Analysis of IEEE802.11 DCF Parameters onAchievable Throughput in Ad hoc Networks

Ehsan Hamadani, Mostafa Mostafavi, Rahim TafazolliCentre for Communication System ResearchUniversity of Surrey Surrey, GU2 7XH, UK

Emails: {e.hamadani, s.mostafavi,r.tafazolli@surrey.ac.uk}

Veselin RakocevicInformation Engineering Research CentreCity University London, EC1 0HB, UK

Email: V.rakocevic@surrey.ac.uk

Abstract—It is well-known that the values of IEEE 802.11 MACparameters directly affect the utilization of the channel capacityand link layer throughput as well as higher layers performance.This paper first studies the throughput of ad hoc network undervarious 802.11 MAC parameters by developing a 3-dimensionalMarkov chain. Based on this model, it is mathematically provedthat the current values of 802.11 parameters result in dramaticthroughput degradation. Then, the optimum values of 802.11parameters that lead to maximum 802.11 MAC throughput willbe proposed.

Index Terms—802.11 MAC, Contention Window, MarkovChain, Saturation Throughput

I. INTRODUCTION

IEEE 802.11 [1] is the most widely used random mediumaccess control in wireless ad hoc network which offerstwo different types of services: a contention-based serviceprovided by the Distributed Coordination Function (DCF)and an optional contention-free service implemented by thePoint Coordination Function (PCF). However, due to lackof infrastructure, DCF is the fundamental access methodcommonly used in ad hoc networks. As shown in previousstudies, the performance of 802.11 DCF can largely affectsthe achievable throughput, delay, and scalability of the adhoc network [2]–[4]. In addition, as shown in [5] and [6],the parameters of 802.11 can have a critical impact on upperlayer performance such as TCP in ad hoc networks. Therefore,due to the complexity of ad hoc networks, it is extremelyimportant to be able to understand and model the impact ofsuch parameters across different layers. Since the introductionof the IEEE 802.11, there have been considerable work on theperformance evaluation of the protocol in ad hoc networksand possible ways to enhance its performance especially fromthroughput point of view. In particular, one of the earliestanalysis of the throughput of DCF was carried out in [7]using a simplified geometrically distributed backoff model. Amore realistic model was proposed by Bianchi [8] where theevolution of the back-off stage at each node is described by aMarkov process; The key importance of Bianchi model is touse a Markov chain to capture the effect of the contentionwindow and binary slotted exponential back-off procedureused by DCF in 802.11. However, his model did not takeinto account the retry limits and the effect of timeout valuesin throughput calculation. Since then, many improvements

to the initial Markov chain have been proposed to capturedifferent aspects of the operation of 802.11 in ad hoc networks.For instance, the study in [9] further improved the modelby considering the retry limit in Markov model. However,it still did not address the issue of multiple retry limits asspecified in 802.11 standard. Finally, the work by [10] take intoaccount the impact of hidden terminals in the analytical model.The main shortcoming of the previous studies is the lack ofcomprehensive and accurate analysis of the impact of different802.11 MAC parameters (e.g. maximum short and long retrylimits, and minimum contention window) on the achievablelink layer throughput. In order to investigate the impact ofdifferent 802.11 parameters on the performance of 802.11MAC, this paper extends the Bianchi two dimensional modelinto a three dimensional model. By using the extended model,we show that for a given number of contending stations, thereexists an optimal value of parameters at which 802.11 achievesthe highest throughput. However, the current version of 802.11parameters do not operate around this optimum values andtherefore causes serious throughput degradation.The rest of the paper is organized as follows. We first brieflyexplain the operation of 802.11 DCF in section II. SectionIII presents the details of the proposed model, includingassumptions and model components. The analytical throughputof the 802.11 and the impact of different 802.11 parameters onlink layer throughput is investigated in section IV. Section Vthen proposes the optimized values of 802.11 parameters forwhich throughput is maximum. Finally Section VI concludesthe paper and sets future directions in this area.

II. IEEE802.11 DCF SCHEME

IEEE 802.11 Distributed Coordination Function (DCF) [1]provides the basic access method of the 802.11 MAC usingthe CSMA/CA (Carrier Sense Multiple Access with CollisionAvoidance) scheme to access the channel. To transmit a packetin DCF, there are two techniques used; a two-way handshakingmechanism, also known as basic access method where apositive MAC acknowledgement(referred as MACK hereafter)is transmitted by the destination station to confirm the suc-cessful packet transmission. The other method is a four-wayhandshaking mechanism, which uses request-to-send/clear-to-send (RTS/CTS) technique to reserve the channel before datatransmission. In both techniques, each station willing to send

978-1-4244-2515-0/09/$25.00 ©2009 IEEE

a packet, generates a random backoff period according toequation 1 as its deferral time before transmitting, unlessthe backoff timer already contains a nonzero value, in whichcase the selection of a random number is not needed and notperformed.

BackoffT ime = Random() ∗ SlotT ime (1)

Here Random() is a random integer number of time slotsdrawn from a uniform distribution over the interval [0,CW ]and CW (Contention Window) is an integer number withinthe range of CWmin and CWmax. After choosing a backofftimer, if no medium activity is indicated, the backoff timer isdecremented by one slot-time. If the medium is determinedto be busy at any time during the backoff process, then thebackoff timer is suspended. This process is repeated until thetime the backoff counter reaches zero where the packet wouldbe transmitted.

At the receiver side (next hop), if the packet is receivedwithout error, the receiver issues a MACK packet, confirmingthe receipt of the packet. If the MACK (or CTS when we havesent RTS) is not received at the sender within the ACKTimeout

(CTSTimeout), the frame is presumed to have been lost andthe emitter schedules a frame retransmission1. This processcontinues until the transmission is successful, or until the Max-Retry-Limit is reached, whichever occurs first. There are twoMax-Retry-Limit defined in 802.11:

• Short-Retry-Limit (S)• Long-Retry-Limit (L)

The short retry counter is incremented whenever a con-trol frame or a short packet frame (i.e. smaller than RTS-Threshold) is retransmitted. Similarly, long retry counter isincremented whenever a long packet frame (i.e. larger thanRTS-Threshold) is retransmitted. Retries for failed transmis-sion attempts will continue until the short retry counter isequal to S or until the long retry counter is equal to L. Wheneither of these limits is reached, the higher layer data packetis discarded from the sender MAC buffer and the CW will bereset to CWmin.

III. ANALYTICAL MODEL

A. Model Assumptions

In order to analyze the protocol, the following assumptionsare made:

• The network consists of finite number of n contendingstations where all stations can hear each other.

• Every station always has a packet to transmit after thecompletion of each successful transmission. In otherwords, the system works in throughput saturation thatdefined as the limit reached by the system throughput asthe offered load increases.

• The probability of a packet collision is constant andindependent of the number of retransmissions attemptson this frame.

1After each retransmission, the value of CW is doubled until it reachesCWmax

B. Model Components

To describe the behaviour of 802.11, let us first denote thestate space F as:

F = {(i, j, k) : 0 ≤ i ≤ L, 0 ≤ j ≤ B ≤ S, k ≥ 0} (2)

where i is the current number of long retries, j is the currentnumber of backoff stage, and k is the current backoff countervalue which can take any value from 0 to Wj , where

Wj ={

W0 ∗ 2j j ≤ BW0 ∗ 2B j > B (3)

Also, in our space, L is the maximum number of long retrylimits, S is the maximum number of short retry limits and Bis the maximum number of backoff stages in the IEEE 802.11protocol 2. Figure 1 depicts in more details the states of thefirst plane (i=0) in the proposed Markov chain.

Fig. 1. Markov chain showing the state of a node in plane i=0

It is important to note that one of the main features ofthe model presented in Figure 1 which has been mostlyignored previously, is the distinction made between packetsthat require RTS/CTS exchange prior to their transmissionand other packets (including 802.11 control packets) in statetransitions. We believe such distinction should be accommo-dated in the model since the probability of packet transmissionand collision for packets that do not use RTS/CTS is clearlydifferent from packets that perform channel reservation priorto their transmission. To this aim, in Figure 1 if a controlpacket or DATA packets smaller then RTS-Threshold collides,the new state is chosen from the next row of the current plane(i.e. a transition along y axis). However, if a DATA packet

2In default operation of 802.11, we have L=4, S=7, and B=5

larger than RTS-Threshold is dropped, the new state is chosenfrom the next row of the next plane (i.e. a transition along zaxis). In other words, if a collision results to an increase in thenumber of short retries, the new state is chosen in the sameplane. However, if the packet drop results in the increase inthe number of long retries, a new state is picked up from thenext plane. The rules of these transitions will be covered inmore details later in this section.3

Let now bi,j,k be the stationary distribution of the Markovchain stochastic process for a given station. As it can be seenin Figure 1, with the total probability of PRTS

col , the stationchooses a new backoff counter from the next row. However,in case of successful RTS/CTS transmission, if the stationsuccessfully transmits its DATA packet with the probability ofPData

suc the chain is reset to initial stage; alternatively if with theprobability of PData

col the DATA packet is dropped, the stationchooses its new backoff value from the plane i = 1 and theprocess continues. In addition, according to equation (3), thenumber of backoff states (Wj) will not increase after a stationreaches stage B (maximum backoff stages) and remains fixed.The state transition diagram shown in figure 1 is governedby transition probabilities given in equations 4 and 5. Also,along with each transition probability, its transition durationis specified to enable us to calculate the average time that astation stays in one state.

• The backoff counter decrements and the station makes atransition from state (i,j,k) to state (i,j,k-1):{

P{(i, j, k − 1)|(i, j, k)} = 1t{(i, j, k − 1)|(i, j, k)} = σ

(4)

where σ represents the system time slot.• The station sends a RTS packet in state (i,j,0), but its RTS

packet collides and the station reaches state (i,j+1,k).{P{(i, j + 1, k)|(i, j, 0)} = P RT S

col

Wj+1

t{(i, j + 1, k)|(i, j, 0)} = TRTScol

(5)

where PRTScol and TRTS

col refer to the probability and averagetime of RTS collision, respectively.

Although the structure of the other planes are similar toFigure 1, it is important to note that different planes donot share exactly the same characteristics. For instance, theminimum value of the current number of backoff stage (j) inplanes i ≥ 1 is greater than or equal to 1 since when thestation is in plane i �= 0, it means there has been minimumone unsuccessful transmission exactly prior to the transition,so J ≥ 1. (Figure 2)

The state transition diagram between different planes inFigure 2 is governed by transition probabilities and durationsgiven below in equations 6 and 7:

• The station sends a RTS/CTS packet successfully in state(i,j,0), but the actual DATA is dropped (due to channel

3For simplicity and sake of argument, in the rest of this study we assume theRTS/CTS exchange is performed for all higher layer data packets and refer tosuch packets as DATA packet to distinguish them from 802.11 control packets.

Fig. 2. The key states of the Markov chain plane’s transition

error, hidden terminal, etc.) and the station reaches state(i+1,j+1,k).

{P{(i + 1, j + 1, k)|(i, j, 0)} = P RT S

suc ∗P Datacol

Wj+1

t{(i + 1, j + 1, k)|(i, j, 0)} = TDatacol

(6)

here PRTSsuc and PData

col are the probability of RTS successand DATA packet collision, respectively. Also, TData

col

refers to the average time the channel is occupied whena DATA packet collision occurs.

• The station sends a Data packet (following a successfulRTS/CTS exchange) successfully in state (i,j,0), andtherefore reaches state (0,0,k).

{P{(0, 0, k)|(i, j, 0)} = P RT S

suc ∗P Datasuc

Wj+1

t{(0, 0, k)|(i, j, 0)} = TDatasuc

(7)

where PDatasuc and TData

suc refer to the probability andaverage time of DATA success event.

Since the chain is regular, for each k ∈ (0,Wj − 1) we havethe following relations:

bi,j,k = bi,j,0 ∗ Wj − k

Wj

b0,j,0 = b0,0,0 ∗ (PRTScoll )j for(i = 0, 0 ≤ j ≤ S − 1)

bi,j,0 = bi,1,0 ∗ (PRTScoll )j−1 for(i �= 0, 1 ≤ j ≤ S)

bi,1,0 = b0,0,0 ∗ [PRTSsuc ∗ PData

col )]i for(i �= 0) (8)

Using the set of equations in (8) and the normalizationcondition, we get:

1 =L∑

i=0

S∑j=0

WJ−1∑k=0

bi,j,k =S−1∑j=0

WJ−1∑k=0

(b0,j,0 ∗ Wj−kWj

)+

L∑i=1

S∑j=1

WJ−1∑k=0

(bi,j,0 ∗ Wj−kWj

) =

b0,0,0

⎡⎢⎢⎢⎣

S−1∑j=0

((PRTS

col )j ∗ Wj+12

)+

L∑i=1

S∑j=1

( [(1 − PRTS

col

) (PData

col

)] i [PRTS

col

]j−1

∗(Wj+12 )

)⎤⎥⎥⎥⎦

(9)

Although in the above equation, b0,0,0 dependson both PData

col and PRTScol , the value of PData

col canstatistically be estimated by each station. This isbecause PData

col =1-[P (Data success)|P (RTS success)]and [P (Data success)|P (RTS success)] can be estimatedby each station since each station may keep track oftwo parameters: (1) The number of occasions that it hastransmitted a DATA packet (after a successful RTS/CTSreservation) and has received a MACK for that packet; and(2) the total number of transmitted DATA packets. Basedon the above values, the node at each time can statisticallycalculate the probability of

(PData

suc |PRTSsuc

)as the ratio of

the first to second parameter. Therefore, this implies thatin equation (9), b0,0,0 only depends on the value of PRTS

col

which can be calculated as follow:

PRTScol = 1 − (1 − τ)n−1 (10)

where τ is the probability of RTS transmission from a givennode in a randomly chosen slot time. On the other hand, sinceRTS transmission occurs when the backoff counter reaches 0,we have:

τ =L∑

i=0

S∑j=0

bi,j,0 =S−1∑j=0

b0,j,0 +L∑

i=1

S∑j=1

bi,j,0 =

b0,0,0

⎡⎢⎢⎣

1−(P RT Scol )S

1−P RT Scol

+L∑

i=1

S∑j=1

([(1 − PRTS

col

) (PData

col

)] i [PRTS

col

]j−1)⎤⎥⎥⎦

(11)

Therefore, equations (9), (10), and (11) represent a nonlinearsystem with two unknowns τ and PRTS

col which can beobtained by numerical results in terms of different n andPData

col . Table I presents the value of τ and its correspondingPRTS

col for different number of n and PDatacol .

IV. IMPACT OF 802.11 PARAMETERS ON THROUGHPUT

In this section, we investigate the impact of different 802.11parameters on the achieved throughput. To this aim, let T bethe normalized saturated throughput, defined as the fractionof time the channel is used to successfully transmit DATA

TABLE Iτ AND ITS CORRESPONDING P RTS

col UNDER DIFFERENT n AND P Datacol

PDatacol = 0.001 PData

col = 0.01

n = 5 P RTScol = 0.0306 P RTS

col = 0.0301τ = 0.0587 τ = 0.0582

n = 10 P RTScol = 0.0987 P RTS

col = 0.0976τ = 0.0541 τ = 0.0537

n = 20 P RTScol = 0.2197 P RTS

col = 0.2184τ = 0.0441 τ = 0.0439

n = 40 P RTScol = 0.3531 P RTS

col = 0.3522τ = 0.0310 τ = 0.0309

payload.4. Assuming the average DATA payload size as E[P],the average amount of DATA payload information successfullytransmitted in a slot time is PData

tr PDatasuc E[P ]. On the other

hand, the average length of a slot time can be obtained byconsidering that a single slot time falls in one of the followingfour cases:

1) With probability P1 =(1 − (PRTS

suc + PRTScol )

), the slot

time is empty2) With probability P2 =

(PData

tr PDatasuc

)the time slot

contains a unsuccessful DATA transmission3) With probability P3 =

(PRTS

col

)the time slot contains a

unsuccessful RTS transmission4) With probability P4 =

(PData

tr PDatacol

)the time slot

contains a successful DATA transmission

Therefore, the normalized throughput can be written as:

T =PData

tr PDatasuc E[P ]

P1σ + P2TDatasuc + P3TRTS

col + P4TDatacol

(12)

or alternatively,

T =E[P ]⎛

⎝(

1−(P RT Ssuc +P RT S

col)

P RT Ssuc

)+

(P RT S

col

P RT Ssuc

∗T RT Scol

σ

)+

(P Data

col∗T Data

colσ

)P Data

suc

⎞⎠+ TData

suc

(13)Note that in the above equation, the terms PData

suc and PDatacol

indeed refer to [P (Data success)|P (RTS success)] and[P (Data collision)|P (RTS success)], respectively which asexplained earlier can be statistically computed by each node.In addition, we have:

PDatatr = PRTS

Suc = nτ(1 − τ)n−1 (14)

Finally, the corresponding values of transition times can becalculated as follows:

TDatasuc = DIFS + Trts + Tcts + H + E[P ] + Tack + 2SIFS + 3δ

TRTScol = DIFS + Trts + SIFS + Tcts_timeout

TDatacol = DIFS + Trts + δ + Tcts + 2SIFS + Tack_timeout

(15)

4As explained earlier, in this paper DATA payload refers to upper layerpackets and does not include the control packets in link layer in contrary to[8] throughput model

Here, Trts, Tcts, and Tack represent the time required totransmit RTS, CTS, and MACK over the channel, respectively.Also, Tcts_timeout (or Tack_timeout) refer to time intervalsbefore a station assumes its RTS transmission (or Datatransmission) has been unsuccessful and triggers a packetretransmission. In addition, H = PHYhdr + MAChdr is thepacket header size and δ is the propagation delay.

It is important to note that based on equations (10),(13), and(14), τ is the only configurable parameter that affects the MACthroughput. Therefore, the issue of investigating the impact of802.11 parameters on throughput can be directly translatedinto analyzing the impact of 802.11 parameters on the valueof τ . Figure 3 depicts the impact of changing different MACparameters on τ under various number of stations and PData

col

by using equation (11) and the system parameters given inTable II5.

Table II: System parameters for MAC and DSSS PHY LayerPacket payload 11680 bits

MAC header 224 bits

PHY header 192 bits

ACK 112 bits + PHY header

RTS 160 bits + PHY header

CTS 112 bits + PHY header

Channel bit rate 2Mbps

Propagation delay 1 μs

Slot time 20 μs

CTST imeout 300 μs

MACKT imeout 300 μs

SIFS 10 μs

DIFS 50 μs

From the above results it is obvious that the value of τis highly sensitive to change of W0 while the number oflong retry limits almost has no impact on τ . Meanwhile, forS>5, the value of τ remains almost constant regardless of thechange in maximum number of short retry limits. Therefore,the main parameter that has a major impact on the 802.11MAC throughput is the size of minimum contention window.

V. THROUGHPUT OPTIMIZATION

Having investigated the impact of different 802.11 param-eters on throughput, the next interesting issue is to find theoptimum value of 802.11 parameters (or equivalently theoptimum τ ) that result in maximum throughput 6. To this aim,we take the derivative of equation (13) with respect to τ andimpose it equal to 0. After some simplifications, the followingequation is obtained:(

(1 − τ) +T ∗RTS

col

(1 − τ)n−1− T ∗RTS

col (1 + nτ − τ))

+

τ

(1 − T ∗RTS

col

(n − 1

(1 − τ)n− (n − 1)

))= 0

(16)

where T ∗RTScol = TRTS

col /σ.

5The system values used in this paper are those specified for the DSSS(Direct Sequence Spread Spectrum) physical layer

6Since the results from figure 3 and table I suggest that the dependency ofτ on P Data

col is very marginal and almost negligible, in the rest of this paperand without the loss of generality we assume P Data

col =0.001 .

0 2 4 6 8 10 12 14 16 18 200.03

0.035

0.04

0.045

0.05

0.055

0.06

Maximum Number of Short Retry Limits (S)

τ

n=5, P(data collision)=0.001n=5, P(data collision)=0.1n=20, P(data collision)=0.001n=20, P(data collision)=0.1n=40, P(data collision)=0.001n=40, P(data collision)=0.1

(a) Impact of maximum short retry limits on τ

0 2 4 6 8 10 12 14 16 18 200.03

0.035

0.04

0.045

0.05

0.055

0.06

Maximum Number of Long Retry Limits (L)

τ

n=5, P(Data collision)=0.001n=5, P(Data collision)=0.1n=20, P(Data collision)=0.001n=20, P(Data collision)=0.1n=40, P(Data collision)=0.001n=40, P(Data collision)=0.1

(b) Impact of maximum long retry limits on τ

0 50 100 150 200 250 300 350 4000

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Minimum Contention Window (slots)

τn=5, P(data collision)=0.001n=5, P(data collision)=0.1n=20, P(data collision)=0.001n=20, P(data collision)=0.1n=40, P(data collision)=0.001n=40, P(data collision)=0.1

(c) Impact of minimum contention window size on τ

Fig. 3. The impact of changing different MAC parameters on τ

Equation 16 shows that the optimum τ at which maximumthroughput occurs is solely dependent on number of con-tending stations (n). Based on this result, Table IV presentsthe default values of τ (based on current values of 802.11parameters) as well as the optimum τ (at which the throughputis maximum) for different number of n.

Table IV: Default and Optimum τ for different number of contendingstations

Number of Stations Default τ Optimum τn=5 τdef = 0.0587 τopt = 0.0480n=10 τdef = 0.0541 τopt = 0.0229n=20 τdef = 0.0441 τopt = 0.0112n=40 τdef = 0.0310 τopt = 0.0055

Comparing the default values of τ (τdef ) and the optimumτ (τopt), it is obvious the default values of 802.11 parameterstend to overshoot the optimum τ in which the maximumthroughput is achieved. In order to understand the importanceof this observation, figure 4 shows the relation between MACthroughput and the parameter τ .

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

τ

Sa

tura

tio

n T

hro

ug

hp

ut

n=5

n=10

n=20

n=40

Maximum Throughput for n=5

Maximum Throughput for n=10

Maximum Throughput for n=20

Maximum Throughput for n=40

Fig. 4. 802.11 saturated throughput versus τ

It is clear from the above figure that as the number of nodesincreases, it becomes increasingly important for the networkto operate very closely to τopt (shown by different symbols infigure 4) as even a slight deviation in the value of τopt canseverely degrade the system throughput.

In order to tune the τdef to τopt, let us recall from figure3 that τ is mainly dependent on minimum contention windowsize (i.e W0). Therefore, the issue of tuning the default τ tooptimum τ can be merely done by adjusting the value of W0.Using equation (11) and (16), figure 5 depicts the values of W0

for different number of n for which τdef = τopt and thereforethe acheived throughput of 802.11 will be maximum.

0 10 20 30 40 500

50

100

150

200

250

300

350

400

Number of Stations

Con

tent

ion

Win

dow

siz

e (s

lots

)

Fig. 5. Optimum value of W0 for different number of stations

The above result suggest that by merely setting the parame-ter W0 according to values in figure 5, the 802.11 will achieveits highest throughput. Nevertheless, it is very important tonote that though the above results suggest the negligible impactof T and L on 802.11 throughput, their values can dramaticallychange the TCP performance. Interested readers are referredto [11] for further discussion on the impact of such parameterson TCP performance.

VI. CONCLUSION

In this paper, we proposed a 3-d Markov chain to accuratelymodel the performance of 802.11 when RTS/CTS mechanismis used. Based on the model, the impact of different 802.11parameters on achievable throughput was investigated and itwas shown that the current values of 802.11 may result in adramatic throughput degradation. It was also shown that thevalue of 802.11 minimum contention window size is the mainconfigurable parameter that needs to be adjusted to maximizethe MAC throughput. The model can be further refined byconsidering unsaturated traffic condition. In addition, the im-pact of 802.11 parameters on other performance metrics suchas service time burstiness can be further studied.

REFERENCES

[1] “IEEE Standards for Wireless LAN Medium Access Control (MAC)and Physical Layer (PHY),Part 11:Technical Specifications,” Tech. Rep.,1999.

[2] O. Tickoo and B. Sikdar, “Queueing analysis and delay mitigation inIEEE 802.11 random access MAC based wireless networks,” Proceed-ings - IEEE INFOCOM, vol. 2, pp. 1404 – 1413, 2004.

[3] M. M. Carvalho and J. J. Garcia-Luna-Aceves, “Delay Analysis of IEEE802.11 in Single-Hop Networks,” 11th IEEE International Conferenceon Network Protocols, 2003.

[4] Y. Barowski, S. Biaz, and P. Agrawal, “Towards the performanceanalysis of IEEE 802.11 in multi-hop ad-hoc networks,” IEEE WirelessCommunications and Networking Conference, WCNC, vol. 1, pp. 100 –106, 2005.

[5] R. Jiang, V. Gupta, and C. V. Ravishankar, “Interactions between TCPand the IEEE 802.11 MAC protocol,” DARPA Information SurvivabilityConference and Exposition, 2003. Proceedings, vol. 1, pp. 273–282,2003.

[6] E. Hamadani and V. Rakocevic, “Evaluating and Improving TCP Per-formance against Contention Losses in Multihop Ad Hoc Networks,”IFIP International Conference on Mobile and Wireless CommunicationNetworks, 2005. Proceedings, vol. 1, 2005.

[7] F. Cali, M. Conti, and E. Gregori, “IEEE 802.11 wireless LAN: capacityanalysis and protocol enhancement,” Proceedings - IEEE INFOCOM,vol. 1, pp. 142 – 149, 1998.

[8] G. Bianchi, “Performance analysis of the IEEE 802.11 distributed coor-dination function,” IEEE Journal on Selected Areas in Communications,vol. 18, no. 3, pp. 535 – 547, 2000.

[9] H. Wu, Y. Peng, K. Long, S. Cheng, and J. Ma, “Performance ofreliable transport protocol over IEEE 802.11 wireless LAN: Analysisand enhancement,” Proceedings - IEEE INFOCOM, vol. 2, pp. 599 –607, 2002.

[10] J. He, D. Kaleshi, A. Munro, Y. Wang, A. Doufexi, J. McGeehan, andZ. Fan, “Performance investigation of IEEE 802.11 MAC in multihopwireless networks,” ACM MSWiM 2005 - Proceedings of the EighthACM Symposium on Modeling, Analysis and Simulation of Wireless andMobile Systems, vol. 2006, pp. 242 – 249, 2006.

[11] Y. Wang, K. Yu, Y. Liu, and H. Zhang, “Effects of MAC retransmissionon TCP performance in IEEE 802.11-based Ad-hoc networks,” IEEEVehicular Technology Conference, vol. 59, no. 4, pp. 2205 – 2209, 2004.