Centralized Quasi-Static Channel Assignment for Multi-Radio Multi-Channel Wireless Mesh Networks

doi:10.4236/wsn.2009.12016

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Wireless Sensor Network, 2009, 2, 61-121

doi:10.4236/wsn.2009.12016 lished Online July 2009 (http://www.SciRP.org/journal/wsn/).

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Centralized Quasi-Static Channel Assignment for

Multi-Radio Multi-Channel Wireless Mesh Networks

Juan REN, Zhengding QIU

Institute of Information Science, Beijing Jiaoton g Un iversity, Beijing , China

Email: {04112047, zdqiu}@bjtu.edu.cn

Received February 19, 2009; rev i se d March 19, 2009; accepted March 20, 2009

Abstract

Employing multiple channels in wireless multihop networks is regarded as an effective approach to increas-

ing network capacity. This paper presents a centralized quasi-static channel assignment for multi-radio

multi-channel Wireless Mesh Networks (WMNs). The proposed channel assignment can efficiently utilize

multiple channels with only 2 radios equipped on each mesh router. In the scheme, the network end-to-end

traffics are first modeled by probing data at wireless access points, and then the traffic load between each

pair of neighboring routers is further estimated using an interference-aware estimation algorithm. Having

knowledge of the expected link load, the scheme assigns channels to each radio with the objective of mini-

mizing network interference, which as a result greatly improves network capacity. The performance evalua-

tion shows that the proposed scheme is highly responsive to varying traffic conditions, and the network per-

formance under the channel assignment significantly outperforms the single-radio IEEE 802.11 network as

well as the 2-radio WMN with static 2 channels.

Keywords: Wireless Mesh Networks, Multihop Network, Channel Assignment, Multi-Radio

1. Introduction

WMN [1] is a promising wireless technology for nu-

merous applications, e.g., broadband home networking,

community and neighborhood networks, enterprise net-

working, building automation, etc. [2,3]. However, in-

terference among wireless links significantly impacts the

performance of WMNs. As a multi-hop wireless network,

the actual goodput available to WMN applications de-

creases a lot when forwarding or relaying packets over

multiple wireless hops.

Fortunately, the IEEE 802.11 PHY specification per-

mits simultaneous operation of multiple non-overlapping

channels. By deploying multi-radio routers in infrastruc-

ture-based networks and assigning radios to non-overlap-

ping channels, the routers can communicate simultane-

ously with little interference in spite of being in direct

interference range of each other. Therefore, the capacity

of wireless networks can be increased. While due to the

limited number of channels available, the interference

cannot be completely eliminated. In addition, the channel

assignment must be restricted to the number of radios on

each wireless node. So it’s a challenging problem de-

serving our research.

In equipping routers with multiple radios, a naive

strategy would be to equip each router with the number

of radios equal to the number of orthogonal channels.

However, this strategy is economically prohibitive due to

the significant number of non-overlapping channels. An-

other channel assignment strategy is to frequently change

channel on the interface, for instance, for each packet

transmission based on current state of the medium. Such

dynamic channel assignment approaches [4-6] require

channel switching at a very fast time scale (per packet or

a handful of packets). The fast-channel switching re-

quirement makes these approaches unsuitable for use

with commodity hardware, where channel switching de-

lays itself can be in the order of milliseconds [4]. Some

of the dynamic channel assignment approaches also re-

*This work is supported by National Basic Research Program of China

(

973 Pro

ram

)

(

2007CB307100

)

J. REN ET AL.

105

quire specialized MAC protocols or extensions of 802.11

MAC layer, making them further unsuitable for use in

commodity 802.11 hardware.

In order to use multiple channels with commodity

hardware, several researches [7-9] focused on develop-

ing techniques that assign channels statically. Such static

assignments can be changed whenever there are signifi-

cant changes to traffic load or network topology. Since

WMN is an infrastructured network and aims to provide

reliable broadband services, such changes are infrequent

enough that the channel-switching delay and traffic

measurement overheads are insignificant. We refer to the

above as quasi-static channel assignments. However,

most of the existing quasi-static channel assignments are

performed offline and bound with routing.

In this paper, we address the problem of quasi-static

channel assignment independent of routing. A central-

ized quasi-static channel assignment algorithm is pro-

posed in the context of networks with multi-radio nodes.

In the channel assignment, we use a novel scheme to

estimate the traffic load on each wireless link. The esti-

mation considers the traffic on the link itself as well as

the interfering traffics introduced by its neighbors. Hav-

ing knowledge of the expected load on each link, the

algorithm can intelligently select different channels for

each radio with the objective of minimizing network

interference, which as a result efficiently improves the

network capacity. To evaluate the algorithm performance,

a corresponding channel assignment protocol is imple-

mented in ns-2 simulations [10] and we incorporate the

well-known WCETT (Weighted Cumulative Expected

Transmission Time) path metric [11], which is tailored

for multi-radio multihop wireless networks, into the

AODV (Ad Hoc On Demand Distance Vector) routing

protocol [12] as our multi-radio routing protocol. The

performance evaluation shows that the proposed scheme

is highly responsive to varying traffic conditions, and the

network performance under the channel assignment sig-

nificantly outperforms the single-radio IEEE 802.11

network as well as the 2-radio WMN with static 2 chan-

nels.

The rest of the paper is organized as follows. Section 2

gives the system architecture of the proposed multi-radio

WMN. In Section 3, we describe the centralized

quasi-static channel assignment scheme. In Section 4, we

evaluate the performance of our channel assignment al-

gorithm using the ns-2 simulations. Section 5 concludes

the paper.

2. System Architecture

In this section, we formulate the interference problem

involved in wireless multihop networks and present the

architecture of multi-radio multi-channel WMNs to re-

solve this problem.

2.1. Interference Problem

Traditional 802.11-based wireless networks can’t trans-

mit data simultaneously as wired networks because of

the intra-path and inter-path interference. For example in

Figure 1, although the two flows transmit separately on

path 1 and path 2, nodes must compete with each other

for a common channel, which reduces network through-

put hardly. If node 3 is in transmission, all the nodes in

interference range of node 3 should keep silence, or a

collision will occur. In contrast, if we assign interfering

hops to different channels, then one collision domain can

be broken into several collision domains with each oper-

ating in a different frequency range. When the in-

gress-egress node pairs that originally pass through the

collision domain now take different paths to route their

traffic, hops using different channels can transmit simul-

taneously and the network throughput will increase.

2.2. Multi-Radio Multi-Channel WMN

Architecture

Figure 2 gives the architecture of multi-radio multi-chan-

nel WMN. The wireless mesh backbone network consists

of mesh routers (MR), mesh access routers (MAR) and

the gateway. Mesh routers provide purely wireless rout-

ing services. Mesh access routers provide not only wire-

less routing services but also wireless access services.

Each WMN has at least one gateway, which can also be

served as the access point for wireless users. The integra-

tion of WMN with other networks such as the Internet,

cellular, IEEE 802.11, IEEE 802.15, IEEE 802.16, sen-

sor networks, etc., can be accomplished through the

gateway and bridging functions in the mesh routers.

In WMN only end users may frequently move and

mesh backbone facilities are almost static once been set-

tled. In addition, both the gateway and the mesh access

routers have aggregation capability. So we use them to

measure the ingress-egress network traffic in our load

estimation algorithm. Although in our network each

router is equipped with only 2 radios, the overall network

can utilize more channels with intelligent channel as-

signment to every link. This is the fundamental reason

for non-linear improvement in throughput with respect to

the increase in number of radios per node.

5 Path 1

Path 2

Sender

Interfere nce

Range

Figure 1. Interference in wireless communication.

J. REN ET AL.

106

MAR

MAR MAR

Internet

Gateway

1 5

5 Virtual Link

Operating on

Channel 1

Figure 2. Architecture of multi-radio multi-channel WMN.

3. Design of Channel Assignment

The goal of channel assignment in multi-radio WMNs is

to bind each radio to a channel in such a way that the

available bandwidth on each link is proportional to its

expected load. The link here means direct communica-

tion on a channel between the routing pair. In this section,

we describe the load estimation and channel assignment

algorithms.

3.1. Traffic Measurement

Optimizations of channel assignment using load estima-

tion require knowledge of network traffic information, so

we propose to measure the end-to-end traffics between

mesh access routers. The traffic measurement procedure

is described as follows:

At first, each mesh access router (including the gate-

way) measures its ingress-egress flows by probing data

periodically (the interval is set to 10s in simulations). For

convenient expression, we use the access router to indi-

cate both the mesh access router and the gateway in the

rest of this paper. Then each access router aggregates its

ingress-egress flows and sends the information to the

gateway. (The gateway is used as the computation center

since it owns the most powerful capacity.)

After receiving the flow information, the gateway

calculates the end-to-end traffic value between every pair

of access routers by further aggregate the flow informa-

tion. In this way, the real time value of the end-to-end

traffic between each pair of access routers is measured at

each echo interval. However, since what the quasi-static

algorithm needed is a long-term measured traffic, the

gateway performs an exponentially weighted moving

average (EWMA) of each end-to-end traffic load to get

an approximate long-term traffic. That is:

(,)(,) (1)(,)

old

TsdT sdTsd



 (1)

where denotes the end-to-end traffic between

access router pair s and d. In simulations, the smoothing

factor α = 0.7.

(, )Tsd

3.2. Initial Expected Load

Having the knowledge of the end-to-end traffics, the

gateway estimates the expected load on each wireless

link and assigns channels to links in order of the ex-

pected loads. The gateway is required to perform a new

estimation of the expected load either when it receives

the traffic information for the first time, or when the dif-

ference between the new traffic and the last one is large

enough.

To initial the load estimation, we assume that there is a

link between each pair of routers in direct communica-

tion range, and each end-to-end traffic load is equally

divided among all the paths with the least hops between

the pair of access routers. (Note that this link won’t

really exist if there is no common channel assigned to the

pair of routers on this link.)

If the number of all shortest paths between node s and

d is, and in those paths there are paths

passes link i, then the initial expected load for link i to

carry is calculated as follow:

(, )Psd (, )

Psd

(, )

()( ,)

(, )

Psd



sd

(2)

Here we only count the shortest paths because the path

with less hops always have much better performance

compared with longer paths in multi-hop wireless net-

works if they all have enough bandwidth.

3.3. Channel Assignment

Having knowledge of the expected loads on all network

links, we start to assign channels to links as follows:

At first, all links are sorted by their expected loads.

Since links expected to carry higher traffic load should

be given more bandwidth, the link with the most ex-

pected load is prior to other links in choosing channels.

Assume every node is equipped with q radios and node a

and b is connected by link i. There are three conditions

for choosing a channel to link i:

1) If both of node a and b have used less than q radios,

then choose a channel with the least interference with

link i. (The chosen channel should also be different from

the used channels of a and b.) Meanwhile, update the

channel lists of node a and b.

2) If one of the two nodes (for example a) has used q

channels, then choose a channel from the channel as-

signment list of node a with the least interference with

link i. Meanwhile update the channel list of node b.

3) When both of node a and b have used q channels: if

there exists a common channel between node a and b,

then assign this channel to link i; else, choose one chan-

nel from the list of node a and b respectively and merge

the two channels to one. Meanwhile update the channel

J. REN ET AL.

107

lists of all nodes which connect to node a or b using the

two channels directly or indirectly.

When link i has been assigned a channel, we remove it

from the unassigned link list. As a result, the link with

lower expected load now owns the priority to choose

channel next. This continues until all network links have

been visited, which is denoted as a cycle of channel as-

signment.

3.4. End or Feedback

After a cycle of channel assignment for all links, we need

to judge whether current channel assignment satisfies all

bandwidth requirements. If so, we terminate the whole

procedure and output the channel assignment results, or

we feedback the expected link loads under new channel

assignment to find a better channel assignment.

It’s easy to see that, if the available bandwidth on each

link is more than the traffic load it’s expected to carry,

no congestion will occur. So at first we need to estimate

the capacity for each link in the network. However, in

wireless networks, channels are shared by all links in the

same interference range. So when estimating the usable

capacity of a link, we should consider all traffic loads in

its interference range. According to the channel assign-

ment rules, the higher load a link is expected to carry, the

more bandwidth it should get. On the other side, the

higher loads its interfering links are expected to carry,

the less bandwidth it could obtain. Thus, the link capac-

ity should be proportional to its traffic load, and be in-

versely proportional to all other interfering loads. So the

capacity for a link i is given by:

()

*()

() ()

jIntfi

Ci j





 (3)

where B is the channel bandwidth and ()

ntf i stands

for the set of links in the interference range of i including

itself.

Then the residual capacity of link i can be obtained as

below:

()() ()RC iC ii



 (4)

We use the minimal residual capacity of all links on a

path as the available bandwidth for this path. Then we

consider the bandwidth requirement of an end-to-end

traffic is satisfied if there exists a path with its available

bandwidth more than the required traffic value.

At the start of the traffic allocation, the initial

of each link is set to the expected capacity , and the

expected load

()RC i

()Ci

()i



of each link is set to 0. When a path

is chosen to carry an end-to-end traffic, all links on the

path reduce their residual capacities and increase their

expected loads by the allocated traffic value.

Assume there are totally N end-to-end traffics with

respectively the value of . We choose

path P with the maximal available bandwidth among all

the paths with both the least hop and enough available

bandwidth for traffic , which is because the WCETT

path metric used in multi-channel routing protocol pre-

fers to choose high throughput paths in multiple channels.

When a path is chosen for traffic , there are two con-

ditions when allocating traffic to this path:

T (1...,)nN

1) If min(( ))

niP

TRC

i



, we can allocate the whole

traffic to path P. So we decrease the residual capacity

and add the expected load of each link i on path P by .

We also decrease the total unallocated traffic by for

a later comparison of each iteration.

2) If, we can only allocate

traffic to path P. So we decrease the re-

sidual capacity and add the expected load of each link i

in path P by . We also decrease the total

unallocated traffic value by .

min(())

niP





( ))RC i

min(( ))

iP RC i



min (

iP

min(( ))

iP RC i



When all the N traffics have been checked, we termi-

nate the whole algorithm if the total unallocated traffic

equals to 0. Or, we compare the total unallocated traffic

of this cycle to the last one. If the unallocated traffic of

this cycle is no less than the last one, it means no im-

provement is made and we also terminate the whole

process. Otherwise, we feedback the new expected link

loads to the channel assignment for a better scheme.

After the whole algorithm ends, the gateway broad-

casts the channel assignment results. Then each mesh

router adjusts its radios to the assigned channels when

they receive the results.

If we combine the traffic measurement, load estima-

tion and channel assignment, the whole algorithm proc-

ess can be depicted as in Figure 3.

4. Simulation Analysis

To evaluate the performance of our channel assignment,

we run simulations using ns-2. We use the IEEE 802.11

MAC protocol with RTS/CTS enabled. The AODV pro-

tocol is used as the routing protocol for the single-radio

IEEE 802.11 network simulations. We modify the AODV

protocol using the WCETT metric, a prevailing path met-

ric, as our multi-radio routing protocol. The topology of

all networks in the simulations is a 25-node square grid.

To simplify and generalize the simulation, we configure

the center node as the gateway and all the other 24 nodes

as mesh access routers. The bandwidth of each channel is

2 Mbps. The ratio between interference and communica-

tion range is 2 and all nodes in multi-channel networks

are equipped with 2 radios.

J. REN ET AL.

108

Channel Assignment

Traffic Measurement

Load Initialization

All traffics have been allocated

Or no improvement exists?

End-to-End

Traffics T

Link L oads



Capacity Estimation

Link Capaciti es C

Traffic Allocation

Link

Loads



Yes

End

Load Estimation

Figure 3. The whole al gorithm proces s .

4.1. Static Traffic Simulation

4.1.1. 10 Flows Simulation

At first, we randomly choose 10 pair of nodes from the

25 nodes and assign each pair with a different CBR UDP

flow. The rate of each flow is chosen randomly between

0 and 0.8Mbps. The packet size is 1000 bytes and flows

run for 100 seconds. We test our channel assignment

algorithm in a 5-channel network with the 10 flows. To

have a good comparison, we also run the same flows in a

standard IEEE 802.11 network and a 2-radio 2-channel

network. The two networks both needn't any channel

assignment and the 2-channel network simulation is just

the original WCETT multi-radio routing simulation. We

evaluate the improvement of network performance by

comparing the aggregate throughputs, the average packet

delays and the average packet drop probabilities of the 3

networks.

The aggregate throughputs of the 3 networks are shown

in Figure 4. The standard 802.11 network throughput is

quite low and the 2-channel network throughput is about

twice of the standard 802.11 network. Although the im-

provement is significant, we can see both the two net-

work throughputs suffer sharp vibration. This is because

all network nodes using common channels is quite easy

to bring great interference. While in the 5-channel net-

work, we can efficiently limit interference to several

small areas using our channel assignment. So the net-

work throughput is quite stable and the value of the

throughput also increases a lot.

The average packet delay of each flow is shown in

Figure 5. We draw the delays of flows that haven’t re-

ceived any data packet successfully in the whole simula-

tion to the max value of the y axis. We can see the

1-channel network performs badly with 1 flow receiving

no packet. Although all flows in the 2-channel network

can transport data, the average packet delays for most

flows are quite large and exceed 0.5s. The maximal av-

erage packet delay of the 2-channel network is up to

1.83s. In the 5-channel network, the average packet de-

lays are much smaller. The maximal delay of all flows is

1.15s and there are 7 flow delays are below 0.5s. This

further proves that under our channel assignment, the

mesh network can efficiently utilize 5 or even more

channels with only 2 radios.

010 2030405060 70 8090100

0.5

1.5

Time ( s)

Network Aggregat e Throughput (Mbps)

A ODV wi th 1 ch annel

W CE TT wit h 2 channels

W CE TT wit h 5 channels

Figure 4. The aggre gate ne twork throughput for 10 flows.

1 23 45678 910

0. 5

1. 5

2. 5

Flow Index

Average Pac ket Delay (s )

AODV wi th 1 channel

W CE TT wit h 2 c hannels

W CE TT wit h 5 c hannels

Figure 5. The average packet delay for 10 flows.

J. REN ET AL.

109

We also compare the average packet drop probability

of each flow in Figure 6. The probabilities for the previ-

ous two networks are all quite large. In the 5-channel

network, the probabilities of 4 flows are quite small with

values below 4%. Although the ones for the other flows

are large, but they have decreased a lot compared to the

previous two networks.

4.1.2. 20 Flows Simulation

To derive the network to saturation, we randomly choose

20 pair of nodes from the 25 nodes and run the same

simulations in the above 3 networks. The aggregate thro-

ughput of the 3 networks is shown in Figure 7. From

Figure 7 we can see the throughput gain for 2-channel

network is not significant under the heavy traffic. While

both the value and the stability of throughput for

5-channel network get further significant increase, which

is due to the sufficient bandwidth brought by efficient

utilization of multiple channels.

The average packet delay of each flow is shown in

Figure 8. We can see that in the 1-channel network, 4

flows received no packet in the whole simulation. In the

2-channel network, the average packet delays for most

flows are also quite large with the maximal average de-

lay up to 2.32s for the 19th flow. While in the 5-channel

network, the average packet delays for most flows are

below 0.5s and the maximal one is only 0.96s.

We compare the average packet drop probability of

each flow in Figure 9. The average packet drop prob-

abilities for all the three networks are all quite large be-

cause of the heavy traffic. While compared to the two

networks without channel assignment, the average packet

drop probability for the 5-channel network is still much

smaller and the average drop probability of 9th flow is

nearly 0, which means it has almost transmitted all the

data packets successfully.

1 23 45 67 8910

100

Fl ow Index

Average Pac ket Drop Probability (%)

A ODV wi t h 1 c h annel

WCETT wit h 2 channels

WCETT wit h 5 channels

Figure 6. The average packet drop probability for 10 flows.

010 2030405060 708090 100

0.5

1.5

2.5

Time ( s)

Network A ggregat e Throughput (Mbps)

A ODV with 1 channel

WCETT wit h 2 channel s

WCETT wit h 5 channel s

Figure 7. The aggre gate ne twork throughput for 20 flows.

12345678910 11 1213 14 15161718 19 20

0.5

1.5

2.5

Fl ow Index

A v e rage P a cket Del ay (s)

A ODV wit h 1 channel

WCE TT wi t h 2 channels

WCE TT wi t h 5 channels

Figure 8. The average packet delay for 20 flows.

12345678910 11 1213 14 1516 17 1819 20

100

Fl ow Index

Average Pack et Drop Probability (%)

AO DV with 1 channel

W CE TT wit h 2 channel s

W CE TT wit h 5 channel s

Figure 9. The average packet drop probability for 20 flows.

J. REN ET AL.

110

Table 1. Comparison of the average aggregate throughput.

AODV WCETT WCETT + Channel

Assignment

Average

Aggregate

Througput

(Mbps) 1 channels 2 channels 3 chan-

nels

4 chan-

nels

5 chan-

nels

10 flows 0.525 0.903 1.231 1.4371.608

20 flows 0.777 0.814 1.448 2.0802.323

020 40 60 80100 120140 160 180

0. 5

1. 5

2. 5

Ti me ( s)

Net work Aggregat e T hrough put (M bps)

Figure 10. Impact of traffic change on aggregate network

throughput.

4.1.3. The Average Aggregate Throughput

Comparison

Without loss of generality, we also run the 10 and 20

flows respectively in the networks with 3 and 4 channels,

using the proposed channel assignment. We list the av-

erage aggregate throughputs of the 5 networks with 2

different traffics in Table 1. We see that the network

throughput increases with more channels assigned to the

network. While the improvement can’t be achieved as

high times as the number of channels, due to the unne-

glectable management packets and probing packets for

both the WCETT path metric and traffic measurement.

4.2. Varying Traffic Simulation

To evaluate the impact of traffic change on network per-

formance, we randomly choose 3 flows in the 10-flow

scene and vary their sending rates in the simulation.

Flows run for 180 seconds. At the 60th second, we in-

crease the sending rate of a flow by 0.2Mbps. Then at the

80th second, we increase the sending rate of another flow

by 0.4Mbps. At the 100th second, we decrease the send-

ing rate of the third flow by 0.4Mbps. After this, the traf-

fic changes are large enough for the gateway to reassign

channels. Because the traffic measurement interval is 10

seconds, the gateway should detect the traffic change and

start to reassign channels at the 110th second. Besides,

each node need to fresh its routing table since the net-

work channel assignment has changed. So we purge the

history information of the WCETT path metric in each

node after every new channel assignment.

Figure 10 shows the serial changes of aggregate net-

work throughput by varying the flow sending rates. We

can see from the 60th second to the 110th second, the ag-

gregate network throughput changes slightly after each

variation of flow sending rate and at last stays around

1.85Mbps. Then at the 110th second, the aggregate

throughput suddenly decreases to 0, which means the

network begins to reassign channels. After about 15

seconds vibration, the aggregate throughput comes back

to near 2.4Mbps which means the new channel assign-

ment as well as the WCETT path metric routing have

terminated.

Compared to the last 1.85Mbps throughput before

channel reassignment, the network now owns a much

better bandwidth assignment. In addition, the channel

reassignment and routing together cost only about 15

seconds. So we can say our channel assignment combin-

ing with the WCETT path metric routing provides a good

choice for the multi-radio WMNs.

5. Conclusions

This paper formulated the channel assignment problem

in multi-radio multi-channel WMNs. Since the backbone

of WMN is an infrastructured network, we assume the

mesh routers are stationary and each of them is equipped

with 2 radios. Based on network traffic measurement and

the load estimation of wireless links, we present a cen-

tralized quasi-static channel assignment with the objec-

tive of minimizing network interference, which as a re-

sult greatly improves network capacity. Extensive simu-

lations show that the proposed scheme is highly respon-

sive to varying traffic conditions, and the network per-

formance under the channel assignment significantly

outperforms the single-radio IEEE 802.11 network as

well as the 2-radio WMN with static 2 channels.

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ACM MOBICOM, pp. 114–128, September 2004.

[12] C. Perkins, E. Royer, and S. Das, “Ad hoc on demand

distance vector (AODV) routing,” IETF Internet Draft,

draft-ietf-manet-aodv2-10.txt, January 24, 2002.