Cooperative Sensing at the MAC Level in Simple Cognitive Personal Area Networks

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 30, NO. 9, OCTOBER 2012 1711

Cooperative Sensing at the MAC Level in SimpleCognitive Personal Area Networks

Jelena Misic

Abstract—We investigate the performance of simple cognitivepersonal area network (CPAN) with cooperative sensing amongthe nodes and CPAN coordinator. Nodes are equipped with smallbuffers of capacity K packets, and each node is allowed totransmit a batch of up to µ packets in one transmission cycle.Upon transmission, each node must support the operation of theCPAN by performing sensing duty. We model this system throughprobabilistic analysis and a queuing model, and demonstrate thetradeoff between the accuracy of cooperative spectrum sensingand node’s ability to communicate.

Index Terms—cooperative sensing, cognitive personal areanetworks, MAC

I. INTRODUCTION

OPPORTUNISTIC or cognitive spectrum access (OSA)aims to make better utilization of available spectrum

by dynamically detecting (i.e., sensing) unused channels andusing them for communication by spectrally agile devices [1].OSA technology can be combined with frequency hoppingspread spectrum (FHSS) technology [10] to offer increasedperformance, reduced interference, and effortless coexistenceof several cognitive personal area networks (CPANs) in thesame physical space. In this setup, a dedicated coordinatorallocates bandwidth to individual nodes upon their request andselects the hopping sequence in an adaptive, pseudo-randommanner, using the map of busy and idle channels (i.e., thechannel map) which is constantly updated by sensing [4].Reliable operation of a CPAN necessitates accurate spectrumsensing, which may be performed by the coordinator alone,or through a cooperative effort of CPAN nodes and thecoordinator.Design of Medium Access Protocol (MAC) which integrates

sensing, reporting and data phases in cognitive networks (CN)has been considered in [2] where nodes deploy dynamic IDnumber in order to regulate access to the medium. Two levelMAC for opportunistic spectrum access which deploys slottedALOHA and CSMA for node’s access is proposed in [5] whileintegration of spectrum sensing rules with CSMA/CA wasconsidered in [8]. Important issue in designing the cognitiveMAC is selection and maintenance of common control chan-nel (CCC). CCC can be static (dedicated) [2] or dynamic.Dynamic CCC can be achieved with support of CSMA [5]or using rendezvous technique if channel hopping is deployed[9], [7].

Manuscript received 20 January 2011; revised 20 June 2011. This researchis partly supported by the NSERC Discovery Grant.The author is with Ryerson University, Toronto, ON. Canada (e-mail:

[email protected]).Digital Object Identifier 10.1109/JSAC.2012.121015.

beacon

channel x

beacon

hop

guard time

time

reservation and admin

reportingdata exchange by devices announced in the beacon

channel y

Fig. 1. Structure of the superframe.

In this paper we investigate a cooperative sensing mech-anism based on a sensing-after-transmission scheme whichmandates that, upon being allowed sufficient bandwidth totransmit their data, nodes must contribute to sensing for aprescribed time interval. Sensing accuracy was modeled in[12] but without considering node’s communication needs.Current work, however, models integrated communicationsand sensing framework for realistic frequency hopping CPANwhich includes recovery procedure and nodes with finitebuffers. In this setup, the coordinator’s role is threefold: first,it allocates bandwidth to each node by following a simpleround-robin mechanism; second, it collects sensing reportsfrom the nodes in order to keep the channel map up to date;and third, it performs sensing itself when there’s no othernode to it. We analyze the performance of this cooperativesensing scheme through probabilistic analysis and a queuingmodel, and show that the aforementioned adverse effects maybe minimized by judicious choice of the cooperation protocoland relevant network parameters. This scheme can work withstatic or rendezvous based CCC.The paper is organized as follows: Section II gives more

details about the operation of a CPAN. Analytical modelis presented in Section III which models the durations oftransmission, sensing and waiting times and in Section IVwhich models sensing accuracy achieved by sensing-after-transmission scheme. Section V presents the performance data.Finally, Section VI concludes the paper.

II. CPAN OPERATION

Nodes in a CPAN group to form a piconet which iscontrolled by a coordinator node. (The role may be undertakenby a dedicated node, or by several nodes on a temporarybasis.) The coordinator emits beacon frames that signal thebeginning of superframes; each superframe takes place on adifferent channel from the chosen RF band. The superframestructure is schematically shown in Fig. 1. The hoppingsequence is dynamically determined by the coordinator, basedon the channel state obtained by sensing, and announced inthe beacon frame.

0733-8716/12/$31.00 c© 2012 IEEE

1712 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 30, NO. 9, OCTOBER 2012

Nodes that have data to transmit request bandwidth duringa predefined reservation interval that precedes the superframe;a single request may contain up to μ packets at a time.The coordinator collects the requests, makes the bandwidthallocation decisions, and announces them within the beaconframe. Allocation includes as many requests as can fit intoone superframe, with remaining requests deferred to the nextone. Requests are serviced in a round-robin fashion.Nodes that receive bandwidth allocation transmit their data

at a designated time during the data exchange period thatfollows the beacon frame, and pay for it by performing sensingduty in subsequent superframe(s). For each transmitted packet,the node has to perform sensing duty for kp subsequentsuperframes, where kp will be referred to as the penaltycoefficient. Sensing results are reported after the data exchangeperiod, but before the reservation requests; if kp > 1, sensingreports are sent in every one of the kp superframes. Inthis manner, nodes that have more data to send can requestadditional bandwidth immediately upon finishing sensing duty.Moreover, the coordinator has sufficient time to analyze therequests and make allocation decisions.All nodes must listen to the beacon in order to learn about

the channel to be used for the next hop, and in order toallow reception. Nodes that are currently sensing will suspendsensing so as to receive data, and resume it in the nextsuperframe; nodes that transmit data can simply switch toreceive mode at an appropriate time since transmission andreception will occur at different times during the data exchangeperiod. In this manner, receiving is not penalized directly, butit does lead to the extension of the packet service cycle dueto the pre-emption of sensing. In the case of collision withprimary user CPAN members enter recovery procedure at CCCin which they choose channel for next regular superframe.

III. MODEL OF JOINT TRANSMISSION AND SENSING

A comprehensive model of CPAN operation can be builtusing a gated μ-limited M/G/1/K system with single vacationand setup time [13]. The system is gated since, once abandwidth reservation request is sent for the packets currentlyin the buffer, any packets that arrive cannot be serviced beforethe pending transmission, subsequent sensing and, possibly,reception, are finished. The timing diagram showing the op-eration of a target node, is shown in Fig. 2 for the case whenthe node buffer, after sensing, is empty (upper diagram) or not(lower diagram). Node’s packet transmission time is randomlypositioned with respect to the beacon and, by extension, to thereporting and reservation periods which immediately precedethe beacon; these periods can be assumed to have a fixedduration. The interval between the end of a transmission andnext beacon is referred to as (beacon) synchronization time.All time intervals are measured in unit slots, so that the total

superframe length is sf unit slots. Let the probability gener-

ating function (PGF) for the packet size be b(z) =

imax∑imin

bizi

with a mean value of b =

imax∑imin

i · bi. Assuming that packetsarrive to the node according to a Poisson process with arrival

optional receptionsensing (kp) and

possible recovery idle

packet arrival

sync setup

reporttransmit transmitrequest

sync

(a) Case with idle period after sensing.

optional reception setup

report, requesttransmit transmit

syncsensing (kp) and possible recovery

packet arrival

(b) Case without idle period after sensing.

Fig. 2. Timing of node’s activities.

rate λ, the offered load is ρ = λb. Nodes are assumed to haveinput buffers with capacity of K packets; packets that arrivewhen the buffer is already full are simply discarded. If wedenote the blocking probability with PB , the carried load fora node will be denoted as ρ′ = (1 − PB)ρ.According to Fig. 2, node service cycle consists of the

following components:

• Waiting time for the beginning of round-robin packetservice starting from a bandwidth reservation request.We call it setup time and it comes in two flavors: setuptime after idle state and regular setup time. In the formercase, (shown in Fig. 2(a)), the node buffer is emptyafter sensing and the node will be idle until a newpacket arrives; the node must, then, synchronize withthe beacon in order to apply for bandwidth and waitfor its transmission. Therefore setup time after idle statecontains synchronization time and waiting time for roundrobin service. The latter case occurs when the node bufferwas not empty after the sensing; the node can, thenimmediately apply for the bandwidth but it still has towait for the actual round-robin allocation.

• packet service time,• synchronization with the beacon, and• spectrum sensing time, possibly preempted (and ex-tended) by packet reception.

These components may be formally described as follows.The regular setup time is the waiting time until all active

nodes with lower addresses that have packets, have beenserved. Assuming the CPAN has M nodes, the PGF forthe time between two occurrences of transmission by thesame node (hereafter referred to as the piconet cycle time)is C(z) = (1 − ρ′ + ρ′S(z))M , where S(z) is the PGF ofthe node service time. Mean duration of the CPAN cycle isC = C

′(1) = Mρ′S. In terms of renewal theory [6], the time

between the bandwidth request and (random) beginning of theservice within the cycle is denoted as ‘elapsed’ piconet cycletime in the discrete time renewal process, where the distribu-tion of the renewal interval is given by the piconet cycle timeC. Elapsed piconet cycle time will be denoted with the PGF

d2(z) =1− C(z)

(1 − z)Cwith the mean value of d2 =

C(2)

2C, where

C(2) = C′′(1) + C

′(1) denotes the second moment of the

cycle time. The number of packet arrivals during the waitingfor round-robin service can be obtained by substituting the

MISIC: COOPERATIVE SENSING AT THE MAC LEVEL IN SIMPLE COGNITIVE PERSONAL AREA NETWORKS 1713

PGF for the number of Poisson packet arrivals during a singleslot e−λ(1−z) in place of variable z in d2(z). We will denote

its PGF as α2(z) =

∞∑i=0

α2,izi = d2(e

−λ(1−z)), where α2,i

denotes mass probability of i packet arrivals during regularsetup time d2.After a packet arrival, the node needs to wait for the next

reservation period before it can apply for bandwidth, as shownin Fig. 1. In the renewal period presented with superframeduration, this synchronization time presents the ‘residual’superframe time. Given that the PGF for the superframeduration is zsf , PGF for beacon synchronization time hasthe form d3(z) = (1− zsf ) /((1 − z)sf ), and the numberof packet arrivals to the node buffer during that time has thePGF of α4(z) = R (e−λ(1−z)). By replacing the variable z ind3(z) with the PGF for the number of Poisson packet arrivalsduring a single slot, we obtain the PGF for the number ofpackets which arrive during synchronization with beacon as

α3(z) =

∞∑i=0

α3,kzk = d3(e

−λ(1−z)).

Setup time after the idle state has the PGF of d3(z)d2(z),and the PGF for the number of packet arrivals during that time

is I(z) = α2(z)α3(z) =

∞∑k=0

ikzk.

The PGF for the number of packet arrivals during packet

service time is αp(z) = b(e−λ(1−z)) =

∞∑k=0

αp,kzk. The PGF

for batch transmission time is denoted as the service periodS(z).Duration of the sensing period after the batch transmission

has the PGF Vs(z). However, packet reception may occurin the same superframe or in a subsequent one. To modelthis effect, we need to find the probability distribution of thedistance (in slots) between the transmission and reception bythe given node. To this end, consider the piconet cycle startedby the transmission by the target node with ID = i, i = 1. .M ;and note that reception will be triggered by some other nodewhose ID, say, j, is uniformly distributed in the range betweeni + 1 and (i + M − 1) mod M . Given that packet size is bslots, and allowing for an extra slot needed for the mandatoryacknowledgement, the PGF for the number of slots betweentransmission and reception by node i is

Θ(z) =

M−1∑k=1

(ρ′S(z) + 1− ρ′)k

M − 1=

(M−1)μ(b+1)∑i=1

θizi (1)

The probability that a node will transmit and receive a batchof packets in the same superframe is Pθ = P (Θ < sf −

δ) =

sf−δ∑i=1

θi, where δ represents the duration of the portion

of the superframe dedicated to the reporting of sensing resultsand bandwidth reservation. The probability that transmissionand reception will occur in separate superframes (in whichcase the sensing duty is preempted by a superframe in whichreception takes place) is 1−Pθ . Then, the PGF for duration ofreception within the same or following superframe is d5(z) =Pθ + (1 − Pθ)z

sf , and the PGF for the number of packets

that arrive to the node buffer during that time is α5(z) =Pθ + (1− Pθ)e

−sf (λ−λz).CPAN recovery time after the collision with primary user is

modeled with PGF Vc(z) = Pczsf+(1−Pc) where Pc denotes

collision probablity. Pc will be calculated in Section IV. Theperiod after the last packet transmission in a batch whichincludes synchronization with the beacon, potential receptionin the following superframe, sensing and potential CPANrecovery, will be referred to as vacation. This time can bedescribed with the PGF of Vtot(z) = d3(z)d5(z)Vs(z)Vc(z),and the number of packets that arrive during that time has the

PGF of F (z) = Vtot(e−λ(1−z)) =

∞∑k=0

fkzk.

a) Number of packets in the node queue: Since nodeshave finite buffers, the Markov points in which the systemwill be modeled are:

1) The moment when the node returns from sensing (in-cluding potential CPAN recovery), at which time the(mass) probability that its buffer has k = 0 . . Kpackets is qk. Let the sum of all mass probabilities be

q =

K∑k=0

qk.

2) The moment when setup time is completed and nodeis ready for transmission, at which time the (mass)probability that its buffer has k = 1 . . K packetsis wk. Let the sum of these mass probabilities bew =

∑Kk=1 wk.

3) When the service period starts after the completion ofsetup time with i packets in the node buffer, the momentwhen m-th, m ∈ 1 . .min(μ, i), packet transmission iscompleted is also considered as a Markov point, and the(mass) probability of having i−m ≤ k < K − 1 in thebuffer at this time is denoted as πm,i

k .

Since bandwidth allocation is done based on request postedat the return from sensing or after packet arrival which endsthe idle state, the PGF for the number of packets transmittedin the service period is built using the corresponding massprobabilities as

Φ(z) =

μ−1∑k=1

qkzk + zμ

∞∑k=μ

qk (2)

+ q0

⎛⎝μ−1∑

k=0

α3,kzk+1 + zμ

∞∑k=μ

α3,k

⎞⎠

with the average value of Φ = Φ′(1). The service period canbe represented with

S(z) =

μ−1∑k=1

qkb(z)k + b(z)μ

K∑k=μ

qk (3)

+ q0

⎛⎝μ−1∑

k=0

α3,kb(z)k+1 + b(z)μ

∞∑k=μ

α3,k

⎞⎠

As explained above, duration of the vacation time due tosensing is kp superframes for each transmission cycle of 1 . .μframes, and its PGF is Vs(z) = zsfkp .


b) Node model at Markov points: Let the mass proba-bilities for the number of packets in the buffer at the end ofthe service period for that node be

h0 =

μ∑i=1

π(i,i)0 (4)

hk =

μ∑i=1

π(i,i)0 +

μ+k∑i=μ+1

π(μ,i) 1 ≤ k ≤ K − μ

hk =

μ∑i=1

π(i,i)0 +

K∑i=μ+1

π(μ,i) K − μ+ 1 ≤ k ≤ K − 1

and let their sum be h =∑K−1

k=0 hk.Then the number of packets in the buffer when the node

returns from sensing (including potential CPAN recovery) is

qk =

k∑j=0

hjfk−j 0 ≤ j ≤ K − 1 (5)

qK =

K−1∑j=0

hj

∞∑k=K−j

fk

When we sum all equations from (5), we obtainK∑

k=0

qk =

K−1∑k=0

hk, or q = h. The length of the packet queue at the end

of setup time may be calculated as

wk =q0ik−1 +

k∑j=1

qjα2,k−1, 1 ≤ k ≤ K − 1 (6)

wK =q0

∞∑k=K

ik−1 +

K∑j=1

qj

∞∑k=K−j

α2,k

where ik, k = 0 . . ∞, are mass probabilities from thedistribution of the setup time after the idle state, while α2,k,k = 0 . . ∞, are mass probabilities from the distribution ofthe number of arrivals during regular setup time. They canbe obtained by expanding I(z) and α2(z) respectively inpower series. When we sum all equations from (6) we obtainK∑

k=1

wk =

K∑k=0

qk, or q = w.

The state of the node buffer at the moments after packetdepartures can be described with

π(1,i)k =wiak−i+1 i− 1 ≤ k ≤ K − 2 (7)

π(1,i)K−1 =wi

∞∑k=K−i

ak

π(m,i)k =

k+1∑j=i−m+1

π(m−1,i)j ak−j+1

m− 1 ≤ i ≤ k, 0 ≤ k ≤ K − 2 (8)

π(m,i)K−1 =

K−1∑j=i−m+1

π(m−1,i)j

∞∑k=K−j

ak

Summing the equations (7) and (8), we obtainK−1∑k=i−1

π(1,i)k =

wi andK−1∑

k=i−m

π(m,i)k =

K−1∑k=i−m+1

π(m−1,i)k = wi, respectively.

Since the sum of the mass probabilities in all Markov pointsmust be 1, we have the normalization condition:

1 =

K∑k=0

qk +

K∑k=1

wk +

μ∑m=1

K∑i=m

K−1∑k=i−m

π(m,i)k (9)

In order to find the carried load and blocking probability,we need to find the average distance between two successiveMarkov points as: η = (q − q0)Vtot + q0(Vtot +

1λ + d3) +

wd2 + (1 − q − w)b. However, ends of vacation and setuptimes correspond to the points when the node is not active,and we need the distance between points when the node startsa vacation and when the node starts the transmission process –i.e., we need to merge vacation and setup times and, possibly,idle times as well. The scaled distance between successivepoints of inactivity and activity has the form:

ηs =(q − q0)

1− w(Vtot + d2) +

q01− w

(Vtot +1

λ+ d3 + d2)

(10)

+(1− q)b

1− w

Then, carried load becomes the ratio:

ρ′ =(1 − q)b

ηs(1− w)(11)

and packet blocking probability becomes PB = 1− ρ′ρ .

When the system of equations (5), (6), (7), (8), (10), (9)and (11) is solved, we obtain probability distributions of nodebuffer occupancy at Markov points, carried load and blockingprobability.

IV. MODELING THE SENSING PROCESS

Without loss of generality, we may assume that the RF bandis partitioned into N working channels, each of which is usedby a distinct primary source, and that active and idle times oneach channel follow a probability distribution with probabilitydensity functions ta,r(x) and ti,r(x) and mean values of Ta,r

and Ti,r, respectively. Let the Laplace-Stieltjes transforms(LSTs) and mean value of channel times be denoted withfollowing expressions where subscript w ∈ (i, a) denotes idleand active time respectively and subscript r refers to real valueof the channel time.

T ∗w,r(s) =

∫ ∞

x=0

e−sxtw,r(x)dx (12)

Tw,r =

∫ ∞

x=0

xtw,r(x)dx = −T ∗′w,r(0)

The mean value of real cycle time will be denoted asTcyc,r = Ta,r + Ti,r. For clarity, we will denote relevantvariables in the active and idle (free) periods of the channelstate, with subscripts a and i, respectively, while the secondsubscript (r and o) stands for real and observed values,respectively, of the relevant network parameters. We consider


the problem of sensing exclusively at the MAC level, assumingthat information obtained by sensing is accurate at the timeof sensing and that it is delivered to the CPAN coordinatorwithout any loss. Any error in sensing is, then, due to the timeinterval between the moment the actual sensing takes placeand the moment when the coordinator uses that informationto decide on the next hop (or hops) in the hopping sequence.Upon completing the access to the medium with up to (and

including) μ frames, the node has to spend kp superframesin sensing over N − 1 primary channels (since one of themwill always be in use by the CPAN). From the queuingmodel, the probability that the node is involved in sensingis Ps = qVs

ηs(1−w) . Then, the PGF for the number of CPANnodes involved in sensing becomes

Ξ(z) =

M∑j=0

(M

j

)P js (1 − Ps)

M−j zj =

M∑j=0

ξjzj (13)

where ξj denotes mass probability that j secondary nodes aresensing.However, when all nodes are busy with transmission or

idle, the coordinator itself takes over the sensing duty, hencethe average number of nodes involved in sensing is Ξ + ξ0.When a node is involved in sensing, it senses one channelevery ds time slots. For simplicity, we assume that channelsare assigned to nodes in a random fashion, without regard towhether the channel is recorded as busy or idle in the channelmap. Upon receiving the sensing reports, the coordinatorupdates the channel map, selects the best channel for thesubsequent frequency hop, and announces it in the next beaconframe.Since sensing is performed in discrete intervals and the

number of nodes available for sensing is typically lower thanthe number of channels, Ξ < N , the information in the channelmap is only partially correct at any given time. The magnitudeof the error (i.e., the mean number of channels with incorrectinformation) and the delay in detecting changes in channelstatus will determine the success rate of CPAN transmissionsand, ultimately, its quality of service. When a small piconetuses a small number of working channels, the delay indetecting the onset of primary user activity is as important asthe corresponding delay in detecting the beginning of spectralopportunity; consequently, idle and active channels will besensed at the same rate.When the node is sensing, the distance between two sensing

events is ds slots and number of sensing actions within thesuperframe is �(sf −δ)/ds�. With each node sensing channelsin the random order, the time between two consecutive sensingevents on the same channel is a random variable with the PGFof

H(z) =ξ0

∞∑k=1

1

N − 1

(N − 2

N − 1

)k−1

zdsk +

M∑min(M,N−2)+1

ξlzds

+

min(M,N−2)∑l=1

∞∑k=1

1

N − 1

(N − 2

N − 1

)k−1

ξlzdsk (14)

and an average value of H = H ′(1). The time between thechange of channel state and next sensing event is presentedin Fig. 3. This time is actually the residual life(time) between

RTa,r

Ta,o

Ti,r

Ti,oR

R

R

channel x

channel y

real activity

observed activity

sensing events

Fig. 3. Detection delays during channel sensing.

two sensing events [6]. The PGF for the residual sensing timeis

R(z) =

Rmax∑k=0

Rkzk =

1−H(z)

H(1− z)(15)

where Rmax is chosen according to M and N .Further derivation of observed inactive and active periods,

probability of sensing error and duration of sensing errorcan be derived following the same steps as in [12] withthe difference that inactive and active channel times havecontinuous probability distributions instead of discrete ones.Probability Px(k) that primary source will become activegiven that it was sensed as idle in k previous superframes isroughly equal to the probability that idle time of the primarysource is larger than ksf and smaller than (k + 1)sf , i.e.Px(k) =

∫ (k+1)sfksf

ti,r(x)dx. If duration of primary source’sidle time does not follow memory-less probability distribution,coordinator can minimize collisions with primary sources bychoosing channel with k smaller than predetermined thresholdkmax. Therefore probability that CPAN will need recoveryprocedure is equal to Pc = Px(kmax) + ps,i, where ps,i isprobability of wrong channel state in the channel table [12].

V. PERFORMANCE EVALUATION

In order to evaluate the performance of the proposedscheme, we have solved the queuing model first and thenused its results to solve the channel sensing model. We haveused Maple 13 software package by Maplesoft, Inc. [11].Analytical model has been validated by simulation modelbuilt in Artifex [3]. In all performance figures numericalvalues are interconnected with lines while simulation valuesare presented as point graphs using box as a symbol. Themodel parameters were:

• The system uses N = 10 channels, each of which has anindependent primary user with active and inactive timesfollowing gamma distribution with mean values of 800and 400 slots, respectively, and standard deviations of667 and 333 slots, respectively.

• The piconet has M nodes; the number M was fixed to10 in some experiments, and made variable in others.

• When a node (or coordinator) is sensing, the distancebetween sensing events (which includes channel switchand actual channel sensing) is ds ∈ 5, 10 slots.

• Superframe duration was set to sf = 100 units, andthe portion of the superframe dedicated to administrative


12

34

5

kp0.05

0.10.15

0.2

rho

0.2

0.4

0.6

0.8

(a) Blocking probability.

12

34

5kp

0.050.1

0.150.2

rho

12

16

20

24

28

(b) Average service time.

12

34

5kp

0.050.1

0.150.2

rho

5101520253035

(c) Average piconet cycle time.

Fig. 4. Transmission performance under variable offered load and sensing penalty; sensing interval ds = 10 slots.

activities (reporting and reservation periods) to δ = 20units.

• The penalty coefficient was varied in range kp = 1 . . 5or kept constant at kp = 1, as noted.

• Node buffer size was set to K = 10 packets, while themaximum number of packets (from a single node) to beserviced in one superframe is μ = 3.

• Packet length distribution (including the acknowledge-ment) was uniform with PGF of b(z) = 0.2

∑12i=8 z

i timeunits with mean value b = 10.

• Packet arrival rate per node was varied to give offer loadvalues in the range of ρ = 0.03 . .0.24, with packet desti-nations uniformly distributed over all remaining piconetnodes.

c) Experiment 1: variable offered load and penalty co-efficient: Our first experiment shows the impact of offeredload ρ and penalty coefficient kp on piconet transmission andsensing, expressed through packet blocking probability, carriedload, average node service time, and average piconet cycletime.Transmission performance is shown in the diagrams in

Fig. 4. From Fig. 4(a), we observe that blocking increaseswith traffic load and/or the penalty coefficient, since highervalues of kp mean that nodes will spend more time in sensingand, thus, have less time for transmission. As a result, inputbuffer will fill up faster, and packets that arrive at a full bufferare blocked – i.e., discarded. In fact, signs of saturation can bedetected at higher loads and values of kp > 3; this is probablybest seen in the diagrams of average service time in Fig. 4(b).By the same token, carried load and average piconet cycletime tend to flatten out at all values of kp ≥ 2, as seen in Fig.4(c).The diagrams in Fig. 5 show measures of sensing perfor-

mance: the observed duration of channel activity and numberof primary channels with wrong status information. Under lowload and small penalty coefficient kp, there are not enoughnodes to do the sensing, and the sensing process exhibits anon-negligible error. Namely, the observed duration of channelactivity and inactivity deviate noticeably from their true valuesof 800 and 400 time slots, respectively, while the number ofprimary channels for which the sensed information is incorrectis over 0.2 (corresponding to an error of 2%). The errordecreases as the traffic load increases but still, the penaltycoefficient should be kept at kp = 1 so as to limit the blockingprobability to reasonably low values.

d) Experiment 2: reduced sensing interval: Fortunately,limiting the penalty coefficient to kp = 1 need not affectthe accuracy of sensing since we have an additional tuningparameter – time interval between two consecutive sensingevents when node is in the sensing state. Of course, shorteningsensing interval requires that a simpler sensing algorithmmust be used: i.e., carrier detection (or even simple energydetection), instead of more complex feature detection [1]. Toillustrate this point, in the second experiment we have changedthe time interval between two sensing events to ds = 5,while keeping all other parameters at the same values (iffixed) or in the same range (if variable). As can be seen fromFig. 6, shortening the sensing interval ds leads to significantimprovement of the sensing accuracy even for kp = 1, asdesired; at the same time, it does not affect the blockingprobability which depends on kp only, but not on ds.

e) Experiment 3: variable piconet size and offered load:In the third experiment we have varies the offered load andpiconet size, while setting the scheduling parameter, penaltycoefficient, and sensing interval to values μ = 3, kp = 1, andds = 5, respectively. Results shown in Fig. 7 demonstrate thatincreasing CPAN size does lead to improved sensing accuracywithout seriously deteriorating the blocking probability of thenode.

f) Experiment 4: variable offered load and schedulingparameter: Finally, in our fourth experiment we set M = 10,kp = 1, ds = 5 and varied frame arrival rate and schedulingparameter μ (which was kept at μ = 3 during all previousexperiments). Results shown in Fig. 8 demonstrate that in-creasing scheduling parameter decreases blocking probability,but also leads to slight deterioration of the sensing accuracy.The latter effect is actually expected since the sensing vacationof a full superframe is taken after every μ transmitted packets.From the experiments described above, the following con-

clusions can be drawn.

1) To keep the blocking probability as low as possible, thevalue of the penalty coefficient kp should be kept low,preferably at kp = 1, esp. at higher traffic load.

2) To increase throughput, esp. in case of high or burstytraffic, the scheduling parameter μ should be as high aspossible but without exceeding transmission time withinsingle superframe. However, this will decrease channelsensing accuracy and lead to increased collisions withprimary users. In order to deal with wrong channelstatus in channel table and with collisions with primary


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Fig. 5. Sensing performance under variable packet arrival rate and sensingpenalty; sensing interval ds = 10 slots.

secondary users, each CPAN packet must contain somekind of error detection code and transmission mustbe acknowledged. Lack of acknowledgement should beinterpreted as a problem with channel availability andtransmission should be stopped immediately. Primaryuser will not be affected by this collision due to lowoperation power of CPAN nodes. CPAN recovery pro-cedure should be started by the coordinator at CCC andnode should re-apply for bandwidth in the superframefollowing the recovery procedure. However, in case of

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Fig. 6. Sensing performance under variable packet arrival rate and sensingpenalty; sensing interval fixed at ds = 5.

high traffic load, smaller values of μ might also bebeneficial if fairness amongst the nodes is required. Thiswill keep the average waiting time low and provide goodsensing accuracy.

3) Cooperation helps reduce sensing error. Furthermore, toreduce sensing error even further, the fastest possiblesensing technique should be used: e.g., energy detec-tion instead of carrier or feature detection. Also, thecoordinator should help with sensing at times when noordinary node is obliged to do so.

The first two observations above indicate that it might bebeneficial to adapt the penalty coefficient and/or the schedulingparameter value(s) so as to improve performance under bothlow and high load. A control problem might be designed toobtain values for kp and μ that would minimize both sensingerror and blocking probability in a given range of CPAN size,superframe duration, number of channels, and offered load. Analternative solution would be to devise a suitable algorithm todynamically adjust the values of kp and μ so as to keep thesensing error and blocking probability within specified limits.We note that sensing error will be lower if the number of

nodes in the piconet is larger, due to the increased pool ofnodes available for sensing; at the same time, the availablebandwidth per node will be reduced. Piconet membership,however, cannot easily be controlled by the network designer.Sensing error might be also reduced by reducing the number ofworking channels; however, this might reduce the probabilityof finding spectral opportunities and, thus, jeopardize theoperation of the CPAN itself.

VI. CONCLUSION

In this paper we have evaluated the performance of simplefrequency hopping MAC for cognitive PANs integrated withspectrum sensing process and CPAN recovery procedure. We


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Fig. 7. Traffic and sensing performance under variable piconet size and offered load.

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Fig. 8. Traffic and sensing performance under variable traffic load and scheduling parameter µ.

have evaluated impacts of CPAN load, CPAN size, schedulingparameter, duration of sensing vacation and distance betweenthe sensing events on operation of the sensing process andoperation of individual node. Our findings indicate that forgiven CPAN size, penalty coefficient and scheduling parametercan be chosen in a way which minimizes both sensing errorand blocking probability.

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[2] A. Alshamrani, X. S. Shen and L-L. Xie A Cooperative MACwith Efficient Spectrum Sensing Algorithm for Distributed Opportunis-tic Spectrum Networks, Journal of Communications, vol.4, no.10,4(10):728–740, 2009.

[3] Artifex v.4.4.2 RSoft Design Group, Inc., San Jose, CA, 2003[4] D. Cabric, S. M. Mishra, D. Willkomm, R. Brodersen, and A. Wolisz.

A cognitive radio approach for usage of virtual unlicensed spectrum.In Proc. 14th IST Mobile Wireless Communications Summit, Dresden,Germany, June 2005.

[5] Q. Chen, Y-C. Liang, M. Motani, W-C Wong A Two-Level MACProtocol Strategy for Opportunistic Specturm Access in Cognitive RadioNetwork IEEE Trans. Veh. Technol., 60(5):2164-2180, Jun 2011.

[6] D. P. Heyman and M. J. Sobel. Stochastic Models in Operations Re-search, Volume I: Stochastic Processes and Operating Characteristics.McGraw-Hill, New York, 1982.

[7] K. Bian, J-M. Park and R. Chen Control Channel Establishment inCognitive Radio Networks using Channel Hopping Journal on SelectedAreas in Communication, 29(4):689-703.

[8] L. Lai, H. ElGamal, H. Jiang, and H. V. Poor Cognitive MediumAccess: Exploration, Exploitation and Competition IEEE Transactionson Mobile Computing, 10(2):239-253, Feb. 2011.

[9] B.F. Lo, I.F. Akyildiz and A.M. Al-Dhelaan Efficient Recovery ControlChannel Design in Cognitive Radio Ad Hoc Networks IEEE Transac-tions on Vehicular Technology, 59(9):4513-4526, Nov. 2010.

[10] P. K. Lee. Joint frequency hopping and adaptive spectrum exploita-tion. In IEEE Military Communications Conference MILCOM2001,volume 1, pages 566–570, October 2001.

[11] Maplesoft, Inc. Maple 13. Waterloo, ON, Canada, 2009.[12] J. Misic and V. B. Misic. Performance of Cooperative Sensing at the

MAC Level: Error Minimization through Differential Sensing. IEEETrans. Veh. Technol., 58(5):2457-2470, May 2009.

[13] H. Takagi. Queueing Analysis, volume 2: Finite Systems. North-Holland, Amsterdam, The Netherlands, 1991.

Jelena Misic (M’91, SM’08) is Professor of Com-puter Science at the Ryerson University, Canada.She has published two books and more than 200 pa-pers in peer reviewed journals and conferences. Herresearch interests include wireless networks, cloudcomputing, M2M communication, and security.

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Cooperative Sensing at the MAC Level in Simple Cognitive Personal Area Networks