Design and Experimental Evaluation of a Cross-Layer Deadline-Based Joint Routing and Spectrum Allocation Algorithm

Transcription

1 Desgn and Expermental Evaluaton of a Cross-Layer Deadlne-Based Jont Routng and Spectrum Allocaton Algorthm Jthn Jagannath, Sean Furman, Tommaso Meloda, and Andrew Drozd Abstract The desgn and mplementaton of a novel dstrbuted deadlne-based routng and spectrum allocaton algorthm for tactcal ad-hoc networks s reported n ths artcle. Dfferent traffc classes ncludng text, voce, survellance vdeo, threat alert among others need to be handled by these networks. Each of these traffc classes have dfferent qualty of servce (QoS) based deadlne requrements. Addtonally, these networks are characterzed by dynamc channel and traffc condtons that vary wth tme and locaton. Even under these condtons, t s crtcal to receve packets before the deadlne expres to make rapd decsons n the battlefeld. Therefore, a tactcal ad-hoc network should be able to adapt to these requrements and maxmze the number of packets delvered to the destnaton wthn the specfed deadlne. A dstrbuted deadlne-based routng and spectrum allocaton algorthm s desgned to maxmze the utlzaton of the avalable resources and ensure delvery of packets wthn the deadlne constrants. To ths end, a weghted vrtual queue (VQ) that s used to construct the network utlty functon s defned. Accordngly, the optmal sesson, next hop, transmt power and frequency s determned by the dstrbuted algorthm to ensure effcent utlzaton of the avalable resources. Hence, maxmzng the delvery of packets to the ntended destnaton wthn the specfed deadlne. The 49 node smulaton shows up to 35 % mprovement n effectve throughput and 26 % mprovement n relablty as compared to jont ROutng and Spectrum Allocaton algorthm (ROSA), whch does not adapt accordng to the deadlne requrements of the data flowng through the network. As a secondary objectve, ths work advances the state of the art of the expermental cross layer framework to address the challenges nvolved n havng such cross layer algorthms mplemented on a testbed. The requred flexblty to change the transmsson parameters on-the-fly s provded by the proposed framework. The network s desgned to enable the data exchange between neghbors usng custom desgned control packets (whch mght be dfferent for dfferent algorthms) snce ths nformaton s crtcal for nodes to perform optmzaton. Cross layer optmzaton s acheved by means of data management and control enttes that enable nformaton exchange between layers. The practcalty of the proposed soluton was proven by havng the novel algorthm mplemented on a fve-node software defned rado testbed whch leverages the proposed cross-layer framework. In contrast to ROSA, the proposed algorthm demonstrated up to 7% mprovement n terms of throughput and relablty. The performance mprovement acheved s expected to ncrease on a larger network deployment. Index Terms Deadlne-based routng, cross-layer optmzaton, cogntve rado, resource allocaton, software defned rado, USRP testbed. INTRODUCTION In a tactcal ad-hoc network, there exsts a constant tenson between avalable resources and the requred qualty of servce (QoS) performance. Nodes n the network have to deal wth severe nterference, spectrum crunch, adversaral jammng and changng network topologes. Addtonally, a typcal tactcal network as depcted n Fg. s requred to handle varous traffc classes ncludng regular samplng data, voce, survellance vdeo, threat alert, among others. Each of these traffc classes have substantally dfferent QoS based deadlne requrements. For example, perodc survellance data mght have looser deadlne constrants when compared to a vdeo or threat alert message. In these delayntolerant networks, only packets that arrve at the destnaton ACKNOWLEDGMENT OF SUPPORT AND DISCLAIMER:(a) Contractor acknowledges Government s support n the publcaton of ths paper. Ths materal s based upon work supported by the US Ar Force Research Laboratory under Award No. FA875-4-C-98 and Award No. FA875-6-C-86. (b) Any opnons, fndngs and conclusons or recommendaton expressed n ths materal are those of the author(s) and do not necessarly reflect the vews of AFRL. A prelmnary shorter verson of ths paper wll appear n the Proceedngs of IEEE Globecom 26. J. Jagannath S. Furman and A. Drozd s wth the ANDRO Advanced Appled Technology, ANDRO Computatonal Solutons, LLC, Rome NY, USA and J. Jagannath s also wth Department of Electrcal and Computer Engneerng, Northeastern Unversty, Boston, MA 25 USA (e-mal: {jjagannath,sfurman,adrozd}@androcs.com) T. Meloda s wth the Department of Electrcal and Computer Engneerng, Northeastern Unversty, Boston, MA 25 USA (e-mal: meloda@ece.neu.edu) wthn the specfed deadlne are vable and contrbute to the overall network throughput. In these scenaros, t becomes mportant to examne the nteracton between spectrum management, routng and sesson management to develop cross-layer control algorthms capable of maxmzng the effectve throughput of the network. In ths paper, we consder only the packets that arrve at the destnaton wthn the specfed deadlne n the computaton of effectve throughput. Vehcular Relays Satellte Lnks Unmanned Aeral Vehcles (UAVs) Arborne Relays Dsmount Solders Usng Rfleman Rados Fg. : Tactcal ad-hoc network Cogntve rado technology along wth varous dynamc spec- Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

2 2 trum access (DSA) technques have been proposed to mprove the spectrum utlzaton of the network by enablng opportunstc access of free spectrum chunks. In prevous work [2], an optmzaton algorthm (ROSA) was proposed to jontly select route and spectrum such that overall network throughput s maxmzed. Ths algorthm combnes the dea of backpressure algorthm [3] wth channel dependent opportunstc routng. Smulatons show that ROSA outperforms the tradtonal algorthms that use ether dynamc spectrum allocaton wth fxed route or dynamc routng wth fxed spectrum allocaton. In ths work, we substantally extend [2] to examne the network performance n terms of effectve throughput and relablty for multple sessons wth dfferent deadlne constrants. Ths enables such routng and resource allocaton algorthms to handle varous QoS based traffc classes effcently. Accordngly, we develop a dstrbuted deadlne-based optmzaton algorthm for tactcal ad-hoc networks. Some of the challenges n desgnng a deadlne-based algorthm are as follows: Each node has to carefully manage multple sessons to meet the deadlne requrements. For example, sessons wth longer backlogs and larger deadlnes can be held back whle acceleratng shorter backlogged-smaller deadlne sessons. Adoptng an effectve resource allocaton procedure that would negotate the access of medum and choose optmal transmsson parameters. In a large network, the spectrum occupancy vares based on locaton and tme, thus nodes may have to use dfferent parts of the spectrum n order to route a sesson n the most effectve manner. Choosng approprate routes to meet the needs of each sesson belongng to dfferent traffc classes. The network should be able to adapt to broken routes or faled nodes by choosng alternate paths. The desgn should be scalable, reduce communcaton overhead and yet enable the network to adjust dynamcally to the avalable resources. Therefore, t s crtcal to desgn a dstrbuted approach that s feasble on a practcal network. Therefore, the overall objectve of ths work s to desgn and evaluate a dstrbuted algorthm that utlzes the avalable resources to determne optmal route, sesson and spectrum to delver maxmum number of packets to ther ntended destnaton wthn the specfed deadlne. The weghted vrtual queue (VQ) used n cross-layer optmzaton ensures proper management of the sessons. The vrtual queue length (VQL) takes nto account deadlnes assocated wth each packet. The jont routng and spectrum allocaton aspects of the algorthm provdes optmal resource allocaton and enables opportunstc routng. The dstrbuted nature of the proposed algorthm along wth forward progress based routng helps the network to recover from broken routes or faled nodes. These features are crtcal n any delayntolerant applcatons and wll be especally useful n tactcal adhoc networks where the delayed delvery of crtcal nformaton n a multhop network can be fatal. 2 RELATED WORK Dynamc spectrum allocaton has been wdely nvestgated wth the objectve to maxmze spectrum utlzaton and s manly dvded nto centralzed [4], [5] and dstrbuted [2], [6] approaches. Whle spectrum allocaton technques are desgned to mprove spectrum utlty based QoS [7], [8], [9], queue length based backpressure (Q-BP) schedulng algorthm was frst proposed n [3] and was shown to be throughput optmal n terms of achevng network stablty under any feasble load. It s well known that the Q-BP algorthm suffers from hgh computatonal complexty and the last packet problem. To reduce the computatonal delay for practcal mplementaton, a greedy maxmum schedulng (GMS) algorthm s studed n [], [], [2]. Ths algorthm frst chooses the lnk l wth maxmum weght from the set of all lnks S and elmnates lnks that nterfere wth l from the set S. Next, t agan pcks the lnk wth maxmum weght among the remanng lnks of set S and elmnates the lnk causng nterference to t. Ths process s repeated untl all lnks have been consdered. The tradeoff here s the reduced network capacty. In [3], the authors solve a centralzed network throughput maxmzaton problem that uses the backpressure algorthm. The study also mplements the soluton on hardware to perform evaluaton. Even though the network acheves throughput mprovement, the network may be prone to last packet problem whch s a crucal hndrance for tactcal networks. The last packet problem of Q-BP algorthm arses because of the assumpton that flows have an nfnte amount of data packets beng njected nto the network. Instead, n practcal networks the flows may be fnte wth some flows termnatng and new flows emergng. When a fnte flow has the last packet n the queue, t may be stagnant for an extended perod of tme because of the presence of other queues wth larger backlog. Ths s referred to as the last packet problem. It has been shown that n these cases, queue length based schemes may not be throughput optmal [4]. Accordngly, there has been consderable work on delay-based schedulng [5], [6], [7], [8], [9], [2], [2] to mprove the delay performance of the network and elmnate the last packet problem. In [5], the authors use a shadow queung archtecture so that each node mantans only one queue per neghbor (rrespectve of sessons) to reduce the complexty of the queung structure and mprove the delay performance at the cost of throughput. Each node stll has to mantan a separate shadow queue (a counter) for every flow gong through the node. The backpressure algorthm s executed usng the shadow queue counters and these counters are updated accordng to the optmal number of shadow packets chosen to be transmtted over each lnk. The key pont here s that the number of shadow packets s lke a permt to transmt on the gven lnk from the real queue but not assocated wth the flow of the shadow packet tself. The packet njecton rate of the shadow queue s kept slghtly hgher than the actual packet njecton rate. The rates are desgned as follows: f the packet njecton rate of the shadow queue s x t (t), the rate of the real queue s gven by βx t (t), where β s a postve real number smaller than one. Therefore, f the number of real packets n the queue s less than the number of shadow packets to be transmtted, all the real packets n the correspondng queue are transmtted. The authors show that the real queue length decreases unformly at every node as the value of β decreases, thus leadng to lower delays by Lttle s law. Ths decrease n delay s accompaned by reduced throughput performance. Mantanng a sngle queue per neghbor s only benefcal n scenaros where the number of flows through a node s much greater than the number of neghbors. Authors propose a self-regulated MaxWeght schedulng algorthm n [22], where each node estmates the aggregated lnk rate. They prove that the self-regulated MaxWeght schedulng s throughputoptmal (.e. stablze any traffc that can be stablzed by any other algorthm) when the traffc flows are assocated wth fxed routes and the packet arrvals follow some statstcal property. Both [5] and [22] are desgned for fxed route scenaros, thus Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

3 3 lackng the mprovement that could be acheved by opportunstc routng. In [6], the authors propose a delay-lmtng algorthm to control the burstness and delays. They adapt the upper lmt for the physcal queues to ensure an upper per-hop delay lmt at the expense of throughput. To ensure that nodes n the network reman operatonal, a lower bound has to be set on the upper queue lmt. If the traffc reduces to a pont such that lower bound comes nto play, the delay-lmtng approach becomes neffectve. There s also a trade-off between delay and the degree of multpath and opportunsm. As the traffc s spread spatally to utlze multple routes, the lower bound on the queue may agan render the delay control neffectve. A cross-layer desgn s proposed n [7] usng VQ structures to provde fnte buffer sze or worst-case delay performance. In [8], authors desgn a delay-aware jont flow control, routng, and schedulng algorthm for multhop network to maxmze network utlzaton. However, due to ther ( [8], [7]) centralzed nature and hgh complexty they are not well suted for practcal dstrbuted mplementaton [23]. In [9], a throughput optmal schedulng algorthm s proposed usng largest weghted delay frst algorthm. The dea s to serve the queue j for whch γ j W j (t)r j (t) s maxmal, where W j ( j) s the weghted delay and r j (t) the achevable capacty for lnk j. Although ths algorthm s an easy and dstrbuted way to acheve throughput optmalty, ths formulaton does not take nto account the dynamc routng possbltes or queung dynamcs of multhop traffc. Snce [9] fals to capture the queung dynamcs of multhop traffc, a new delay metrc s defned n [2] to establsh a lnear relaton between queue length and delay. The authors also propose a greedy algorthm that s smlar to GSM dscussed earler, but uses delay dfferental rather than queue length. Smulatons show that the average queue length of the network s smlar n Q-BP and delay-based backpressure (D- BP) but the tal of the delay dstrbuton s much longer for Q-BP. Ths mples that some queues are stagnant over extended perods of tme n Q-BP whereas D-BP reduces ths problem. Unlke the proposed algorthm, D-BP s desgned for fxed routes and does not consder dynamc routng. In [2], a delay-drven MaxWeght scheduler s presented that gets around the last packet problem and addresses nstablty of the queue length based algorthms caused by rate varatons. However, t has been shown n [24], [25] that there are other factors that contrbute to the neffcency of the back-pressure algorthm ncludng, neffcent spatal reuse, falure to opportunstcally explot better lnk rates, underutlzed lnk capacty and neffcent routng because of nsuffcent path nformaton. Deadlne-based routng has been recently studed n [26], [27] and [28]. In [26], an utlty-based algorthm s proposed for cyclc moble socal networks under the assumpton that nodes follow cyclc moblty, perodcally encounterng each other wth hgh probablty. It s dffcult to extend [26] to tactcal ad-hoc networks wthout apror knowledge of the encounter probablty. To ncrease the packet delvery rato, [27] adopts an epdemc based routng algorthm and [28] proposes a capacty-constraned routng algorthm that decdes whch packets have to be replcated. The replcaton strateges proposed n [27] and [28] to mprove packet delvery rato may adversely affect the achevable throughput. The major contrbutons of ths work can be outlned as follows, We propose a novel deadlne-based jont routng and spectrum allocaton algorthm for tactcal ad-hoc networks to meet the deadlne requrements of multple sessons. To the best of our knowledge, ths s the frst work that combnes the nteracton of opportunstc routng, spectrum allocaton and deadlne constrants to maxmze the effectve throughput of tactcal ad-hoc networks. The proposed algorthm s able to adapt to the needs of a dynamc network by managng multple sessons wth varable QoS. Ths s accomplshed by makng an optmal choce about the sesson, route, spectrum and power allocaton used to maxmze the utlzaton of avalable resources. A dstrbuted approach s formulated to enable the mplementaton of the proposed algorthm n a scalable manner. Performance of the proposed algorthm s extensvely evaluated under varous smulated scenaros. Another major contrbuton of ths artcle s the testbed mplementaton. To prove the practcalty of the proposed algorthm, we successfully mplement the deadlne-based jont routng and spectrum allocaton algorthm on a software defned testbed. A secondary objectve of ths paper s to further advance the cross-layer framework and show how novel cross-layer algorthms can be mplemented on testbed usng the expermental framework. The rest of the paper s organzed as follows. In Secton 3, we descrbe the system model. We dscuss the desgn of deadlnebased routng algorthm n Secton 4. Next n Secton 5, we smulate a 49 node ad-hoc network, to evaluate the performance of the proposed algorthm. The desgn and confguraton of the testbed s descrbed n Secton 6. The expermental evaluaton of the proposed algorthm on a SDR based testbed s dscussed n Secton 7. Fnally, conclusons are dscussed n Secton 8. 3 SYSTEM MODEL Consder a multhop tactcal ad-hoc network wth M prmary users and N secondary users modeled as a drected connectvty graph G(U,E), where U = {u,u,...,u N+M } s a fnte set of wreless transcever (nodes), and (, j) E represents undrectonal wreless lnk from node u to node u j (for smplcty, we also refer to them as node and node j). We assume G s lnk symmetrc,.e., f (, j) E, then ( j,) E. The nodes from the subset PU = {u,...,u M } are desgnated as prmary users, and nodes from the subset SU = {u M+,...,u M+N } are desgnated as secondary users. The secondary network s composed of cogntve nodes capable of adaptng to the current spectrum usage. The prmary user holds the lcense for the specfc spectrum bands and have full access to the spectrum wthout nterference from any other users. In relevant scenaros, the prmary user can also be a non-cooperatve node (the adversary). Snce the entre spectrum s not always used by prmary users, the am of the secondary user n a cooperatve scenaro s to maxmze spectrum utlty whle ensurng no nterference to prmary users. Thus, a secondary user has to use the spectrum holes [2] to maxmze the spectrum usage. The secondary network wll also allocate resources such that t maxmzes the number of packets delvered at the destnaton wthn ther respectve deadlne. Only packets that reach the destnaton wthn the specfed deadlne contrbute towards the effectve throughput computaton. The set of neghbors for node s gven by N B { j : (, j) E}. The secondary users are equpped wth cogntve rados capable of scannng the avalable spectrum to reconfgure ther transcevers on-the-fly. The entre avalable spectrum s gven by BW. The cogntve transcever s capable of tunng to a set of contguous frequency bands [ f, f + B], where B s the Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

4 TABLE : Defnton of notatons Notatons Defnton M Number of prmary users N Number of secondary users u Node (also refered to as ) P U Subset of prmary users SU Subset of secondary users Drected connectvty graph, where U G(U, E) s a fnte set of nodes, and E represents set of undrectonal wreless lnks N B Set of neghbors for node BW Total avalable spectrum [ f, f + B] Set of contguous frequency bands, where B s the bandwdth of the cogntve rado b Bandwdth of each subband f mn Mnmum frequency that node can tune to f max Maxmum frequency that node can tune to s S Set of sesson n node t Tme slot λ s (t) Arrval rate of sesson s at t Λ Set of arrval rates q s (t) A packet n sesson s at t Q s (t) Vrtual queue length of sesson s at t L(q s ) Length of the packet n bts T r (q s ) Remanng lfe tme of the packet D(q s ) Deadlnes of the packet T d (q s ) Tme to the destnaton of packet as estmated at node. w q s (L,T d,t r ) Weght of the packet R Communcaton range of the node T h Average tme spent by packet at each hop α Delay estmaton factor a(q s, j,t) a = represent packet q s Qs (t) s transmtted to node j at tme slot t, and a = otherwse. A Vector or routng profle r s j (t) Transmsson rate for lnk (, j) E R Vector of transmsson rates P Selected power levels n each subband F Selected frequency subbands U j Utlty functon for lnk (, j) E C j Achevable channel capacty for lnk (, j) E P ( f ) Transmt power of node on the frequency f PL j ( f ) Transmsson loss due to path loss from to j G Processng gan N j ( f ) Recever nose on frequency f I j ( f ) Interference experenced by the recevng node j BER PU BER guarantees requred for prmary user BER SU BER guarantees requred for secondary user SINR th PU SINR thresholds requred to acheve the target BER for the prmary user SINR th SU SINR thresholds requred to acheve the target BER for the secondary user O j ( f ) Spectrum Opportunty between and j P max Maxmum power that can be used by the ( f ) secondary node on the frequency f P mn Mnmum power requred to reach the requred ( f ) SINR th SU at the ntended secondary recever CW Contenton wndow η Effectve throughput ρ Relablty bandwdth of the cogntve rado and B < BW. We also assume that the transmt power can be vared to explot any avalable spectrum opportunty. We defne spectrum opportunty as the lmted avalablty of spectrum that mght currently be used by nodes (prmary or secondary users) but can be further exploted by adjustng the transmt power such that t does not volate the bt error rate (BER) constrant of the exstng transmsson. Ths work s ntended for any general physcal layer but we assume that multple transmssons can occur concurrently on the same frequency band, e.g., wth dfferent spreadng codes. The total spectrum, BW s dvded nto separate channels, a common control channel (CCC) and a data channel. All secondary nodes use CCC to share local nformaton for spectrum negotaton and data channel s used exclusvely for data communcaton. The data channel s dvded nto dscrete set of carrers { f mn, f mn+,..., f mn, f max }, each of bandwdth b and dentfed by a unque dscrete ndex. The cogntve rado of the secondary user can tune nto a consecutve set of carrers from [ f mn, f max ]. Let the traffc n the network consst of multple sessons characterzed by the source-destnaton par and the applcaton generatng the sesson. The arrval rates of each sesson s S at node s gven by λ s (t), and characterzed by vector of arrval rates Λ. 4 DEADLINE-BASED ROUTING AND SPECTRUM ALLO- CATION In ths secton, we dscuss the deadlne-based dstrbuted routng and spectrum allocaton algorthm n detal. Here, we wll defne the utlty functon, that has to be maxmzed to acheve the goal of the proposed soluton. 4. Network Utlty Functon Consder that the tactcal ad-hoc network s assumed to operate over a tme slotted channel. The spectrum utlty functon s calculated by node for every tme slot t when node s backlogged and not already transmttng or recevng packets. Each node mantans a separate VQ for each sesson. We defne Q s (t) as the VQL formed by packets of sesson s n node at tme slot t. Unlke tradtonal queue length, the VQL gets nflated as tme passes to penalze the node for holdng packets whose deadlne s approachng. More detals about the desgn of VQL s dscussed below. For each packet q s Qs (t), that belongs to sesson s and stored at node, a set of felds are defned, ncludng, L(q s ) s the length of the packet n bts, T r (q s ) s the remanng lfe tme of the packet, whch s based on the deadlne D(q s ) assgned to the packet at the source node, T d (q s ) s the tme to the destnaton as estmated at node. Based on these parameters, a weght w q s [L(q s ),T d(q s ),T r(q s )] can be defned for each packet q s Qs (t) as follows, w q s (L(q s ),T d (q s ),T r (q s L(q s )) = ) max(t r (q s ),τ)max(t r(q s ) T d(q s ),τ) () As we can see n () the weght w q s assgned to each packet s drectly proportonal to L (for smplcty, we removed q s from these notatons) and nversely proportonal to T r and T d. The τ n () s a very small value used to avod negatve and nfnte weghts. The parameter T r helps to get rd of the well-known last packet problem, snce T r wll ncrease the VQL as tme elapses. Ths can be nterpreted as the holdng penalty mposed for packets 4 Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

5 5 beng stagnant n the queue for extended perod of tme. Snce T r s dependent on the assgned deadlne, t helps the nodes to manage dfferent sessons by pushng crtcal packets faster even f the actual queue length s comparatvely smaller. Consderng just the deadlnes alone wll not help n cases where there are two sessons wth the same deadlne but one s farther away from the destnaton than the other. In such cases, T d wll ensure that the sesson farther away from the destnaton moves through the network at a faster rate compared to smlar sessons closer to the destnaton. Therefore, T d can be consdered as a varable that ether amplfes or dmnshes the effect of T r dependng on the tme requred to reach the destnaton. T d also encourages packets to take shorter routes f all other factors lke queue length and spectrum are the same for two dfferent routes. The ratonale wll become more evdent when we dscuss the network utlty functon used for the proposed algorthm. Among these three parameters, the exact value of T d s not avalable at each node and has to be estmated at each hop. For a centralzed network, assumng global knowledge of the network, T d can be estmated usng average queung delays, transmsson rate, propagaton delays and usng the knowledge of average delays experenced prevously by packets wth the same destnaton. Estmatng T d becomes further challengng n a dstrbuted network where each node s requred to make decsons wthout global knowledge of the network. One soluton s to estmate T d by usng queung delay experenced by the sesson n the node tself. We use ths nformaton and slghtly over estmate the delay by assumng that the packet has to route through more than one node wthn ts transmsson range tself. Underestmatng T d would ncrease the rsk of packets not reachng the destnaton wthn the specfed deadlne. Therefore, n our desgn T d s slghtly over estmated accordng to the characterstcs of the network. Snce T d s updated at every hop, the estmaton error/margn decreases as the packet moves closer to the destnaton. Ths method does not lead to any error propagaton snce the value s updated at each hop. A smple way to estmate T d s based on dstance to destnaton (d), communcaton range (estmated based of maxmum transmt power) of the nodes deployed (R) and average tme spent by the packet durng each hop (T h ) (estmated based processng delay, queung delay, transmsson delay and propagaton delay). The dea s to assume that a hop s requred every half range of a node and s gven by α = R/2. Accordngly, we get an estmate of how much tme s requred to reach the destnaton as, T d = d T h α = 2d T h R. (2) The value of α can be vared accordng to the densty of the network. Now from the defnton of weghts, t can be seen that hgher value s assgned to packets wth more bts to transmt, lower T r and whch are farther away from the destnaton. Accordngly, we defne a VQL of a sesson s n node as follows, Q s (t) = w q s (L,T d,t r ). (3) q s Qs (t) Now, let a(q s, j,t) = represent a packet qs Qs (t) s transmtted to node j at tme slot t, and a(q s, j,t) = otherwse. The routng profle of node s defned as a s (t) = [a(qs j SU/, j,t)] q s Qs (t), and A represents the vector of routng profle a s (t) of all nodes n the network at nstant t. We also defne the transmsson rate on lnk (, j) durng tme slot t as r s j (t), and R as the vector of rates. Then, the VQL of node can be updated as, [ Q s (t + ) = Q s (t) + w q s j (L,T d,t r )a(q s j,,t) q s j Qs j (t) j N / j N / q s Qs (t) w q s (L,T d,t r )a(q s, j,t) ] +. Accordngly, the network lnk utlty functon U j for lnk (, j) E for sesson s can be defned as, (4) U j (a s (t)) = C j [Q s (t) Q s j(t)] +, (5) where [Q s (t) Qs j (t)]+ represents the dfferental VQL and C j s the achevable channel capacty of the lnk (, j) E at tme slot t for a selected frequency ( f ) and the transmsson strategy can be gven by, [ C j ( f,p ( f )) b.log 2 + P ] ( f )PL j ( f )G (6) f [ f, f + f N ] j ( f ) + I j ( f ) In the above equaton, P ( f ) represents the transmt power of node on the frequency f, PL j ( f ) s defned as the transmsson loss due to path loss (can be computed based on the chosen path loss model) from to j, G represents the processng gan, whch would be the length of the spreadng code when applcable, N j ( f ) s the recever nose on frequency f and I j ( f ) s the nterference experenced by the recevng node j. We assume a quas-statc channel,.e. channel condtons reman constant for the duraton n between sensng and transmsson of a packet. Ths can be acheved wth an effcent sensng mechansm and havng dedcated recever that perform sensng n parallel to the regular transcever. As we can see n (6), the achevable capacty prmarly depends on selected frequency F = [ f, f + f ], power allocaton P = [P ( f )], SU,, f and the schedulng polcy. Therefore, the overall noton of ths network utlty functon s to couple the constrants of packet deadlne to the tradtonal queue length used n the dfferental backlog algorthm. Ths s then weghted by the dynamc spectrum avalablty nformaton to provde a jont routng and spectrum allocaton decson. Moreover, algorthms lke ROSA [29], [3] does not handle QoS requrements of dfferent traffc classes. Snce ths s essental for mprovng the relablty of tactcal ad-hoc networks, the redefnng of the the queue length to form the new VQL s where the proposed algorthm extends [2]. We wll dscuss and evaluate the benefts of ths n detal n Secton Dstrbuted Deadlne-based Routng and Spectrum Allocaton Algorthm The overall optmzaton problem s to maxmze the utlty functon dscussed n (5). Let us denote the BER guarantees requred for prmary and secondary users as BER PU and BER SU respectvely. Accordngly, we can represent the requred sgnalto-nterference-plus-nose power rato (SINR) thresholds requred to acheve the target BER for the secondary and prmary user as SINR th PU and SINRth PU respectvely. Thus, the global objectve of the optmzaton problem s to fnd the optmal global vectors R, F and P that wll maxmze the sum of the network utltes, under the power and BER constrants. The formulaton of the optmzaton problem s provded n Appendx. Snce solvng the overall optmzaton problem needs global knowledge of feasble rates and the worst-case complexty of ths centralzed problem Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

6 6 s exponental, t necesstates the need to desgn a dstrbuted algorthm that s scalable for practcal mplementaton. The resource allocaton of the proposed algorthm conssts of spectrum and power allocaton. A spectrum opportunty for lnk (, j) s a set of contguous subbands where O j ( f ), when O j ( f ) s gven by, O j ( f ) = P max ( f ) P mn ( f ), (7) where P max ( f ) s defned as the maxmum power that can be used by the secondary node on the frequency f such that t satsfes the BER constrants of prmary and secondary users. It s mportant to note that P max ( f ) wll be constraned by the maxmum transmt power of the wreless rado used n the network. On the other hand, P mn ( f ) denotes the mnmum power requred to reach the requred SINR th SU at the ntended secondary recever. In other words, P mn ( f ) and P max ( f ) provde the lower bound and upper bound of transmt power respectvely for node on frequency f. The P mn ( f ) and P max ( f ) values are determned by a node by gatherng spectrum and resource allocaton nformaton from ts neghbors. Ths nformaton s gathered usng collaboratve vrtual sensng (CVS) usng the control packets n the network. We do not nclude detals about resource allocaton, CVS and the medum access control (MAC) protocol employed as t s smlar to that n ROSA. We urge readers to refer to [2] for further detals. Accordngly, we propose the dstrbuted Deadlne-based crosslayer Routng and Spectrum allocaton algorthm (DRS) to maxmze the throughput of a tactcal ad-hoc network. In the dstrbuted network, each node makes an adaptve decson to choose optmal sesson, next hop, power allocaton and spectrum to use durng the next tme slot based on the nformaton gathered from the neghbors usng CVS. Ths decson wll be dfferent from tradtonal ROSA [2] because the network utlty defned here s a functon of VQL and not the actual queue lengths. Once a backlogged node senses an dle CCC, t performs the Algorthm to obtan the optmal resource allocaton decson: Algorthm Deadlne-based Resource Allocaton : t =, =, C j =, U j = 2: for s S do All Actve sessons 3: for j u,u 2,...u k do Next feasble hops 4: for f [ f mn,..., f max f ] do 5: 6: Calculate P t ( f ) smlar to [2] Calculate C temp as n (6) 7: f C temp > C j then 8: C j = C temp 9: [ f, j,p,j ]=[ f,p t ] : end f : end for 2: U s j = C j [Q s Q s j ] 3: f U s j > U j then 4: U j = U s j 5: [ f opt 6: end f 7: end for 8: end for 9: Return [ f opt,p opt,s opt,p opt,s opt, j opt ], j opt ]=[ f, j,p,j,s, j] ) The proposed algorthm assumes that locaton of the ntended destnaton node s known to the source node. Ths nformaton s carred by the packet through the ntermedate nodes. Each node selects a feasble set of next hops for each backlogged sesson j (u s,us 2,...,us k ), whch are neghbors wth postve advance towards the ntended destnaton. 2) The maxmum capacty for each node s calculated by consderng all possble spectrum opportuntes. The maxmum capacty of each feasble neghbor s used along wth the correspondng dfferental VQL to determne the network utlty. The optmal decson s taken such that, U s j (s opt, j opt ) = arg max(u s j). (8) As seen earler, the network utlty functon comprses of dfferental VQL and achevable capacty. The dfferental VQL s a functon of deadlne and estmaton of T d. Thus, the sessons that have smaller deadlnes or are further away from the ntended destnaton wll be scheduled more often f the avalable spectrum for all sessons are comparable. The adaptve routng wll also provde most traffc to VQs that are lghtly backlogged. 3) The optmal frequency and power allocaton ( f opt,p opt ) correspond to the values that provde maxmum Shannon capacty C j over the wreless lnk (, j opt ), where j opt s the best next hop. In ths work, we use a contenton based medum access control protocol n the control channel before transmttng the packet on the selected data channel. In the contenton based MAC protocol, the probablty of accessng the medum s calculated based on the U j. Nodes generate a backoff counter from the range [,2CW ], where CW s the contenton wndow. The CW s a decreasng functon of U j. Ths wll ensure that heavly backlogged VQs wth more spectrum resources wll have a hgher probablty of transmsson. The computatonal complexty of the DRS algorthm at a node s drectly proportonal to the number of neghbors, number of channels and number of actve sessons. Therefore, for a constant number of channels and sessons n a network the computatonal complexty for node s gven as O( N B ). 5 PERFORMANCE EVALUATION THROUGH SIMULATION In ths secton, we compare the performance of DRS wth ROSA n a multhop ad-hoc network. To evaluate DRS, we use a objectorented packet-level dscrete-event smulator smlar to [2], whch mplements the features descrbed n the earler sectons of ths paper. The metrc used for ths evaluaton s effectve throughput (η) and relablty (ρ) of the network. Effectve throughput was defned based on the number of packets receved wthn the deadlne. The relablty s defned as the rato of packets receved at the destnaton wthn the specfed deadlne wth respect to the number of packets generated at the source node. The evaluaton s conducted on a grd topology n a 6 m x 6 m area. The sessons are ntated between dsjont random source-destnaton pars and the packet sze of the packets are set at 25 bytes and number of packets transmtted per sesson s set to 5. The total avalable spectrum (BW) s set to be 54 MHz-72 MHz The bandwdth usable by cogntve rados are restrcted to be 2, 4 and 6 MHz. The bandwdth of the common control channel s set as 2 MHz. Each result was obtaned by averagng the values obtaned from 5 random seeds unless specfed dfferently. Next, we descrbe dfferent scenaros under whch the proposed algorthm s compared to ROSA. In all fgures except Fg. 6, the Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

7 Throughput (Mbts/sec) 5 5 ROSA (Exp:) DRS (Exp:) ROSA (Exp:2) DRS (Exp:2) Relablty ROSA (Exp:) DRS (Exp:) ROSA (Exp:2) DRS (Exp:2) Throughput (Mbts/sec) ROSA (Exp:3) DRS (Exp:3) ROSA (Exp:4) DRS (Exp:4) ROSA (Exp:5) DRS (Exp:5) Number of sessons Number of sessons Rate (Mbts/sec) Fg. 2: Scenaro : η vs No. of sessons. Fg. 3: Scenaro : ρ vs No. of sessons. Fg. 4: Scenaro 2: η vs No. of sessons. blue lnes represent performance of DRS and red lnes denote the performance of ROSA. 5. Scenaro : Network performance as the number of sesson ncreases (All sessons started at random tme) TABLE 2: Parameters of scenaro Parameter Experment Experment 2 Source Rate 2 Mbts/s 2 Mbts/s Sesson duraton 5 s 5 s Sesson start randomly from randomly from No. of sessons Deadlne of each sesson t = [,5] s 2, 4, 6, 8,, 2 4, 6, 8, 2, 22 2 s t = [,5] s 2, 4, 6, 8,, 2 4, 6, 8, 2, 22 Odd sesson.5 s Even sesson s In scenaro, we evaluate the network performance as the number of actve sessons n the network ncrease. The parameters used durng the two experments for scenaro are lsted n Table 2. The only dfference between the two experments are the deadlnes assgned to dfferent sessons. In experment, all the sessons have a deadlne of 2 s, whch represents a hghly constraned network. Instead, n experment 2 the odd numbered sessons have a deadlne of.5 s and even numbered sessons have a deadlne of s. Experment 2 can be consdered as a scenaro where one sesson carres perodc weather montorng data through the network. These sessons are delay tolerant to an extent, hence have a longer deadlne. The second type of data can have extremely small deadlne, consstng of delay-ntolerant data lke threat detecton, ncomng mssle alert or real-tme vdeo streamng. The proposed algorthm should be able to adapt to the varyng requrements of dfferent sessons and maxmze the effectve throughput of the network. The sesson are set to start randomly any tme between start of the smulaton (t = s) and sesson duraton (t = 5 s). Ths ensures that all sessons are actve at some pont durng the smulaton but the number of actve sessons wll vary throughout the smulaton. The parameters of both the experments ( and 2) are lsted n Table 2. Examnng Fg. 2 and Fg. 3 show that DRS performs much better than ROSA n terms of relablty and effectve throughput n experment 3 and 4. In these scenaros, tradtonal backpressure based algorthm may suffer from the last packet problem. Snce DRS s formulated based on VQL whch takes nto account the deadlnes of each packet n the queue, the penalty for holdng packets n the queue grows as tme elapses elmnatng the last packet problem. 5.2 Scenaro 2: Network performance as the data rate of the sessons ncrease Relablty ROSA (Exp:3) DRS (Exp:3) ROSA (Exp:4) DRS (Exp:4) ROSA (Exp:5) DRS (Exp:5) Rate (Mbts/sec) Fg. 5: Scenaro 2: ρ vs No. of sessons. TABLE 3: Parameters of scenaro 2 Parameter Exp: 3 Exp: 4 Exp: 5 Source Rate to to to Mbts/s Mbts/s Mbts/s Sesson duraton 5 s 5 s 5 s No. of sessons Deadlne of Odd sesson.5 s 2 s each sesson Even sesson s 2 s In these set of experments, we evaluate the network performance n a scenaro where number of sessons are kept constant and the data rate njected at the source node ncreases from Mbts/s to Mbts/s. We conduct three experments n ths scenaro. Experments 3 and 4 are smlar to the experments of prevous scenaro, varyng only n deadlne as shown n Table 3. Experment 7 evaluates the performance of DRS when the packet sze s larger (25 bts). As we can see n Fg. 4 and 5, all three experments show that DRS outperforms ROSA. Though the effectve throughput of both ROSA and DRS ncreased wth hgher packet sze (dotted lnes), the relablty of ROSA decreased more compared to the decrease n DRS. Hence, the dfference n performance between DRS and ROSA ncreased when larger packets (fewer number of packets per second) were used n the network. 5.3 Scenaro 3: Examnng the effect of dfferent components of DRS Here, we try to evaluate the effect of dfferent components used durng the formulaton of DRS. In experment 6, we evaluate how Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

8 Throughput (Mbts/sec) DRS DRS(w Tr -T d ) DRS(w Tr ) ROSA Number of sessons Fg. 6: Scenaro 3: η vs No. of sessons. Throughput (Mbts/sec) TABLE 4: Parameters of scenaro 3 Parameter Exp: 6 Exp: 7 Exp: 8 Source Rate 2 Mbts/s 2 Mbts/s 2 Mbts/s Sesson duraton 5 s 5 s 5 s No. of sessons 5 8 Deadlnes 2 s 2 s 5 s No. of seeds = parameter DRS (8 Sessons) DRS (6 Sessons) Fg. 7: Scenaro 3: η vs Parameter τ. 2, 4, 6, 8,, 2, 4, 6 T r and T r T d affect the proposed algorthm ndvdually. Accordngly, we run the smulaton wth the parameter shown under experment 6 n Table 4 usng two dfferent weght defntons as shown below, L w Tr = max(t r,τ) (9) L w Tr T d = max(t r T d,τ) () Fgure 6 shows that both the DRS usng weghts seen n (9) and () perform consderably better than ROSA but does not maxmze the effectve throughput lke orgnal DRS. Ths s because orgnal DRS that uses the weght shown n () derves the benefts of both weghts ((9) and ()). Hence, ths shows why t s advantageous to have both T r and T r T d n the denomnator of the weght used to calculate VQL. Further, t s nterestng to note that n cases where t s dffcult to estmate T d, one can stll acheve moderately good performance by usng weght shown n (9). Next, we evaluate the effect of the parameter τ on the effectve throughput of the network. Equaton () uses a very small value τ to ensure correctness of weght, such that nstances wth nfnte value do not occur. Fgure 7 depcts the effectve throughput of the network as τ changes whle keepng the number of sessons and source data rates constant. The other parameters of experment 7 are depcted n Table 4. The result shows that the effectve throughput of the network usng DRS s consstently hgh for values of τ over a range between 8 to.99. Any value greater than takes away the effect of deadlnes and has a degradng effect on effectve throughput. As the value of τ moves closer to, the VQL becomes more and more equvalent to tradtonal queue length. On the other hand, choosng τ to be smaller than 8 also affects the algorthm adversely snce t bloats the VQL to an extent where the capacty component of the network utlty functon becomes nsgnfcant. Ths lower bound would depend on the characterstcs of the network, specfcally, the achevable capacty determned by the bandwdth of the transcever. Fgure 7 also shows that the range of Throughput (Mbts/sec) DRS ( Sesson) DRS (8 Sesson) R/.5 R/ R/.5 R/2 R/2.5 R/3 R/3.5 R/4 R/4.5 R/5 Fg. 8: Scenaro 3: η vs Parameter α. values for τ outsde whch the effectve throughput of the network starts declnng s same for both cases (6 and 8 sessons). Ths shows that the acceptable value for τ does not change accordng to the number of actve sessons n the network. Snce we have shown that DRS performs consstently well over a large range of values of τ, one can choose any value wthn the acceptable range dependng on the network setup. Next, we use the same parameters as n experment 7 to analyze how errors n estmaton of T d affect the network s effectve throughput. In ths case, we set the number of sessons to eght and ten. We vary α from R/.5 to R/5 as shown n Fg. 8. When α = R/.5, T d s underestmated and when α = R/5, T d s overestmated. As expected, f T d s underestmated, fewer packets are delvered to the destnaton wthn the deadlne thereby decreasng the effectve throughput. Meanwhle, overestmaton does not mpact the throughput negatvely because T d s calculated at each hop and hence error/margn decreases as the packet moves closer to the destnaton. Fnally, n experment 8, we evaluate the performance of DRS n a network havng sessons wth very long deadlne (5 s). Ths experment examnes how the network behaves n scenaros where the deadlnes of the sessons are long enough such that packets lost due to expraton of deadlne are neglgble. Ths experment evaluates whether there s any loss n throughput whle usng DRS as compared to ROSA n network that are delay tolerant (have extremely long deadlnes). As we can see from Fg. 9, the throughput of both algorthms are equal as the number of sessons n the network ncrease. Ths shows that there s no dsadvantage n usng DRS over ROSA even n scenaros where deadlnes are nsgnfcant. Throughput (Mbts/sec) ROSA (Exp:) DRS (Exp:) Number of sessons Fg. 9: Scenaro 3: η vs No. of sessons (Deadlne=5 s). Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

9 9 6 TESTBED IMPLEMENTATION 6. Challenges In the past decade, cross-layer protocols have been extensvely studed and varous solutons have been proposed [3], [32], [33], [34]. Even wth these advances n lterature, most of the solutons are evaluated only usng smulaton tools lke MATLAB, ns-3, OPNET among others. The goal of cross-layer optmzaton technques are to utlze the nformaton between dfferent layers and enable ther nteracton to jontly optmze objectves ncludng throughput, relablty, delay among others. Ths nvokes the need for an archtecture that enables these nteractons and promotes desgn and development of cross-layer optmzaton algorthms. Lack of such platforms has lead to the growng gap between number of solutons proposed n lterature versus the number of solutons that are mplemented and tested usng actual hardware. There are only lmted efforts that extend the mplementaton of the optmzaton algorthms to actual hardware and evaluate the performance on a cross-layer testbed [3], [35], [36], [37], [38], [39]. The major challenge n achevng ths mplementaton s the lack of a flexble archtecture that facltates mplementaton of the cross-layer optmzaton algorthm on mult-node networks. Ths defcency s beng recognzed by the communty and some solutons are beng proposed to acheve the requred flexblty. GNU rado s an open-source sgnal processng software that provdes great flexblty specfcally at the physcal layer of SDRs. GNU rado comprses of varous sgnal processng and dgtal communcaton blocks and s an excellent tool to control SDR. However, the majorty of the contrbuton s lmted only to the Physcal layer. There have also been efforts to relocate some of the processng functons to a Feld-programmable gate array (FPGA) [4] to mprove the delay performance. Ths makes t dffcult to ntegrate new algorthms for testng and evaluaton purpose. Some other work [4], [42] ams to provde reconfgurable MAC protocols by decomposng the overall desgn nto core fundamental blocks. In [4], the mplementaton of these fundamental blocks are splt between PC and FPGA dependng on the tme crtcal nature of the blocks. In [42], the authors mplement an abstract executon machne on a resource-constraned commodty WLAN (wreless local area network) card. Recently, software defned network (SDN) usng an Open-Flow [43] based approach has been proposed for evaluatng routng protocols. The overall concept of Open-Flow s to keep the data path on the Open-Flow swtch tself whle movng the hgh-level routng decson to a separate controller (server). The swtch performs the packet forwardng based on the flow table defned by the controller and use Open- Flow protocol to communcate wth each other. The majorty of the work on OpenFlow has been concentrated at the network layer of the protocol stack. Even wth these advancements, a major challenge to transtonng algorthms and protocols to commercal hardware s the lack of a software defned testbed wth a flexble archtecture that enables easy mplementaton of cross-layer technologes. These testbeds are essental to corroborate the results obtaned n smulatons and evaluate how to refne these algorthms to ensure a successful transton to relevant hardware. Some of the requrements of such a testbed nclude, a flexble cross-layer based protocol stack [38], modularty to ntegrate new algorthms wth ease, a framework to accommodate both centralzed and dstrbuted solutons, real-tme network performance montorng tools, and havng the ablty to run unsupervsed scrpted experments over extended perods of tme. Havng such a testbed expedtes the desgn and development process of next-generaton wreless communcaton technologes destned for a commercal SDR system. The CrOss-layer Based testbed wth Analyss Tool (COmBAT) frst ntroduced n [39] was developed to serve as a software defned testbed to enable the mplementaton of cross-layer optmzaton algorthms. In COm- BAT, a Adaptve cross-layer (AXL) communcaton framework facltates easy ntegraton of new protocols and algorthms. The desgn of AXL s dscussed n detal n the next secton. 6.2 Adaptve Cross-Layer (AXL) The overall AXL framework depcted n Fg. conssts manly of the applcaton layer, sesson manager, decson plane, control plane, regster plane and the physcal layer. Each node n the network uses the AXL framework n place of a tradtonal protocol stack and are therefore referred to as AXL nodes throughout ths paper. In mplementaton, the AXL framework conssts of Python multprocessng processes whch are ntalzed at node start up usng an AXL daemon. The daemon mports the man modules and propertes that are used n the dfferent layers/planes of the framework as requred. The propertes nclude predefned values for the network such as data tmeout duraton used by MAC protocol, node IP and MAC addresses and payload szes. However, most of the propertes are dynamc n nature and they can be reconfgured on-the-fly based on network optmzaton strateges or user nput. The processes that are started by the daemon run contnuously untl shutdown. These processes nclude the regster plane, sesson manager, control plane and the physcal layer. The decson plane s not a process but a collecton of functons that can be called by the framework when needed. Each plane/layer can share nformaton wth each other by a combnaton of three methods; drect functon calls, shared memory (regster plane) or by overhearng global events (global wth respect to the framework, not the entre network) that can be trggered by any process n the framework. These functonaltes allow for a flexble crosslayer communcaton between all network protocols. Applcaton (APP) Layer. The current AXL software package provdes data generaton APPs that a user may choose from n order to evaluate the performance of the network. The APPs can operate n packet streamng mode or packet-by-packet mode. For streamng mode, the source data s repeated untl a user specfed amount of data has been generated. The streamng mode s generally used n experments requrng a constant bt rate (CBR) source for a fxed duraton of tme. The APPs connect to the AXL daemon va a TCP/IP socket. For each APP that connects, a unque connecton object s created that manages data transfer between the APP and the AXL framework. Each packet contans the user generated and QoS parameters. Ths s where deadlne of the packet can be defned. The packet s parsed and then sent to the sesson manager for the next processng stage. Sesson Manager. In the AXL framework, the sesson manager provdes the capablty of smultaneous mult-sesson management. When a packet arrves at the sesson manager, the sesson manager creates a sesson object based on the packet parameters, whch nclude process ID that s created by the OS, source and destnaton IP, data type, any QoS parameters (such as deadlne), and the packet number generated at packet creaton. Packets that correspond to exstng sesson objects are appended to ther approprate sesson queue. Packets receve ther network headers based on the parameters n the sesson object mentoned Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

10 Control Lnk N Data lnk,2 2,2 MIMO Cable,2 Regster Plane Applcaton Layer Sngle Packet or Streamng TEXT Voce Vdeo Sesson Manager Packet management, N 2 N 3, Ethernet connecton N 4 2, Data management/ storage Decson Plane Algorthms/Decsons ROSA RFA RDA Thresh S S 2 Control Plane Medum access controller CSMA/CA TDMA FDMA N 5,, 2, Fg. : Layout of the Fve-node grd topology. earler. In mplementaton, the sesson manager s desgned as a multprocessng frst-n, frst-out (FIFO) wth a user specfed update perod. The update perod dctates the tmeout for updatng packet queues. Durng each update perod, the sesson manager stores the current queue length of each sesson n the regster plane and trggers an event flag whch ndcates that the transmtter has backlogged sesson ready for routng decsons. Decson Plane. As the name suggests, ths s the component where all the logcal decson makng and algorthm executon takes place. These algorthms pertan to routng algorthms, spectrum allocaton, automatc modulaton classfcaton and other resource allocaton decsons. The complexty of the algorthms can vary from threshold decson to teratve algorthms lke the Expectaton and Maxmzaton (EM) algorthms or solvers for convex optmzaton problems. Decson algorthms can () modfy a parameter n a protocol, () trgger swtchng among dfferent modes wthn a protocol, () enable swtchng among dfferent protocols altogether [38]. In regards to ths paper, there are currently two routng algorthms (ROSA and DRS), stored as software modules, avalable for use (dscussed n detal n Secton 7). Ths s where all the calculaton for routng algorthm takes place. Each routng module can be passed as an nstance for the decson plane to use durng runtme. The chosen and actve routng module s requested by the sesson manager to execute the algorthm when a sesson s backlogged. The results of the executed algorthm s stored n the regster plane for other layers/planes to access. Conversely, to execute the algorthm, the decson plane obtans the nformaton from the regster plane. After executng the algorthm wthn the routng module, the decson plane trggers an event flag that prompts the control plane to schedule a transmsson. It should be noted that the nteractons between the decson plane and the other layers/planes n AXL take place va the regster plane, through global events or through drect functon calls. There are some DRS specfc requrements that had to be ncluded n the expermental framework to ensure successful mplementaton. Each packet s requred to carry tme of generaton n ts header. Ths enables nodes to calculate VQL at each hop. Ths feature had to be ncluded n the expermental framework to enable the operaton of such deadlne-based cross-layer algorthm. In ths work, we ncluded the ablty to track the tme nstant of generaton and arrval of packets at each node ncludng the destnaton. In tradtonal network, the backlog does not change wth tme unless a packet s receved or transmtted by the Physcal Layer Wreless Data channel & Control channel/ Wred lnk for NEAT Fg. : Adaptve cross-layer (AXL) Framework. node. In case of DRS, the VQL changes contnuously wth tme. Ths was a crucal component necessary to mplement DRS-lke protocols that am to elmnate the last packet problem. Therefore, n ths current verson of the framework, the VQL s constantly updated at the decson plane of each node. All of these nclusons have bolstered the expermental envronment/framework to handle algorthms that are requred to handle tme-based queue lengths. Control Plane. The control plane houses the control logc used to access the wreless medum. The control plane contans the fnte state machne (FSM) used to mplement dfferent MAC protocols. The chosen MAC protocol defnes the exact set of states, events, condtons and actons requred to operate FSM. The control plane can be ntalzed to use multple dfferent MAC protocols dependng on the stuatonal awareness gathered from other layers/planes of the stack as shown n [38]. Each MAC protocol should have ts FSM mplemented n the control plane as a separate FSM ntalzaton functon. Future developers can take advantage of the baselne FSM model that s already defned n the control plane by modfyng ts states and actons as needed by the protocol. An example of a state transton dagram for a carrer sensng multple access wth collson avodance (CSMA/CA) based MAC protocol [2] s gven n Fg. 2. We would lke to pont out that CSMA/CA based MAC protocol operates only on the CCC and does not restrct concurrent feasble (n terms of BER constrants) transmsson from occurrng on the data channel. The state transton dagram descrbes the nteracton between all possble states, events and actons for the receve and transmt paths. As shown n Fg. 2, when an event Data_avalable s set, Send_RT S (request-to-send) acton s taken as the FSM goes from an IDLE state to WAIT CT S (clear-to-send) state. The next event that the FSM s lookng for s ether CT S_receved or CT S_tmeout and the FSM transtons dependng on whch event was observed. If CT S was receved and CT S_receved s set, Send_DT S (Data Transmsson reservaton) acton s taken as the FSM goes from WAIT CT S state to SEND DATA state. The rest of the state transton dagram can be nterpreted n a smlar manner. The FSM s generally n an IDLE state untl the correspondng global AXL events are flagged to nvoke a state transtonng process. These global AXL events are used n cross-layer communcaton between dfferent layers/planes and should not be confused wth the events used by the FSM tself. The events n the FSM are strctly defned by the chosen MAC protocol and dctate the state transtonng process that allows the control Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

11 Ack_receved or Ack_tmeout < Th, Clear_state Data_sent, Clear_state SEND DATA CTS_receved, Send_DTS WAIT ACK Ack_tmeout >= Th, Clear_state Data_tmeout, Clear_state CTS_tmeout, Clear_state WAIT CTS Transmt Path Ack_receved, Clear_state IDLE Data_avalable, Send_RTS RTS_receved, Send_CTS Data_tmeout >= TH, Clear_state Data_receved, Send_ACK, clear_state Receve Path Fg. 2: FSM of MAC protocol. WAIT DATA Partal_data or Data_tmeout < TH, Send_ACK, clear_state Event, Acton plane to manage medum access. The global events such as SESSION_ROUT ING that transton the FSM from an IDLE state are usually set n the decson plane after routng decson has been made. Some other examples of such global events used throughout AXL nclude SESSION_PROCESSING event whch s used by the sesson manager to ndcate that the node s busy processng a sesson and START _SENSE event that sgnals the PHY layer that t s tme to perform spectrum sensng. Therefore, we can state that the overall AXL framework follows an event drven desgn. Regster Plane. The regster plane s essentally a node database used to share nformaton across layers/planes. Although the regster plane does not perform any computaton and does not have any decson makng ablty, t s an ntegral part n the overall cross-layer desgn. The regster plane can be consdered as a central nformaton hub that can be accessed by dfferent layers/planes of the AXL framework. Data sharng among multple processes s acheved through Python manager dctonares. The global nformaton that needs to be shared among all layers s stored n a manager dctonary whch allows for only one process to read or wrte nformaton n the regster plane at a tme. The man dctonares that resde n the regster plane are a global regster dctonary (GRD), global values dctonary (GVD) and sesson backlog dctonares (SBD). AXL nodes learn about ther envronment by overhearng control packets on the CCC. Each node stores local nformaton n a node dctonary n the GRD. The node dctonary s appended to every control packet sent on the CCC. The nformaton n the node dctonary s contnuously updated as new nformaton becomes avalable. Node dctonary nformaton ncludes IP and MAC addresses, the node locaton, local nose plus nterference, sesson packet queue lengths, current routng algorthm among others. Nodes mantan a copy of ther own node dctonary, as well as a copy of ts neghbor s dctonary n the GRD. The GRD also contans nformaton lke the desgnated frequences, possble next hops and neghbors. The GVD stores the current routng decson parameters as well as the current state of the FSM. SBD has a lst of all local sessons and ther most up to date packet queue lengths. The routng algorthm s able to access ths nformaton stored n the regster plane as t optmzes the routng parameters. Other layers/planes can smlarly read or wrte nformaton n the regster plane as needed. Physcal (PHY) Layer. The PHY layer s easly separable from the rest of the framework as the goal s to allow the ntegraton of dfferent rado front ends and sgnal processng software. The PHY layer conssts of a herarchcal mplementaton where the lowest level ncludes sgnal processng software specfc lbrares such as GNU Rado and a unversal hardware drver (UHD) nterface used wth the unversal software rado perpheral (USRP) famly of products from Ettus. The PHY herarchcal module, conssts of functons that are drectly accessble by the control layer and the regster plane. Ths mplementaton allows for a very smple nterface between AXL and a PHY layer makng ths desgn SDR hardware agnostc. Fgure. depcts the current confguraton of a fve-node network that s arranged n the form of a grd topology. Each node conssts of two USRP N2s (one for control lnk and the other used as data lnk) connected to each other va MIMO cable. The SBX and CBX daughterboards are used wth the rados, whch cover frequency ranges from 4 MHz to 4.4 GHz and.2 GHz to 6 GHz respectvely. The recevers (USRP N2s), provde up to 4 MHz of nstantaneous analog bandwdth. The analog-todgtal and dgtal-to-analog converters on the motherboard use a MHz master clock and sample at MS/s and 4 MS/s respectvely. The on-board Xlnx Spartan 3A-DSP 34 FPGA performs the requred dgtal nterpolaton or decmaton to provde the requred samplng rate. The host PC nterfaces wth the USRP usng a Ggabt Ethernet (GgE) connecton as shown n Fg.. The USRPs are controlled usng GNU Rado sgnal processng modules and a UHD nterface. DRS s mplemented on the AXL framework and the results are dscussed n Secton 7. 7 TESTBED EVALUATION N N 5 2 N 4 N N 3 Fg. 3: Fve-node USRP testbed. We dscuss the expermental evaluaton of DRS usng the AXL framework mplemented on fve-nodes USRP testbed shown n Fg. 3. In these set of tests, both DRS and ROSA use the same MAC protocol wth FSM as shown n Fg. 2. The PHY layer uses Gaussan mnmum shft keyng (GMSK) mplemented usng GNU rado wth USRPs as the transcevers of the AXL node. The USRP s able to operate at frequency, bandwdth and transmt power level as specfed by algorthms (DRS or ROSA) runnng n the decson plane. Ths framework provdes the requred flexblty to mplement and evaluate the two cross-layer optmzaton algorthm (ROSA and DRS). For rapd mplementaton and feasblty analyss, the AXL framework and the routng algorthms are mplemented usng Python programmng language. The advantage of usng the Python s ease of programmng and faster development turnaround tme. The drawback s large delays ncurred by the framework [44], [45]. In future, we plan to move the mplementaton of the AXL framework to the kernel space usng C programmng language. Therefore, t s mportant to obtan a baselne for the delay experenced n the network so that we can choose approprate deadlnes for the expermentaton process. Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

12 2 End-to-End delay (mnutes) Average packet delay Average sesson delay Throughput (Kbts/s) ROSA DRS Relablty ROSA DRS Number of sessons Fg. 4: EED vs No. of Sessons Number of sesson Fg. 5: Test : η vs No. of sessons Number of sessons Fg. 6: Test : ρ vs No. of sessons. TABLE 5: Parameters to baselne end-to-end delay Parameters Baselne Test Packet sze 25 Bytes Number of packets 3 Number of sessons to 6 Source rate 2 kbts/s Source duraton 5 s Routng algorthm ROSA No. of seeds 5 Maxmum transmt power 2 dbm 7. Establshng Baselne In ths secton, we establsh a baselne for the average end-toend delay (EED) experenced by the SDR based testbed whch s the ntended platform for evaluatng DRS. To accomplsh ths, ROSA s used as the the default routng algorthm. Accordngly, we calculate the average delay experenced by packets to traverse from source to destnaton as the number of sesson ncreases. The network parameters used for determnng the EED experenced by the current confguraton of the SDR based ad-hoc network s lsted n Table. 5. Fgure 4 depcts the average EED experenced by each packet and the average EED experenced by the entre sesson as the number of sessons n the network ncrease. The EED experenced by the packet s calculated as the duraton between the packet generaton at the source node and packet arrval at the destnaton node. Smlarly, EED experenced by each sesson represents the tme between the generaton of the frst packet at the source node and the arrval of the last packet at the destnaton node. As expected, the average delay ncreases n both cases wth ncreasng number of sessons. Examnng these delay values, we can clearly see the mpact of the Python based mplementaton of the overall framework. Nevertheless, we can use ths baselne to choose approprate deadlnes for ths network that would enable us to compare the performance of two cross-layered algorthms (DRS and ROSA). Accordngly, for the next set of tests, we choose 3 mn as the smaller strngent deadlne and 5 mn as the larger deadlne. 7.2 DRS and ROSA In ths secton, the effectve throughput and relablty of DRS and ROSA are evaluated on the fve-node USRP testbed. In the current mplementaton, the MAC protocol s able to recover any loss of packet that occurs due to channel usng retransmsson. Therefore, loss of packets only takes place at the destnaton when the packet reach after the specfed deadlnes. In addton to parameters lsted n Table. 5, ths set of experments use the parameters n Table 6. TABLE 6: Parameters for testbed evaluaton Param. Test Test 2 Test 3 Deadlnes Odd sess. 3 mn Odd sess. 3 mn Even sess. 5 mn Even sess. 5 mn 6 mn Sesson start t = s t = [,2] mn t = s τ Seeds In the frst set of tests, we used multple sessons that started at the same tme and performance of the SDR based network was evaluated as the number of sessons ncreased. It s evdent from Fg. 5 that for the gven network confguratons DRS outperforms ROSA n terms of effectve throughput as soon as there are two sessons n the network. The performance trend contnues as the number of sesson ncreases achevng up to 7% mprovement over ROSA. Ths s because DRS s able to manage multple sessons adaptvely to ensure that the effectve throughput of the network s maxmzed. Smlar behavor s also observed n Fg. 6 whch compares the relablty of DRS and ROSA as the number of packet ncreases. In contrast to the frst test, the source nodes are set to choose a random tme to start the sesson n the second test. Ths would mply that dfferent sessons wll end at dfferent tmes leadng to the last packet problem n networks usng tradtonal backpressure based algorthm lke ROSA. The effectve throughput and relablty of Test 2 are depcted n Fg. 7 and Fg. 8. As expected DRS outperforms ROSA n terms of effectve throughput (up to 2% mprovement) and relablty (up to 3% mprovement) even when the the network has fnte sessons startng at dfferent tmes. Ths s due to the fact that the use of VQL prevents the network from experencng the last packet problem. VQL keeps ncreasng wth tme even f the actual queue length does not change. In the fnal test, we use a very large deadlne (6 mn). The goal was to evaluate f there s any degradaton n performance of DRS compared to ROSA when the deadlnes are large enough to be close to neglgble. As shown n Fg. 9, there s no sgnfcant loss n performance on usng DRS compared to ROSA even n scenaros where the deadlnes are long enough to be nsgnfcant. Overall, these tests follow the same trend as smulatons dscussed n Secton 5. The gans observed wth experments s much smaller than wth smulatons due to the smaller network sze. We beleve larger beneft can be attaned on a larger network deployment. Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.

13 ROSA DRS 35 ROSA DRS Throughput (Kbts/s) ROSA DRS Relablty Throughput (Kbts/s) Number of sessons Fg. 7: Test : η vs No. of sessons Number of sessons Fg. 8: Test : ρ vs No. of sessons Number of sessons Fg. 9: Test 3: η vs No. of sessons. These set of tests provde valdty to the proposed algorthm to be effectve n cogntve network that provdes the flexblty to adapt accordng to the gven scenaros. 8 CONCLUSIONS We proposed a novel dstrbuted deadlne-based jont routng and spectrum allocaton algorthm to maxmze the effectve network throughput. The DRS adapts accordng to avalable resources and s capable of handlng sessons wth dfferent deadlne requrements. DRS enables every node n the network to choose optmal sesson, next hop, frequency and transmt power wth an objectve to delver maxmum number of packets to ther ntended destnaton before the specfed deadlne. Though DRS s desgned for tactcal ad-hoc networks, ts applcaton can be extended to any wreless ad-hoc network that handles sessons wth dfferent QoS based deadlne requrements. We have performed extensve smulatons to compare the performance of DRS wth ROSA and showed up to 35 % mprovement n effectve throughput and up to 26 % mprovement n relablty of the network. Furthermore, we have successfully overcome the challenges of mplementng the proposed algorthm on a cross-layer framework based software defned testbed. The experments conducted on the testbed showed DRS outperformng ROSA n terms of effectve throughput (up to 7%) and relablty (up to 3%). Ths helped us accomplsh the secondary objectve of ths paper whch was to valdate the flexblty and further advance the COmBAT framework by mplementng novel cross-layer DRS algorthm. REFERENCES [] J. Jagannath, T. Meloda, and A. 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Jthn Jagannath s a Ph.D. canddate n the Department of Electrcal and Computer Engneerng at Northeastern Unversty. He s currently conductng research n the Wreless Networks and Embedded Systems Laboratory under the gudance of Prof. Tommaso Meloda. Mr. Jagannath receved hs Master of Scence degree n Electrcal Engneerng from Unversty at Buffalo, The State Unversty of New York n 23. He has experence developng SDR based prototypes whch ncludes desgnng, mplementng and testng dfferent communcaton protocols and optmzed algorthms on hardware for wreless sensor networks. Mr. Jagannath s also a Sr. Assocate Scentst/Engneer at ANDRO Computatonal Solutons n Rome, NY. He s currently PI of a DHS SBIR effort that ams to develop a novel nterference detecton devce for frst responders. He has also been the techncal lead n multple SBIR/STTR efforts ncludng cross-layer networkng technology, mult-sensor automatc modulaton classfcaton, cyber securty, and compressve sensng. Hs research nterest ncludes software defned rado, vsble lght communcaton, cogntve networks, underwater sensor network and automatc modulaton classfcaton. Sean Furman receved hs BSECE degree n 27 from the State Unversty of New York Polytechnc Insttute n Utca, New York. In the academc year, he worked on VANETTA (Vehcular Ad- Hoc Network for Transportaton Automaton) as hs senor project. The project nvolved desgn and mplementaton of vehcular platoonng protocols on scaled remote control cars. He joned ANDRO Computatonal Solutons n 25 and has contrbuted to projects nvolvng software defned rado networks. Hs research nterests nclude wreless communcaton, automatc control systems, and computng. Tommaso Meloda receved the Ph.D. degree n electrcal and computer engneerng from the Georga Insttute of Technology, Atlanta, GA, USA, n 27. He s an Assocate Professor wth the Department of Electrcal and Computer Engneerng, Northeastern Unversty, Boston, MA, USA. Hs research has been supported by the Natonal Scence Foundaton, Ar Force Research Laboratory, and the Offce of Naval Research, among others. Hs current research nterests are n modelng, optmzaton, and expermental evaluaton of networked communcaton systems, wth applcatons to ultrasonc ntra-body networks, cogntve and cooperatve networks, multmeda sensor networks, and underwater networks. Prof. Meloda was a recpent of the Natonal Scence Foundaton CAREER Award and coauthored a paper that was recognzed as the ISI Fast Breakng Paper n the feld of Computer Scence for February 29. He was the Techncal Program Commttee Vce Char for IEEE GLOBECOM 23 and the Techncal Program Commttee Vce Char for Informaton Systems for IEEE INFOCOM 23. He serves on the edtoral boards of the IEEE TRANSACTIONS ON MO- BILE COMPUTING, the IEEE TRANSACTIONS ON WIRELESS COMMUNICA- TIONS, the IEEE TRANSACTIONS ON MULTIMEDIA, and Computer Networks Andrew Drozd s the Presdent and Chef Scentst of ANDRO Computatonal Solutons n Rome, NY. Hs felds of expertse nclude wreless communcaton, dynamc spectrum access, computatonal electromagnetcs (CEM), development of electromagnetc envronmental effects (E3) tools for coste analyss, spectrum explotaton and nterference rejecton technologes. Mr. Drozd was Presdent of the IEEE EMC Socety (26-27), a past member of the EMC-S Board of Drectors (998-28), and s an IEEE Fellow. He was also on the Board of Drectors of the Appled Computatonal Electromagnetcs Socety (ACES) (24-2). He s an NARTE certfed EMC Engneer and has authored over 6 techncal papers, reports, and journal artcles. Copyrght (c) 28 IEEE. Personal use s permtted. For any other purposes, permsson must be obtaned from the IEEE by emalng pubs-permssons@eee.org.