Distributed Channel Assignment for Multi-Radio

Wireless Mesh N etw ork s

S adeq Ali Mak ram

Dep artment of Comp uter S c ienc e

Communic ation and Distributed S y stems, Informatik 4

RWT H Aac hen U niv ersity , G ermany

mak ram@ nets.rw th-aac hen.de

Mesut G ü nes

Dep artment of Comp uter S c ienc e

Distributed, embedded S y stems

F U B erlin, G ermany

guenes@ inf.fu-berlin.de

Abstract—Wireless mesh networks are a special kind of adhoc networks in which most nodes are static. T he applicationpu rpose of wireless mesh networks is also different than that ofad hoc networks and is focu sed on b roadb and access serv icesto the I nternet. R ecent stu dies hav e shown that nodes in awireless mesh network hav e to b e eq u ipped with sev eral radiointerfaces for hig h performance. H owev er, one of the challeng esthat still faces hig h performance wireless mesh networks is thecapacity redu ction du e to interference of wireless links. I n thispaper, we address the prob lem of assig ning channels to nodesin wireless mesh networks. F or this, we propose and stu dy theC lu ster C hannel A ssig nment (C C A ) approach with the g oal ofredu cing the network interference to increase the ov erall networkperformance.

I n d e x T e rm s—C hannel assig nment, M u lti-channel, M u lti-radio, and Wireless M esh N etworks.

I . I N T RO DU CT I O N

Rec ently , w ireless mesh netw ork s (WMN s) are in the fo-

c us of ac ademia and industry researc h. T he reason is that

WMN s hav e sev eral interesting c harac teristic s suc h as self-

organiz ation, self-c onfi guration, reliable serv ic es, and Internet

c onnec tiv ity . A WMN is a multihop w ireless netw ork w hic h

c onsists of mesh routers and mesh c lien ts. Mesh routers hav e

minimal mobility and form the bac k bone of the w ireless mesh

netw ork w hic h p rov ide ac c ess to the mesh c lients, w hic h c an

be stationary or mobile [1 ] , [ 2 ] .

O ne c hallenge of WMN s is the c ap ac ity reduc tion due to

the interferenc e of w ireless link s. T herefore, many ap p roac hes

w ere p rop osed to ov erc ome this p roblem. T hese ap p roac hes

c an be distinguished into tw o c lasses. T he fi rst c lass is foc used

in utiliz ing multip le c hannels w ith a single w ireless netw ork

interfac e c ard (WN IC) p er mesh router [3 ] – [ 5 ] . H ow ev er,

these ap p roac hes are ineffi c ient, sinc e they req uire c hannel

sw itc hing w hic h c auses signifi c ant delay s. T he delay c an be

in order of millisec onds w ith IE E E 8 0 2 .1 1 c ards. T his is higher

than the normal p ac k et transmission time w hic h is in the order

of mic rosec onds. Additionally , these ap p roac hes are unsuitable

to be used w ith c ommodity I E E E 8 0 2 .1 1 hardw are, sinc e they

req uire modifi c ations of the MAC lay er or hardw are.

T he sec ond c lass assumes eac h mesh router be eq uip p ed

w ith multip le WN ICs and ex p loits multip le c hannels at the

same time [6 ] – [ 1 0 ] . T hese ap p roac hes mak e it easy for a

mesh router to utiliz e multip le c hannels av ailable in the net-

w ork w ithout the req uirement of c hannel sw itc hing. Moreov er,

these ap p roac hes c an be used w ith c ommodity I E E E 8 0 2 .1 1

hardw are. F urthermore, using multip le radios p er mesh router

p ermits it to transmit and rec eiv e simultaneously or it c an

transmit on different c hannels c onc urrently . H ow ev er, the k ey

issue is how to assign these c hannels to WN ICs in a w ay that

minimiz es the interferenc e and max imiz es the c ap ac ity . T his

q uestion summariz es the c hannel assignment p roblem.

In p artic ular, the main c ontribution of this p ap er is a

distributed c hannel assignment algorithm using c lustering. We

fi rst p rop osed to use c lustering for c hannel assignment and

desc ribed how to ex p loit the p erformanc e p otential of the

ap p roac h in [1 1 ] . F eatures of the ap p roac h inc lude. F ully

distributed c hannel assignment. F air c hannel distribution for

the c lusters based on the number of the nodes w ithin a c luster.

Re-assignment w ith the c onsideration of the distanc e.

T he rest of this p ap er is struc tured as follow s. In S ec tion II

related w ork is disc ussed. S ubseq uently in S ec tion II I , w e

desc ribe the netw ork model and introduc e the terminology

used throughout this p ap er. S ec tion IV p resents the D istrib uted

C luster C ha n n el A ssig n men t A p p roa c h (CCA) in detail. S ec -

tion V ev aluates the p erformanc e of CCA c omp aring to other

ap p roac hes p resented in the related w ork sec tion. F inally ,

S ec tion V I c onc ludes the p ap er.

I I . RE L AT E D WO RK

S ev eral ap p roac hes w hic h assume multip le w ireless netw ork

interfac es p er node [1 2 ] . K o et al. [ 1 0 ] assume that a node

c an transmit on a single c hannel but c an listen to all av ailable

c hannels w ithin its loc al domain at the same time. In this

ap p roac h, the nodes selec t the c hannel w hic h minimiz es the

interferenc e from the set of nodes w ithin their interferenc e

range. S hin et al. [ 1 3 ] show that op timal c hannel assignment

is N P -hard and p rop ose to assign as many distinc t c hannels as

p ossible to a node to imp rov e the p erformanc e w hile satisfy ing

the c onstraints of limited w ireless netw ork interfac es and av ail-

able c hannels. T he c hannel assignment to a p artic ular w ireless

netw ork interfac e is done randomly . T here are tw o ap p roac hes

272

that are close to our presented approach: Tabu-based [9] and

CLICA-SCE [7 ]. Subramanian et al. [9] designed a centralized

Tabu-based algorithm and a distributed greedy algorithm. Both

algorithms assign channels to wireless network interfaces with

the objective of minimizing network interference. The Tabu-

based algorithm consists of two phases. The first phase tries

to find a good solution with minimum interference. However,

this solution may violate interface constraints which is handled

in the second phase. Furthermore, Tabu-based does not work

well when the number of radio interfaces is limited. Marina et

al. [7 ] propose a polynomial-time heuristic algorithm (CLICA)

for assigning channels. The algorithm assumes each node a

given priority and depending on this priority the coloring,

i.e. channel assigning, decision is done. Starting from the

node with the highest priority the algorithm tries to color all

uncolored incident links from this node.

III. SY STEM MODEL AND PROBLEM FORMULATION

A. Network Model

A WMN can be represented as an undirected graph

G(V, E, K) called the connectivity graph, where V ={i1, i2, . . . , in} is the set of vertices in the graph that represent

mesh routers, K = {k1, k2, . . . , kc} the set of available

channels, and E = {(i, j, k)|i, j ∈ N ∧ k ∈ K} the set of

wireless links between the mesh router i and its neighbor

j on channel k. The wireless link is constructed between

any two mesh routers if they are located within each other’s

transmission range. For the sake of simplicity we will denote

lij = (i, j) ∈ E as the wireless link between mesh router i

and j. A mesh router i with mi wireless network interfaces

may allocate up to mi different channels if available. The

set of assigned channels to mesh router i is denoted as

Ki = {k1, k2, . . . , kri}, ri ≤ mi.

B . Clustering

Our approach assumes a clustering, where the mesh routers

are grouped into clusters Ci, the set of all clusters are given by

C = {C1, C2, . . . , Cq}. We deploy the H ighest Connectiv ity

Cluster (HCC) algorithm [14], where a node is elected as

the cluster head (CH ) if it is the most highly connected

node (having the highest number of neighbor nodes). It is

also possible to employ any other clustering algorithm which

realizes a uniform clustering and where the cluster head is in

the center of the cluster.

IV. DISTRIBUTED CLUSTER CHANNEL ASSIGNMENT

In this section, we present a distributed channel assignment

algorithm that utilizes only local topology information to

perform channel assignment computation. After the clustering

computation mentioned in Section III-B is done. This infor-

mation is collected by a clusterhead from the nodes within

its cluster and two hop neighborhood clusters. Based on these

assumptions and using the terminology defined in Table I, we

discuss the Distributed Cluster Channel Assignment in three

stages. These stages are channel division and selection for the

neighbor clusters, re-assignment of channels and the channel

assignment for the nodes within a cluster. We describe these

stages in detail as next.

A. Channel div ision and selection for neighbor clusters

The available channels K in the network are equally dis-

tributed to the clusters in a way that two neighbored clusters

get disjoint sets of channels as shown in Figure 1. In the

figure, we assume that the clustering is done uniformly and

each cluster has 6 neighbors. In the beginning, a cluster in

the center of the neighbors is elected as a head of clusterhead

(CH H ), where each cluster only belongs to one CH H . For

example, C1 is the head of neighbors (C2, C3, . . . , C7) and

so on. After that, the CH H divides the available channels

(|K|

|N Ci∪Ci|) into subsets of channels {A, B, . . . , F} ⊂ K. For

simplicity, the channels are divided equally, where A∩B = ∅.Each cluster Cx ∈ NCi ∪ Ci gets a set of these subsets

(e.g. C1 ← A, C2 ← B, .. .C7 ← F ). This distribution of

the channels is repeated for all CH H s in the network. We

allow by this distribution the other clusters to reuse the same

channels as those used by CH H i and its neighbors.

In the case that the clustering is not uniform, we distribute

the available channels K to neighbored clusters as follows:

|KCi| =

{

∣

∣ni ·Kn

∣

∣, if |K| > |NCi|

1, otherwise(1)

KCi⊂

{

K \ (K ∩KN Ci), if |KN Ci

| < |K|

min(KN Ci), otherwise

(2)

Equation (1) determines the number of channels |KCi|

that can be assigned to cluster Ci. |KCi| is proportional to

the number of nodes ni in cluster Ci where the number of

available channels |K| is higher than the number of neighbors

NCi otherwise each cluster is assigned only one channel.

Equation (2) determines the set of channels KCito be assigned

to cluster Ci. A cluster is marked as assigned when it gets

a set of channels. In the case of small number of available

channels, we reuse the least used channels from KN Ciwhich

are already assigned to some neighbor clusters. For example, if

there are only 3 available channels |K| = 3 and NCi = 4 , then

each cluster is assigned only one channel where each cluster

gets a disjoint channel. In this case, we can reuse the least

used channel from KN Ciand then assign it to the rest of the

neighbor clusters. This distribution of the channels is repeated

for all unassigned clusters in the network. We allow by this

distribution the other clusters to reuse the same channels as

those used by cluster Ci and its neighbors.

B . Channel re-assignment

In consideration of a re-assignment, we should forbid the

co-channel interference. Therefore, the distance requirement

for the assignment of the same set of channels have to be

met which at least two clusters far away from the previous

assignment.

To illustrate this case, (see Figure 2(a)) the set of channels

B is assigned to cluster C2 and re-assigned to cluster C10 ,

where the distance from the previous assignment is only one

273

Table INOTATION

Symbol Definition

n Number of nodes in the networkCi Cluster iCH i Cluster head of cluster iK Set of available channelsKCi

Set of channels of cluster iNCi Set of neighbor clusters of cluster iKN Ci

Set of assigned channels to neighbor clusters of cluster iBCi

Set of border nodes of cluster ii, j A node in the network, e.g. a mesh routerNi Set of neighbor nodes of node imi The number of WNICs of node ikx A channel in KKi Set of assigned channels to node i|Ki| The number of divers channels

allocated to the WNICs of node i

cluster. To minimize such type of interference, by knowing

the information of the previous assignment of neighbors. To

illustrate this idea see Figure 2(b), when the assignment turn is

on C10, it selects the set of channels K \{KNC10∪KNNC10

}that was not previously assigned to its neighbors NC10 and

also to its neighbors of neighbors NNC10 as follows:

C10 ← {?}

NC10 = {C7, C6, C9, C13}

KNC10= {G, F, ∅, A}

NNC10 = {C2, C1, C6}∪

{C7, C1, C5, C8, C9}∪

{C6, C8, C11, C12, C13}∪

{C9, C12, C14 }

KNNC10= {B, A, F} ∪ {G, A, E, ∅, ∅}∪

{F, ∅, ∅, ∅, ∅} ∪ {∅, ∅, ∅}

KNC10∪KNNC10

= {G, F, A, B, E}

K \ {KNC10∪KNNC10

} = {A, B, C, D, E, F, G}\

{G, F, A, B, E}

C10 ← {C ∨D}

This procedure is repeated for all unassigned clusters in the

network as shown in Figure 2(c) and Figure 1. By applying

this idea, the overall interference in the network is minimized.

C. Phase 1: Default CCA

At the beginning, each clusterhead assigns a single common

channel to all nodes which belong to its cluster. We assume

that each node has a distinct number of WNICs and the com-

mon channel is allocated to one of its WNICs, thus the nodes

can communicate with each other using the same channel. In

the case where a node is a border node being in the range

of neighboring clusters. For those border nodes a common

channel is agreed to allow an inter-cluster communication.

Algorithm 1 Phase 2: CCA

1: Each cluster Ci

2: for each node i in Ci do

3: AssingChannels (i)4: end for

1: P RO CE D U RE AssignChannels (i)2: for each link lij , j ∈ Ni do

3: if (|Ki| < mi) then

4: if and (|Kj | < mj)) then

5: { /* Both i and j have free WNIC * /}

6: if i, j ∈ Ci then

7: k = min{KCi}|k 6∈ {Ki ∪Kj}

8: else

9: k = min{KCi∪KCj

}|k 6∈ {Ki ∪Kj}10: end if

11: Assign k to lij , Ki = {k ∪Ki}, Kj = {k ∪Kj}12: else

13: { /* Only i has free WNIC * /}

14: k = min{Kj}15: Assign k to lij , Ki = {k ∪Ki}16: end if

17: else

18: if (|Kj | < mj)) then

19: { /* Only j has free WNIC * /}

20: k = min{Ki}21: Assign k to lij , Kj = {k ∪Kj}22: else

23: { /* No free WNIC for both i and j * /}

24: k = min{Ki ∩Kj}25: end if

26: end if

27: end for

GB

FAC

ED

GB

FAC

ED GB

FAC

ED

GB

FAC

EDG

7

B

2

F

6

A

1

C

3

E

5

D

4

GB

FAC

ED

GB

FAC

ED

Figure 1. Repetition of channels using clustering

D. Phase 2 : CCA

This part is given in Algorithm 1. In this phase each cluster

head assigns the remainder of the channels (kx \KCi) to the

rest of unassigned WNICs of the nodes within the cluster.

The central idea of the algorithm is to use the least used

channel of KCiwhen assigning channels. The reason for that

is to minimize the interference in the neighborhood, since the

interference range is normally twice the transmission range.

274

F

14

A

13

B

10

E

12

C

9

A

16

C

15

G

11

D

8

G

7

B

2

F

6

A

1

C

3

E

5

D

4

(a) Distance, One cluster

14

A

13

?

10

129 16

15118

G

7

B

2

F

6

A

1

C

3

E

5

D

4

(b) Re-assignment for C10

F

14

A

13

C

10

E

12

D

9

B

16

C

15

G

11

B

8

G

7

B

2

F

6

A

1

C

3

E

5

D

4

(c) Distance, Two clusters

Figure 2. Aware of the distance of re-assignment channels

Thus by using the least used channel of KCicontributes in

minimizing the interference within the neighborhood which in

turn decreases the overall network interference. Throughout

the algorithm the expression min{< set of channels >}denotes the channel with minimum number of assigned links

of {< set of channels >} and these information are updated

after each case.

Before starting the CCA algorithm, each cluster head col-

lects the information (mi, Ni, and Ki) from each node in

its cluster as determined from the neighbor information sent

by each node. Such knowledge can be gathered through

local message exchanges between neighbors during network

initialization and discovery using a single channel. In addition,

the input of the algorithm is based on the connectivity graph

of the network in the single channel mode.

Based on these assumptions we describe the second phase of

our algorithm as next. The algorithm distinguishes four cases.

Each case takes into consideration the difference between the

number of WNICs on the node to be assigned a channel

and the number of distinct channels assigned to this node.

Additionally, it takes into account the difference between the

number of WNICs on the node’s neighbors and the number of

distinct channels assigned to this neighbor. Each case checks

the above two conditions and then decides which channel to

use. The first case (lines 4−11) is given if both nodes i and j

has a free WNIC, then the cluster head selects the least used

channel among all available channels KCiand assigns it to

the link lij . In the case if j is a border node the algorithm

chooses the least used channel among all available channels

KCi∪KCj

and assigns it to the link lij .

The second case (lines 12 − 15 ) is given if only node i

has a free WNIC, then the cluster head chooses one of the

channels of node j and assigns it to the link lij . The decision

is taken based on the fact that node j cannot be assigned a new

channel, otherwise the interface constraint is violated. In order

to minimize the interference, we select the least used channel

within Ci and Cj of node j. The third case (lines 17 − 2 1) is

given if only j has free WNIC, that means i cannot be assigned

another new channel. Accordingly, the chosen channel for the

link lij must be one of the channels of node i. Again, the

cluster head selects the least used channel within Ci and Cj

of node i.

The fourth case (lines 2 2 − 2 7 ) is given if there is no free

WNIC at i and its neighbor j, this means that both nodes

cannot be assigned a new channel. Accordingly, the cluster

head selects the minimum common channel between i and j

from {Ki ∩ Kj} and assigns it to the link lij . These steps

are repeated for all clusters in the network until a stable

configuration is reached. As it can been noticed from the above

explanation CCA does not refer to the interference model

when selecting channels as it is the case in Tabu and CLICA

algorithms, however, it uses the idea of selecting the least used

channel within the cluster Ci and its neighbor Cj which in turn

reduces the network interference.

V. PERFORMANCE EVALUATION

In this section we discuss the performance of CCA and

other related approaches in two different ways: from a graph

theoretical point of view, where we consider a wireless mesh

network as a graph and with the aid of a network simulator,

i.e. ns-2 [15]. For both studies we implemented the approaches

in appropriate simulation environments, where the first is a

simulator entirely developed by us and the latter is a well

known and popular network simulator.

A. Graph theoretical study of the approaches

In this section we discuss the performance of our algo-

rithm and three other related algorithms: Tabu, CLICA, and

a random algorithm. Furthermore, we present lower bounds

obtained from Linear Programming as main competitive solu-

tion. For the study a set of random networks with n nodes

are generated and the approaches are run in the simulation

environment.

We consider two different kinds of random networks: dense

networks and sparse networks. Both network kinds are gen-

erated by randomly distributing 50 nodes on 5 0 0 × 5 0 0 m2

for dense topology and 8 0 0 × 8 0 0 m2 for sparse networks.

The dense networks show an average node degree of 10 while

sparse networks show an average node degree of 5. We assume

that each node has the same number of WNICs and a uniform

transmission and interference range of 150 m. Two nodes share

a wireless communication link if they are located within each

others transmission range. Two links (vertices in the confl ict

graph) are connected with each other if and only if one of the

communication endpoints lies within the interference range of

the other communication endpoint. For example, if we have

two communication links (i, j) and (a, b), we say that the two

links interfere with each other if and only if either i or j lies

within the interference range of a or b.

In this study we consider the F ractional Network Inter-

ference (FNI) as the performance metric, since it represents

the capability of a channel assignment protocol in terms of

interference and allows the comparison of different approaches.

The Fractional Network Interference is defined as the ratio

of network interference and the total number of edges in

275

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R a n d o m

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C C A

T a b u - b a s e d

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L P

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Figure 3. Fractional network interference vs. different number of channels

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F ig u re 4 . F ractio nal netw o rk interference v s. d ifferent nu mb er o f rad iointerfaces per no d e

the co nfl ict g raph o r mo re fo rmally , the nu mb er o f co nfl icts

that remain after channel assig nment relativ e to the nu mb er

o f co nfl icts in a sing le channel netw o rk . T his represents the

remaining ratio o f interference after apply ing the channel

assig nment alg o rithm.

F ig u re 3 d epicts the F N I as a fu nctio n o f the nu mb er

o f av ailab le channels and F ig u re 4 d epicts the F N I as a

fu nctio n o f the nu mb er o f W N I C s per no d e. In b o th g raphs

the lo w er b o u nd s o b tained fro m L inear P ro g ramming d efi nes

the b est av ailab le resu lts f o r the co nsid ered netw o rk s. T hu s,

the perfo rmance o f C C A is o n b o th cases the clo sest o ne and

sho w s g o o d resu lts. T hese resu lts are reached b y C C A b y fi rst

cu tting d o w n the pro b lem o f channel assig nment into smaller

g ro u ps and the appro ach o f try ing to u nif o rmly assig ning o f

av ailab le channels to the no d es in a clu ster. A sto nishing ly , the

resu lts f o r d ense netw o rk s are in b o th cases w o rse. H o w ev er,

this is d u e to the larg er nu mb er o f clu sters w hich are g enerated

and partially d etermined b y the co mmu nicatio n rang e. H ence,

the mo re clu sters there are the mo re channels hav e to to

b e reserv ed to each o f them. T hu s, resu lting less av ailab le

channels fo r ad d itio nal u se lead ing to hig her interference.

B. Evaluation with the network simulator

In ad d itio n to the g raph theo retical stu d y o f the appro aches

w e d iscu ss the perfo rmance o f the appro aches b y means

o f netw o rk thro u g hpu t, since in real w o rld w ireless mesh

netw o rk s o ther facto rs than the o nes w hich co u ld b e co n-

sid ered theo retically are impo rtant to o . F o r that, w e hav e

implemented o u r appro ach fo r the ns-2 netw o rk simu lato r and

perfo rmed series o f ex periments. Since the stand ard ns-2 co d e

d o es no t su ppo rt mu ltiple W N I C simu latio ns, w e u sed the

mo d ifi ed v ersio n o f ns-2 w hich su ppo rts su ch simu latio ns [16 ] .

B ef o re d iscu ssing the ns-2 simu latio n resu lts, w e d escrib e the

simu latio n env iro nment.S imulation Environment: T he 5 0 mesh ro u ters are rand o mly

placed o n an area o f 1000×1000 m2. T he phy sical and M A C

lay ers o f ns-2 are set u p to simu late IE E E 8 0 2.11a w ith a

max imu m b it rate o f 24 M b ps and a transmissio n rang e o f

25 0 m. T o g enerate traffi c lo ad s, w e d eplo y C onstant Bit R ate

(C B R ) so u rces, w hich g enerate pack ets w ith siz e o f 10 0 0 b y tes.

W e ru n the simu latio n fo r three d ifferent traffi c mo d els lik e

in [9 ] :

• Sing le-ho p traffi c mo d el: T his mo d el d istrib u tes traffi c

eq u ally in all co mmu nicatio n link s. I t is u sed to ev alu ate

the perfo rmance w hen all link s in the netw o rk carry the

same lo ad .

• M u lti-ho p peer-to -peer traffi c mo d el: In this mo d el, 25

rand o mly selected no d es are u sed as so u rce no d es and the

remaining 25 are u sed as d estinatio n no d es. T he no d es

co mmu nicate u sing mu lti-ho p ro u tes. T he ro u tes are

co mpu ted statically u sing the sho rtest path as the metric,

and d o n’t chang e fo r the lifetime o f the simu latio n.

• M u lti-ho p g atew ay traffi c mo d el: In this mo d el, 4 ran-

d o m no d es are selected as g atew ay s and 25 no d es are

selected as so u rce no d es. E ach so u rce no d e send s traffi c

to the nearest g atew ay . R o u tes are d etermined as in the

prev io u s mo d el. T he mu lti-ho p traffi c mo d el is a co mmo n

mo d el w hen w ireless mesh netw o rk s are u sed f o r Internet

co nnectiv ity .

T o ev alu ate the perfo rmance o f the alg o rithms w e u se a

metric called S aturation T hroug hp ut. Satu ratio n thro u g hpu t is

d efi ned as the limit reached b y the sy stem thro u g hpu t as the

o ffered lo ad increases, and it represents the max imu m lo ad

that the sy stem can carry in stab le co nd itio ns [17 ] .

W e co nsid er tw o d ifferent scenario s. In the fi rst scenario

there are 12 av ailab le channels and d ifferent nu mb er o f rad io

interfaces per no d e. W e ru n ex periments fo r each traffi c mo d el

d escrib ed ab o v e and f o r d ifferent nu mb er o f interfaces per

no d e (u p 4 W N I C s) and 12 av ailab le channels. T he reaso n fo r

limiting the nu mb er o f W N I C s is that 4 W N I C s per no d e is

eno u g h to create nearly all po ssib le to po lo g ies. F u rthermo re,

the satu ratio n thro u g hpu t remains appro x imately the same

ev en if w e increase the nu mb er o f W N I C s w ith the same

nu mb er o f channels. T he resu lts f o r this scenario are d epicted

in F ig u re 5 . T he fi g u re sho w s the satu ratio n thro u g hpu t f o r

each alg o rithm as fu nctio n o f d ifferent nu mb er o f rad io

interfaces per no d e and v ario u s traffi c mo d els. A s ex pected

the perfo rmance is increased w ith the nu mb er o f av ailab le

interfaces and channels. T he perfo rmance o f C C A is all cases

su perio r to the o thers. T he reaso n fo r that is, that b y assig ning

the av ailab le channels u nif o rmly to the clu sters the interference

is red u ced and the perfo rmance is increased . T hese resu lts also

co nfi rm the resu lts presented in the prev io u s g raph theo retical

d iscu ssio n.

276

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

1 2 3 4

Sat

urat

ion

Thr

ough

put

(Mbp

s)

N u m b e r o f R a d io I n te r f a c e s p e r N o d e

C C AT a b u - b a s e d

C L I C A - S C ES in g le C h a n n e l

(a) Single hop, 12 channels

0

5

1 0

1 5

1 2 3 4

Satu

rati

on

Th

rou

gh

pu

t (M

bp

s)

N u m b e r o f R a d io I n te r f a c e s p e r N o d e

C C AT a b u - b a s e d

C L I C A - S C ES in g le C h a n n e l

(b) Multi-hop-peer-to-peer, 12 chan-nels

0

1 0

2 0

3 0

4 0

5 0

1 2 3 4

Sat

ura

tion T

hro

ughput

(Mbps)

N u m b e r o f R a d io I n te r f a c e s p e r N o d e

C C AT a b u - b a s e d

C L I C A - S C ES in g le C h a n n e l

(c) Multi-hop G ateway, 12 channels

0

1 0

2 0

30

4 0

5 0

6 0

7 0

3 1 2 1 5

Satu

rati

on

Th

rou

gh

pu

t (M

bp

s)

N u m b e r o f C h a n n e ls

C C AT a b u - b a s e d

C L I C A - S C ES in g le C h a n n e l

(d) Single hop, two radio interfacesper node

Figure 5. Saturation throughput for various traffic models

The second scenario considers two radio interfaces per node

and different number of non-overlapping channels available.

We performed experiments only for the single-hop traffic

model. Figure 5(d) shows the results for this scenario. We

assumed 3, 12, and 15 available channels according to IEEE

802.11b, IEEE 802.11a, and 802.11a,g respectively. According

to the figure, all algorithms perform well when the number of

channels is increased from 3 to 12 and there is no significant

enhancement when 15 channels are available. The performance

of CCA is again superior compared to the Tabu and CLICA

algorithms.

V I. CO NCLU SIO N

In this paper we address the problem of channel assignment

for wireless mesh networks. We have developed a distributed

channel assignment algorithm with the objective of minimizing

the network interference and accordingly to maximize the

available network throughput. O ur approach consists of cluster-

ing and three stages of distribution and assignment. In the first

stage the available channels are divided between the neighbor

clusters where two neighbors get distinct sets of channels. In

the second stage, the channel assignment is done for the nodes

within a cluster by a clusterhead. In the last stage, the channels

can be re-assigned within a distance of two clusters of those

previously assigned ones to avoid and minimize interference.

We studied the algorithms in two different ways. The firs

analysis is from a graph theoretical point of view and the

second one on a widely used network simulator. The results

obtained from both studies demonstrate the effectiveness of

the approach on minimizing network interference on both

dense and sparse random networks. The results obtained from

the network simulator, however show that by deploying the

approach a significant improvement in aggregated throughput

can be achieved.

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