Design and Experimental Evaluation of a Cross-Layer ...

Design and Experimental Evaluation of aCross-Layer Deadline-Based Joint Routing

and Spectrum Allocation AlgorithmJithin Jagannath , Sean Furman, Tommaso Melodia , and Andrew Drozd, Fellow, IEEE

Abstract—The design and implementation of a novel distributed deadline-based routing and spectrum allocation algorithm for tactical

ad-hoc networks is reported in this article. Different traffic classes including text, voice, surveillance video, and threat alert among

others need to be handled by these networks. Each of these traffic classes have different quality of service (QoS) based deadline

requirements. Additionally, these networks are characterized by dynamic channel and traffic conditions that vary with time and location.

Even under these conditions, it is critical to receive packets before the deadline expires to make rapid decisions in the battlefield.

Therefore, a tactical ad-hoc network should be able to adapt to these requirements and maximize the number of packets delivered to

the destination within the specified deadline. A distributed deadline-based routing and spectrum allocation algorithm is designed to

maximize the utilization of the available resources and ensure delivery of packets within the deadline constraints. To this end, a

weighted virtual queue (VQ) that is used to construct the network utility function is defined. Accordingly, the optimal session, next hop,

transmit power, and frequency is determined by the distributed algorithm to ensure efficient utilization of the available resources.

Hence, maximizing the delivery of packets to the intended destination within the specified deadline. The 49 node simulation shows up to

35 percent improvement in effective throughput and 26 percent improvement in reliability as compared to joint ROuting and Spectrum

Allocation algorithm (ROSA), which does not adapt according to the deadline requirements of the data flowing through the network. As

a secondary objective, this work advances the state of the art of the experimental cross-layer framework to address the challenges

involved in having such cross-layer algorithms implemented on a testbed. The required flexibility to change the transmission

parameters on-the-fly is provided by the proposed framework. The network is designed to enable the data exchange between

neighbors using custom designed control packets (which might be different for different algorithms) since this information is critical for

nodes to perform optimization. Cross-layer optimization is achieved by means of data management and control entities that enable

information exchange between layers. The practicality of the proposed solution was proven by having the novel algorithm implemented

on a five-node software defined radio testbed which leverages the proposed cross-layer framework. In contrast to ROSA, the proposed

algorithm demonstrated up to 17 percent improvement in terms of throughput and reliability. The performance improvement achieved is

expected to increase on a larger network deployment.

Index Terms—Deadline-based routing, cross-layer optimization, cognitive radio, resource allocation, software defined radio, USRP testbed

Ç

1 INTRODUCTION

IN a tactical ad-hoc network, there exists a constant tensionbetween available resources and the required quality of ser-

vice (QoS) performance. Nodes in the network have to dealwith severe interference, spectrum crunch, adversarial jam-ming and changing network topologies. Additionally, a typi-cal tactical network as depicted in Fig. 1 is required to handlevarious traffic classes including regular sampling data, voice,

surveillance video, threat alert, among others. Each of thesetraffic classes have substantially different QoS based deadlinerequirements. For example, periodic surveillance data mighthave looser deadline constraintswhen compared to a video orthreat alert message. In these delay-intolerant networks, onlypackets that arrive at the destination within the specifieddeadline are viable and contribute to the overall networkthroughput. In these scenarios, it becomes important to exam-ine the interaction between spectrum management, routingand session management to develop cross-layer control algo-rithms capable of maximizing the effective throughput of thenetwork. In this paper, we consider only the packets thatarrive at the destination within the specified deadline in thecomputation of effective throughput.

Cognitive radio technology along with various dynamicspectrum access (DSA) techniques have been proposed toimprove the spectrum utilization of the network by enablingopportunistic access of free spectrum chunks. In previouswork [2], an optimization algorithm (ROSA) was proposed tojointly select route and spectrum such that overall networkthroughput is maximized. This algorithm combines the ideaof backpressure algorithm [3] with channel dependent

� J. Jagannath is with ANDRO Advanced Applied Technology, ANDROComputational Solutions, LLC, Rome, NY 13440, and the Department ofElectrical and Computer Engineering, Northeastern University, Boston,MA 02115. E-mail: [email protected].

� S. Furman and A. Drozd are with ANDRO Advanced Applied Technol-ogy, ANDRO Computational Solutions, LLC, Rome, NY 13440.E-mail: {sfurman, adrozd}@androcs.com.

� T. Melodia is with the Department of Electrical and Computer Engineer-ing, Northeastern University, Boston, MA 02115.E-mail: [email protected].

Manuscript received 19 Dec. 2017; revised 16 May 2018; accepted 14 Aug.2018. Date of publication 20 Aug. 2018; date of current version 28 June 2019.(Corresponding author: Jithin Jagannath.)For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference the Digital Object Identifier below.Digital Object Identifier no. 10.1109/TMC.2018.2866093

1774 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 18, NO. 8, AUGUST 2019

1536-1233� 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See ht _tp://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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opportunistic routing. Simulations show that ROSA outper-forms the traditional algorithms that use either dynamic spec-trum allocation with fixed route or dynamic routing withfixed spectrum allocation. In this work, we substantiallyextend [2] to examine the network performance in terms ofeffective throughput and reliability for multiple sessions withdifferent deadline constraints. This enables such routing andresource allocation algorithms to handle various QoS basedtraffic classes efficiently. Accordingly, we develop a distrib-uted deadline-based optimization algorithm for tactical ad-hoc networks. Some of the challenges in designing a deadline-based algorithm are as follows:

� Each node has to carefully manage multiple sessionsto meet the deadline requirements. For example, ses-sions with longer backlogs and larger deadlines canbe held back while accelerating shorter backlogged-smaller deadline sessions.

� Adopting an effective resource allocation procedurethat would negotiate the access of medium andchoose optimal transmission parameters. In a largenetwork, the spectrum occupancy varies based onlocation and time, thus nodes may have to use differ-ent parts of the spectrum in order to route a sessionin the most effective manner.

� Choosing appropriate routes to meet the needs ofeach session belonging to different traffic classes.

� The network should be able to adapt to brokenroutes or failed nodes by choosing alternate paths.

� The design should be scalable, reduce communica-tion overhead and yet enable the network to adjustdynamically to the available resources. Therefore, itis critical to design a distributed approach that is fea-sible on a practical network.

Therefore, the overall objective of this work is to designand evaluate a distributed algorithm that utilizes the avail-able resources to determine optimal route, session and spec-trum to deliver maximum number of packets to theirintended destination within the specified deadline. Theweighted virtual queue (VQ) used in cross-layer optimiza-tion ensures proper management of the sessions. The virtualqueue length (VQL) takes into account deadlines associatedwith each packet. The joint routing and spectrum allocationaspects of the algorithm provides optimal resource

allocation and enables opportunistic routing. The distrib-uted nature of the proposed algorithm along with forwardprogress based routing helps the network to recover frombroken routes or failed nodes. These features are critical inany delay-intolerant applications and will be especially use-ful in tactical ad-hoc networks where the delayed deliveryof critical information in a multihop network can be fatal.

2 RELATED WORK

Dynamic spectrum allocation has been widely investigatedwith the objective to maximize spectrum utilization and ismainly divided into centralized [4], [5] and distributed [2],[6] approaches. While spectrum allocation techniques aredesigned to improve spectrum utility based QoS [7], [8],[9], queue length based backpressure (Q-BP) schedulingalgorithm was first proposed in [3] and was shown to bethroughput optimal in terms of achieving network stabilityunder any feasible load. It is well known that the Q-BP algo-rithm suffers from high computational complexity and thelast packet problem. To reduce the computational delay forpractical implementation, a greedy maximum scheduling(GMS) algorithm is studied in [10], [11], [12]. This algorithmfirst chooses the link l with maximum weight from the set ofall links S and eliminates links that interfere with l from theset S. Next, it again picks the link with maximum weightamong the remaining links of set S and eliminates the linkcausing interference to it. This process is repeated until alllinks have been considered. The trade-off here is the reducednetwork capacity. In [13], the authors solve a centralized net-work throughput maximization problem that uses the back-pressure algorithm. The study also implements the solutionon hardware to perform evaluation. Even though the networkachieves throughput improvement, the network may beprone to last packet problem which is a crucial hindrance fortactical networks. The last packet problem of Q-BP algorithmarises because of the assumption that flows have an infiniteamount of data packets being injected into the network.Instead, in practical networks the flows may be finite withsome flows terminating and new flows emerging. When afinite flow has the last packet in the queue, it may be stagnantfor an extended period of time because of the presence ofother queueswith larger backlog. This is referred to as the lastpacket problem. It has been shown that in these cases, queuelength based schemes may not be throughput optimal [14].Accordingly, there has been considerable work on delay-based scheduling [15], [16], [17], [18], [19], [20], [21] toimprove the delay performance of the network and eliminatethe last packet problem.

In [15], the authors use a shadow queuing architecture sothat each node maintains only one queue per neighbor (irre-spective of sessions) to reduce the complexity of the queuingstructure and improve the delay performance at the cost ofthroughput. Each node still has to maintain a separateshadow queue (a counter) for every flow going through thenode. The backpressure algorithm is executed using theshadow queue counters and these counters are updatedaccording to the optimal number of shadowpackets chosen tobe transmitted over each link. The key point here is that thenumber of shadow packets is like a permit to transmit on thegiven link from the real queue but not associated with theflow of the shadow packet itself. The packet injection rate ofthe shadow queue is kept slightly higher than the actualpacket injection rate. The rates are designed as follows: if the

Fig. 1. Tactical ad-hoc network.

JAGANNATH ETAL.: DESIGN AND EXPERIMENTAL EVALUATION OFA CROSS-LAYER DEADLINE-BASED JOINT ROUTING AND SPECTRUM... 1775

packet injection rate of the shadow queue is xtðtÞ, the rate ofthe real queue is given by bxtðtÞ, where b is a positive realnumber smaller than one. Therefore, if the number of realpackets in the queue is less than the number of shadow pack-ets to be transmitted, all the real packets in the correspondingqueue are transmitted. The authors show that the real queuelength decreases uniformly at every node as the value of bdecreases, thus leading to lower delays by Little’s law. Thisdecrease in delay is accompanied by reduced throughput per-formance. Maintaining a single queue per neighbor is onlybeneficial in scenarios where the number of flows through anode is much greater than the number of neighbors. Authorspropose a self-regulated MaxWeight scheduling algorithm in[22], where each node estimates the aggregated link rate.They prove that the self-regulated MaxWeight scheduling isthroughput-optimal (i.e., stabilize any traffic that can be stabi-lized by any other algorithm)when the traffic flows are associ-ated with fixed routes and the packet arrivals follow somestatistical property. Both [15] and [22] are designed for fixedroute scenarios, thus lacking the improvement that could beachieved by opportunistic routing. In [16], the authors pro-pose a delay-limiting algorithm to control the burstiness anddelays. They adapt the upper limit for the physical queues toensure an upper per-hop delay limit at the expense ofthroughput. To ensure that nodes in the network remain oper-ational, a lower bound has to be set on the upper queue limit.If the traffic reduces to a point such that lower bound comesinto play, the delay-limiting approach becomes ineffective.There is also a trade-off between delay and the degree of mul-tipath and opportunism. As the traffic is spread spatially toutilize multiple routes, the lower bound on the queue mayagain render the delay control ineffective.

A cross-layer design is proposed in [17] using VQ struc-tures to provide finite buffer size or worst-case delay perfor-mance. In [18], authors design a delay-aware joint flowcontrol, routing, and scheduling algorithm for multihop net-work to maximize network utilization. However, due to their([18], [17]) centralized nature and high complexity they arenot well suited for practical distributed implementation [23].In [19], a throughput optimal scheduling algorithm is pro-posed using largest weighted delay first algorithm. The ideais to serve the queue j for which gjWjðtÞrjðtÞ is maximal,where WjðjÞ is the weighted delay and rjðtÞ the achievablecapacity for link j. Although this algorithm is an easy anddistributedway to achieve throughput optimality, this formu-lation does not take into account the dynamic routing possi-bilities or queuing dynamics of multihop traffic. Since [19]fails to capture the queuing dynamics of multihop traffic, anewdelaymetric is defined in [20] to establish a linear relationbetween queue length and delay. The authors also propose agreedy algorithm that is similar to GSM discussed earlier, butuses delay differential rather than queue length. Simulationsshow that the average queue length of the network is similarin Q-BP and delay-based backpressure (D-BP) but the tail ofthe delay distribution is much longer for Q-BP. This impliesthat some queues are stagnant over extended periods of timein Q-BP whereas D-BP reduces this problem. Unlike the pro-posed algorithm, D-BP is designed for fixed routes and doesnot consider dynamic routing. In [21], a delay-driven Max-Weight scheduler is presented that gets around the last packetproblem and addresses instability of the queue length basedalgorithms caused by rate variations. However, it has beenshown in [24], [25] that there are other factors that contributeto the inefficiency of the back-pressure algorithm including,

inefficient spatial reuse, failure to opportunistically exploitbetter link rates, underutilized link capacity and inefficientrouting because of insufficient path information. Deadline-based routing has been recently studied in [26], [27] and[28]. In [26], an utility-based algorithm is proposed for cyclicmobile social networks under the assumption that nodes fol-low cyclic mobility, periodically encountering each otherwith high probability. It is difficult to extend [26] to tacticalad-hoc networks without apriori knowledge of the encounterprobability. To increase the packet delivery ratio, [27] adoptsan epidemic based routing algorithm and [28] proposes acapacity-constrained routing algorithm that decides whichpackets have to be replicated. The replication strategies pro-posed in [27] and [28] to improve packet delivery ratio mayadversely affect the achievable throughput.

The major contributions of this work can be outlined asfollows,

� We propose a novel deadline-based joint routing andspectrum allocation algorithm for tactical ad-hoc net-works to meet the deadline requirements of multiplesessions. To the best of our knowledge, this is thefirst work that combines the interaction of opportu-nistic routing, spectrum allocation and deadlineconstraints to maximize the effective throughput oftactical ad-hoc networks.

� The proposed algorithm is able to adapt to the needsof a dynamic network by managing multiple sessionswith variable QoS. This is accomplished by makingan optimal choice about the session, route, spectrumand power allocation used tomaximize the utilizationof available resources.

� A distributed approach is formulated to enable theimplementation of the proposed algorithm in a scal-able manner.

� Performance of the proposed algorithm is exten-sively evaluated under various simulated scenarios.

� Another major contribution of this article is thetestbed implementation. To prove the practicality ofthe proposed algorithm, we successfully implementthe deadline-based joint routing and spectrum allo-cation algorithm on a software defined testbed.

� A secondary objective of this paper is to furtheradvance the cross-layer framework and show hownovel cross-layer algorithms can be implemented ontestbed using the experimental framework.

The rest of the paper is organized as follows. In Section 3,we describe the system model. We discuss the design ofdeadline-based routing algorithm in Section 4. Next inSection 5, we simulate a 49 node ad-hoc network, to evalu-ate the performance of the proposed algorithm. The designand configuration of the testbed is described in Section 6.The experimental evaluation of the proposed algorithm ona SDR based testbed is discussed in Section 7. Finally, con-clusions are discussed in Section 8. Table 1 summarizesnotations used in the paper.

3 SYSTEM MODEL

Consider a multihop tactical ad-hoc network with M pri-mary users and N secondary users modeled as a directedconnectivity graph GðU; EÞ, where U ¼ fu0; u1; . . . ; uNþMg isa finite set of wireless transceiver (nodes), and ði; jÞ 2 E rep-resents unidirectional wireless link from node ui to node uj(for simplicity, we also refer to them as node i and node j).


We assume G is link symmetric, i.e., if ði; jÞ 2 E, thenðj; iÞ 2 E. The nodes from the subset PU ¼ fu1; . . . ; uMg aredesignated as primary users, and nodes from the subsetSU ¼ fuMþ1; . . . ; uMþNg are designated as secondary users.The secondary network is composed of cognitive nodescapable of adapting to the current spectrum usage. The pri-mary user holds the license for the specific spectrum bandsand have full access to the spectrum without interferencefrom any other users. In relevant scenarios, the primaryuser can also be a non-cooperative node (the adversary).Since the entire spectrum is not always used by primaryusers, the aim of the secondary user in a cooperative sce-nario is to maximize spectrum utility while ensuring nointerference to primary users. Thus, a secondary user has touse the spectrum holes [2] to maximize the spectrum usage.The secondary network will also allocate resources suchthat it maximizes the number of packets delivered at thedestination within their respective deadline. Only packetsthat reach the destination within the specified deadline con-tribute towards the effective throughput computation. Theset of neighbors for node i is given byNBi , fj : ði; jÞ 2 Eg.

The secondary users are equipped with cognitive radioscapable of scanning the available spectrum to reconfiguretheir transceivers on-the-fly. The entire available spectrum isgiven by BW . The cognitive transceiver is capable of tuningto a set of contiguous frequency bands ½f; f þ DB�, where DBis the bandwidth of the cognitive radio and DB < BW . Wealso assume that the transmit power can be varied to exploitany available spectrum opportunity. We define spectrumopportunity as the limited availability of spectrum thatmight currently be used by nodes (primary or secondaryusers) but can be further exploited by adjusting the transmitpower such that it does not violate the bit error rate (BER)constraint of the existing transmission. This work is intendedfor any general physical layer but we assume that multipletransmissions can occur concurrently on the same frequencyband, e.g., with different spreading codes.

The total spectrum, BW is divided into separate channels,a common control channel (CCC) and a data channel. All sec-ondary nodes use CCC to share local information for spec-trum negotiation and data channel is used exclusively fordata communication. The data channel is divided into dis-crete set of carriers ffmin; fminþ1; . . . ; fmin�1; fmaxg, each ofbandwidth b and identified by a unique discrete index. Thecognitive radio of the secondary user can tune into a consecu-tive set of carriers from ½fmin; fmax�. Let the traffic in the net-work consist of multiple sessions characterized by the source-destination pair and the application generating the session.The arrival rates of each session si 2 Si at node i is given by�si ðtÞ, and characterized by vector of arrival ratesL.

4 DEADLINE-BASED ROUTING AND SPECTRUM

ALLOCATION

In this section, we discuss the deadline-based distributedrouting and spectrum allocation algorithm in detail. Here,we will define the utility function, that has to be maximizedto achieve the goal of the proposed solution.

4.1 Network Utility FunctionConsider that the tactical ad-hoc network is assumed tooperate over a time slotted channel. The spectrum utilityfunction is calculated by node i for every time slot t whennode i is backlogged and not already transmitting or

TABLE 1Definition of Notations

Notations Definition

M Number of primary usersN Number of secondary usersui Node i (also refered to as i)PU Subset of primary usersSU Subset of secondary usersGðU; EÞ Directed connectivity graph, where U is

a finite set of nodes, and E represents setof unidirectional wireless links

NBi Set of neighbors for node iBW Total available spectrum½f; f þ DB� Set of contiguous frequency bands, where

DB is the bandwidth of the cognitive radiob Bandwidth of each subbandfmin Minimum frequency that node can tune tofmax Maximum frequency that node can tune tosi 2 Si Set of session in node it Time slot�si ðtÞ Arrival rate of session si at t

L Set of arrival ratesqsi ðtÞ A packet in session si at t

Qsi ðtÞ Virtual queue length of session si at t

Lðqsi Þ Length of the packet in bits

Trðqsi Þ Remaining life time of the packet

Dðqsi Þ Deadlines of the packet

Tdðqsi Þ Time to the destination of packet as estimatedat node i.

wqsiðL; Td; TrÞ Weight of the packet

R Communication range of the nodeTh Average time spent by packet at each hopa Delay estimation factoraðqsi ; j; tÞ a ¼ 1 represent packet qsi 2 Qs

i ðtÞ is transmittedto node j at time slot t, and a ¼ 0 otherwise.

A Vector or routing profilersijðtÞ Transmission rate for link ði; jÞ 2 ER Vector of transmission ratesP Selected power levels in each subbandF Selected frequency subbandsUij Utility function for link ði; jÞ 2 ECij Achievable channel capacity for link ði; jÞ 2 EPiðfÞ Transmit power of node i on the frequency fPLijðfÞ Transmission loss due to path loss from i to jG Processing gainNjðfÞ Receiver noise on frequency f

IjðfÞ Interference experienced by the receivingnode j

BERPU BER guarantees required for primary userBERSU BER guarantees required for secondary user

SINRthPU SINR thresholds required to achieve the

target BER for the primary userSINRth

SU SINR thresholds required to achieve thetarget BER for the secondary user

OijðfÞ Spectrum Opportunity between i and j

Pmaxi ðfÞ Maximum power that can be used by the

secondary node i on the frequency fPmini ðfÞ Minimum power required to reach the

required SINRthSU at the intended secondary

receiverCW Contention windowh Effective throughputr Reliability


receiving packets. Each node i maintains a separate VQ foreach session. We define Qs

i ðtÞ as the VQL formed by packetsof session s in node i at time slot t. Unlike traditional queuelength, the VQL gets inflated as time passes to penalize thenode for holding packets whose deadline is approaching.More details about the design of VQL is discussed below.For each packet qsi 2 Qs

i ðtÞ, that belongs to session s andstored at node i, a set of fields are defined, including,

� Lðqsi Þ is the length of the packet in bits,� Trðqsi Þ is the remaining life time of the packet, which

is based on the deadline Dðqsi Þ assigned to the packetat the source node,

� Tdðqsi Þ is the time to the destination as estimated atnode i.

Based on these parameters, a weight wqsiLðqsi Þ; Tdðqsi Þ;�

Trðqsi Þ� can be defined for each packet qsi 2 Qsi ðtÞ as follows,

wqsiðLðqsi Þ; Tdðqsi Þ; Trðqsi ÞÞ ¼

Lðqsi Þmax Trðqsi Þ; t

� �max Trðqsi Þ � Tdðqsi Þ; t

� � :

(1)

As we can see in (1) the weight wqsiassigned to each packet

is directly proportional to L (for simplicity, we removed qsifrom these notations) and inversely proportional to Tr and Td.The t in (1) is a very small value used to avoid negative andinfinite weights. The parameter Tr helps to get rid of the well-known last packet problem, since Tr will increase the VQL astime elapses. This can be interpreted as the holding penaltyimposed for packets being stagnant in the queue for extendedperiod of time. Since Tr is dependent on the assigned dead-line, it helps the nodes to manage different sessions by push-ing critical packets faster even if the actual queue length iscomparatively smaller. Considering just the deadlines alonewill not help in cases where there are two sessions with thesame deadline but one is farther away from the destinationthan the other. In such cases, Td will ensure that the sessionfarther away from the destinationmoves through the networkat a faster rate compared to similar sessions closer to the desti-nation. Therefore, Td can be considered as a variable thateither amplifies or diminishes the effect of Tr depending onthe time required to reach the destination. Td also encouragespackets to take shorter routes if all other factors like queuelength and spectrum are the same for two different routes.The rationale will become more evident when we discuss thenetwork utility function used for the proposed algorithm.

Among these three parameters, the exact value of Td isnot available at each node and has to be estimated at eachhop. For a centralized network, assuming global knowledgeof the network, Td can be estimated using average queuingdelays, transmission rate, propagation delays and using theknowledge of average delays experienced previously bypackets with the same destination. Estimating Td becomesfurther challenging in a distributed network where eachnode is required to make decisions without global knowl-edge of the network. One solution is to estimate Td by usingqueuing delay experienced by the session in the node itself.We use this information and slightly over estimate the delayby assuming that the packet has to route through more thanone node within its transmission range itself. Underestimat-ing Td would increase the risk of packets not reaching thedestination within the specified deadline. Therefore, in ourdesign Td is slightly over estimated according to the charac-teristics of the network. Since Td is updated at every hop,

the estimation error/margin decreases as the packet movescloser to the destination. This method does not lead to anyerror propagation since the value is updated at each hop. Asimple way to estimate Td is based on distance to destina-tion ðdÞ, communication range (estimated based of maxi-mum transmit power) of the nodes deployed ðRÞ andaverage time spent by the packet during each hop ðThÞ (esti-mated based processing delay, queuing delay, transmissiondelay and propagation delay). The idea is to assume that ahop is required every half range of a node and is given bya ¼ R=2. Accordingly, we get an estimate of how muchtime is required to reach the destination as,

Td ¼ d Th

a¼ 2d Th

R: (2)

The value of a can be varied according to the density of thenetwork. Now from the definition of weights, it can be seenthat higher value is assigned to packets with more bits totransmit, lower Tr and which are farther away from the des-tination. Accordingly, we define a VQL of a session s innode i as follows,

Qsi ðtÞ ¼

X

qsi2Qs

iðtÞwqs

iðL; Td; TrÞ: (3)

Now, let aðqsi ; j; tÞ ¼ 1 represent a packet qsi 2 Qsi ðtÞ is

transmitted to node j at time slot t, and aðqsi ; j; tÞ ¼ 0 other-wise. The routing profile of node i is defined as asi ðtÞ ¼½aðqsi ; j; tÞ�j22SU=iqs

i2Qs

iðtÞ, and A represents the vector of routing pro-

file asi ðtÞ of all nodes in the network at instant t. We alsodefine the transmission rate on link ði; jÞ during time slot tas rsijðtÞ, and R as the vector of rates. Then, the VQL of nodei can be updated as,

Qsi ðtþ 1Þ ¼

�Qs

i ðtÞ þX

j2N =i

X

qsj2Qs

jðtÞwqs

jðL; Td; TrÞaðqsj ; i; tÞ

�X

j2N =i

X

qsi2Qs

iðtÞwqs

iðL; Td; TrÞaðqsi ; j; tÞ

�þ:

(4)

Accordingly, the network link utility function Uij for linkði; jÞ 2 E for session s can be defined as,

Uijðasi ðtÞÞ ¼ Cij½Qsi ðtÞ �Qs

jðtÞ�þ; (5)

where ½Qsi ðtÞ �Qs

jðtÞ�þ represents the differential VQLand Cij is the achievable channel capacity of the linkði; jÞ 2 E at time slot t for a selected frequency ðfÞ and thetransmission strategy can be given by,

Cijðf; PiðfÞÞ ,X

f2½fi;fiþDfi�b:log 2 1þ PiðfÞPLijðfÞG

NjðfÞ þ IjðfÞ� �

: (6)

In the above equation, PiðfÞ represents the transmitpower of node i on the frequency f , PLijðfÞ is defined as thetransmission loss due to path loss (can be computed basedon the chosen path loss model) from i to j, G represents theprocessing gain, which would be the length of the spreadingcode when applicable, NjðfÞ is the receiver noise on fre-quency f and IjðfÞ is the interference experienced by thereceiving node j. We assume a quasi-static channel, i.e.,channel conditions remain constant for the duration inbetween sensing and transmission of a packet. This can be


achieved with an efficient sensing mechanism and havingdedicated receiver that perform sensing in parallel to the reg-ular transceiver. As we can see in (6), the achievable capacityprimarily depends on selected frequency F ¼ ½fi; fiþDfi �,power allocation P ¼ ½PiðfÞ�, 8i 2 SU; 8f and the schedulingpolicy. Therefore, the overall notion of this network utilityfunction is to couple the constraints of packet deadline to thetraditional queue length used in the differential backlogalgorithm. This is then weighted by the dynamic spectrumavailability information to provide a joint routing and spec-trum allocation decision. Moreover, algorithms like ROSA[29], [30] does not handle QoS requirements of different traf-fic classes. Since this is essential for improving the reliabilityof tactical ad-hoc networks, the redefining of the the queuelength to form the newVQL iswhere the proposed algorithmextends [2]. We will discuss and evaluate the benefits of thisin detail in Section 5.

4.2 Distributed Deadline-Based Routing andSpectrum Allocation Algorithm

The overall optimization problem is to maximize the utilityfunction discussed in (5). Let us denote the BER guaranteesrequired for primary and secondary users as BERPU andBERSU respectively. Accordingly, we can represent therequired signal-to-interference-plus-noise power ratio (SINR)thresholds required to achieve the targetBER for the second-ary and primary user as SINRth

PU and SINRthPU respectively.

Thus, the global objective of the optimization problem is tofind the optimal global vectors R, F and P that will maximizethe sum of the network utilities, under the power and BERconstraints. The formulation of the optimization problem isprovided in Appendix. Since solving the overall optimizationproblem needs global knowledge of feasible rates and theworst-case complexity of this centralized problem is exponen-tial, it necessitates the need to design a distributed algorithmthat is scalable for practical implementation.

The resource allocation of the proposed algorithmconsists of spectrum and power allocation. A spectrumopportunity for link ði; jÞ is a set of contiguous subbandswhere OijðfÞ � 0, when OijðfÞ is given by,

OijðfÞ ¼ Pmaxi ðfÞ � Pmin

i ðfÞ; (7)

where Pmaxi ðfÞ is defined as the maximum power that can

be used by the secondary node i on the frequency f suchthat it satisfies the BER constraints of primary and second-ary users. It is important to note that Pmax

i ðfÞ will be con-strained by the maximum transmit power of the wirelessradio used in the network. On the other hand, Pmin

i ðfÞdenotes the minimum power required to reach the requiredSINRth

SU at the intended secondary receiver. In other words,Pmini ðfÞ and Pmax

i ðfÞ provide the lower bound and upperbound of transmit power respectively for node i on fre-quency f . The Pmin

i ðfÞ and Pmaxi ðfÞ values are determined

by a node i by gathering spectrum and resource allocationinformation from its neighbors. This information is gatheredusing collaborative virtual sensing (CVS) using the controlpackets in the network. We do not include details aboutresource allocation, CVS and the medium access control(MAC) protocol employed as it is similar to that in ROSA.We urge readers to refer to [2] for further details.

Accordingly, we propose the distributed Deadline-basedcross-layer Routing and Spectrum allocation algorithm

(DRS) to maximize the throughput of a tactical ad-hoc net-work. In the distributed network, each node makes an adap-tive decision to choose optimal session, next hop, powerallocation and spectrum to use during the next time slotbased on the information gathered from the neighbors usingCVS. This decision will be different from traditional ROSA[2] because the network utility defined here is a function ofVQL and not the actual queue lengths. Once a backloggednode senses an idle CCC, it performs the Algorithm 1 toobtain the optimal resource allocation decision:

Algorithm 1. Deadline-based Resource Allocation

1: t ¼ 1, D ¼ 1, Cij ¼ 0, U�ij ¼ 0

2: for si 2 Si do " All Active sessions3: for j 2 u1; u2; :::uk do " Next feasible hops4: for fi 2 ½fmin; . . . ; fmax�Dfi � do5: Calculate Pt

i ðfÞ similar to [2]6: Calculate Ctemp as in (6)7: if Ctemp > Cij then8: Cij ¼ Ctemp

9: [f�i;j;P

�i;j] = [fi;P

ti]

10: end if11: end for12: Us

ij ¼ Cij � ½Qsii �Q

sij �

13: if Usij > U�

ij then

14: U�ij ¼ Us

ij

15: [fopti ;P

opti ; sopti ; jopt] = [f�

i;j;P�i;j; si; j]

16: end if17: end for18: end for19: Return [fopt

i ;Popti ; sopti ; jopt]

(1) The proposed algorithm assumes that location of theintended destination node is known to the sourcenode. This information is carried by the packetthrough the intermediate nodes. Each node selects afeasible set of next hops for each backlogged sessionj 2 ðus

1; us2; . . . ; u

skÞ, which are neighbors with positive

advance towards the intended destination.(2) The maximum capacity for each node is calculated

by considering all possible spectrum opportunities.The maximum capacity of each feasible neighbor isused along with the corresponding differential VQLto determine the network utility Us

ij. The optimaldecision is taken such that,

ðsopt; joptÞ ¼ argmaxðUsijÞ: (8)

As seen earlier, the network utility function com-prises of differential VQL and achievable capacity.The differential VQL is a function of deadline andestimation of Td. Thus, the sessions that have smallerdeadlines or are further away from the intended des-tination will be scheduled more often if the availablespectrum for all sessions are comparable. The adap-tive routing will also provide most traffic to VQs thatare lightly backlogged.

(3) The optimal frequency and power allocation(fopt

i ;Popti ) correspond to the values that provide

maximum Shannon capacity Cij over the wirelesslink (i; jopt), where jopt is the best next hop.


In this work, we use a contention based medium accesscontrol protocol in the control channel before transmitting thepacket on the selected data channel. In the contention basedMACprotocol, the probability of accessing themedium is cal-culated based on the U�

ij. Nodes generate a backoff counter

from the range ½0; 2CW�1�, where CW is the contention win-dow. The CW is a decreasing function of U�

ij. This will ensurethat heavily backlogged VQs with more spectrum resourceswill have a higher probability of transmission.

The computational complexity of the DRS algorithm at anode i is directly proportional to the number of neighbors,number of channels and number of active sessions. Therefore,for a constant number of channels and sessions in a networkthe computational complexity for node i is given asOðjNBijÞ.

5 PERFORMANCE EVALUATION THROUGH

SIMULATION

In this section, we compare the performance of DRS withROSA in a multihop ad-hoc network. To evaluate DRS, weuse a object-oriented packet-level discrete-event simulatorsimilar to [2], which implements the features described inthe earlier sections of this paper. The metric used for thisevaluation is effective throughput (h) and reliability (r) ofthe network. Effective throughput was defined based on thenumber of packets received within the deadline. The reli-ability is defined as the ratio of packets received at the desti-nation within the specified deadline with respect to thenumber of packets generated at the source node. The evalu-ation is conducted on a grid topology in a 6000 m x 6000 marea. The sessions are initiated between disjoint randomsource-destination pairs and the packet size of the packetsare set at 2500 bytes and number of packets transmitted persession is set to 500. The total available spectrum (BW ) isset to be 54 MHz-72 MHz The bandwidth usable by cogni-tive radios are restricted to be 2; 4 and 6 MHz. The band-width of the common control channel is set as 2 MHz. Eachresult was obtained by averaging the values obtained from50 random seeds unless specified differently. Next, wedescribe different scenarios under which the proposed algo-rithm is compared to ROSA. In all figures except Fig. 6, theblue lines represent performance of DRS and red linesdenote the performance of ROSA.

5.1 Scenario 1: Network Performance As theNumber of Session Increases (All SessionsStarted at Random Time)

In scenario 1, we evaluate the network performance as thenumber of active sessions in the network increase. The param-eters used during the two experiments for scenario 1 are listed

in Table 2. The only difference between the two experimentsare the deadlines assigned to different sessions. In experiment1, all the sessions have a deadline of 2 s, which represents ahighly constrained network. Instead, in experiment 2 the oddnumbered sessions have a deadline of 1:5 s and even num-bered sessions have a deadline of 10 s. Experiment 2 can beconsidered as a scenario where one session carries periodicweathermonitoring data through the network. These sessionsare delay tolerant to an extent, hence have a longer deadline.The second type of data can have extremely small deadline,consisting of delay-intolerant data like threat detection,incoming missile alert or real-time video streaming. The pro-posed algorithm should be able to adapt to the varyingrequirements of different sessions and maximize the effectivethroughput of the network. The session are set to start ran-domly any time between start of the simulation (t ¼ 0 s) andsession duration (t ¼ 5 s). This ensures that all sessions areactive at some point during the simulation but the number ofactive sessions will vary throughout the simulation. Theparameters of both the experiments (1 and 2) are listed inTable 2. Examining Figs. 2 and 3 show that DRS performsmuch better than ROSA in terms of reliability and effectivethroughput in experiment 3 and 4. In these scenarios, tradi-tional backpressure based algorithm may suffer from the lastpacket problem. Since DRS is formulated based on VQLwhich takes into account the deadlines of each packet in thequeue, the penalty for holding packets in the queue grows astime elapses eliminating the last packet problem.

5.2 Scenario 2: Network Performance As the DataRate of the Sessions Increase

In these set of experiments, we evaluate the network perfor-mance in a scenario where number of sessions are kept

TABLE 2Parameters of Scenario 1

Parameter Experiment 1 Experiment 2

Source Rate 2 Mbits=s 2 Mbits=sSession duration 5 s 5 sSession start randomly from

t ¼ ½0; 5� srandomly fromt ¼ ½0; 5� s

No. of sessions 2, 4, 6, 8, 10, 12 14,16, 18, 20, 22

2, 4, 6, 8, 10, 12 14,16, 18, 20, 22

Deadline ofeach session

2 s Odd session 1:5 sEven session 10 s

Fig. 2. Scenario 1: h versus No. of sessions.

Fig. 3. Scenario 1: r versus No. of sessions.


constant and the data rate injected at the source nodeincreases from 1 Mbits=s to 10 Mbits=s. We conduct threeexperiments in this scenario. Experiments 3 and 4 are simi-lar to the experiments of previous scenario 1, varying onlyin deadline as shown in Table 3. Experiment 7 evaluatesthe performance of DRS when the packet size is larger(25000 bits). As we can see in Figs. 4 and 5, all three experi-ments show that DRS outperforms ROSA. Though the effec-tive throughput of both ROSA and DRS increased withhigher packet size (dotted lines), the reliability of ROSAdecreased more compared to the decrease in DRS. Hence,the difference in performance between DRS and ROSAincreased when larger packets (fewer number of packetsper second) were used in the network.

5.3 Scenario 3: Examining the Effect of DifferentComponents of DRS

Here, we try to evaluate the effect of different componentsused during the formulation of DRS. In experiment 6, weevaluate how Tr and Tr � Td affect the proposed algorithmindividually. Accordingly, we run the simulation with the

parameter shown under experiment 6 in Table 4 using twodifferent weight definitions as shown below,

wTr ¼L

max Tr; tð Þ (9)

wTr�Td ¼L

max Tr � Td; tð Þ (10)

Fig. 6 shows that both the DRS using weights seen in (9)and (10) perform considerably better than ROSA but doesnot maximize the effective throughput like original DRS.This is because original DRS that uses the weight shown in(1) derives the benefits of both weights ((9) and (10)). Hence,this shows why it is advantageous to have both Tr andTr � Td in the denominator of the weight used to calculateVQL. Further, it is interesting to note that in cases where itis difficult to estimate Td, one can still achieve moderatelygood performance by using weight shown in (9). Next, weevaluate the effect of the parameter t on the effectivethroughput of the network. Equation (1) uses a very smallvalue t to ensure correctness of weight, such that instanceswith infinite value do not occur.

Fig. 7 depicts the effective throughput of the network as tchanges while keeping the number of sessions and sourcedata rates constant. The other parameters of experiment 7are depicted in Table 4. The result shows that the effectivethroughput of the network using DRS is consistently highfor values of t over a range between 10�8 to 0.99. Any valuegreater than 10�1 takes away the effect of deadlines and hasa degrading effect on effective throughput. As the value of tmoves closer to 1, the VQL becomes more and more equiva-lent to traditional queue length. On the other hand, choos-ing t to be smaller than 10�8 also affects the algorithmadversely since it bloats the VQL to an extent where thecapacity component of the network utility function becomesinsignificant. This lower bound would depend on the char-acteristics of the network, specifically, the achievable


Parameter Exp: 3 Exp: 4 Exp: 5

Source Rate1 to 10Mbits=s

1 to 10Mbits=s

1 to 10Mbits=s

Session duration 5 s 5 s 5 sNo. of sessions 5 5 5Deadline ofeach session

2 sOdd session 1:5 sEven session 10 s

2 s


Fig. 5. Scenario 2: r versus No. of sessions.


Parameter Exp: 6 Exp: 7 Exp: 8

Source Rate 2 Mbits=s 2 Mbits=s 2 Mbits=sSession duration 5 s 5 s 5 s

No. of sessions 5 82, 4, 6, 8, 10,12, 14, 16

Deadlines 2 s 2 s 50 sNo. of seeds 50 50 20



capacity determined by the bandwidth of the transceiver.Fig. 7 also shows that the range of values for t outsidewhich the effective throughput of the network starts declin-ing is same for both cases (6 and 8 sessions). This showsthat the acceptable value for t does not change according tothe number of active sessions in the network. Since we haveshown that DRS performs consistently well over a largerange of values of t, one can choose any value within theacceptable range depending on the network setup.

Next, we use the same parameters as in experiment 7 toanalyze how errors in estimation of Td affect the network’seffective throughput. In this case, we set the number of ses-sions to eight and ten. We vary a from R=0:5 to R=5 asshown in Fig. 8. When a ¼ R=0:5, Td is underestimated andwhen a ¼ R=5, Td is overestimated. As expected, if Td isunderestimated, fewer packets are delivered to the destina-tion within the deadline thereby decreasing the effectivethroughput. Meanwhile, overestimation does not impactthe throughput negatively because Td is calculated at eachhop and hence error/margin decreases as the packet movescloser to the destination.

Finally, in experiment 8, we evaluate the performance ofDRS in a network having sessions with very long deadline(50 s). This experiment examines how the network behavesin scenarios where the deadlines of the sessions are longenough such that packets lost due to expiration of deadlineare negligible. This experiment evaluates whether there isany loss in throughput while using DRS as compared toROSA in network that are delay tolerant (have extremelylong deadlines). As we can see from Fig. 9, the throughputof both algorithms are equal as the number of sessions inthe network increase. This shows that there is no disadvan-tage in using DRS over ROSA even in scenarios where dead-lines are insignificant.

6 TESTBED IMPLEMENTATION

6.1 ChallengesIn the past decade, cross-layer protocols have been exten-sively studied and various solutions have been proposed[31], [32], [33], [34]. Even with these advances in literature,most of the solutions are evaluated only using simulationtools like MATLAB, ns-3, OPNET among others. The goal ofcross-layer optimization techniques are to utilize the infor-mation between different layers and enable their interactionto jointly optimize objectives including throughput, reliabil-ity, delay among others. This invokes the need for an archi-tecture that enables these interactions and promotes designand development of cross-layer optimization algorithms.Lack of such platforms has lead to the growing gap betweennumber of solutions proposed in literature versus the num-ber of solutions that are implemented and tested using actualhardware. There are only limited efforts that extend theimplementation of the optimization algorithms to actualhardware and evaluate the performance on a cross-layertestbed [13], [35], [36], [37], [38], [39]. The major challenge inachieving this implementation is the lack of a flexible archi-tecture that facilitates implementation of the cross-layer opti-mization algorithm on multi-node networks. This deficiencyis being recognized by the community and some solutionsare being proposed to achieve the required flexibility.

GNU radio is an open-source signal processing softwarethat provides great flexibility specifically at the physicallayer of SDRs. GNU radio comprises of various signal proc-essing and digital communication blocks and is an excellenttool to control SDR. However, the majority of the contribu-tion is limited only to the Physical layer. There have alsobeen efforts to relocate some of the processing functions to aField-programmable gate array (FPGA) [40] to improve thedelay performance. This makes it difficult to integrate newalgorithms for testing and evaluation purpose. Some otherwork [41], [42] aims to provide reconfigurable MAC proto-cols by decomposing the overall design into core fundamen-tal blocks. In [41], the implementation of these fundamentalblocks are split between PC and FPGA depending onthe time critical nature of the blocks. In [42], the authorsimplement an abstract execution machine on a resource-constrained commodity WLAN (wireless local area net-work) card. Recently, software defined network (SDN)using an Open-Flow [43] based approach has been pro-posed for evaluating routing protocols. The overall conceptof Open-Flow is to keep the data path on the Open-Flowswitch itself while moving the high-level routing decision toa separate controller (server). The switch performs thepacket forwarding based on the flow table defined by the

Fig. 8. Scenario 3: h versus Parameter a.

Fig. 9. Scenario 3: h versus No. of sessions (Deadline = 50 s).Fig. 7. Scenario 3: h versus Parameter t.


controller and use Open-Flow protocol to communicatewith each other. The majority of the work on OpenFlow hasbeen concentrated at the network layer of the protocol stack.

Evenwith these advancements, amajor challenge to transi-tioning algorithms and protocols to commercial hardware isthe lack of a software defined testbed with a flexible architec-ture that enables easy implementation of cross-layer technolo-gies. These testbeds are essential to corroborate the resultsobtained in simulations and evaluate how to refine these algo-rithms to ensure a successful transition to relevant hardware.Some of the requirements of such a testbed include, a flexiblecross-layer based protocol stack [38], modularity to integratenew algorithmswith ease, a framework to accommodate bothcentralized and distributed solutions, real-time network per-formance monitoring tools, and having the ability to rununsupervised scripted experiments over extended periods oftime. Having such a testbed expedites the design and devel-opment process of next-generation wireless communicationtechnologies destined for a commercial SDR system. TheCrOss-layer Based testbed with Analysis Tool (COmBAT)first introduced in [39] was developed to serve as a softwaredefined testbed to enable the implementation of cross-layeroptimization algorithms. In COmBAT, a Adaptive cross-layer(AXL) communication framework facilitates easy integrationof new protocols and algorithms. The design of AXL is dis-cussed in detail in the next section.

6.2 Adaptive Cross-Layer (AXL)The overall AXL framework depicted in Fig. 11 consistsmainly of the application layer, session manager, decisionplane, control plane, register plane and the physical layer.Each node in the network uses the AXL framework in place ofa traditional protocol stack and are therefore referred to asAXL nodes throughout this paper. In implementation, theAXL framework consists of Pythonmultiprocessing processeswhich are initialized at node start up using an AXL daemon.The daemon imports the main modules and properties thatare used in the different layers/planes of the framework asrequired. The properties include predefined values for thenetwork such as data timeout duration used by MAC proto-col, node IP andMACaddresses and payload sizes. However,most of the properties are dynamic in nature and they can bereconfigured on-the-fly based on network optimization strate-gies or user input. The processes that are started by the dae-mon run continuously until shutdown. These processesinclude the register plane, sessionmanager, control plane andthe physical layer. The decision plane is not a process but acollection of functions that can be called by the frameworkwhen needed. Each plane/layer can share information witheach other by a combination of three methods; direct functioncalls, sharedmemory (register plane) or by overhearing globalevents (global with respect to the framework, not the entirenetwork) that can be triggered by any process in the frame-work. These functionalities allow for a flexible cross-layercommunication between all network protocols.

Application (APP) Layer. The current AXL software pack-age provides data generation APPs that a user may choosefrom in order to evaluate the performance of the network.The APPs can operate in packet streaming mode or packet-by-packet mode. For streaming mode, the source data isrepeated until a user specified amount of data has been gen-erated. The streaming mode is generally used in experi-ments requiring a constant bit rate (CBR) source for a fixedduration of time. The APPs connect to the AXL daemon via

a TCP/IP socket. For each APP that connects, a unique con-nection object is created that manages data transfer betweenthe APP and the AXL framework. Each packet contains theuser generated and QoS parameters. This is where deadlineof the packet can be defined. The packet is parsed and thensent to the session manager for the next processing stage.

Session Manager. In the AXL framework, the session man-ager provides the capability of simultaneous multi-sessionmanagement. When a packet arrives at the session manager,the session manager creates a session object based on thepacket parameters, which include process ID that is createdby the OS, source and destination IP, data type, any QoSparameters (such as deadline), and the packet number gen-erated at packet creation. Packets that correspond to exist-ing session objects are appended to their appropriatesession queue. Packets receive their network headers basedon the parameters in the session object mentioned earlier.In implementation, the session manager is designed as amultiprocessing first-in, first-out (FIFO) with a user speci-fied update period. The update period dictates the timeoutfor updating packet queues. During each update period, thesession manager stores the current queue length of each ses-sion in the register plane and triggers an event flag whichindicates that the transmitter has backlogged session readyfor routing decisions.

Decision Plane.As the name suggests, this is the componentwhere all the logical decisionmaking and algorithm executiontakes place. These algorithms pertain to routing algorithms,spectrum allocation, automatic modulation classification andother resource allocation decisions. The complexity of thealgorithms can vary from threshold decision to iterative algo-rithms like the Expectation and Maximization (EM) algo-rithms or solvers for convex optimization problems. Decisionalgorithms can (i) modify a parameter in a protocol, (ii) triggerswitching among different modes within a protocol, (iii)enable switching among different protocols altogether [38].

In regards to this paper, there are currently two routingalgorithms (ROSA and DRS), stored as software modules,available for use (discussed in detail in Section 7). This iswhere all the calculation for routing algorithm takes place.Each routingmodule can be passed as an instance for the deci-sion plane to use during runtime. The chosen and active rout-ing module is requested by the session manager to executethe algorithmwhen a session is backlogged. The results of theexecuted algorithm is stored in the register plane for otherlayers/planes to access. Conversely, to execute the algorithm,the decision plane obtains the information from the registerplane. After executing the algorithm within the routing mod-ule, the decision plane triggers an event flag that prompts thecontrol plane to schedule a transmission. It should be notedthat the interactions between the decision plane and the otherlayers/planes in AXL take place via the register plane,through global events or through direct function calls.

There are some DRS specific requirements that had to beincluded in the experimental framework to ensure successfulimplementation. Each packet is required to carry time of gen-eration in its header. This enables nodes to calculate VQL ateach hop. This feature had to be included in the experimentalframework to enable the operation of such deadline-basedcross-layer algorithm. In this work, we included the abilityto track the time instant of generation and arrival of packetsat each node including the destination. In traditional net-work, the backlog does not change with time unless a packetis received or transmitted by the node. In case of DRS, the


VQL changes continuously with time. This was a crucialcomponent necessary to implement DRS-like protocols thataim to eliminate the last packet problem. Therefore, in thiscurrent version of the framework, the VQL is constantlyupdated at the decision plane of each node. All of these inclu-sions have bolstered the experimental environment/frame-work to handle algorithms that are required to handle time-based queue lengths.

Control Plane. The control plane houses the control logicused to access the wireless medium. The control plane con-tains the finite state machine (FSM) used to implement differ-ent MAC protocols. The chosen MAC protocol defines theexact set of states, events, conditions and actions required tooperate FSM. The control plane can be initialized to usemulti-ple different MAC protocols depending on the situationalawareness gathered from other layers/planes of the stack asshown in [38]. Each MAC protocol should have its FSMimplemented in the control plane as a separate FSM initializa-tion function. Future developers can take advantage of thebaseline FSM model that is already defined in the controlplane by modifying its states and actions as needed by theprotocol. An example of a state transition diagram for a carriersensingmultiple accesswith collision avoidance (CSMA/CA)based MAC protocol [2] is given in Fig. 12. We would like topoint out that CSMA/CA basedMAC protocol operates onlyon the CCC and does not restrict concurrent feasible (in termsof BER constraints) transmission from occurring on the datachannel. The state transition diagramdescribes the interactionbetween all possible states, events and actions for the receiveand transmit paths. As shown in Fig. 12, when an eventData available is set, Send RTS (request-to-send) action istaken as the FSM goes from an IDLE state to WAIT CTS(clear-to-send) state. The next event that the FSM is lookingfor is eitherCTS received orCTS timeout and the FSM transi-tions depending on which event was observed. If CTS wasreceived and CTS received is set, Send DTS (Data Transmis-sion reServation) action is taken as the FSM goes fromWAITCTS state toSENDDATA state. The rest of the state transitiondiagram can be interpreted in a similarmanner.

The FSM is generally in an IDLE state until the corre-sponding global AXL events are flagged to invoke a statetransitioning process. These global AXL events are used incross-layer communication between different layers/planesand should not be confused with the events used by theFSM itself. The events in the FSM are strictly defined by thechosen MAC protocol and dictate the state transitioningprocess that allows the control plane to manage mediumaccess. The global events such as SESSION ROUTING thattransition the FSM from an IDLE state are usually set in thedecision plane after routing decision has been made. Someother examples of such global events used throughout AXLinclude SESSION PROCESSING event which is used bythe session manager to indicate that the node is busy proc-essing a session and START SENSE event that signals thePHY layer that it is time to perform spectrum sensing.Therefore, we can state that the overall AXL framework fol-lows an event driven design.

Register Plane. The register plane is essentially a node data-base used to share information across layers/planes.Although the register plane does not perform any computa-tion and does not have any decision making ability, it is anintegral part in the overall cross-layer design. The registerplane can be considered as a central information hub that canbe accessed by different layers/planes of the AXL framework.

Data sharing among multiple processes is achieved throughPython manager dictionaries. The global information thatneeds to be shared among all layers is stored in amanager dic-tionary which allows for only one process to read or writeinformation in the register plane at a time. Themain dictionar-ies that reside in the register plane are a global register dictio-nary (GRD), global values dictionary (GVD) and sessionbacklog dictionaries (SBD).

AXL nodes learn about their environment by overhearingcontrol packets on the CCC. Each node stores local informa-tion in a node dictionary in the GRD. The node dictionary isappended to every control packet sent on the CCC. The infor-mation in the node dictionary is continuously updated asnew information becomes available. Node dictionary infor-mation includes IP and MAC addresses, the node location,local noise plus interference, session packet queue lengths,current routing algorithm among others. Nodes maintain acopy of their own node dictionary, as well as a copy of itsneighbor’s dictionary in the GRD. The GRD also containsinformation like the designated frequencies, possible nexthops and neighbors. The GVD stores the current routingdecision parameters as well as the current state of the FSM.SBD has a list of all local sessions and their most up to datepacket queue lengths. The routing algorithm is able to accessthis information stored in the register plane as it optimizesthe routing parameters. Other layers/planes can similarlyread or write information in the register plane as needed.

Physical (PHY) Layer. The PHY layer is easily separablefrom the rest of the framework as the goal is to allow the inte-gration of different radio front ends and signal processingsoftware. The PHY layer consists of a hierarchical implemen-tation where the lowest level includes signal processing soft-ware specific libraries such as GNU Radio and a universalhardware driver (UHD) interface usedwith the universal soft-ware radio peripheral (USRP) family of products from Ettus.The PHY hierarchical module, consists of functions that aredirectly accessible by the control layer and the register plane.This implementation allows for a very simple interfacebetween AXL and a PHY layer making this design SDR hard-ware agnostic.

Fig. 10 depicts the current configuration of a five-nodenetwork that is arranged in the form of a grid topology.Each node consists of two USRP N210s (one for control linkand the other used as data link) connected to each other viaMIMO cable. The SBX and CBX daughterboards are usedwith the radios, which cover frequency ranges from400 MHz to 4:4 GHz and 1:2 GHz to 6 GHz respectively. Thereceivers (USRP N210s), provide up to 40 MHz of instanta-neous analog bandwidth. The analog-to-digital and digital-to-analog converters on the motherboard use a 100 MHzmaster clock and sample at 100 MS=s and 400 MS=s respec-tively. The on-board Xilinx Spartan 3A-DSP 3400 FPGA per-forms the required digital interpolation or decimation toprovide the required sampling rate. The host PC interfaceswith the USRP using a Gigabit Ethernet (GigE) connectionas shown in Fig. 10. The USRPs are controlled using GNURadio signal processing modules and a UHD interface. DRSis implemented on the AXL framework and the results arediscussed in Section 7.

7 TESTBED EVALUATION

We discuss the experimental evaluation of DRS using theAXL framework implemented on five-nodes USRP testbed


shown in Fig. 13. In these set of tests, both DRS and ROSAuse the same MAC protocol with FSM as shown in Fig. 12.The PHY layer uses Gaussian minimum shift keying(GMSK) implemented using GNU radio with USRPs as thetransceivers of the AXL node. The USRP is able to operateat frequency, bandwidth and transmit power level as speci-fied by algorithms (DRS or ROSA) running in the decisionplane. This framework provides the required flexibility toimplement and evaluate the two cross-layer optimizationalgorithm (ROSA and DRS). For rapid implementation andfeasibility analysis, the AXL framework and the routingalgorithms are implemented using Python programminglanguage. The advantage of using the Python is ease of pro-gramming and faster development turnaround time. Thedrawback is large delays incurred by the framework [44],[45]. In future, we plan to move the implementation of the

AXL framework to the kernel space using C programminglanguage. Therefore, it is important to obtain a baseline forthe delay experienced in the network so that we can chooseappropriate deadlines for the experimentation process.

7.1 Establishing BaselineIn this section, we establish a baseline for the average end-to-end delay (EED) experienced by the SDR based testbedwhichis the intended platform for evaluating DRS. To accomplishthis, ROSA is used as the the default routing algorithm.Accordingly, we calculate the average delay experienced bypackets to traverse from source to destination as the numberof session increases. The network parameters used for deter-mining the EED experienced by the current configuration ofthe SDR based ad-hoc network is listed in Table. 5.

Fig. 14 depicts the average EED experienced by eachpacket and the average EED experienced by the entire ses-sion as the number of sessions in the network increase. TheEED experienced by the packet is calculated as the durationbetween the packet generation at the source node andpacket arrival at the destination node. Similarly, EED expe-rienced by each session represents the time between thegeneration of the first packet at the source node and thearrival of the last packet at the destination node. As

Fig. 11. Adaptive cross-layer (AXL) Framework.

Fig. 12. FSM of MAC protocol.

Fig. 10. Layout of the Five-node grid topology.

Fig. 13. Five-node USRP testbed.

TABLE 5Parameters to Baseline End-to-End Delay

Parameters Baseline Test

Packet size 1250 BytesNumber of packets 3000Number of sessions 1 to 6Source rate 200 kbits=sSource duration 150 sRouting algorithm ROSANo. of seeds 5Maximum transmit power 20 dBm

Fig. 14. EED versus No. of Sessions.


expected, the average delay increases in both cases withincreasing number of sessions. Examining these delay val-ues, we can clearly see the impact of the Python basedimplementation of the overall framework. Nevertheless, wecan use this baseline to choose appropriate deadlines forthis network that would enable us to compare the perfor-mance of two cross-layered algorithms (DRS and ROSA).Accordingly, for the next set of tests, we choose 3 min as thesmaller stringent deadline and 15 min as the larger deadline.

7.2 DRS and ROSAIn this section, the effective throughput and reliability of DRSand ROSA are evaluated on the five-node USRP testbed. Inthe current implementation, the MAC protocol is able torecover any loss of packet that occurs due to channel usingretransmission. Therefore, loss of packets only takes place atthe destination when the packet reach after the specifieddeadlines. In addition to parameters listed in Table. 5, this setof experiments use the parameters in Table 6.

In the first set of tests, we used multiple sessions thatstarted at the same time and performance of the SDR basednetwork was evaluated as the number of sessions increased.It is evident from Fig. 15 that for the given network

configurations DRS outperforms ROSA in terms of effectivethroughput as soon as there are two sessions in the network.The performance trend continues as the number of sessionincreases achieving up to 17 percent improvement overROSA. This is because DRS is able to manage multiple ses-sions adaptively to ensure that the effective throughput ofthe network is maximized. Similar behavior is also observedin Fig. 16 which compares the reliability of DRS and ROSAas the number of packet increases.

In contrast to the first test, the source nodes are set tochoose a random time to start the session in the second test.This would imply that different sessions will end at differ-ent times leading to the last packet problem in networksusing traditional backpressure based algorithm like ROSA.The effective throughput and reliability of Test 2 aredepicted in Figs. 17 and 18. As expected DRS outperformsROSA in terms of effective throughput (up to 12 percentimprovement) and reliability (up to 13 percent improve-ment) even when the the network has finite sessions startingat different times. This is due to the fact that the use of VQLprevents the network from experiencing the last packetproblem. VQL keeps increasing with time even if the actualqueue length does not change. In the final test, we use avery large deadline ð60 minÞ. The goal was to evaluate ifthere is any degradation in performance of DRS comparedto ROSA when the deadlines are large enough to be close tonegligible. As shown in Fig. 19, there is no significant lossin performance on using DRS compared to ROSA even inscenarios where the deadlines are long enough to be insig-nificant. Overall, these tests follow the same trend as simu-lations discussed in Section 5. The gains observed withexperiments is much smaller than with simulations due tothe smaller network size. We believe larger benefit can beattained on a larger network deployment. These set of tests

TABLE 6Parameters for Testbed Evaluation

Param. Test 1 Test 2 Test 3

DeadlinesOdd sess. 3 minEven sess. 15 min

Odd sess. 3 minEven sess. 15 min

60 min

Session start t ¼ 0 s t ¼ ½0; 2� min t ¼ 0 st 10�6 10�6 10�6

Seeds 30 30 30

Fig. 15. Test 1: h versus No. of sessions.

Fig. 16. Test 1: r versus No. of sessions.


Fig. 18. Test 1: r versus No. of sessions.


provide validity to the proposed algorithm to be effective incognitive network that provides the flexibility to adaptaccording to the given scenarios.

8 CONCLUSIONS

Weproposed a novel distributed deadline-based joint routingand spectrum allocation algorithm to maximize the effectivenetwork throughput. The DRS adapts according to availableresources and is capable of handling sessions with differentdeadline requirements. DRS enables every node in the net-work to choose optimal session, next hop, frequency and tra-nsmit power with an objective to deliver maximum numberof packets to their intended destination before the specifieddeadline. Though DRS is designed for tactical ad-hoc net-works, its application can be extended to any wireless ad-hocnetwork that handles sessions with different QoS based dead-line requirements. We have performed extensive simulationsto compare the performance of DRS with ROSA and showedup to 35 percent improvement in effective throughput and upto 26 percent improvement in reliability of the network. Fur-thermore, we have successfully overcome the challenges ofimplementing the proposed algorithm on a cross-layer frame-work based software defined testbed. The experiments con-ducted on the testbed showed DRS outperforming ROSA interms of effective throughput (up to 17 percent) and reliability(up to 13 percent). This helped us accomplish the secondaryobjective of this paper which was to validate the flexibilityand further advance the COmBAT framework by implement-ing novel cross-layerDRS algorithm.

ACKNOWLEDGMENTS

(a) Contractor acknowledges the Government’s support inthe publication of this paper. This material is based uponwork supported by the US Air Force Research Laboratoryunder Award No. FA8750-14-C-0098 and Award No.FA8750-16-C-0086. (b) Any opinions, findings, and conclu-sions or recommendations expressed in this material arethose of the author(s) and do not necessarily reflect theviews of AFRL. A preliminary shorter version of this paperappeared in the Proceedings of IEEE Globecom 2016 [1].

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Jithin Jagannath received the master of sciencedegree in electrical engineering from the Univer-sity at Buffalo, The State University of New York,in 2013. He is working toward the PhD degree inthe Department of Electrical and Computer Engi-neering, Northeastern University. He is currentlyconducting research with the Wireless Networksand Embedded Systems Laboratory under theguidance of Prof. Tommaso Melodia. He hasexperience developing SDR based prototypeswhich includes designing, implementing, and

testing different communication protocols and optimized algorithms onhardware for wireless sensor networks. He is also a Sr. associate scien-tist/engineer at ANDRO Computational Solutions in Rome, NY. He is cur-rently PI of a DHS SBIR effort that aims to develop a novel interferencedetection device for first responders. He has also been the technicallead in multiple SBIR/STTR efforts including cross-layer networkingtechnology, multi-sensor automatic modulation classification, cybersecurity, and compressive sensing. His research interest includes soft-ware defined radio, visible light communication, cognitive networks,underwater sensor network, and automatic modulation classification.

Sean Furman received the BSECE degree fromthe State University of New York PolytechnicInstitute, in Utica, New York, in 2017. In the 2016-2017 academic year, he worked on VANETTA(Vehicular Ad-Hoc Network for TransportationAutomation) as his senior project. The projectinvolved design and implementation of vehicularplatooning protocols on scaled remote controlcars. He joined ANDRO Computational Solutionsin 2015 and has contributed to projects involvingsoftware defined radio networks. His research

interests include wireless communication, automatic control systems,and computing.

Tommaso Melodia received the PhD degree inelectrical and computer engineering from theGeor-gia Institute of Technology, Atlanta, GA, in 2007. Heis an associate professor with the Department ofElectrical and Computer Engineering, Northeast-ern University, Boston, MA. His research has beensupported by the National Science Foundation,Air Force Research Laboratory, and the Office ofNaval Research, among others. His current rese-arch interests include modeling, optimization, andexperimental evaluation of networked communica-

tion systems, with applications to ultrasonic intra-body networks, cognitiveand cooperative networks, multimedia sensor networks, and underwaternetworks. He was a recipient of the National Science Foundation CAREERAward and coauthored a paper that was recognized as the ISI Fast Break-ing Paper in the field of computer science for February 2009. He was thetechnical program committee vice chair for IEEE GLOBECOM 2013 andthe technical program committee vice chair for Information Systems forIEEE INFOCOM 2013. He serves on the editorial boards of the IEEETransactions on Mobile Computing, the IEEE Transactions on WirelessCommunications, the IEEE Transactions on Multimedia, and ComputerNetworks.

Andrew Drozd is the president and chief scientistof ANDRO Computational Solutions in Rome, NY.His fields of expertise include wireless communica-tion, dynamic spectrum access, computationalelectromagnetics (CEM), development of electro-magnetic environmental effects (E3) tools for cositeanalysis, spectrum exploitation, and interferencerejection technologies. He was president of theIEEE EMC Society (2006-2007), a past member ofthe EMC-S Board of Directors (1998-2008), and isan IEEE fellow. He was also on the Board of Direc-

tors of the Applied Computational Electromagnetics Society (ACES)(2004-2010). He is an iNARTE certified EMC Engineer and has authoredmore than 160 technical papers, reports, and journal articles.

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