogntve rado networks. In [13], Hou et al. formulated a crosslayer optmzaton problem for a network wth cogntve rados, whose objectve s to mnmze the requred network-wde rado spectrum resource needed to support traffc for a gven set of user sessons. The problem s formulated as a mxed nteger non-lnear problem (MINLP), and a sequental fxng (SF) algorthm s developed where the nteger varables are determned teratvely va a sequence of lnear programs, whch provdes a near-optmal soluton to the orgnal problem. However, the work of [13] does not consder power control, whch s addressed n our work. In [6], a routng and spectrum assgnment algorthm s proposed to acheve lower cumulatve delay caused by channel swtchng, queueng and collsons. However, the algorthm s under the assumpton that the node that has data to transmt knows the frequency band choce of every nodes along the route to destnaton, whch requres global nformaton. Conversely, our soluton performs routng and spectrum assgnment wthout global knowledge of the network state. Fnally, n [15], Khalfe et al. proposed a routng and spectrum selecton algorthm for cogntve rado networks. The algorthm chooses the path that has the hghest probablty to satsfy the demands of secondary users n terms of capacty. The work n [15] does not cover the ssues of schedulng and power control, whch are addressed n our work n detal. 3. SYSTEM MODEL We consder a cogntve rado network consstng of M prmary users and N secondary users. Prmary users are nodes holdng lcenses for specfc spectrum bands, and can only occupy ther assgned spectrum. Snce prmary users are lcensed users, they wll be provded wth a hghly relable communcaton envronment whenever and wherever needed. Secondary users do not have any lcensed spectrum and opportunstcally send ther data by utlzng dle prmary spectrum. Let the mult-hop wreless network be modeled by a drected connectvty graph G(V, E), where V = {v 1,..., v N+M } s a fnte set of wreless transcevers (nodes), wth V = N + M, and (, j) E represent a undrectonal wreless lnk from node v to node v j (referred to also as node and node j, respectvely, for smplcty). Nodes from the subset PU = {v 1,..., v M } are desgnated as prmary users, and nodes from subset SU = {v M+1,..., v M+N } are desgnated as secondary users. We assume G s lnk symmetrc,.e., f (, j) E, then (j, ) E. Let S {j : (, j) E)} be the set of neghbors for node. We assume that all the secondary users are equpped wth cogntve rados whch consst of a reconfgurable transcever and a scanner, smlar for example to the KNOWS prototype from Mcrosoft [26]. The transcever can tune to a set of contguous frequency bands [f, f + B], where B s the bandwdth of the cogntve

3 rado. We keep the physcal layer model general. However, we assume that multple transmssons can concurrently occur n a frequency band, e.g., wth dfferent spreadng codes. Among others, our physcal layer model could represent orthogonal frequency dvson multplexng (OFDM)-based transmsson, whch s based on a flexble subcarrers pool, and s thus a promsng canddate technology for cogntve rado networks. Alternatvely, the consdered abstracton could model mult-channel tme-hoppng mpulse rado ultra wde band system n low SINR regme [8][18]. The avalable spectrum s assumed to be organzed n two separate channels. A common control channel (CCC) s used by all secondary users for spectrum access negotaton, and s assumed to be tme slotted. A data channel (DC) s used for data communcaton. The data channel conssts of a set of dscrete mnbands {f mn, f mn+1,, f max 1, f max }, each of bandwdth w and dentfed by a dscrete ndex. For example, the nterval [f, f + f ] represents the (dscrete) set of mnbands selected by secondary user between f and f + f, wth bandwdth w f. Each secondary user that has packets to send contends for spectrum access on the control channel f cc, where f cc / [f mn, f max ]. All secondary users n the network exchange local nformaton on the common control channel. Traffc flows are, n general, carred over mult-hop routes. Let the traffc demands consst of a set S = 1, 2,, S, where S = S, of uncast sessons. Each sesson s S s characterzed by a fxed source-destnaton node par. We ndcate the arrval rate of sesson s at node as λ s (t), and wth Λ the vector of arrval rates. Each node mantans a separate queue for each sesson s for whch t s ether a source or an ntermedate relay. At tme slot t, defne Q s (t) as the number of queued packets of sesson s watng for transmsson at secondary user. Defne rj(t) s as the transmsson rate on lnk (, j) for sesson s durng tme slot t, and R as the vector of rates. For SU, the queue s updated as follows: Q s (t + 1) = Q s (t) + rk(t) s rl(t) s + λ s (t). k V,k l V,l 4. JOINT ROUTING AND DYNAMIC SPECTRUM ALLOCATION In ths secton, we present the dstrbuted jont ROutng and dynamc Spectrum Allocaton (ROSA) algorthm. To ths am, we ntroduce the notons of spectrum utlty and spectrum hole. Opportuntes to transmt are assgned based on the concept of spectrum utlty, and routes are explored based on the presence of spectrum holes wth the objectve of maxmzng the spectrum utlty. Our goal s to desgn a dstrbuted cross-layer control scheme that allows secondary users to jontly control the routng, spectrum and power allocaton functonaltes to maxmze the global spectrum utlty. The scheme should be easy to mplement and yeld hgh throughput and low delay. 4.1 Spectrum Utlty The control channel s assumed to be tme slotted. At each tme slot for whch node s backlogged and not already transmttng, node can evaluate the spectrum utlty for lnk (, j), defned as U j(t) = c j(t) (Q s ) (t) Q s j (t), (1) where s s the sesson wth maxmal dfferental backlog on lnk (, j). Note that, for the sake of smplcty, we wll drop all tme dependences n the followng. Note also that the noton of spectrum utlty s defned for a specfc lnk (, j). In the expresson above, c j(t) represents the achevable capacty for lnk (, j) gven the current spectrum condton, and s defned as c j (f, P (f)) [ w log P ] (f)l j (f)g. N j (f) + I j (f) (2) In (2), G s the processng gan, e.g., length of the spreadng code, and L j(f) s the transmsson loss from to j. P (f) represents the transmt power of on frequency f. I j (f) represents nterference at j. Fnally, N j (f) s the recever nose on frequency f. The achevable values of c j depend on ) the schedulng polcy; ) dynamc spectrum allocaton polcy,.e., spectrum selecton vector F = [f, f + f ], SU, and power allocaton vector P = [P (f)], SU, f. The noton of spectrum utlty can be thought of as a dfferental backlog, nspred by dynamc resource allocaton polces that react to the dfference (Q s Q s j) of queue backlogs for a gven sesson [9][11][22], weghted wth dynamc spectrum avalablty nformaton. A desrable soluton should dynamcally utlze the avalable spectrum and power effcently to provde BER guarantees for both prmary and secondary users. Denote SINRP th U and SINR th the sgnal-to-nterference-plus-nose power rato (SINR) thresholds SU as to acheve a target bt error rate BERP U for prmary users and BERSU for secondary users, respectvely. Thus, at each tme slot the global objectve s to fnd global vectors R, F, P that maxmze the sum of spectrum utltes over the actvated lnks, under gven BER and power constrants. Ths s expressed by the problem below. P1 : Gven : F nd : Maxmze : Subject to : BERSU, BERP U, G(V, E), P Bgt, Q s R, F, P SU j SU,j Uj (3) rj s c j, SU, j SU \ (4) s S SINR k SINR th P U (BER P U ), k PU, f (5) SINR l SINR th SU (BER SU ), l SU, f (6) P (f) P Bgt, SU (7) In the problem above, constrant (4) mposes that the total amount of traffc transported on lnk (, j) s lower than the capacty of the physcal lnk. Constrant (5) states that the target BER of prmary users should be guaranteed rrespectve of the secondary users presence. Constrant (6) mposes that secondary user transmssons should also satsfy a gven BER performance, whle sharng the spectrum wth other secondary users. In (7), P Bgt represents the nstantaneous power avalable at the cogntve rado. Solvng the problem above requres global knowledge of feasble rates, s centralzed and ts complexty s worst-case exponental. Ths provdes the motvaton for our dstrbuted algorthm, whose objectve s to maxmze (3) under the constrants ntroduced by cogntve rado networks n a dstrbuted fashon. In addton, we show how the dstrbuted algorthm can be mplemented n a practcal protocol. In the followng, we frst ntroduce a rgorous noton of spectrum hole (Secton 4.2). Then, n Secton 4.3 we outlne the algorthm for spectrum and power allocaton executed n a dstrbuted fashon at each secondary user. Fnally, we present the core ROSA algorthm n Secton Spectrum Holes In ths secton, we provde a rgorous defnton of the noton of spectrum hole. We ndcate a mnband by smply referrng to ts

4 central frequency. For frequency f, secondary user needs to (1) satsfy the BER requrement when t transmts to secondary user j and (2) avod nterferng wth ongong communcatons of nodes l S. The frst constrant can be expressed by P (f) L j(f) G N j (f) + k S j,k P k(f)l kj (f) SINRth SU (BERSU ),, j SU. (8) The second constrant represents the fact that communcaton for a node l S s not mpared by s transmsson. We can also ndcate nterference at node l S, l j as NI l (f)+ I l (f), where NI l (f) represents nose plus nterference at l before s transmsson, and I l (f) represents the addtonal nterference caused at l by s transmsson,.e., P (f)l l (f). Ths can be expressed as Pl R (f) NI l (f) + I l (f) SINRth (BER ), l S, l j, (9) where Pl R (f) represents the sgnal power beng receved at recever l. Snce ths has to be true for every node n the neghborhood of, the constrant can be wrtten as where Il max (f) = l P (f) mn L l (f) P max (f) (10) l S I max Pl R (f) SINR th P U (BER P U ) NI l(f), Pl R (f) SINR th l PU, NI l(f), l SU. SU (BER SU ) (11) The constrant n (8) states that the SINR at recever j needs to be above a certan threshold, whch means the power receved at recever j on frequency f needs be suffcently hgh to allow recever j to successfully decode the sgnal gven ts current nose and nterferences. The constrant n (10) states that the nterference generated by s transmsson on each frequency should not exceed the threshold value that represents the maxmum nterference that can be tolerated by the most vulnerable of s neghbors l S, l j. Hence, s transmt power needs to be bounded on each frequency. Constrant n (8) represents a lower bound and constrant n (10) represents an upper bound on the transmt power for each frequency. By combng constrants (8) and (10), we defne a control varable S j(f) for lnk (, j) and frequency f as S j(f) = P max (f) P mn (f), (12) where P max (f) s defned n (10) and P mn (f) s the value of P (f) for whch equalty n (8) holds. In cogntve rado networks, avalable spectrum holes should provde users wth lmted nterference level spectrum bands to satsfy user requrements, and power lmtatons to enable spectrum sharng wthout harmful nterference. Hence, to capture these characterstc of the avalable spectrum holes, we ntroduce the followng defnton. DEFINITION 1. A spectrum hole for lnk (, j) s a set of contguous mnbands where S j (f) Spectrum and Power Allocaton In ths secton we present the spectrum and power allocaton algorthm executed n a dstrbuted fashon at each secondary user to maxmze the lnk capacty gven the current spectrum condton. In cogntve rado networks local spectrum resource and transmt power allocaton may change from tme to tme, hence lnk capacty s tme-varyng and can be maxmzed through spectrum and power allocaton. Maxmzng the capacty of lnk (, j) means selectng spectrum [f, f + f ] and correspondng transmt power P (f) that maxmze the Shannon capacty wthn the spectrum holes. P2 : Gven : P k k S, k, L j (, j) E F nd : [f, f + f ], P (f) Maxmze : c j (13) Subject to : c j = P mn [ w log P ] (f)l j (f)g ; (14) N j (f) + I j (f) (f) P (f) P max (f) f [f, f + f ]; (15) P (f) P Bgt. (16) The objectve of the problem above s to fnd a spectrum hole wth maxmal capacty, gven spectrum condton and hardware lmtatons of the cogntve rado. Note that (14) represents the capacty of lnk (, j), constrant (15) mposes the presence of a spectrum hole, and (16) ndcates the hardware restrctons. For a fxed contguous set of mnbands [f, f + f ], we can obtan a soluton to the problem above by relaxng constrants (15) and (16). Hence, we can express the dual objectve functon as + g(p, Υ) = [υ f mn,f mn (P +υ BGT ( where Υ = [υ f mn υf +1 mn υf + f mn [ w log P ] (f)l j (f)g + N j(f) + I j(f) P (f))+υ f max(p (f) P max,f )]+ P (f) P BGT ), (17) υ f maxυ f +1 max υ f + f max υ BGT ] (18) s the vector of Lagrange multplers, Υ 0. A soluton to problem P2 s obtaned as descrbed n Algorthm 1. The algorthm provdes a dual-based teratve soluton to the problem. Specfcally, for a gven spectrum wndow between frequency f and f + f, at each teraton t the algorthm assgns power P t (f) sequentally for each frequency as n (20) (see followng page). Equaton (20) s obtaned by settng dg(p,υ) dp = 0. (f) Then, Lagrange multplers are updated followng a gradent descent algorthm. In Algorthm 1, th represents a target precson, whle ɛ s a small constant used n the gradent stepsze 1+ɛ. Fnally, Γ(t) represents a sutable gradent at step t, t+ɛ.e., Γ(t) = [(P mn,f ( P max,f P t 1 (f )) (P mn,f + f P t 1 (f + f ) + P t 1 (f )) (+P max,f + f P t 1 (f + f )) ( f + f f=f P t 1 (f) P BGT )]. (19) 4.4 Dstrbuted Routng and Dynamc Spectrum Allocaton Algorthm We now present the cross-layer ROutng and dynamc Spectrum Allocaton algorthm (ROSA), whch ams at maxmzng throughput through jont opportunstc routng, dynamc spectrum allocaton and transmt power control, whle performng schedulng n a dstrbuted way. Every backlogged node, once t senses an dle common control channel, performs the followng jont routng and schedulng algorthm:

5 P t (f) = wl gg (N (f) + k S j,k P k(f)l kj (f))(υ BGT,t υ mn,t f + υ max,t f )log e 2 L j G(υ BGT,t υ mn,t f + υ max,t f )log e 2 (20) Algorthm 1 Spectrum and Power Allocaton 1: t = 1, =, c j = 0 2: for f [f mn,, f max f ] do 3: whle > th do 4: t = t + 1 5: for f [f,, f + f ] do 6: Assgn P t (f) as n (20) 7: end for 8: Update Lagrange Multplers 9: = Υ(t) Υ(t 1) 2 10: end whle 11: Calculate c temp as n (14) 12: f c temp > c j then 13: c j = c temp 14: [f, P ] = [f, P ] 15: end f 16: end for 17: Return soluton as [f, P, c j] Υ(t) = [Υ(t 1) ɛ t + ɛ Γ(t)]+ (21) 1. Fnd the set of feasble next hops {n s 1, n s 2,..., n s k} for the backlogged sesson s, whch are neghbors wth postve advance towards the destnaton of s. Node n has postve advance wth respect to ff n s closer to the destnaton than. Calculate c j for each lnk (, j), where j {n s 1, n s 2,..., n s k}, usng Algorthm Schedule s wth next hop j such that (s, j ) = arg max(u s j). (22) Note that U s j defned n (1) depends on both the capacty and the dfferental backlog of lnk (, j). Hence, routng s performed n such a way that lghtly backlogged queues wth more spectrum resource receve most of the traffc. 3. Once spectrum selecton, power allocaton and next hop are determned, the probablty of accessng the medum s calculated based on the value of U s j. Nodes wth hgher U s j wll get a hgher probablty of accessng the medum and transmt. Note that U s j defned n (1) s an ncreasng functon of (Q s Q s j),.e., lnks wth hgher dfferental backlog may have hgher spectrum utlty, thus have hgher probablty of beng scheduled for transmsson. Ths probablty s mplemented through the contenton wndow at MAC layer. The transmttng node generates a backoff counter chosen unformly from the range [0, 2 CW 1 ], where CW s the contenton wndow, whose value decreases when U s j ncreases. Wth ths mechansm, heavly backlogged queues wth more spectrum resources are gven hgher probablty of transmsson. 5. COLLABORATIVE VIRTUAL SENSING As dscussed earler, we assume that each node s equpped wth two transcevers, one of whch s a reconfgurable transcever that can dynamcally adjust ts waveform and bandwdth for data transmsson. 2 The other s a conventonal transcever employed on the 2 Implementatons of ROSA that rely on a sngle transcever are also possble, for example by lettng the reconfgurable transcevers perodcally tune to the common control channel to exchange control nformaton. Ths s the subject of ongong research. common control channel. Handshakes on the CCC are conducted n parallel wth data transmssons on the data channel. The man challenge n mplementng ROSA s to let nodes learn about the envronment to make dstrbuted decsons on routng, spectrum, and power allocaton wth bounded nterference. One possble way to learn about the envronment s to rely on extensve spectrum sensng. However, conventonal CSMA/CA mechansms cannot meet the challengng rado senstvty requrements and wdeband frequency aglty needed n cogntve rado networks. A cogntve rado devce should have the capablty of scannng a wdeband spectrum and obtanng the nformaton about the statstcs of the spectrum envronment. In addton, RF and sgnal processng technques have been consdered for spectrum feature detecton [4]. The performance of these technques s lmted by the receved sgnal strength, whch may be severely degraded because of multpath fadng and shadowng [4]. As an alternatve, we propose a new scheme called Collaboratve Vrtual Sensng (CVS), whch ams at provdng nodes wth accurate spectrum nformaton based on a combnaton of physcal sensng and of local exchange of nformaton. Scanner-equpped cogntve rados can detect prmary users transmssons by sensng the data channel. In addton, collaboratve vrtual sensng s acheved by combnng scannng results and nformaton from control packets exchanged on the control channel that contan nfo about transmssons and power used on dfferent mnbands. ROSA s medum access control logc s llustrated n Fg. 1. Smlar to the IEEE two-way RTS (request-to-send) and CTS (clear-to-send) handshake, backlogged nodes contend for spectrum access on the common control channel (CCC). In partcular, backlogged nodes must frst sense an dle control channel for a tme perod of Dstrbuted Inter-Frame Spacng (DIFS), and then generate a backoff counter. The values of backoff counter are determned under the objectve that nodes wth hgher spectrum utlty should have a hgher channel access probablty. As dscussed n Secton 4.4, the backoff counter s chosen randomly (wth a unform dstrbuton) wthn the nterval [0, 2 CW 1 ], where CW represents the contenton wndow, whose value s a decreasng functon Φ() of the spectrum utlty. The functon Φ() must be desgned carefully to mnmze the collson rate and provde hgh channel utlzaton. Dfferent choces of Φ lead to dfferent system performance n terms of farness, overall throughput, and delay. The sender nforms the recever of the selected frequency nterval [f, f + f ] usng an RTS packet. On recevng the RTS packet, the recever responds by usng a CTS packet after the Short Inter- Frame Space (SIFS) and tunes ts transcever for data transmsson on the frequency specfed n the RTS packet. As n [27], an addtonal control packet, DTS (Data Transmsson reservaton), s needed for the transmtter to announce the spectrum reservaton and transmt power to ts neghbors. Here, we modfy the RTS/CTS/DTS packets and nclude channel allocaton nformaton to allow the nodes to make adaptve decsons. By actvely collectng RTS, CTS, and DTS packets transmtted on the CCC, each node learns the spectrum envronment and queue nformaton of ts neghborhood. Once RTS/CTS/DTS are successfully exchanged, sender and recever tune ther transcevers to the selected spectrum porton. Before transmttng, they sense the selected spectrum and, f t s dle, the sender begns data transmsson wthout further delay. Note that t s possble that the sender or the recever fnd the selected spectrum busy just before data transmsson. Ths can be caused by the presence of prmary users, or by conflctng reservatons caused by

6 Fgure 1: ROSA s Medum Access Control. losses of control packets. In ths case, the node gves up the selected spectrum, and goes back to the control channel for further negotaton. Durng the RTS/CTS/DTS exchange, f the sender s selected spectrum can not be entrely used,.e. the recever just sensed a prmary user s presence, the recever wll not send a CTS. The sender wll go back to the control channel for further negotaton once the watng-for-cts tmer expres and the RTS retransmsson lmt s acheved. If data are successfully receved, an ACK wll be sent by the recever. The transacton s consdered completed after the ACK s successfully receved. 6. INTERPRETATION OF ROSA AS A NUM SOLVER In ths secton, we show how ROSA can be nterpreted as a dstrbuted dual-based soluton to a cross-layer network utlty maxmzaton problem for cogntve rado ad hoc networks under the system model descrbed n the prevous sectons. A jont congeston control, routng, and dynamc spectrum allocaton problem for cogntve rado networks can be formulated as follows. where P3 : Gven : F nd : Maxmze : Subject to : λ s + rk s = c j k SU,k BER SU, BER P U, G(V, E), P Bgt SU l SU,l Λ, R, C s S U (λ s ); (23) r s l, SU, s S (24) rj s c j, SU, j SU \ (25) s S [ w log P ] (f)l j (f)g N j (f) + I j (f) (26) P (f) P Bgt (27) SINR k SINR th P U (BER P U ), k PU, f (28) SINR l SINR th SU (BER SU ), l SU, f (29) In the problem above, the objectve s to maxmze a sum of utlty functons U (λ s ), whch are assumed to be smooth, ncreasng, concave, and dependent on local rate at node only [7]. Constrant (24) expresses conservaton of flows through the routng varables r s j, whch represent the traffc from sesson s that s beng transported on lnk (, j). Fnally, constrant (25) mposes that the total amount of traffc transported on lnk (, j) s lower than the capacty of the physcal lnk. Note that f C s the feasble set of the physcal rates, values of c j Co(C),.e., they are constraned to be wthn the convex hull of the feasble rate regon [11][16]. The achevable values of c j depend on ) the schedulng polcy; ) allocaton of resources at the physcal layer, as expressed by constrants (26), (27), (28) and (29). By takng a dualty approach, the Lagrange dual functon of P3 can be obtaned by relaxng constrant (24) through Lagrange multplers Q = [Q s ], wth{ SU and s S. } L(Q) = max (U (λ s ) Q s λ s ) + Λ SU s S max R,C SU j SU,j s S r s j ( Q s Q s j), (30) where varables ndcatng data rates are stll constraned to be c j Co(C), and where C s defned by constrants (26)- (29). In the above decomposton, the frst term of (30) represents the congeston control functonalty (whch can be carred out ndependently), whle the second term represents routng, schedulng, and physcal rate allocaton. Let Λ (Q), R (Q), C (Q) be the vectors of optmum values for a gven set of Lagrange multplers Q. Whle λ s, (Q) can be computed locally at each source of sesson s, R (Q), C (Q) requre global knowledge and centralzed algorthms. To solve the above problem, the followng actons need to be performed at each tme slot t: Update the congeston control varables. For each sesson s and for each source node : λ s (t) = sup λ s {U (λ s ) Q s λ s } (31) Schedulng and Routng. For each lnk (, j), choose the sesson that maxmzes the dfferental backlog between transmtter and recever: s j = arg max s { Q s Q s j} (32) Then, set r s j j (t) = c j(t). Assgn lnk rates c j (t) to maxmze the weghted sum of the lnk rates of the network, where the weghts correspond to dfferental backlogs: C(t) = arg max C SU j SU,j c j ( Q s j ) Q s j j (33) Note that the maxmzaton above s analogous to the dynamc backpressure algorthm n [11][23]. Update Lagrange multplers (queues) as Q s (t + 1) = Q s (t) + ɛ rk(t) s rl(t) s + λ s (t) k SU,k l SU,l (34) Note that the Lagrange functon s always convex, and thus the multplers can be computed usng a subgradent algorthm. A subgradent for a gven vector of Lagrange multplers s the vector consstng of all multplcatve terms n the Lagrange functon, whch are the results of the maxmzaton above. Hence, the Lagrange multplers are computed usng an teratve algorthm whch updates them based on the value of the local subgradent. When ɛ = 1, the Lagrange multplers behave lke real queues. However, ɛ needs to be small for the algorthm to converge to the global optmum. Clearly, the bottleneck of the above soluton les n the routng and schedulng component n (33). Solvng (33) requres global knowledge of feasble rates, s centralzed and ts complexty s worstcase exponental. Exact dstrbuted soluton of (33) s thus nfeasble. However, t can be shown that the closer a polcy gets to maxmzng (33), the closer the polcy gets to the capacty regon of the +

7 Farness ndex RFA RDA ROSA 0.7 Farness ndex Table 1: PARAMETERS OF THE MODEL USED FOR SIM- ULATIONS. network [11]. Ths provdes the ratonale for our dstrbuted algorthm, whose objectve s to maxmze (33) under the constrants expressed by (27), together wth (28) and (29) for cogntve rado ad hoc networks. 7. PERFORMANCE EVALUATION In ths secton, we analyze the performance of ROSA n a multhop cogntve rado network, and compare t to the performance of other schemes. We concentrate on evaluatng network throughput, delay, and farness. To evaluate ROSA, we developed an objectorented packet-level dscrete-event smulator, whch mplements the features descrbed n ths paper. In all smulaton scenaros, we consdered a common set of parameters, whch s reported n Table 1. A grd topology of 49 nodes s deployed, n a 6000 m x 6000 m area. We ntate sessons between randomly selected but dsjont source-destnaton pars. Sessons are CBR sources wth a data rate of 2 Mbt/s each. We set the avalable spectrum to be 54 MHz - 72 MHz. We restrct the bandwdth usable by cogntve rados to be 2, 4 and 6 MHz. The bandwdth of the CCC s 2 MHz. We compare the performance of ROSA wth two alternatve schemes. In partcular, we consder Routng wth Fxed Allocaton (RFA) as the soluton where routng s based on dfferental backlog (as n Secton 4) wth pre-defned channel and transmt power, and to Routng wth Dynamc Allocaton (RDA) as the soluton where routng s based on shortest path wth dynamc channel selecton and power allocaton wthout consderng dfferental backlog. We compare aganst the three solutons by varyng the number of sessons njected nto the network and plot the network throughput (sum of ndvdual sesson throughput) n Fg. 2(a), whch shows that ROSA outperforms RFA and RDA. When there are a few actve sessons, e.g., 2 or 4, ROSA, RDA and RFA obtan smlar throughput performance. However, wth more actve sessons, ROSA and RDA perform much better than RFA snce they use the best among possble spectrum allocatons and routes adaptvely. The mprovement obtaned by ROSA s more vsble when the number of actve sessons grows large. In fact, ROSA acheves hgher network throughput than RDA when there are more than 10 actve sessons n the network. Fg. 2(b) shows the delay performance for the three solutons. RFA, on average, delvers a larger delay than the other two solutons. The above delay performance gap grows as the number of sessons ncreases. As shown n Fg. 2(b), ROSA provdes very low and stable delay performance as the number of sessons ncreases. ROSA and RDA yeld almost the same delay performance. Fg. 2(c) shows the mpact of source data rate per sesson on the performance of throughput. We evaluate the throughput as the traffc load per sesson ncreases from 100 Kbt/s to 8 Mbt/s. As Number of Actve Sessons Fgure 3: Farness Index. shown n Fg. 2(c), the throughput acheved by ROSA ncreases lnearly as the load per sesson ncreases. As the load ncreases, ROSA obtans a sgnfcant throughput gan. Fgure 3 shows Jan s farness ndex, calculated as ( r ) 2 /n (r ) 2, where r s the throughput of sesson, and n s the total number of actve sessons. As shown n the fgure, the overall farness among competng sessons s mproved by ROSA usng prortzed channel access scheme. When the sessons are dynamc, the protocol s supposed to be stable snce the algorthm adaptvely adjusts channel selecton and power allocaton accordng to the current transmssons. 8. CONCLUSIONS We proposed, dscussed and analyzed ROSA, a dstrbuted algorthm for jont opportunstc routng and dynamc spectrum access n mult-hop cogntve rado networks. ROSA was derved by decomposng a cross-layer network utlty maxmzaton problem formulated under the constrants of cogntve rado networks. As dscussed n Secton 6, a congeston control module that nteracts wth the other functonaltes can also be easly ntegrated based on t. Through dscrete-event smulaton, ROSA was shown to outperform smpler solutons. Future work wll am at dervng a theoretcal lower bound on the performance of ROSA, studyng an effcent sngle-rado mplementaton of the algorthm, and evaluatng the performance of the algorthm n conjuncton wth a congeston control module. In addton, we are currently mplementng ROSA on GNU rado and Unversal Software Rado Perpheral (USRP2). 9. REFERENCES [1] I. F. Akyldz, W.-Y. Lee, and K. Chowdhury. CRAHNs: Cogntve Rado Ad Hoc Networks. Ad Hoc Networks Journal (Elsever), 7(5): , Jul [2] I. F. Akyldz, W.-Y. Lee, M. C. Vuran, and S. Mohanty. NeXt Generaton/Dynamc Spectrum Access/Cogntve Rado Wreless Networks: A Survey. Computer Networks Journal(Elsever), 50: , Sept [3] V. Brk, E. Rozner, S. Banerjee, and P. Bahl. DSAP: A Protocol for Coordnated Spectrum Access. In IEEE Intl. Symp on New Fronters n Dynamc Spectrum Access Networks (DySPAN), Nov [4] D. Cabrc, S. M. Mshra, and R. W. Brodersen. Implementaton Issues n Spectrum Sensng for Cogntve Rados. In Conference Record of the 38th Aslomar Conference on Sgnals, Systems and Computers, volume 1, pages , Nov

8 30 Average throughput vs Number of Actve Sessons ROSA RFA RDA Average Delay vs Number of Actve Sessons ROSA RFA RDA ROSA RFA RDA Impact of the sesson load Average throughput [Mbt/s] Average Delay [s] Average throughput [Mbt/s] Number of Actve Sessons Number of Actve Sessons Load per sesson [Mbt/s] Fgure 2: (a): Average throughput vs number of actve sessons; (b): Delay vs number of actve sessons; (c): Impact of Source Data Rate per Sesson on Throughput. [5] L. Cao and H. Zheng. SPARTA: Stable and Effcent Spectrum Access n Next Generaton Dynamc Spectrum Networks. In Proc. of IEEE Intl. Conf. on Computer Communcatons (INFOCOM), pages , Apr [6] G. Cheng, W. Lu, Y. L, and W. Cheng. Jont On-demand Routng and Spectrum Assgnment n Cogntve Rado Networks. In IEEE Intl. Conf. on Communcatons(ICC), pages , Jun [7] M. Chang, S. Low, A. Calderbank, and J. Doyle. Layerng as Optmzaton Decomposton: A Mathematcal Theory of Network Archtectures. Proceedngs of the IEEE, 95(1): , Jan [8] F. Cuomo, C. Martello, A. Baocch, and F. Caprott. Rado Resource Sharng for Ad-hoc Networkng wth UWB. IEEE Journal on Selected Areas n Communcatons, 20(9): , Dec [9] A. Erylmaz and R. Srkant. Jont Congeston Control, Routng, and MAC for Stablty and Farness n Wreless Networks. IEEE Journal on Seclected Areas n Communcatons, 24(8): , Aug [10] S. Gandh, C. Buragohan, L. Cao, H. Zheng, and S. Sur. A General Framework for Wreless Spectrum Auctons. In IEEE Intl. Symp on New Fronters n Dynamc Spectrum Access Networks (DySPAN), Apr [11] L. Georgads, M. J. Neely, and L. Tassulas. Resource Allocaton and Cross-layer Control n Wreless Networks. Found. Trends Netw., 1(1):1 144, [12] P. Gupta and P. Kumar. The capacty of wreless networks. IEEE Trans. on Informaton Theory, 46(2): , Mar [13] Y. T. Hou, Y. Sh, and H. D. Sheral. Optmal spectrum sharng for mult-hop software defned rado networks. In Proc. of IEEE Intl. Conf. on Computer Communcatons (INFOCOM), May [14] D. B. Johnson and D. A. Maltz. Dynamc Source Routng n Ad Hoc Wreless Networks. In T. Imelnsk and H. Korth, edtors, Moble Computng, pages Kluwer Academc Publshers, [15] H. Khalfe, S. Ahuja, N. Malouch, and M. Krunz. Jont Routng and Spectrum Selecton for Multhop Cogntve Rado Networks. Techncal Report, UPMC - Pars 6, [16] X. Ln, N. Shroff, and R. Srkant. A tutoral on cross-layer optmzaton n wreless networks. IEEE Journal on Selected Areas n Communcatons, 24(8): , Aug [17] K. Lu and Q. Zhao. A Restless Bandt Formulaton of Opportunstc Access: Indexablty and Index Polcy. In Proc. of the 5th IEEE Conference on Sensor, Mesh and Ad Hoc Communcatons and Networks (SECON) Workshops, Jun [18] T. Meloda and I. F. Akyldz. Cross-layer Qualty of Servce Support for UWB Wreless Multmeda Sensor Networks. In Proc. of IEEE Intl. Conf. on Computer Communcatons (INFOCOM), Mn-Conference, Apr [19] J. Mtola and G. Magure. Cogntve Rado: Makng Software Rados More Personal. IEEE Personal Communcatons, 6:13 18, Aug [20] I. N. Psaromlgkos and S. N. Batalama. Rapd Combned Synchronzaton/Demodulaton Structures for DS-CDMA Systems - Part II: Fnte data record performance analyss. IEEE Transactons on Communcatons, 51: , Jul [21] I. N. Psaromlgkos, S. N. Batalama, and M. J. Medley. Rapd Combned Synchronzaton/Demodulaton Structures for DS-CDMA Systems - Part I: Algorthmc developments. IEEE Transactons on Communcatons, 51: , Jun [22] L. Tassulas and P. P. Bhattacharya. Allocaton of Interdependent Resources for Maxmal Throughput. Stochastc Models, 16(1), [23] L. Tassulas and A. Ephremdes. Stablty Propertes of Constraned Queueng Systems and Schedulng Polces for Maxmum Throughput n Multhop Rado Networks. IEEE Transactons on Automatc Control, 37(12): , Jan [24] Q. Wang and H. Zheng. Route and Spectrum Selecton n Dynamc Spectrum Networks. In IEEE Consumer Communcatons and Networkng Conference (CNCC), Jan [25] C. Xn, B. Xe, and C.-C. Shen. A novel layered graph model for topology formaton and routng n dynamc spectrum access networks. In IEEE Intl. Symp on New Fronters n Dynamc Spectrum Access Networks (DySPAN), pages , Nov [26] Y. Yuan, P. Bahl, R. Chandra, P. A. Chou, J. I. Ferrell, T. Moscbroda, S. Narlanka, and Y. Wu. KNOWS: Kogntv Networkng Over Whte Spaces. In IEEE Intl. Symp on New Fronters n Dynamc Spectrum Access Networks (DySPAN), Apr [27] Y. Yuan, P. Bahl, R. Chandra, T. Moscbroda, and Y. Wu. Allocatng dynamc tme-spectrum blocks n cogntve rado networks. In Proc. of ACM Intl. Symp. on Moble Ad Hoc Networkng and Computng (MobHoc), pages , New York, NY, USA, [28] X. Zhou, S. Gand, S. Sur, and H. Zheng. ebay n the Sky: Strategy-Proof Wreless Spectrum Auctons. In ACM Intl. Conf. on Moble Computng and Networkng (MobCom), Sept