Channel Assignment in Multi-Rate 802.11n WLANs

Dawei Gong, Miao Zhao and Yuanyuan Yang

Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA

Abstract—As the latest IEEE 802.11 standard, 802.11n allowsa maximum physical data rate as high as 600Mbps, making ita desirable candidate for wireless local area network (WLAN)deployment. In WLANs, access points (APs) are often denselydeployed, and thus neighboring APs should be assigned withorthogonal channels to avoid performance degradation causedby interference. It is challenging to find the optimal channelassignment strategy, as the number of channels is very lim-ited. Many channel assignment schemes have been proposedfor WLANs in the literature. However, most of them werenot designed for 802.11n WLANs, and did not consider thechallenges from the new channel bonding and frame aggregationmechanisms. Moreover, the impact of multi-rate clients onchannel assignment is not fully investigated yet. In this paper,we study channel assignment in multi-rate 802.11n WLANs,aiming at maximizing the network throughput. We first presenta network model and an interference model, and estimate theclient throughput based on them. We then formulate the channelassignment problem into a throughput optimization problem. Asthe formulated problem is NP-hard, we propose a distributedchannel assignment algorithm to provide practical solutions. Wehave conducted extensive simulations to evaluate the proposedalgorithm and the results show that the network throughput canbe significantly improved compared with existing schemes.

Index Terms—Wireless Local Area Networks (WLANs), IEEE802.11n, Channel Assignment, Channel Bonding.

I. INTRODUCTION AND RELATED WORK

Recently, IEEE 802.11n based wireless local area networks

(WLANs) [1]–[5] have been widely deployed in homes,

universities, airports, enterprises, etc. Typically, access points

(APs) are densely deployed to provision anytime anywhere

Internet access, and WLAN clients associate with a nearby

AP to access the network. An AP and its associated clients

are referred to as a basic service set (BSS). In a WLAN,

neighboring APs should be assigned with orthogonal channels

to avoid interference among each other. However, it is non-

trivial to assign channels in conventional 802.11a/b/gWLANs

to achieve optimal network performance, as the number of

channels is very limited. It becomes more challenging to

assign channels in 802.11n WLANs, because the limited

channel band is further congested by the new channel bonding

mechanism, which uses two non-overlapping 20MHz chan-

nels together for data transmissions.

There has been some previous work on channel assignment

in WLANs. One approach is to formulate channel assignment

into a vertex coloring problem [6]–[9], where each BSS is

denoted by a vertex, two vertices are connected by an edge

if the two corresponding BSSs interfere with each other, and

each available channel is represented by a color. However,

as each BSS is regarded as a vertex in this approach, the

interference experienced by clients is not considered.

Another approach of channel assignment is to examine the

interference of each client separately. A client-driven channel

assignment scheme was proposed in [10], where each client

maintains an interference set including all the conflicting

BSSs. The objective of the scheme is to maximize the number

of conflict-free clients. In the channel assignment scheme

from [11], a weight was defined for each BSS, including the

traffic demand and the interference degree of each associated

client, aiming at maximizing the network throughput. It

was shown in [12] that better network performance can be

achieved by considering the interference of clients individu-

ally. Nevertheless, the impact of channel bonding in 802.11n

WLANs was not studied in above schemes. In fact, although

a bonded (40MHz) channel may double the throughput of

a BSS [2], it is sensitive to interference. It was shown in

[4] that the throughput of a BSS drops drastically even if

the interference only exists in one of the bonded 20MHz

channels.

On the other hand, stations (APs or clients) in a WLAN can

transmit frames at different data rates, so as to adapt to various

channel conditions. In a BSS, if all clients have the same

frame length, they have generally the same throughput, which

is determined by the client with the lowest data rate [13].

This is because that the carrier sense medium access with

collision avoidance (CSMA/CA) guarantees each station an

equal medium access opportunity in the long term, regardless

of its data rate. Consequently, the benefit of assigning a

bonded channel to a BSS could be restricted, if the BSS

has a client with poor channel quality requiring low data

rates. In addition, the interfering impact of a client on a

nearby BSS is related to the data rate of the client, as a

low rate client occupies the wireless medium for a long time.

Moreover, a frame aggregation mechanism was introduced in

802.11n WLANs, where multiple frames are aggregated into

a large frame before transmission to enhance MAC efficiency.

The frame aggregation level (the number of sub-frames in an

aggregated frame) of a station depends on its traffic load.

Thus the interfering impact of a client is also related to its

frame aggregation level. A joint channel assignment and AP

association algorithm for 802.11n WLANs was presented in

[14]. However, the effects of frame aggregation were not

discussed.

In this paper, we study channel assignment in multi-rate

802.11n WLANs, aiming at maximizing network throughput.

We first introduce a network model for channel assignment in

802.11n WLANs, considering the new channel bonding and

frame aggregation mechanisms of 802.11n. We then discuss

the interference relationship among clients from nearby BSSs,

and develop an analytical model to estimate client throughput.

Based on this analytical model, we formulate the channel

assignment problem into an integer linear program (ILP),

which is NP-hard. To provide practical solutions, we first

978-1-4673-5939-9/13/$31.00 ©2013 IEEE978-1-4673-5939-9/13/$31.00 ©2013 IEEE

2013 IEEE Wireless Communications and Networking Conference (WCNC): MAC2013 IEEE Wireless Communications and Networking Conference (WCNC): MAC

392

D

I

F

S

Back

Off

Sub

Frame

1

S

I

F

S

BACKSub

Frame

2...

Sub

Frame

an-1

Sub

Frame

an

Aggregated Frame

Fig. 1. Transmission of an aggregated frame in 802.11n WLANs.

propose a measurement-based protocol to determine the inter-

ference among all clients. After that, we present a distributed

channel assignment algorithm for 802.11n WLANs. Finally,

we conduct extensive simulations to evaluate the performance

of the proposed algorithm. The results show that the proposed

algorithm greatly outperforms the compared algorithms.

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

presents the network model, analytical model and problem

formation of the channel assignment problem. Section III

describes the proposed channel assignment algorithm. Sec-

tion IV provides the simulation results. Finally, Section V

concludes the paper.

II. SYSTEM MODELS AND PROBLEM FORMULATION

In this section, we describe the network model for chan-

nel assignment in multi-rate 802.11n WLANs, estimate the

throughput for each client, and formulate the problem into an

integer linear program.

A. Network Model

Consider a 802.11n WLAN consisting of a set of APs,

and each AP is associated with a number of clients. We use

A and N to denote the set of APs and the set of clients,

respectively. For each AP a ∈ A, we define a set Na ⊆ N to

denote all clients associated with it. Similarly, we use variable

an ∈ A to denote the associated AP of client n. We assume

that the channel condition varies slowly, and thus there is

an optimal data rate between each client and its associated

AP. For client n, we use variable rn to denote its optimal

data rate. Note that the optimal data rate rn may vary if the

channel bandwidth (20/40MHz) of its associated AP changes.

The corresponding data rate can be determined by looking

up the data rate table of 802.11n, under the condition that

the modulation and coding scheme remains the same. For

simplicity, we assume that all clients have the same packet

length and denote it as l. In addition, we use variable fn to

represent the frame aggregation level of client n. Then the

average length of an aggregated frame for client n is l · fn.

Note that the definition of the available channel set for a

802.11n WLAN in this paper is different from the ones in

the literature. Typically, the available channel set is defined

as the collection of all non-overlapping channels in the

frequency band. There is no interference between any two

channels in the set. In a 802.11n WLAN, a bonded 40Mhz

channel interferes with any channel that overlaps the 40Mhz

bandwidth. Therefore, we define an available channel set K ,

including both the non-overlapping 20Mhz channels and the

bonded 40Mhz channels. We further define an interference

table I to denote the interference relationship between any

TABLE IINTERFERENCE MATRIX FOR 802.11N CHANNELS AT 2.4GHZ

Channels 1 (20) 6 (20) 11 (20) 1 6 (40) 6 11 (40)

1 (20) 1 0 0 1 0

6 (20) 0 1 0 1 1

11 (20) 0 0 1 0 1

1 6 (40) 1 1 0 1 1

6 11 (40) 0 1 1 1 1

two channels in K . Specifically, the available channel set

at 2.4Ghz band is defined as K = {1, 6, 11, 1 6, 6 11},where 1 6 and 6 11 stand for the 40Mhz channels bonded

by channel 1 and channel 6, channel 6 and channel 11,

respectively. The corresponding interference table is given

in Table I. We use variable ka ∈ K to denote the channel

assigned to AP a.

Stations in a 802.11n WLAN use the CSMA/CA mecha-

nism to access the medium as shown in Fig. 1. If a station

has pending traffic, it first senses the medium for a distributed

inter frame space (DIFS) period. If the medium is free

during this period, the station defers its transmission for a

random period of time. After the backoff duration, the station

aggregates multiple frames to the receiver into a large frame

and transmits the aggregated frame. If an aggregated frame

is received, the receiver sends back a Block ACK (BACK)

frame after waiting for a short inter frame space (SIFS)

period. In the BACK frame, a bitmap is included to indicate

the reception of each sub-frame in the aggregated frame.

We define the period from sensing the wireless medium to

successfully receiving the BACK frame as the transaction

time of a station. The transaction time of a station can be

given byTtran = Tdifs + Tcont + Tprot + Taggr + Tsifs + Tback (1)

where Tdifs, Tsifs, Tcont, Tprot, Taggr and Tback stand for

the DIFS duration, SIFS duration, contention duration for the

random backoff, duration of protection frames (RTS/CTS),

transmission time for the aggregated frame, and transmission

time for the BACK frame, respectively. In Equation (1),

Tdifs, Tsifs and Tback are constants. The contention duration

Tcont is related to the number of contending stations, and we

will discuss it in detail later. The transmission time Taggr

of an aggregated frame to or from client n depends on its

aggregation level as well as its optimal data rate, and can be

approximately expressed as

Taggr(n) =l · fn

rn

Generally, the protection time Tprot is zero since RTS/CTS

is disabled by default. However, if a station is affected by

hidden terminals, it has to enable RTS/CTS to protect the

transmission. In such a case, Tprot is a positive constant.

B. Interference Model

In this paper, we consider interference from the perspective

of clients. For two clients from neighboring BSSs, we say

they interfere with each other if one client or its associated AP

can sense the transmission of the other client or the associated

AP of the other client. Two clients need to contend and share

393

the wireless medium if they interfere with each other. For any

two clients m, n ∈ N , we use a binary variable im,n to denote

their interference relationship. im,n is one if they interfere

with each other; otherwise, it is zero. As shown in Fig. 2,

for downlink traffic from APs to clients, the hidden terminal

problem may occur between two interfering clients if their

associated APs are assigned with overlapped channels while

not within the carrier sense range of each other. Similarly,

for uplink traffic from clients to APs, the hidden terminal

problem may occur between two interfering clients if they are

not within the carrier sense range of each other while their

associated AP are assigned with overlapped channels. We

assume that RTS/CTS is enabled for clients that are affected

by hidden terminals to prevent severe transmission failures.

C. Throughout Estimation Model

In this subsection, we introduce a model to estimate the

throughput of each client in a 802.11n WLAN, by using

the above network and interference models. Accordingly, the

network throughput of the WLAN can be estimated, and

the objective of channel assignment becomes maximizing

network throughput. We will focus on downlink traffic in

this paper, as previous studies [15] have shown that downlink

traffic is dominating in many deployed WLANs. However,

it should be pointed out that our system models and the

proposed algorithm can be applied to uplink traffic as well.

To estimate the throughput of a client, we first need to

determine its transaction time, which can be derived from

Equation (1). As discussed earlier, the contention duration

Tcont of a station depends on the number of contending

stations and the collision probability. Given M competing

wireless stations, the contention duration can be approximated

using equations from [13], expressed as follows

pMcol ≈1 −

(

1 −1

CWmin

)M−1

T Mcont ≈Tslot ·

1 + pMcol

2M·CWmin

2(2)

In above equations, pMcol and T M

cont stand for the collision

probability and contention duration with M stations, while

Tslot and CWmin both are constants, standing for the du-

ration of a time slot and the minimum contention window

size, respectively. However, these equations cannot be used

in our model directly. This is because that for downlink

traffic, only APs transmit data frames and each AP transmits

to multiple clients alternatively. An AP only contends the

wireless medium with a neighboring AP if they are assigned

with overlapping channels and the receiving clients of their

transmissions interfere with each other. It is almost impossible

to accurately predict the receiving client of an AP at a specific

time, as it depends on the traffic loads of all clients and the

scheduling strategy of the AP. For simplicity, we assume that

all clients associated with AP a have the same contention

duration, which is determined by the average number of

competing APs of AP a. For each neighboring AP b that has

AP2AP1

client1

client4

client2

client3

AP2

AP1client1 client2

(a) downlink traffic (b) uplink traffic

Fig. 2. Examples of the hidden terminal problem for downlink and uplinktraffic. (a) AP1 and AP2 cannot sense the transmission of each other. (b)client1 and client2 cannot sense the transmission of each other.

an overlapping channel with AP a, we denote the probability

that AP a contends the wireless medium with AP b as follows

Pcont(a, b) =

∑

m∈Nbmaxn∈Na

{im,n}

|Nb|

Then the average number of competing APs for AP a

(including AP a itself) can be given by

Ma = 1 +∑

b∈A,b6=a

(I(ka, kb) · Pcont(a, b)) (3)

By plugging Equation (3) into Equation (2), the contention

duration of AP a and each client n ∈ Na can be derived.

Then the transaction time of client n ∈ Na can be obtained.

As an AP usually has more than one associated client,

its clients share the transmitting opportunity of the AP to

receive downlink traffic. We assume the AP transmits frames

to its associated clients alternatively in a round-robin fashion.

However, it should be pointed out that our proposed network

model and algorithm can be easily applied to other trans-

mission scheduling schemes for APs with multiple clients.

Under this assumption, after receiving an aggregate frame,

a client has to wait for the AP to transmit an aggregated

frame to all other clients before receiving the next frame to

it. Moreover, the AP also has to share the wireless medium

with neighboring APs that are assigned with an overlapping

channel and have clients interfere with its own clients. Thus,

we further assume that a client also has to wait for a downlink

transmission to each interfering clients in neighboring BSSs

before receiving its next frame, because CSMA/CA provides

each contending station the same medium access opportunity

in the long term. Accordingly, the duration between two

transmissions to client n ∈ N can be expressed as follows

T (n) =∑

m∈Nan

Ttran(m)

+∑

b∈A,b6=an

(I(kan, kb) ·

∑

p∈Nb

(in,p · Ttran(p))) (4)

Since the design objective of our throughput estimation

model is to optimize the channel assignment of the network

rather than accurately predict the throughput, we ignore the

throughput degradation caused by collisions here. Then the

estimated throughput S(n) of client n can be expressed as

the average length of its aggregated frames, divided by the

duration T (n) between two consecutive transmissions to it,

that is, S(n) = l·fn

T (n) .

394

D. Formulation of Channel Assignment Problem

The channel assignment problem in a multi-rate 802.11n

WLAN can be formally described as follows. Given a 802.11n

WLAN consisting of a set of APs, A, a set of clients,

N , and a set of available channels, K , assign a channel

k ∈ K to each AP a ∈ A, such that the estimated network

throughput is maximized. The channel assignment problem

can be formulated into the following optimization problem.

Maximize∑

∀n∈N

S(n)

Subject to

ka ∈ K, ∀a ∈ A (5)

T (n) =∑

m∈Nan

Ttran(m)

+∑

b∈A,b6=an

(I(kan, kb) ·

∑

p∈Nb

(in,p · Ttran(p))) (6)

S(n) =l · fn

T (n), ∀n ∈ N (7)

In the above formulation, Equation (5) ensures that each

AP is assigned with a channel from K; Equations (6) and

(7) derive the estimated throughput of client n according to

the channel assignment of all APs. Clearly, this optimization

problem is an integer linear program (ILP), as the only

unknown variables ka ∈ K are integers. Moreover, these

integer constraints can be further rewritten as binary integer

constraints. Then the optimization problem becomes a binary

integer program, which is well known to be NP-hard.

III. DISTRIBUTED CHANNEL ASSIGNMENT ALGORITHM

As shown in the previous section, the interference rela-

tionship between any two clients from different BSSs is re-

quired to estimate the throughput of a BSS. This information

is essential to our proposed channel assignment algorithm.

Therefore, in this section, we first present a protocol to obtain

the interference relationship among clients from neighbor-

ing BSSs. We then give a distributed channel assignment

algorithm, named as throughput-maximizing channel assign-

ment (TMCA) algorithm, aiming at maximizing the network

throughput. The network throughput is derived by putting

the measured interference into the throughput estimation

model presented in the previous section. In TMCA, each BSS

iteratively updates its channel assignment until the network

throughput in its neighborhood cannot be further improved.

The protocol and the algorithm are described in detail in the

following subsections.

A. Protocol for Local Information Exchange

In most of current 802.11n WLAN deployments, APs

are connected to a wireless controller or the Internet via

wired local area network (LAN) links. Therefore, we assume

that each AP is directly connected to a LAN infrastructure

and the LAN interfaces of neighboring APs are within the

same broadcast domain. Under this assumption, information

exchange among neighboring BSSs is carried out through

LAN broadcast, thus the transmission failure due to collision

and poor channel condition in the protocol can be ignored.

The protocol for local information exchange includes three

phases: client list and measurement schedule announcement

phase, carrier sense and report phase, and the interference

and hidden terminal announcement phase.

1) Client List and Measurement Schedule Announcement:

In this phase, each AP broadcasts a client list and mea-

surement schedule (CLMS) message including the associ-

ated client information over the LAN interface, such that

other APs in the neighborhood can obtain the data rate and

aggregation level of its associated clients. Furthermore, to

determine the interference among clients, the AP schedules a

measurement message for every station in its BSS (including

the AP itself), thus other clients in neighboring BSSs can

determine whether the station transmitting the measurement

message is in its carrier sense range by sensing the wireless

medium at the scheduled time. The measurement schedule

for associated clients is included in the CLMS message as

well. An AP rebroadcasts the received CLMS message in the

wireless medium such that its associated clients can obtain

the measurement schedule. In addition, an AP updates its

schedule if it has not broadcast the CLMS message and its

scheduled time conflicts with the schedule in the received

messages. All measurement messages are transmitted in the

wireless medium over the default channel.

2) Carrier Sense and Report: At the end of the last phase,

each station is aware of the measurement schedule of all

other stations in the neighborhood. In this phase, each station

constructs a carrier sense list including all the stations within

its carrier sense range. At each scheduled measurement time a

station determines whether the transmitting station is within

the carrier sense range by sensing the wireless medium. If

the medium is busy, the transmitting station is added into its

carrier sense list. A station broadcasts its own message at the

schedule time. After completing the broadcast measurement

of all scheduled stations, a client sends a report message to

its associated AP including its carrier sense list. Based on the

carrier sense lists from its associated clients and itself, an AP

can derive a set of interfering clients and hidden terminals

for each client associated with it.

3) Interference and Hidden Terminal Announcement:

Given the channel assignment of neighboring BSSs and the

information acquired in the last two phases, an AP can

estimate the throughput of its associated clients based on the

proposed system models. However, to obtain a sub-optimal

channel assignment in a distributed manner, each AP should

be able to estimate the throughput of neighboring BSSs, so as

to choose the best channel assignment. Hence in this phase,

each AP broadcasts an interference and hidden terminal

(IHT) message to the wire infrastructure, including the set of

interfering clients and hidden terminals for each associated

client. After receiving the IHT messages from neighboring

395

BSSs, an AP is capable of estimating the throughput of its

own BSS and its neighbors.

This local information exchange procedure should be per-

formed periodically to reflect the variance of client associa-

tions and channel conditions.

B. Throughput-Maximizing Channel Assignment Algorithm

We describe a distributed throughput-maximizing channel

assignment (TMCA) algorithm for 802.11n WLANs, where

each AP aims at maximizing the local network throughput,

which is defined as the overall throughput of all BSSs in

the neighborhood. TMCA algorithm works in a distributed

manner, as each AP is capable of estimating the throughput of

every neighboring BSS. Initially, each AP randomly chooses

a channel from the available channel set and broadcasts a

channel announcement (CA) message via its LAN interface.

When receiving a CA message, an AP first estimates its local

throughput by putting the inference relationship among clients

into the throughput estimation model in the previous section.

Then it checks whether the local network throughput can be

improved if assigning a different channel. If so, it assigns

the channel that improves the local throughput most and

broadcasts a CA message via the LAN interface. Otherwise, it

keeps waiting for CA messages from other APs. The pseudo

code of TMCA algorithm is given in Table II.

The TMCA algorithm is triggered repeatedly at each AP

by the received CA messages from other APs. An AP stops

triggering the channel assignment of neighboring APs when it

cannot further improve its local throughput and stops broad-

casting CA message. The TMCA algorithm terminates when

no further CA message is broadcast. Note that the throughput

is enhanced every time and the maximum throughput is

limited by channel capacity, thus the algorithm will terminate.

Both the interference measurement and the TMCA algorithm

can be executed when the traffic load is light, hence their

impact on the network performance is negligible. Moreover,

a minimum threshold for throughput improvement can be

placed to the iterations, so as to improve the convergence

speed.

IV. PERFORMANCE EVALUATIONS

In this section, we evaluate the proposed channel assign-

ment algorithm via simulations and compare it with the

CFAssign-RaC algorithm in [10], which outperformed most

of other channel assignment algorithms in the literature. The

throughput of a least congested channel assignment (LCCA)

scheme is compared as well, where each BSS chooses the

channel that is least used by its neighbors. Three sets of

simulations are conducted to study the performance of the

propose algorithm with different client densities, different

frame aggregation levels, and different number of orthogonal

20MHz channels, respectively.

In the simulations, 25 APs are deployed over a 1000 ×1000m2 field. The APs are randomly scattered around a grid

of the field such that most area of the field is covered and

the interference among APs is not deterministic. A number of

TABLE IITHROUGHPUT-MAXIMIZING CHANNEL ASSIGNMENT ALGORITHM

Input:Transaction time of all clients in nearby BSSsInterference relationship among clients in nearby BSSs

Output:Channel assignment ka for each AP a ∈ A

Algorithm:1: for each AP a ∈ A

2: Assign a random channel k ∈ K

3: Broadcast a CA message4: Idle and wait for CA messages5: If receiving a CA message6: Determine local throughput S

7: Determine the maximal local throughput S′

8: If assigned another channel k′

9: If S′ > S

10: Assign k′ to AP a

11: Broadcast a new CA message12: end if

13: end if

14: Go back to step 315: end for

clients are randomly distributed in the filed. The transmission

range is set to 100m and the carrier sense range is set to

200m. Each client is associated with the closest AP. If not

otherwise specified, the three 20MHz orthogonal channels at

the 2.4GHz frequency band are used. Each AP has saturated

UDP traffic to its associated clients and the length of UDP

packets is fixed at 1K bytes.

We first examine the network throughput of the TMCA

algorithm under various client densities. The simulation re-

sults are plotted in Fig. 3(a), where the number of clients

increases from 50 to 350 in a step of 50, while the average

aggregation level of all clients is fixed at 15. We can see that

the network throughput of all channel assignment algorithms

decreases as the number of clients increases. This is because

that all clients have pending traffic and a higher number

of clients lead to higher collision probability. Notably, our

proposed TMCA algorithm performs best compared to other

schemes regardless of the client densities, showing that the

estimated local throughput is a better metric than the number

of collision-free clients for channel assignment. Furthermore,

the advantage of the TMCA algorithm becomes more evident

when the client density is high, which reveals the importance

of distinguishing the effect of an interfering client according

to its transaction time.

50 100 150 200 250 300 3500

200

400

600

800

Number of clients

Netw

ork

Thro

ughput (M

bps)

TMCACFAssign−RaCLCCA

1 5 10 15 20 25 300

200

400

600

800

1000

1200

Average aggregation level

Netw

ork

Thro

ughput (M

bps)

TMCACFAssign−RaCLCCA

(a) (b)

Fig. 3. Network throughput under various client densities and aggregationlevels. (a) Network throughput vs. Number of clients. (b) Network throughputvs. Average aggregation level.

396

We now evaluate the network throughput of the proposed

algorithm under different frame aggregation levels. The sim-

ulation results are given in Fig. 3(b), where the number

of clients is set to 200, and the average aggregation level

increases from 1 to 30 in a step of 5. It can be observed that

as the aggregation level increases, the network throughput

of all algorithms grows, validating the effectiveness of the

frame aggregation mechanism in boosting MAC efficiency.

In addition, the proposed TMCA algorithm always leads to

the highest network throughput, regardless of the aggregation

level. Moreover, the benefit of TMCA algorithm is more

remarkable when the average frame aggregation level is high.

The reason is that given a high aggregation level, clients with

low data rates occupy the wireless medium for a much longer

time, compared with clients with high data rates. Therefore,

their interfering effects to clients in neighboring BSSs become

more severe, which is considered in other compared channel

assignment schemes.

Finally, we study the impact of the number of 20MHz

channels on the network throughput. The simulation results

are shown in Fig. 4, where the number of clients is fixed at

200 and the average aggregation level is 15. The simulations

are conducted in the 5GHz frequency band, in which more

orthogonal 20MHz channels are provisioned. We consider

three cases with 4, 6 and 8 non-overlapping 20Mhz channels,

respectively. The corresponding number of bonded channel

is 2, 3 and 4 according to the 802.11n specification. We

can note that the network throughput is boosted when the

number of non-overlapping 20Mhz channels is increased from

4 to 6. However, the throughput improvement is relatively

small when the number of channels is increased from 6 to 8,which has two reasons. First, for the network topology under

simulation, most of co-channel interference among clients is

already eliminated with 6 channels. Second, although bonded

channels can be assigned to BSSs to increase the data rate

and thus the throughput of clients when there are plenty of

channel resources, the improvement of network throughput is

limited by the clients that have poor channel qualities. Similar

to [14], we also observe that the throughput of some clients

even drops slightly if their associated APs are assigned with

a bonded channel. Thus it is not always beneficial to assign

bonded channels to BSSs even if there are sufficient channels.

4 6 80

500

1000

1500

Number of 20MHz channels

Netw

ork

Thro

ughput (M

bps)

TMCACFAssign−RaCLCCA

Fig. 4. Network throughput given various numbers of orthogonal 20MHzchannels.

V. CONCLUSIONS

In this paper, we have studied channel assignment in

multi-rate 802.11n WLANs, especially the new challenges

introduced by channel bonding and frame aggregation mech-

anisms. We have introduced a model to estimate the client

throughput in 802.11n WLANs and formulated the channel

assignment problem into an integer linear program. We have

provided a distributed channel assignment algorithm to give

practical solutions to the problem, and have conducted ex-

tensive simulations to evaluate their performance. The results

show that network throughput can be significantly boosted,

compared to other schemes. In our future work, we will

study the fairness among multi-rate clients as well as the

fairness among neighboring BSSs using the proposed channel

assignment algorithm.

VI. ACKNOWLEDGEMENTS

This work was supported by US National Science Foun-

dation under grant number ECCS-0801438 and US Army

Research Office under grant number W911NF-09-1-0154.

REFERENCES

[1] IEEE 802.11n Standard, “Part 11: Wireless LAN Medium Access Con-trol (MAC) and Physical Layer (PHY) Specifications: Enhancementsfor Higher Throughput,”, IEEE, October, 2009.

[2] V. Shrivastava, S. Rayanchu,J. Yoonj and S. Banerjee, “802.11n Underthe Microscope,” ACM IMC, pp. 105-110, 2008.

[3] T. Selvam and S. Srikanth, “Performance Study of IEEE 802.11nWLANs,” First International Communication Systems and Networks

and Workshops COMSNETS 2009, pp. 1-6, 2009.[4] S. Fiehe, J. Riihijvi and P. Mahonen, “Experimental Study on Perfor-

mance of IEEE 802.11n and Impact of Interferers on the 2.4 GHz ISMBand,” ACM IWCMC, pp. 47-51, 2010

[5] D. Gong and Y. Yang, “AP Association in 802.11n WLANs withHeterogeneous Clients,” IEEE INFOCOM, pp. 1440-1448, 2012.

[6] K. K. Leung and B. J. Kim, “Frequency Assignment for IEEE 802.11Wireless Networks,” IEEE VTC, pp. 1422-1426, 2003.

[7] M. Yu, H. Luo and K. K. Leung, “A Dynamic Radio Resource Man-agement Technique for Multiple APs in WLANs,” IEEE Transactions

on Wireless Communications, vol. 5, no. 7, pp. 1910-1919, 2006.[8] A. Mishra, S. Banerjee and W. Arbaugh, “Weighted Coloring Based

Channel Assignment for WLANs,” ACM SIGMOBILE, vol. 9, pp. 19-31, 2005.

[9] Y. Liu, W. Wu, B. Wang, T. He, S. Yi and Y. Xia, “Measurement-BasedChannel Management in WLANs,” IEEE WCNC, pp. 1-6, 2010.

[10] A. Mishra, V. Brik, S. Banerjee, A. Srinivasan and W. Arbaugh, “AClient-Driven Approach for Channel Management in Wireless LANs,”IEEE INFOCOM, pp. 1-12, 2006.

[11] J. K. Chen, G. de Veciana and T. S. Rappaport, “ImprovedMeasurement-Based Frequency Allocation Algorithms for WirelessNetworks,” IEEE GLOBECOM, pp. 4790-4795, 2007.

[12] S. Chieochan, E. Hossain and J. Diamond, “Channel AssignmentSchemes for Infrastructure-based 802.11 WLANs: A Survey,” IEEE

Communications Surveys & Tutorials, vol. 12, no. 1, pp. 124-136, FirstQuarter 2010.

[13] M. Heusse, F. Rousseau, G. Berger-Sabbatel and A. Duda, “Perfor-mance Anomaly of 802.11b,” IEEE INFOCOM, pp. 836-843, 2003.

[14] M. Arslan, K. Pelechrinis, I. Broustis, S. Krishnamurthy, S. Addepalliand K. Papagiannaki, “Auto-Configuration of 802.11n WLANs,” ACM

CoNext, 2010.[15] Y. Cheng, J. Bellardo, P. Benko, A. C. Snoeren, G. M. Voelker and

S. Savage, “Jigsaw: solving the puzzle of enterprise 802.11 analysis,”ACM SIGCOMM, pp. 39-50, Sept. 2006.

397