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Cross-layer Routing and Dynamic SpectrumAllocation in Cognitive Radio Ad Hoc Networks

Lei Ding, Tommaso Melodia, Stella N. Batalama, John D. Matyjas, and Michael J. Medley

Abstract—Throughput maximization is one of the main chal-lenges in cognitive radio ad hoc networks, where the availabilityof local spectrum resources may change from time to timeand hop-by-hop. For this reason, a cross-layer opportunisticspectrum access and dynamic routing algorithm for cognitiveradio networks is proposed, called ROSA (ROuting and SpectrumAllocation algorithm). Through local control actions, ROSA aimsat maximizing the network throughput by performing jointrouting, dynamic spectrum allocation, scheduling, and transmitpower control.

Specifically, the algorithm dynamically allocates spectrumresources to maximize the capacity of links without generatingharmful interference to other users while guaranteeing boundedbit error rate (BER) for the receiver. In addition, the algorithmaims at maximizing the weighted sum of differential backlogsto stabilize the system by giving priority to higher-capacitylinks with high differential backlog. The proposed algorithm isdistributed, computationally efficient, and with bounded BERguarantees.

ROSA is shown through numerical model-based evaluationand discrete-event packet-level simulations to outperform base-line solutions leading to a high throughput, low delay, and fairbandwidth allocation.

Index Terms—Cognitive radio networks, routing, dynamicspectrum allocation, cross-layer design, ad hoc networks.

I. INTRODUCTION

COGNITIVE 1 radio networks [2] have recently emergedas a promising technology to improve the utilization

efficiency of the existing radio spectrum. In a cognitiveradio network, users access the existing wireless spectrumopportunistically, without interfering with existing users. Akey challenge in the design of cognitive radio networks isdynamic spectrum allocation, which enables wireless devicesto opportunistically access portions of the spectrum as theybecome available. Consequently, techniques for dynamic spec-trum access have received significant attention in the last fewyears, e.g., [3] [4] [5] [6] [7].

In addition to this, in cognitive radio networks with multi-hop communication requirements (i.e., cognitive radio ad

Copyright (c) 2010 IEEE. Personal use of this material is permitted.However, permission to use this material for any other purposes must beobtained from the IEEE by sending a request to pubs-permissions@ieee.org.

L. Ding, T. Melodia and S. Batalama are with the Department of ElectricalEngineering, The State University of New York at Buffalo, Buffalo, NY14260, USA. e-mail: {leiding,tmelodia,batalama}@buffalo.edu.

J. Matyjas and M. Medley are with the U.S. Air ForceResearch Laboratory, RIGF, Rome, NY 13441, USA. e-mail:{john.matyjas,michael.medley}@rl.af.mil.

1This material is based upon work supported by the US Air Force ResearchLaboratory under Award No. 45790. Approved for Public Release; Distribu-tion Unlimited: 88ABW-2010-0960 date 3 March 2010. A preliminary shorterversion of this work [1] appeared in the Proc. of ACM Intl. Conf. on Modeling,Analysis and Simulation of Wireless and Mobile Systems (MSWiM) 2009.

hoc networks), the dynamic nature of the radio spectrumcalls for the development of novel spectrum-aware routingalgorithms. In fact, spectrum occupancy is location-dependent,and therefore in a multi-hop path the available spectrum bandsmay be different at each relay node. Hence, controlling theinteraction between the routing and the spectrum managementfunctionalities is of fundamental importance. While cross-layer design principles have been extensively studied by thewireless networking research community in the recent past, theavailability of cognitive and frequency agile devices motivatesresearch on new algorithms and models to study cross-layerinteractions that involve spectrum management-related func-tionalities.

For the reasons above, in this paper we consider inter-actions between spectrum management and dynamic routingfunctionalities. With this respect, we propose a distributedalgorithm that jointly addresses the routing, dynamic spectrumassignment, scheduling and power allocation functionalitiesfor cognitive radio ad hoc networks. The objective of theproposed algorithm is to allocate resources efficiently, distribu-tively, and in a cross-layer fashion.

We further show how our algorithm can be interpreted asa distributed solution to a centralized cross-layer optimizationproblem. While the optimization problem is centralized andhard to solve, our algorithm is practically and distributivelyimplementable. We show how a cross-layer solution that solvesrouting and spectrum allocation jointly at each hop outper-forms approaches where routes are selected independentlyof the spectrum assignment, with moderate computationalcomplexity. Our main contributions can be outlined as follows:• We derive a distributed and localized algorithm for joint

dynamic routing and spectrum allocation for multi-hopcognitive radio networks. The proposed algorithm jointlyaddresses routing and spectrum assignment with powercontrol under the so-called physical interference model,which computes the interference among secondary usersusing a SINR-based model. The proposed algorithmconsiders and leverages the unique characteristics ofcognitive radio including the availability of spectrumholes at a particular geographic location and their possiblevariability with time;

• In the proposed algorithm each cognitive radio makesreal-time decisions on spectrum and power allocationbased on locally collected information. Nodes can adjusttheir transmission power to maximize the link capacityon the selected spectrum portion;

• We introduce a notion of “spectrum hole” that con-siders interference from neighboring secondary as well

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as primary users, and leverage it to optimize resourceutilization at a low computational cost;

• We discuss a practical implementation of the proposedalgorithm that relies on a dual radio with a commoncontrol channel and a frequency-agile data channel;

• We show how the proposed algorithm can be interpretedas a distributed and practical solution to a cross-layeroptimal resource allocation problem, whose performanceis close to the optimum.

The remainder of this paper is organized as follows. InSection II, we review related work. In Section III, we introducethe system model. In Section IV we propose ROSA, ourdistributed algorithm for joint routing and dynamic spectrumallocation. Section V addresses implementation details. InSection VI we show how ROSA can be interpreted as adistributed solution to a centralized cross-layer network utilitymaximization problem for cognitive radio ad hoc networks.Section VII evaluates the performance of the algorithm. Fi-nally, Section VIII concludes the paper.

II. RELATED WORK

Recent work has investigated algorithms and protocols fordynamic spectrum allocation in cognitive radio networks. Pro-posed approaches to assign spectrum can be broadly classifiedinto centralized and distributed schemes. For example, theDynamic Spectrum Access Protocol (DSAP) [8] is centralized,and thus requires a central controller to allocate spectrum. In[7], a distributed spectrum assignment algorithm is proposed,which aims at solving the spectrum allocation problem: whichnode should use how wide a spectrum-band at what center-frequency and for how long. Our work differs significantlyfrom [7], which assumes mutually exclusive transmissionswith zero interference tolerance.

Spectrum band auctions [9][10] have been proposed toallocate wireless spectrum resources, in which bidders obtaindifferent spectrum channels to minimize the interference. Incontrast, our proposed solution jointly considers spectrumallocation and routing in a cross-layer fashion, since theavailable spectrum bands may be different at each hop.

Some recent work has made initial steps in the direction ofleveraging interactions between routing and spectrum alloca-tion. In [11], each source node finds candidate paths based onDynamic Source Routing (DSR) [12] and collects informationon link connectivity and quality. For each candidate route, thealgorithm finds all feasible spectrum assignment combinationsand estimates the end-to-end throughput performance for eachcombination. Based on this, it selects the route and spectrumassignment with maximal throughput and schedules a conflict-free channel for this route. In [13], a connectivity-based rout-ing scheme for cognitive radio ad hoc networks is proposed,where the connectivity of different paths is evaluated bytaking into account primary user activities. The authors in [14]propose a layered graph model, where each layer correspondsto a channel, and find shortest paths based on the layeredgraph. Both [11] and [14] are channel-based solutions, i.e.,the available spectrum is divided into predefined channels, anddevices are assigned opportunities to transmit on channels on arelatively long time scale. However, cognitive radio networks

require spectrum allocation on a short time scale since theavailable spectrum bands will vary continuously based on theactivities of primary and secondary users. In addition, thealgorithms in [11] and [14] are based on the so-called protocolmodel [15], in which two links either interfere destructivelyor do not interfere at all. Although simple, this model fails tocapture the cumulative effect of interference. Conversely, ourwork assumes a richer interference model, which accounts forthe fact that advanced transmission techniques, including code-division multiple access (CDMA) [3] [16], allow concurrentco-located communications so that a message from node i tonode j can be correctly received even if there is a concurrenttransmission close to j.

Recent work has started investigating cross-layer opti-mizations for cognitive radio networks. In [17], Hou et al.formulate a cross-layer optimization problem for a networkwith cognitive radios, whose objective is to minimize therequired network-wide radio spectrum resource needed tosupport traffic for a given set of user sessions. The problemis formulated as a mixed integer non-linear problem, and asequential fixing algorithm is developed where the integervariables are determined iteratively via a sequence of linearprograms. Shi et al. studied the joint optimization of powercontrol, scheduling, and routing for a multi-hop cognitive radionetwork via a centralized approach [18] and a distributedapproach [19]. In [19] the authors developed a distributedoptimization algorithm with the objective of maximizing datarates for a set of sessions. The performance of the algorithmis shown to be in average within 88% of the performance ofthe optimal (centralized) algorithm.

III. SYSTEM MODEL

We consider a cognitive radio network consisting of Mprimary users and N secondary users. Primary users holdlicenses for specific spectrum bands, and can only occupy theirassigned portion of the spectrum. Secondary users do not haveany licensed spectrum and opportunistically send their data byutilizing idle portions of the primary spectrum.

Let the multi-hop wireless network be modeled by a directedconnectivity graph G(V , E), where V = {v1, ..., vN+M} isa finite set of nodes, with |V| = N + M , and (i, j) ∈ Erepresent a unidirectional wireless link from node vi to nodevj (referred to also as node i and node j, respectively, forsimplicity). Nodes from the subset PU = {v1, ..., vM} aredesignated as primary users, and nodes from subset SU ={vM+1, ..., vM+N} are designated as secondary users.

We assume that all secondary users are equipped withcognitive radios that consist of a reconfigurable transceiverand a scanner, similar for example to the KNOWS prototypefrom Microsoft [20]. The transceiver can tune to a set ofcontiguous frequency bands [f, f+∆B], where ∆B representsthe maximum bandwidth of the cognitive radio. We keepthe physical layer model general. However, we assume thatmultiple transmissions can concurrently occur in a frequencyband, e.g., with different spreading codes. Among others, ourphysical layer model could represent orthogonal frequencydivision multiplexing (OFDM)-based transmission, which isbased on a flexible subcarriers pool, and is thus a promising

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candidate technology for cognitive radio networks. Alterna-tively, the considered abstraction could model a multi-channeltime-hopping impulse radio ultra wide band system [21].

The available spectrum is assumed to be organized in twoseparate channels. A common control channel (CCC) is usedby all secondary users for spectrum access negotiation, andis assumed to be time slotted. A data channel (DC) is usedfor data communication. The data channel consists of a set ofdiscrete minibands {fmin, fmin+1, · · · , fmax−1, fmax}, eachof bandwidth w and identified by a discrete index. For ex-ample, the interval [fi, fi+∆fi

] represents the (discrete) set ofminibands selected by secondary user i between fi and fi+∆fi ,with bandwidth w∆fi. If we let w∆fB denote the maximumbandwidth of the cognitive radio, where ∆fB denotes themaximum number of minibands, we have ∆fi ≤ ∆fB repre-senting the constraint of maximum bandwidth of the cognitiveradio. Each backlogged secondary user contends for spectrumaccess on the control channel fcc, where fcc /∈ [fmin, fmax].All secondary users exchange local information on the com-mon control channel.

Traffic flows are, in general, carried over multi-hop routes.Let the traffic demands consist of a set S = 1, 2, · · · , S,where S = |S|, of unicast sessions. Each session s ∈ Sis characterized by a fixed source-destination node pair. Weindicate the arrival rate of session s at node i as λs

i (t), andwith Λ the vector of arrival rates.

Each node maintains a separate queue for each session s forwhich it is either a source or an intermediate relay. At time slott, define Qs

i (t) as the number of queued packets of session swaiting for transmission at secondary user i. Define rs

ij(t) asthe transmission rate on link (i, j) for session s during timeslot t, and R as the vector of rates. For ∀i ∈ SU , the queueis updated as follows:

Qsi (t+1) =

Qs

i (t) +∑

k∈SU,k 6=i

rski(t)−

∑

l∈SU,l6=i

rsil(t) + λs

i (t)

+

.

(1)

IV. JOINT ROUTING AND DYNAMIC SPECTRUMALLOCATION

In this section, we present the distributed joint ROuting anddynamic Spectrum Allocation (ROSA) algorithm. We start byintroducing the notions of spectrum hole and spectrum utilityin Sections IV-A and IV-B, respectively. Opportunities totransmit are assigned based on the concept of spectrum utility,and routes are explored based on the presence of spectrumholes with the objective of maximizing the spectrum utility.Then, in Section IV-C we outline the algorithm for spectrumand power allocation executed in a distributed fashion at eachsecondary user. Finally, we present the core ROSA algorithmin Section IV-D.A. Spectrum Holes

For frequency f , secondary user i needs to (i) satisfy theBER requirement when it transmits to secondary user j, and(ii) avoid interfering with ongoing receivers. Denote SINRth

PU

and SINRthSU as the SINR thresholds to achieve a target

bit error rate BER∗PU for primary users and BER∗SU forsecondary users, respectively.

The first constraint can be expressed as

Pi(f) · Lij(f) ·GNj(f) +

∑k∈V,k 6=i Pk(f)Lkj(f)

≥ SINRthSU (BER∗SU ),

(2)where G is the processing gain, e.g., length of the spreadingcode. Pi(f) represents the transmit power of i on frequencyf . Lij(f) represents the the transmission loss from nodei to j. The expression

∑k∈V,k 6=i Pk(f)Lkj(f) represents

interference at node j. Finally, Nj(f) is the receiver noiseon frequency f .

The second constraint models the condition that receiver l isnot impaired by i’s transmission. We can also indicate interfer-ence at node l ∈ V, l 6= j as NIl(f)+∆Iil(f), where NIl(f)represents noise plus interference at l before i’s transmission,and ∆Iil(f) represents the additional interference at l causedby i’s transmission, i.e., Pi(f)Lil(f). This is expressed as

PRl (f)

NIl(f) + ∆Iil(f)≥ SINRth(BER∗), l ∈ V, l 6= j, (3)

where PRl (f) represents the signal power being received at re-

ceiver l. Since this has to be true for all ongoing transmissions,the constraint can be written as

Pi(f) ≤ minl∈V

∆Imaxl

Lil(f), Pmax

i (f) (4)

where

∆Imaxl (f) =

{P R

l (f)

SINRthP U (BER∗P U )

−NIl(f), l ∈ PU ,

P Rl (f)

SINRthSU (BER∗SU )

−NIl(f), l ∈ SU . (5)

The constraint in (2) states that the SINR at receiver jneeds to be above a pre-defined threshold, which means thatthe power received at receiver j on frequency f needs besufficiently high to allow receiver j to successfully decode thesignal given its current noise and interferences. The constraintin (4) states that the interference generated by i’s transmissionon each frequency should not exceed the threshold value thatrepresents the maximum interference that can be tolerated bythe most vulnerable of i’s neighbors l ∈ V, l 6= j. Hence, i’stransmit power needs to be bounded on each frequency. Theconstraint in (2) represents a lower bound and the constraintin (4) represents an upper bound on the transmit power foreach frequency. By combing constraints (2) and (4), we candefine for link (i, j) and frequency f

Sij(f) = Pmaxi (f)− Pmin

i (f), (6)

where Pmaxi (f) is defined in (4) and Pmin

i (f) is the value ofPi(f) for which equality in (2) holds. Let vectors Pmax

i =[Pmax

i (fmin), Pmaxi (fmin+1), · · · , Pmax

i (fmax)] andPmin

i = [Pmini (fmin), Pmin

i (fmin+1), · · · , Pmini (fmax)]

denote the maximum and minimum transmit power constraintsfor link (i, j). Hence, we introduce the following definition.

Definition 1: A spectrum hole for link (i, j) is a set ofcontiguous minibands where Sij(f) ≥ 0.

Figure 1 illustrates the notion of spectrum hole. As shownin the figure, the spectrum portion [fi, fi+∆fi ] is a possiblespectrum hole for link (i, j).

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Fig. 1. Illustration of a Spectrum Hole.

B. Spectrum Utility

The control channel is assumed to be time slotted. At eachtime slot for which node i is backlogged and not alreadytransmitting, node i can evaluate the spectrum utility for link(i, j), defined as

Uij(t) = cij(t) ·[Qs∗

i (t)−Qs∗j (t)

]+

, (7)

where s∗ is the session with maximal differential backlogon link (i, j). The spectrum utility function is defined basedon the principle of dynamic back-pressure, first introducedin [22], where the authors showed that a policy that jointlyassigns resources at the physical/link layers and routes tomaximize the weighted sum of differential backlogs (withweights given by the achievable data rates on the link) isthroughput-optimal, in the sense that it is able to keep allnetwork queues finite for any level of offered traffic that iswithin the network capacity region. As will be discussed inSection VI, the same result can be derived from a cross-layer network utility maximization problem, which can bedecomposed into two subproblems. After the decomposition,the solution of the routing and scheduling subproblem requiresmaximization of a weighted sum of differential backlogs.

Note that, for the sake of simplicity, we will drop all timedependencies in the following. Note also that the notion ofspectrum utility is defined for a specific link (i, j). In (7),cij(t) represents the achievable capacity for link (i, j) giventhe current spectrum condition, and is defined as

cij(Fi,Pi) ,∑

f∈Fi=[fi,fi+∆fi]

w · log2

[1 +

Pi(f)Lij(f)GNj(f) + Ij(f)

],

(8)where Ij(f) represents the interference at j on f . The achiev-able values of cij depend on the dynamic spectrum allocationpolicy, i.e., spectrum selection vector Fi = [fi, fi+∆fi ], andpower allocation vector Pi = [Pi(f)], ∀i ∈ SU ,∀f ∈ Fi. Thenotion of spectrum utility can be thought of as a differentialbacklog, inspired by dynamic resource allocation policies thatreact to the difference (Qs

i−Qsj) of queue backlogs for a given

session [23][24], weighted with dynamic spectrum availabilityinformation. Routing with consideration of differential backlogcan reduce the probability of relaying data through a congested

relay node. A large queue size at an intermediate node isinterpreted as an indicator that the path going through thatnode is congested and should be avoided, while a small queuesize at an intermediate node indicates low congestion on thepath going through that node. Therefore, in ROSA nodes witha smaller queue size have a higher probability of being selectedas next hop.

We let A indicate active links of secondary users on thedata channel, i.e., aij = 1 indicates that link (i, j) is active,while aij = 0 indicates that the link is not active. Similarly,we denote AP as the link status of primary users, i.e., aP

ij = 1indicates that link between primary users i and j is active(input to the problem). Thus, at each time slot the globalobjective is to find global vectors P = [P1,P2, · · · ,PN ], F =[F1,F2, · · · ,FN ], A (and, implicitly, C) that maximize thesum of spectrum utilities over the activated links, under givenBER and power constraints. This is expressed by the problembelow.

P1 : Given : BER∗SU , G(V, E), PBgt, Q, AP

Find : P, F, A

Maximize :∑

i∈SU∑

j∈SU,j 6=i Uij · aij (9)Subject to :

SINRfkl ≥ SINRth

PU (BER∗PU ) · aPkl, ∀k, l ∈ PU , ∀ f ∈ Fk

(10)SINRf

ij ≥ SINRthSU (BER∗SU ) · aij , ∀i, j ∈ SU , ∀ f ∈ Fi

(11)∑

f∈Fi

Pi(f) ≤ PBgt, ∀i ∈ SU , (12)

0 ≤ ∆fi ≤ ∆fB , ∀i ∈ SU . (13)

In the problem above, we denote SINRij as the SINRfor link (i, j). Constraint (10) indicates that primary usertransmissions should not be impaired. Constraint (11) imposesthat secondary user transmissions should also satisfy a givenBER performance, while sharing the spectrum with othersecondary users. In (12), PBgt represents the instantaneouspower available at the cognitive radio. Constraint (13) imposesa limit on the bandwidth of cognitive radios. In addition,∑

i∈SU (aij + aji) ≤ 1,∀j ∈ SU must hold.Solving the problem above requires global knowledge of

feasible rates, is centralized and its complexity is worst-caseexponential. This provides the motivation for our distributedalgorithm, whose objective is to maximize (9) under the con-straints introduced by cognitive radio networks in a distributedfashion. In addition, we show how the distributed algorithmcan be implemented in a practical protocol.

C. Spectrum and Power Allocation

In this section we present the spectrum and power allocationalgorithm executed in a distributed fashion at each secondaryuser to maximize the link capacity given the current spectrumcondition. Maximizing the capacity of link (i, j) means select-ing spectrum Fi = [fi, fi+∆fi ] and corresponding transmitpower Pi(f) on each frequency to maximize the Shannoncapacity.

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P2 : Given : (i, j), Ij,Nj,Lij, Pmini , Pmax

i , PBgt

Find : [fi, fi+∆fi], Pi

Maximize : cij (14)Subject to :

Pmini (f) ≤ Pi(f) ≤ Pmax

i (f), ∀ f ∈ [fi, fi+∆fi]; (15)

∑

f∈[fi,fi+∆fi]

Pi(f) ≤ PBgt, ∀i ∈ SU ; (16)

0 ≤ ∆fi ≤ ∆fB , ∀i ∈ SU , (17)

where Ij = [Ij(fmin), Ij(fmin+1), · · · , Ij(fmax)], Nj =[Nj(fmin), Nj(fmin+1), · · · , Nj(fmax)], and Lij =[Lij(fmin), Lij(fmin+1), · · · , Lij(fmax)] with i, j ∈ SU .

The objective of the problem above is to find the spec-trum hole with maximal capacity, given spectrum conditionand hardware limitations of the cognitive radio. Note thatconstraint (15) imposes the presence of a spectrum hole, andconstraints (16) and (17) indicate the hardware restrictions.

For a fixed contiguous set of minibands [fi, fi+∆fi ], we canobtain a solution to the problem above by relaxing constraints(15) and (16). Hence, we can express the dual objectivefunction as

g(Pi,Υ) =∑

f∈[fi,fi+∆fi]

w · log2

[1 +

Pi(f)Lij(f)GNj(f) + Ij(f)

]+

∑

f∈[fi,fi+∆fi]

[υfmin(Pmin

i (f)−Pi(f))+υfmax(Pi(f)−Pmax

i (f))]

+υBgt(∑

f∈[fi,fi+∆fi]

Pi(f)− PBgt), (18)

whereΥ = [υfi

minυfi+1min · · · υ

fi+∆fimin υfi

maxυfi+1max · · · υ

fi+∆fimax υBgt] (19)

is the vector of Lagrange multipliers, Υ º 0.

Algorithm 1 Spectrum and Power Allocation.Inputs: (i, j), Ij, Nj, Lij, Pmin

i , Pmaxi , PBgt.

1: [f∗i , ∆f∗i ] = ∅, P∗i = 02: for ∆fi ∈ [0, ∆fB ] do3: m = 1, ∆ = ∞, cij = 04: for fi ∈ [fmin, · · · , fmax−∆fi ] do5: while ∆ > ∆th do6: m = m + 17: for f ∈ [fi, · · · , fi+∆fi ] do8: Assign P m

i (f) as in (20)9: end for

10: Update Lagrange Multipliers

Υ(m) = [Υ(m− 1) +1 + ε

m + εΓ(m)]+ (21)

11: ∆ = |Υ(m)−Υ(m− 1)|212: end while13: Calculate ctemp as in (8)14: if ctemp > cij then15: cij = ctemp

16: [f∗i , ∆f∗i ,P∗i ] = [fi, ∆fi,Pi]17: end if18: end for19: end for20: Return solution as [f∗i , ∆f∗i ,P∗i , cij ]

A solution to problem P2 is obtained as described in Algo-

rithm 1, which provides a dual-based iterative solution to theproblem. Specifically, for a given spectrum window betweenfrequency fi and fi+∆fi

, at each iteration m the algorithmassigns power Pm

i (f) sequentially for each frequency as in(20). Equation (20) is obtained by setting dg(Pi,Υ)

dPi(f) = 0. Then,Lagrange multipliers are updated following a gradient descentalgorithm. In Algorithm 1, ∆th represents a target precision,while ε is a small constant used in the gradient stepsize 1+ε

m+ε .Finally, Γ(m) represents a suitable gradient at step m, i.e.,Γ(m) = [(P min

i (fi)−P m−1i (fi))...(P

mini (fi+∆fi)−P m−1

i (fi+∆fi))

(−P maxi (fi) + P m−1

i (fi))...(−P maxi (fi+∆fi) + P m−1

i (fi+∆fi))

(∑

f=[fi,fi+∆fi]

P m−1i (f)− P Bgt)]. (22)

D. Routing and Dynamic Spectrum Allocation AlgorithmWe now present the cross-layer ROuting and dynamic Spec-

trum Allocation algorithm (ROSA), which aims at maximizingthroughput through joint opportunistic routing, dynamic spec-trum allocation and transmit power control, while performingscheduling in a distributed way.

Every backlogged node i, once it senses an idle commoncontrol channel, performs the following joint routing andscheduling algorithm:

1) Find the set of feasible next hops {ns1, n

s2, ..., n

sk} for

the backlogged session s, which are neighbors withpositive advance towards the destination of s. Node nhas positive advance with respect to i iff n is closer tothe destination than i. Calculate cij for each link (i, j),where j ∈ {ns

1, ns2, ..., n

sk}, using Algorithm 1.

2) Schedule s∗ with next hop j∗ such that

(s∗, j∗) = arg max(Usij). (23)

Note that Usij depends on both the capacity and the

differential backlog of link (i, j). Hence, routing isperformed in such a way that lightly backlogged queueswith more spectrum resource receive most of the traffic.

3) Once spectrum selection, power allocation and nexthop have been determined, the probability of accessingthe medium is calculated based on the value of Us∗

ij∗ .Nodes with higher Us∗

ij∗ will get a higher probabilityof accessing the medium and transmit. Note that Us

ij

defined in (7) is an increasing function of (Qsi−Qs

j), i.e.,links with higher differential backlog may have higherspectrum utility, thus have higher probability of beingscheduled for transmission.This probability is implemented by varying the sizeof the contention window at the MAC layer. Thetransmitter i generates a backoff counter BCi chosenuniformly from the range [0, 2CWi−1], where CWi isthe contention window of transmitter i, whose value isa decreasing function Φ() of the optimal spectrum utilityUs∗

ij∗ as below

CWi = −α · Us∗ij∗∑

k∈SU ,(k,l)∈E Uskl

+ β, α > 0, β > 0

(24)where

∑k∈SU ,(k,l)∈E Us

kl represents the total spectrumutility of the competing nodes. Note that sender i col-

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Pmi (f) =

wLij(f)Glog2e− (Nj(f) + Ij(f))(υf,mmin + υf,m

max − υBgt,m)

Lij(f)G(υf,mmin + υf,m

max − υBgt,m)(20)

lects the spectrum utilities of its neighbors by overhear-ing the control packets on the common control channelas discussed in Section V. Nodes with smaller values ofthe backoff counter will have higher priority in allocatingresources for transmission than nodes with larger valueof the backoff counter. With this mechanism, heavilybacklogged queues with more spectrum resources aregiven higher probability of transmitting.

Algorithm 2 ROSA Algorithm.1: At backlogged node i2: Ui = 0, [f∗i , ∆f∗i ] = ∅, Pi = 03: for each backlogged session s do4: for j ∈ {ns

1, ns2, ..., n

sk} do

5: Calculate cij , [fi, ∆fi] and Pi using Algorithm 16: Utemp = cij · (Qs

i −Qsj)

7: if Utemp > Ui then8: Ui = Utemp

9: [s∗, j∗, Us∗ij∗ , f

∗i , ∆f∗i ,P∗i ] = [s, j, Ui, fi, ∆fi,Pi]

10: aij∗ = 111: end if12: end for13: end for14: Set contention window CWi = Φ(Us∗

ij∗)15: Generate backoff counter BCi ∈ [0, 2CWi−1]

16: Return [s∗, j∗, BCi, Us∗ij∗ , f

∗i , ∆f∗i ,P∗i , aij∗ ]

The details are shown in Algorithm 2.ROSA calculates the next hop opportunistically depend-

ing on queueing and spectrum dynamics, according to thespectrum utility function in (7). Hence, each packet willpotentially follow a different path depending on queueing andspectrum dynamics. Hence, packets from the same sessionmay follow different paths. At every backlogged node, thenext hop is selected with the objective of maximizing thespectrum utility. The combination of next hops leads to amulti-hop path. The multi-hop path discovery terminates whenthe destination is selected as the next hop. If the destination isin the transmission range of the transmitter (either a source oran intermediate relay node for that session), the differentialbacklog between the transmitter and the destination is noless than the differential backlogs between the transmitter andany other nodes, because the queue length of the destinationis zero. Hence, the destination has a higher probability ofbeing selected as next hop than any other neighboring nodeof the transmitter. Note that the transmitter may still select anode other than the destination as the next hop even if thedestination is in the transmission range. This can happen, forexample, if there is no available miniband (low interference)between the transmitter and destination, or if the interferenceon all minibands at that time is high, which results in low linkcapacity between the transmitter and the destination.

V. COLLABORATIVE VIRTUAL SENSING IN ROSA

As discussed earlier, we assume that each node is equippedwith two transceivers, one of which is a reconfigurabletransceiver that can dynamically adjust its waveform and

Fig. 2. ROSA’s Medium Access Control.

bandwidth for data transmission2. The other is a conven-tional transceiver employed on the common control channel.Handshakes on the CCC are conducted in parallel with datatransmissions on the data channel.

We propose a new scheme called Collaborative VirtualSensing (CVS), which aims at providing nodes with accuratespectrum information based on a combination of physical sens-ing and of local exchange of information. Scanner-equippedcognitive radios can detect primary users transmissions bysensing the data channel. In addition, collaborative virtualsensing is achieved by combining scanning results and infor-mation from control packets exchanged on the control channelthat contain information about transmissions and power usedon different minibands.

ROSA’s medium access control logic is illustrated in Fig.2. Similar to the IEEE 802.11 two-way RTS (request-to-send)and CTS (clear-to-send) handshake, backlogged nodes contendfor spectrum access on the CCC. In particular, backloggednodes must first sense an idle control channel for a time periodof Distributed Inter-Frame Spacing (DIFS), and then generatea backoff counter. The values of backoff counter are deter-mined under the objective that nodes with higher spectrumutility should have a higher channel access probability.

The sender informs the receiver of the selected frequencyinterval [fi, fi+∆f ] using an RTS packet. On receiving theRTS packet, the receiver responds by using a CTS packet afterthe Short Inter-Frame Space (SIFS) and tunes its transceiverfor data transmission on the frequency specified in the RTSpacket. As in [7], an additional control packet, DTS (DataTransmission reServation), is needed for the transmitter toannounce the spectrum reservation and transmit power to itsneighbors. Here, we modify the RTS/CTS/DTS packets andinclude channel allocation information to allow the nodes tomake adaptive decisions. The control packets carry addressfields of the sender and the receiver, the spectrum reservation

2Implementations of ROSA that rely on a single transceiver are alsopossible, for example by letting the reconfigurable transceivers periodicallytune to the common control channel to exchange control information. This isthe subject of ongoing research.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. X, NO. X, XXXX 2010 7

[fi, fi+∆f ], reservation duration field (t0, t0+∆t), informationon queue length Q, and power constrains Pmax,Pmin, all ofwhich are input parameters of ROSA. Based on the collectedinformation, each node learns the spectrum environment andqueue length information from its neighborhood. Each back-logged node performs ROSA to adaptively select the portionof the spectrum to be used and the next hop. Note that ∆t isthe reservation time for the data channel, and includes the timeneeded for transmitting all the remaining data in the scheduledqueue ∆Qs

i /cij , plus the time needed to ACK packets. Byactively collecting RTS, CTS, and DTS packets transmitted onthe CCC, each node learns spectrum and queue informationof its neighborhood.

Once RTS/CTS/DTS are successfully exchanged, senderand receiver tune their transceivers to the selected spectrumportion. Before transmitting, they sense the selected spectrumand, if it is idle, the sender begins data transmission withoutfurther delay. Note that it is possible that the sender or thereceiver find the selected spectrum busy just before datatransmission. This can be caused by the presence of primaryusers, or by conflicting reservations caused by losses of controlpackets. In this case, the node gives up the selected spectrum,and goes back to the control channel for further negotiation.During the RTS/CTS/DTS exchange, if the sender-selectedspectrum can not be entirely used, i.e., the receiver justsensed primary user presence, the receiver will not send aCTS. The sender will go back to the control channel forfurther negotiation once the waiting-for-CTS timer expiresand the RTS retransmission limit is achieved. When data aresuccessfully received, an ACK will be sent by the receiver.The transaction is considered completed after the ACK issuccessfully received.

VI. INTERPRETATION OF ROSA AS A NUM SOLVER

In this section, we show how ROSA can be interpreted as adistributed dual-based solution to a cross-layer network utilitymaximization problem for cognitive radio ad hoc networksunder the system model described in the previous sections. Ajoint congestion control, routing, and dynamic spectrum allo-cation problem for cognitive radio networks can be formulatedas follows.P3 : Given : BER∗SU , BER∗PU , G(V, E), PBgt, AP

Find : Λ, R, C

Maximize :∑

i∈SU∑

s∈S Ui(λsi ); (25)

Subject to :

λsi +

∑

k∈SU ,k 6=i

rski =

∑

l∈SU ,l 6=i

rsil, ∀i ∈ SU , ∀s ∈ S (26)

∑

s∈Srsij ≤ cij , ∀i ∈ SU , ∀j ∈ SU \ i. (27)

Note that if C is the feasible set of the physical rates, valuesof cij ∈ Co(C), i.e., they are constrained to be within theconvex hull of the feasible rate region [24][25]. The feasibleset of the physical rates is expressed by

cij ,∑

f∈[fi,fi+∆fi]

w · log2

[1 +

Pi(f)Lij(f)GNj(f) + Ij(f)

](28)

SINRfkl ≥ SINRth

PU (BER∗PU )·aPkl,∀k, l ∈ PU ,∀f ∈ [fk, fk+∆fk ]

(29)SINRf

ij ≥ SINRthSU (BER∗SU ),∀i, j ∈ SUs.t.

∑s∈S

rsij ≥ 0, ∀f ∈ Fi

(30)∑

f∈[fi,fi+∆fi]

Pi(f) ≤ PBgt, ∀i ∈ SU , (31)

0 ≤ ∆fi ≤ ∆fB , ∀i ∈ SU . (32)

In the problem above, the objective is to maximize asum of utility functions Ui(λs

i ), which are assumed to besmooth, increasing, concave, and dependent on local rate atnode i only [26]. Constraint (26) expresses conservation offlows through the routing variables rs

ij , which represent thetraffic from session s that is being transported on link (i, j).Finally, constraint (27) imposes that the total amount of traffictransported on link (i, j) is lower than the capacity of thephysical link.

By taking a duality approach, the Lagrange dual functionof P3 can be obtained by relaxing constraint (26) throughLagrange multipliers Q = [Qs

i ], with i ∈ SU and s ∈ S .

L(Q) = maxΛ

{ ∑

i∈SU

∑

s∈S(Ui(λs

i )−Qsi λ

si )

}+

+maxR,C

∑

i∈SU

∑

j∈SU,j 6=i

∑

s∈Srsij

(Qs

i −Qsj

) , (33)

where variables indicating data rates are still constrained to becij ∈ Co(C), and C is defined by constraints (28)- (32).

In the above decomposition, the first term of (33) rep-resents the congestion control functionality (which can becarried out independently), while the second term repre-sents routing, scheduling, and physical rate allocation. LetΛ∗(Q),R∗(Q),C∗(Q) be the vectors of optimum values fora given set of Lagrange multipliers Q. While λs,∗

i (Q) canbe computed locally at each source i of session s, R∗(Q),C∗(Q) require global knowledge and centralized algorithms.

To solve the above problem, the following actions need tobe performed at each time slot t:

• Update the congestion control variables. For each sessions and for each source node i:

λsi (t) = sup

λsi

{Ui(λsi )−Qs

i λsi} (34)

• Scheduling and Routing. For each link (i, j), choose thesession that maximizes the differential backlog betweentransmitter and receiver:

s∗ij = arg maxs

{Qs

i −Qsj

}(35)

Then, set rs∗ij

ij (t) = cij(t). Assign link rates cij(t) tomaximize the weighted sum of the link rates of thenetwork, where the weights correspond to differentialbacklogs:

C(t) = arg maxC

∑

i∈SU

∑

j∈SU ,j 6=i

cij

(Q

s∗ij

i −Qs∗ij

j

)(36)

Note that the maximization above is analogous to the

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. X, NO. X, XXXX 2010 8

dynamic backpressure algorithm in [24][22].• Update Lagrange multipliers (queues) as

Qsi (t + 1) =

Qs

i (t) + ε

∑

k∈SU,k 6=i

rski(t)−

∑

l∈SU ,l 6=i

rsil(t) + λs

i (t)

+

(37)Note that the Lagrange function is always convex, andthus the multipliers can be computed using a subgradientalgorithm.

Clearly, the bottleneck of the above solution lies in the routingand scheduling component in (36). Solving (36) requiresglobal knowledge of feasible rates and centralized algorithm. Ithas been shown that the complexity of this family of scheduleproblems is worst-case exponential [22][27]. Exact distributedsolution of (36) is thus infeasible. However, it can be shownthat the closer a policy gets to maximizing (36), the closer thepolicy gets to the capacity region of the network [24]. Thisprovides the rationale for our distributed algorithm, whoseobjective is to maximize (36) under the constraints expressedby (30) and (31), together with (32) for cognitive radio ad hocnetworks.

VII. SIMULATION RESULTS

To evaluate ROSA, we have developed an object-orientedpacket-level discrete-event simulator, which models in detailall layers of the communication protocol stack as describedin this paper. We first concentrate on evaluating the networkthroughput, delay, and fairness. Then, we compare the perfor-mance of the proposed distributed algorithm and the central-ized algorithm. In all simulation scenarios, we considered acommon set of parameters. A grid topology of 49 nodes isdeployed, in a 6000m x 6000m area. We initiate sessions be-tween randomly selected but disjoint source-destination pairs.Sessions are CBR sources with a data rate of 2Mbit/s each.We set the available spectrum to be 54MHz - 72MHz, aportion of the TV band that secondary users are allowed touse when there is no licensed (primary) user operating on it.We restrict the bandwidth usable by cognitive radios to be2, 4 and 6MHz. The bandwidth of the CCC is 2MHz. Theduration of a time slot is set to 20 microseconds. Parameters αand β in (24) are set to 10 and 10 respectively. A Larger CWcan reduce the collision rate but may lead to lower utilizationof the control channel caused by backoff. These values areimplicitly optimized based on the network size in the paper.The SU SINR threshold is 9 dB, and the PU SINR thresholdis 19 dB in the simulation.

We average over multiple trials to obtain a small relativeerror (within 10% of the average value). The data rate is a step-wise approximation of (8), which can model, among others,different modulation schemes available for different SINRvalues. Fig. 3(a) illustrates the network throughput achieved byROSA with time as the number of active sessions varies. Witha higher number of active sessions, ROSA achieves higheroverall network throughput by adaptively adjusting bandwidthto enable concurrent parallel transmissions.

We compare the performance of ROSA with two alternativeschemes, both of which rely on the same knowledge of the

environment as ROSA. In particular, we consider Routing withFixed Allocation (RFA) as the solution where routing is basedon differential backlog (as in Section IV) with pre-definedchannel and transmit power, and to Routing with DynamicAllocation (RDA) as the solution where routing is based onshortest path with dynamic channel selection and transmitpower allocation without considering differential backlog.

We compare against the three solutions by varying thenumber of sessions injected into the network and plot thenetwork throughput (sum of individual session throughput) inFig. 4(a), which shows that ROSA outperforms RFA and RDA.When there are a few active sessions, e.g., 2 or 4, ROSA, RDAand RFA obtain similar throughput performance. However,with more active sessions, ROSA and RDA perform muchbetter than RFA since they use the best among possible spec-trum allocations and routes adaptively. RDA restricts packetsforwarding to the receiver that is closest to the destination,even if the link capacity is very low or the receiver is heavilycongested. In contrast, ROSA, by considering both the linkcapacity and the differential backlog, is more flexible and mayroute packets along paths that temporarily take them fartherfrom the destination, especially if these paths eventually leadto links that have higher capacity and/or that are not as heavilyutilized by other traffic. The improvement obtained by ROSAis more visible when the number of active sessions increases.

Fig. 4(b) shows the delay performance for the three solu-tions. RFA, on average, delivers a larger delay than the othertwo solutions. The above delay performance gap grows as thenumber of sessions increases. As shown in Fig. 4(b), ROSAprovides very low and stable delay performance as the numberof sessions increases. ROSA and RDA yield almost the samedelay performance.

Fig. 4(c) shows the impact of source data rate per sessionon the performance of throughput and delay. We evaluatethe throughput and delay performance as the traffic load persession increases from 100Kbit/s to 8Mbit/s. As shown inFig. 4(c), the throughput achieved by ROSA increases linearlyas the load per session increases. As the load increases, ROSAobtains a significant throughput gain.

Fig. 3(b) shows Jain’s fairness index, calculated as(∑

rs)2/S ∗ ∑(rs)2, where rs is the throughput of session

s, and S is the total number of active sessions. As shown inthe figure, the overall fairness among competing sessions isimproved by ROSA using prioritized channel access scheme.When the sessions are dynamic, the protocol is supposed to bestable since the algorithm adaptively adjusts channel selectionand power allocation according to the current transmissions.

We compare the performance in terms of network spectrumutility defined in (9) of P1 between our distributed algo-rithm and the centralized algorithm. We consider a cognitiveradio network with 10 nodes. We assume that there are7 secondary users and 3 primary users associated with 3different minibands. Every primary user holds a license forone specific miniband, and can only occupy its assignedminiband. We activate sessions between randomly selectedbut disjoint source-destination pairs among the 7 secondaryusers. Fig. 3(c) shows that even though ROSA uses onlylocal information and has low complexity, the performance

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. X, NO. X, XXXX 2010 9

0 10 20 30 40 50 600

2

4

6

8

10

12

14

16

Time [s]

Thr

ough

put [

Mbi

t/s]

Throughput vs Time,

2 active sessions4 active sessions6 active sessions8 active sessions

4 8 12 16 20 240

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Number of Active Sessions

Fai

rnes

s in

dex

Fairness index

RFARDAROSA

0 0.2 0.4 0.6 0.8 1 1.20

2

4

6

8

10

12

14

16

18x 10

18

Normalized Traffic Load

Net

wor

k S

pect

rum

Util

ity

Performance Comparison Between ROSA and Centralized Algorithm

Centralized AlgorithmROSA

(a) (b) (c)Fig. 3. (a): Throughput for Different Number of Active Sessions; (b): Fairness Index; (c): Performance Comparison Between ROSA and Centralized Algorithm

5 10 15 200

5

10

15

20

25

30

Number of Active Sessions

Ave

rage

thro

ughp

ut [M

bit/s

]

Average throughput vs Number of Active Sessions

ROSARFARDA

5 10 15 200

50

100

150

200

250

300

350

400

450

Number of Active Sessions

Ave

rage

Del

ay [s

]

Average Delay vs Number of Active Sessions

ROSARFARDA

0 1 2 3 4 5 6 7 80

5

10

15

20

25

30

35

Load per session [Mbit/s]

Ave

rage

thro

ughp

ut [M

bit/s

]

Impact of the session load

ROSARFARDA

(a) (b) (c)Fig. 4. (a): Throughput vs Number of Active Sessions; (b): Delay vs Number of Active Sessions; (c): Impact of Source Data Rate per Session on Throughput

is within 75% of the optimal (centralized) solution. However,the centralized solution is obtained with global informationand has exponential computational complexity.

VIII. CONCLUSIONS

We proposed, discussed and analyzed ROSA, a distributedalgorithm for joint opportunistic routing and dynamic spec-trum access in multi-hop cognitive radio networks. ROSAwas derived by decomposing a cross-layer network utilitymaximization problem formulated under the constraints ofcognitive radio networks. Through discrete-event simulation,ROSA was shown to outperform simpler solutions for inelastictraffic. Future work will aim at deriving a theoretical lowerbound on the performance of ROSA. In addition, we arecurrently implementing ROSA on a software defined radioplatform based on an open source platform built on GNU radioand USRP2.

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[26] M. Chiang, S. Low, A. Calderbank, and J. Doyle, “Layering as Optimiza-tion Decomposition: A Mathematical Theory of Network Architectures,”Proceedings of the IEEE, vol. 95, no. 1, pp. 255–312, January 2007.

[27] G. Sharma, N. B. Shroff, and R. R. Mazumdar, “On the complexityof scheduling in wireless networks,” in Proc. of ACM Intl. Conf. onMobile Computing and Networking (MobiCom), Los Angeles, CA, USA,September 2006.

Lei Ding received the B.S. degree from SichuanUniversity, Chengdu, China, in 2003, and the M.S.degree Beijing Institute of Technology, Beijing,China in 2006, both in Electrical Engineering. From2006 to 2007, she was an engineer with the R&Dgroup of Motorola GTSS (Global Telecom SolutionsSector) China Design Center, Beijing, China. Cur-rently, she is a Ph.D. student under the supervision ofDr. Tommaso Melodia with the Wireless Networksand Embedded Systems Laboratory, Department ofElectrical Engineering, State University of New York

at Buffalo. She was the recipient of the State University of New York atBuffalo Dean’s Scholarship in 2008.

Tommaso Melodia (M’2007) is an Assistant Pro-fessor with the Department of Electrical Engineeringat the University at Buffalo, The State University ofNew York (SUNY), where he directs the WirelessNetworks and Embedded Systems Laboratory. Hereceived his Ph.D. in Electrical and Computer En-gineering from the Georgia Institute of Technologyin 2007. He had previously received his “Laurea”(integrated B.S. and M.S.) and Doctorate degrees inTelecommunications Engineering from the Univer-sity of Rome “La Sapienza”, Rome, Italy, in 2001

and 2005, respectively. He is the recipient of the BWN-Lab Researcher of theYear award for 2004. He coauthored a paper that was was recognized as theFast Breaking Paper in the field of Computer Science for February 2009 byThomson ISI Essential Science Indicators. He is an Associate Editor for theComputer Networks (Elsevier) Journal, Transactions on Mobile Computingand Applications (ICST) and for the Journal of Sensors (Hindawi). He was thetechnical co-chair of the Ad Hoc and Sensor Networks Symposium for IEEEICC 2009. His current research interests are in modeling and optimizationof multi-hop wireless networks, cross-layer design and optimization, cogni-tive radio networks, multimedia sensor networks, and underwater acousticnetworks.

Stella N. Batalama (S’91, M’94) received theDiploma degree in computer engineering and science(5-year program) from the University of Patras,Greece in 1989 and the Ph.D. degree in electricalengineering from the University of Virginia, Char-lottesville, VA, in 1994.

From 1989 to 1990 she was with the ComputerTechnology Institute, Patras, Greece. In 1995 shejoined the Department of Electrical Engineering,State University of New York at Buffalo, Buffalo,NY, where she is presently a Professor. Since 2009,

she is serving as the Associate Dean for Research of the School of Engineeringand Applied Sciences. During the summers of 1997-2002 she was VisitingFaculty in the U.S. Air Force Research Laboratory (AFRL), Rome, NY. FromAug. 2003 to July 2004 she served as the Acting Director of the AFRL Centerfor Integrated Transmission and Exploitation (CITE), Rome NY.

Her research interests include small-sample-support adaptive filtering andreceiver design, adaptive multiuser detection, robust spread-spectrum com-munications, supervised and unsupervised optimization, distributed detection,sensor networks, covert communications and steganography.

Dr. Batalama was an associate editor for the IEEE Communications Letters(2000-2005) and the IEEE Transactions on Communications (2002-2008).

John D. Matyjas received the A.S. degree in pre-engineering from Niagara University in 1996 andthe B.S., M.S., and Ph.D. degrees in electrical en-gineering from the State University of New Yorkat Buffalo in 1998, 2000, and 2004, respectively.He was a Teaching Assistant (1998-2002) and aResearch Assistant (1998-2004) with the Commu-nications and Signals Laboratory, Department ofElectrical Engineering, State University of New Yorkat Buffalo. Currently, he is employed since 2004 bythe Air Force Research Laboratory in Rome, NY,

performing R&D in the information connectivity branch. His research interestsare in the areas of wireless multiple-access communications and networking,statistical signal processing and optimization, and neural networks. Addi-tionally, he serves as an adjunct faculty in the Department of ElectricalEngineering at the State University of New York Institute of Technologyat Utica/Rome. Dr. Matyjas is the recipient of the 2009 Mohawk ValleyEngineering Executive Council ”Engineer of the Year” Award and the 2009Fred I. Diamond Basic Research Award for ”best technical paper.” He alsowas the recipient of the State University of New York at Buffalo PresidentialFellowship and the SUNY Excellence in Teaching Award for GraduateAssistants. He is a member of the IEEE Communications, Information Theory,Computational Intelligence, and Signal Processing Societies; chair of the IEEEMohawk Valley Chapter Signal Processing Society; and a member of the TauBeta Pi and Eta Kappa Nu engineering honor societies.

Michael J. Medley (S’91-M’95-SM’02) receivedthe B.S., M.S. and Ph.D. degrees in electrical engi-neering from Rensselaer Polytechnic Institute, Troy,NY, in 1990, 1991 and 1995, respectively.

Since 1991, he has been a research engineerfor the United States Air Force at the Air ForceResearch Laboratory, Rome, NY, where he has beeninvolved in communications and signal processingresearch related to adaptive interference suppression,spread spectrum waveform design, covert messag-ing, and airborne networking and communications

links. In 2002, he joined the State University of New York Institute ofTechnology in Utica, NY where he currently serves as an Associate Professorand Coordinator of the Electrical Engineering program.