Effective Bandwidth Evaluation for VoIPApplications in IEEE 802.11 Networks

Cristina Ortiz, Jean-Francois Frigon, Brunilde Sanso and Andre GirardElectrical Engineering DepartmentEcole Polytechnique de Montreal

C.P. 6079, succ. centre-ville, Montreal, QC, Canada, H3C 3A7Email: {cristina.ortiz, j-f.frigon, brunilde.sanso}@polymtl.ca, andre@emt.inrs.ca

Abstract— The convergence of different classes of traffic withdifferent priorities over the wireless network has become a reality.To insure that the users of key applications such as VoIP aresatisfied with the service they receive, one must insure that theQoS criteria for these applications, such as delay, jitter or packetloss, are met. This in turn means that some form of ConnectionAdmission Control must be used. In this paper, we show howthe notion of Effective bandwidth that had been previously usedin wired systems can be used for CAC in WLAN IEEE 802.11band 802.11g networks. Effective Bandwidth simplifies connectionadmission since a new application can be accepted on a linkwhenever its effective bandwidth is lower than the bandwidthstill available on the link. The paper presents empirical resultsobtained by extensive simulations showing that the admissionregion is nearly linear so that it is possible to design an effectivelinear CAC policy based solely on the effective bandwidth of aconnection. We also propose a more practical method that relatesto per packet effective bandwidth.

Index Terms— Connection Admission Control, Effective Band-width, MAC, Network Capacity, QoS, Regions of Admission,VoIP, WLAN, Simulation, 802.11.

I. INTRODUCTION

IEEE 802.11 Wireless LANs [1] are amongst the mostpopular technologies used in private enterprises and publichot spots so that there is a fast growing demand for supportof real-time applications in WLANs. A major roadblock for aseamless implementation of an effective support for stringentreal-time applications, such as VoIP, is the common MAClayer of the 802.11 standard. This standard provides twoMAC operation modes: a mandatory Distributed CoordinationFunction (DCF) for data applications and an optional PointCoordinator Function (PCF) for real-time applications. MostWLANs use the DCF mode, which was not designed tosupport real-time traffic and does not offer any service guaran-tee [2]. Furthermore, in the current standard, voice applicationsmust directly compete with data applications for access to themedium without any priority.

Initial work on the DCF mode present analytic and simula-tion models to evaluate its performance in terms of through-put [3]. The ability of the DCF mode to support voice trafficwas also evaluated by theoretical and experimental analysisin [4]–[7]. These works estimated the maximum number oftwo-way VoIP calls that the DCF mode can support andshowed that the voice capacity remains small despite a large

access bandwidth. The network performance with VoIP andbackground data traffic was also evaluated in [8].

The results presented so far in the WLAN literature havedemonstrated that there is a critical point after which addinganother voice connection results in an unacceptable qualityfor all ongoing calls. Thus, it is necessary to implement aConnection Admission Control (CAC) mechanism to protectthe quality of ongoing connections and to optimize the utiliza-tion of network resources. One important work on CAC for802.11 DCF WLAN is presented in [9] which uses channelutilization estimation (CUE) as the acceptance metric for anew connection. On the other hand, the concept of EffectiveBandwidth (EB) is widely used in ATM wireline networksto perform CAC [10], [11]. The EB is the minimum amountof bandwidth required by a flow to maintain an acceptableQoS during its lifetime. This can be used for CAC simplyensuring that the sum of the EBs of all connections is alwaysless than or equal to the total link bandwidth. The objectiveof the work presented in this paper is to use the same conceptof EB to do CAC as a function of different traffic classes inWLANs. Using this approach, we demonstrate that for IEEE802.11b/802.11g networks with mixed VoIP and data traffic,the admission regions are approximately bounded by a linearfunction. Unlike previous work on CAC in WLANs [9], [12],[13], we are thus able to propose a simple CAC algorithmbased on a general EB metric which takes into account thefunctionality of the 802.11 MAC layer, different traffic classesand QoS metrics.

The remainder of this paper is organized as follows. Sec-tion II provides background information on the 802.11 MAClayer and EB. In Section III, we introduce the simulationplatform and traffic models used in the study. The results arepresented and discussed in Section IV. Finally, the paper isconcluded in Section V.

II. BACKGROUND

A. IEEE 802.11 MAC Layer

In this paper, we focus our analysis on the most commonDCF access mode that is based on Carrier Sense MultipleAccess with Collision Avoidance (CSMA/CA). In this listen-before-talk mechanism, terminals must first monitor the sharedmedium and determine if it is free before attempting transmis-sion.

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+

ACK(14 Bytes)

DIFS

...

Channel Busy Backoff Phase

SIFS

tsender

receiver

= time slot

THEADERS

TDATA

+THEADERS

TDATA

Channel Busy

Fig. 1. Basic DCF Procedure for IEEE 802.11

The basic operation of the 802.11 DCF is described inFig. 1. Before a transmission, the station senses the channeland if the channel is idle for a time greater than a DistributedInterFrame Space (DIFS), the packet is transmitted. Other-wise, the station enters into the backoff phase where it choosesa random backoff interval time, uniformly distributed between[0, CW] time slots, where CW is the current ContentionWindow and is initialized to CWmin. As soon as the randombackoff has reached 0, the node starts the transmission. If thereceiver successfully receives the packet, it confirms the recep-tion with a positive Acknowledge frame (ACK) after a ShortInterFrame Space (SIFS). The absence of an acknowledgmentindicates to the sender a lost or corrupted transmission. Inthis case, the station schedules a retransmission using thebackoff process again; however, the CW value doubles until itreaches the maximum value of CWmax. In the DCF, there isan optional mode with an exchange of Request to Send (RTS)and Clear to Send (CTS) messages used to solve the hiddennode problem. This feature is generally used with nodes inmovement. RTS/CTS mode is not considered in our study dueto the fact that it introduces an additional delay that does notwork for small packets such as VoIP packets.

B. Effective Bandwidth-Based Admission Regions

The task of the CAC mechanism is to decide whethera new connection can be admitted in the network withoutviolating the QoS requirements of all active connections. Thisis related to the notion of admission region which can bedefined as follows. Consider for the sake of simplicity a systemthat supports only two types of connections with differentcharacteristics and QoS requirements. Let ni, i = 1, 2 be thenumber of connections of each type that are present on thesystem at a given time. Consider points in the (n1, n2) plane.For some of these points, all sessions will get their requiredQoS while this will not be true for others, e.g., if the valuesof ni become too large. The region of the plane where allthe QoS requirements are met is called the admission region.This is the set of values of (n1, n2) that are acceptable. Thecalculation of this region, and in particular of its boundary, isquite difficult except for the simplest systems and is not linearin general.

However, the use of the EB assigned to each traffic sourceis a simple and scalable solution for CAC. The EB conceptis based on connection-oriented networks where a portion ofthe total bandwidth is allocated to each connection basedon its traffic class. The EB thus approximates this valueof the minimum bandwidth required by each traffic flowand depends on several factors such as QoS requirements,

the number of users multiplexed together and the networkresources and mode of operation. A precise definition of theeffective bandwidth can be found in [14].

For the purpose of this paper, it is enough to state that weevaluate the EB γi of traffic class i by computing, for thistraffic class only, the maximum number of connections N∗

i

that can be accepted without violating its QoS requirement.γi is then given by dividing the total capacity C of the linkby the maximum number of connections admitted:

γi = C/N∗i . (1)

The effective bandwidth can be used for connection admissionif we assume that the boundary of the admission region islinear. In that case, the EB is additive over all classes and it ispossible to sum up all the per-connection EBs on a link. Thevector of connections {n1, n2, . . . nI}, where I is the numberof traffic classes, is thus admissible if:

I∑

i=1

γiNi ≤ C. (2)

An important question is then whether the linear approxima-tion for the admission region is valid for a given system, inour case for 802.11 WLANs. This is what we now set out toshow.

III. SIMULATION PLATFORM AND ASSUMPTIONS

The simulation study was conducted using the NetworkSimulator NS-2 [15]. This section presents the simulationscenario, traffic models and the QoS metrics considered inour experimental study.

A. The Simulation Scenario

The network topology used in the simulations is depictedin Fig. 2. Each session is between a wireless station and awired end-point, or vice versa. The communication sessionsare one-way (uplink or downlink) for CBR sources or two-wayfor VoIP sources. We examined two scenarios, one with voicetraffic only and the other with mixed voice and data traffic.Furthermore, one of our goals is to isolate the MAC layerand evaluate the impact of simple DCF operation on voicetransmission. For this reason, all stationary stations are withinthe range of others and no hidden node is simulated. RTS/CTSmechanism in not consider and, wireless and wireline channeldo not introduce errors, packet loss or propagation delay.

First we ran the simulation for each service at a time andmeasure the various QoS parameters for increasing values ofni. The largest value for which all QoS requirements are met isthen taken as N∗

i in Eq. (1). To determine the actual admissionregions, we vary both n1 and n2 to determine the points whereit is no longer possible to increase both values at the same timeand maintain all the QoS. These points are then taken as theboundary of the admission region.

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IdealWireline network

. . .

. . .

Wireless link

AP

Fig. 2. Topology used by our study

PHY Preamble + PLCP

MAC header(28 Bytes)

IP header(20 Bytes)

UDP header(8 Bytes)

PACKET(Variable length)

Fig. 3. Wireless packet structure

B. Protocol Headers and MAC Parameters Values

We assumed UDP as the transport protocol so UDP, IP andMAC headers are added to the payload for all traffic classes. Inaddition to these protocol headers, a PLCP header and a PHYlayer preamble are added to every packet for transmission overthe wireless medium. The final packet structure is shown inFig. 3 and the values of the parameters of the DCF operationused in our simulation, as specified by the IEEE 802.11b andIEEE 802.11g standards, are summarized in Table I. We usedthe maximum data rate available for data transmission in allour simulations.

C. Traffic Characteristics

We have used two traffic classes in the simulations.a) Voice over IP (VoIP): Voice traffic is modeled based

on the G711 CODEC. The voice is sampled at 64 kbit/s andframes are generated at a 10 ms or 20 ms interval. We chosethis commonly use nowadays CODEC due to its similaritywith the Public Switched Telephone Network (PSTN). Previ-ous work [4], [6] also demonstrated that other CODECs withbetter compression don’t significantly improve the maximumcapacity of WLAN networks while degrading voice quality.

TABLE I

PARAMETERS VALUES OF IEEE 802.11 B AND IEEE 802.11G

ParameterValue

802.11b 802.11g

TIME SLOT (μs) 20 9

SIFS (μs) 10 10

DIFS (μs) 50 28

PLCP and Preamble (μs) 192 20

Basic Rate (Mbps) 1 6

CWmin 31 15

CWmax 1023 1023

Maximum Data Rate (Mbps) 11 54

Our experimental model could also be implemented with otherCODECs and other packetisation intervals. The full duplexVoIP calls are represented by two streams: one from a wirelessstation to a wired station and another the other way around.Each stream is modeled using an independent exponentialON/OFF process with a mean silence (OFF) period of 600 msand a mean talk (ON) period of 400 ms.

b) DATA flows: Data traffic is emulated as a unidirec-tional CBR source and originates either in the wireline or thewireless network. The packet length and data rate are variableparameters. To analyze the capacity inferior limit in terms ofdata admitted connections, we need data sources that havealways a packet to send to the network as CBR sources do.

D. Performance Metrics

To characterize the unidirectional Quality-of-Service (QoS)of voice traffic, we used three performance metrics. The Delay,defined as the time between the packet generation and itsarrival at the destination, the Loss Rate, defined as the ratio oflost and late packets over the total number of packets sent, andthe Jitter which is the absolute value of the difference betweenthe delay of two consecutive packets. All these metrics areaveraged on a simulation run for each one-way VoIP stream.

The maximum one-way delay budget of 75 ms for voicetraffic in the WLAN was established based on a 200 ms end-to-end delay including coding/decoding time, packetization andassuming transmission over another interconnected network.Due to the delay-bounded nature of VoIP traffic, packets aredropped at the receiver if they arrive too late to be used. Thus,no more than 1% of real-time traffic must be delivered nolater than the maximum tolerable delay. Additionally, the jitterbound for VoIP traffic was set to 20 ms. Note that althoughdata traffic does not have any QoS requirements, we added adelay bound of 2 seconds in order to avoid driving the networkin an artificially over-congested mode.

IV. NUMERICAL RESULTS

In this section, we present the quantitative results obtainedby simulations. Each scenario was simulated for 120 secondsand repeated 10 times to obtain statistically significant numberof connections results within an error margin of ±1 connectionfor 802.11b and ±5 connections for 802.11g.

A. Voice-only Scenario

We first studied the case where only voice users are activein the network. Table II presents the maximum number ofconnections that can be admitted without violating the QoSrequirements. It also shows the Effective Bandwidth for eachtraffic source computed using Eq. (1). First, note that althoughthe actual mean data rate of a VoIP source is low (25.6 kbit/s),its EB is extremely large. This is due to two factors: the 802.11MAC introduces a large delay and there is a bottleneck atthe AP since all down-link streams must be served by theAP which creates a large queuing delay. We also see thatthe performance improves with a larger packetization intervalbecause fewer packets are transmitted and the impact of the

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TABLE II

EFFECTIVE BANDWIDTH OF VOIP SOURCES

Standard Traffic Payload Capacity EB (kbit/s)

802.11b Voice10 ms 9 1 222

20 ms 19 579

802.11g Voice10 ms 68 794

20 ms 122 443

0 20 40 60 80 100 120 1400

10

20

30

40

50

60

Number of CBR connections

Nu

mb

er o

f V

oIP

Co

nn

ecti

on

s

4 Mbps2 Mbps1 Mbps512 Kbps128 Kbps

CBR DATA RATE

Fig. 4. VoIP admission regions with CBR sources (1440 Bytes packet lengthfor 802.11g)

MAC overhead is reduced. Also, at a higher data rate for802.11g, the EB improves due to better statistical multiplexing.The results for voice-only scenarios are in accordance with thepreviously reported results in literature [4], [7], [8].

B. Voice and DATA Scenario

In this section, we analyze the impact of data rate, packetsize and flow direction od CBR sources on VoIP/CBR admis-sion regions. Fig. 4 shows an example of the VoIP admissionregions with uplink CBR traffic for a 802.11g WLAN. TheVoIP payload was fixed at 10 ms and the CBR packet size to1440 Bytes. For this region of operation, the results clearlyindicate that the number of voice connections than can beadmitted without violating their QoS requirements is a linearfunction of the number of active CBR connection.

Fig. 5 illustrates the VoIP admission region for 128 kbit/sCBR connections with different packet sizes. We can againobserve the linear VoIP admission region. Furthermore, as wasthe case for the voice-only scenario, the network can supporta larger number of voice connections when the CBR packetsize increases due to the significant MAC overhead incurredfor every packet transmission. Similar linear admission regionsfor voice connections versus CBR connections were obtainedfor various network configurations (802.11b or 802.11g) anddifferent traffic source parameters such as voice packetizationdelay, CBR data rate, CBR packet size and uplink or downlinkCBR streams. These simulations results indicate that it ispossible to perform CAC for mixed voice and CBR data traffic

0 20 40 60 80 100 120 1400

10

20

30

40

50

60

Number of CBR connections

Nu

mb

er o

f V

oIP

co

nn

ecti

on

s

100 Bytes500 Bytes1024 Bytes1440 Bytes

CBR PACKET SIZE

Fig. 5. VoIP admission regions with 128 Kbps CBR sources for 802.11g

based on the EB of both traffic sources. The EB of voicesources with 10 and 20 ms packetization delays are givenin Table II and the EB of a CBR source is given by thenetwork data rate divided by the abscissa of the admissionregion. However, this abscissa value for EB CBR source variesas a function of the data rate and packet size and it mustbe evaluated experimentally in each case. This leads to apotentially large number of traffic classes in Eq. (2) and canbe hardly applied in a real network.

C. Simplified CAC

In order to simplify the CAC algorithm based on the EB,we want to eliminate some of the parameters such as theCODEC interval packetization and CBR data rate using adifferent definition of EB. As previously noted, due to theMAC overhead incurred for each packet transmission, thenumber of packets per second sent by a source is criticalto the actual bandwidth that is going to be required. Theseobservations lead us to define the admission region as thenumber of VoIP packets per second that can be transmitted,without violating the VoIP QoS requirements, as a function ofthe number packets per second transmitted by the CBR datasources. Fig. 6 presents the redefined admission regions givenin Fig. 4. Using this rescaling, the new EB (θi) of a source oftraffic class i is now defined as:

θi = C/P ∗i bits/packet, (3)

where P ∗i is the maximum number of packets/second of class

i that can be transmitted. In Fig. 7, we show that admissionregions for different voice packetization interval and downlinkCBR streams are close to the admission regions presented inFig. 6. On the other hand, we also observe in this figure thatthe linear admission region varies as a function of the CBRpacket size. Thus, for a given packet size and a traffic class,it is possible to use a common value of the redefined EB (θi),regardless of the other source parameters (CBR data rate, CBRdirection flow and VoIP packetization interval).

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0 500 1000 1500 2000 25000

1000

2000

3000

4000

5000

6000

7000

Number of CBR packets per second

Nu

mb

er o

f V

oIP

pac

kets

per

sec

on

d

4 Mbps2 Mbps1 Mbps512 Kbps128 Kbps

CBR DATA RATES

Fig. 6. VoIP/CBR admission regions in terms of packets per second (1440Bytes packet length for 802.11g)

0 1000 2000 3000 4000 50000

1000

2000

3000

4000

5000

6000

7000

Number of CBR packets per second

Nu

mb

er o

f V

oIP

pac

kets

per

sec

on

d

Voice: 10 ms − DATA: 2 Mbps, 1440B, UPVoice: 10 ms − DATA: 128 Kbps, 1440B, UPVoice: 20 ms − DATA: 128Kbps, 1440B, UP Voice: 10 ms − DATA: 128Kbps, 1440B, DNVoice: 10 ms − DATA: 128 Kbps, 100B, UP

Fig. 7. VoIP/CBR admission regions in terms of packets per second (802.11g)

To verify our simulation results, we computed the theo-retical upper bound of CBR packets per second that can beadmitted by the WLAN network. Let us denote this boundby θ, let TP be the time to transmit P Bytes, and Tover

the overhead time taken by the MAC operation and headerstransmission. The average time needed to successfully transmita packet over the wireless network is TP + Tover seconds.Thus, the maximum number of packets that can be transmittedper second is:

γ = 1/(TP + Tover), (4)

where Tover = 2TPHY + TMAC + TIP + TUDP + DIFS +TBCK + SIFS + TACK . TBCK is the backoff time used byeach station and is a function of the collision probability andthus of the number of users. This probability is consideredvery small in an infrastructure mode. Then, TBCK can beapproximated as (CWmin/2)TSLOT [4], [6], [7]. Tover is thusequal to 900μs in 802.11b WLANs and to 172μs in 802.11gWLANs. In 802.11g, the theoretical upper bound θ for CBR

TABLE III

EXPERIMENTAL EB (BITS/PACKET) OF VOIP AND CBR SOURCES

Standard TrafficPacket Size Maximum EB (θ)

(Bytes) Pck/Sec (kbits/Pck)

802.11b

Voice80 900 12.22

160 950 11.57

CBR

1440 388.88 28.28

1024 468.75 23.47

500 704 15.62

100 1120 9.82

802.11g

Voice80 6800 7.94

160 6100 8.85

CBR

1440 1611.11 33.52

1024 1937.5 27.87

500 2944 18.34

100 4800 11.25

sources with 1440 Bytes packet size is 2595.17 packets persecond which is close to the results obtained by simulationsas shown in Fig. 6. The difference between theoretical andexperimental values, especially for 1440 Bytes CBR sourcesat 128 kbit/s where the abscissa is 1611 pck/sec instead of2595 pck/sec, can be explained by the following two factors:1) the abscissa in the linear admission region represents thesmallest CBR traffic for which we can’t admit a VoIP call,as opposed to the maximum CBR traffic computed in thetheoretical bound; and 2) when there is a large number of CBRsources, the approximation for TBCK is no longer accurate asillustrated by the larger deviation with lower data rate sourceswhere there are 145 sources at 128 kbit/s admitted.

Based on these results, we propose the following CACalgorithm. First, the number of packets/second Pi,j that willbe transmitted by a new connection of traffic class i and withpacket length j is evaluated. Then, this new connection isadmitted if the set of Pi,j’s, including ongoing connectionsand the new connection, satisfy the following inequality:

∑

i

∑

j

θi,jPi,j ≤ C (5)

where θi,j is the EB of traffic class i and packet size j. In orderto ensure the QoS requirements of VoIP traffic, the abscissaused to compute the EB of CBR sources is set by the minimumoperation region found in our simulations. Table III presentsthe experimental EBs for VoIP and CBR data traffic that canbe used in the proposed simplified CAC algorithm.

D. The standard IEEE 802.11e and the CAC mechanism

The IEEE 802.11e is the new proposition for QoS inWLANs. It implements new schemes for service differenti-ation and for different transmission priorities. Our simulationsdemonstrate that admission regions of IEEE 802.11e networksare no longer linear. In fact, the differentiation mechanismsadmit more high priority connections avoiding fast degradationof its QoS criteria. In these cases, our linear approximationsof admission regions are very conservative and our CAC

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proposition always maintains the network below its maximumcapacity. Non-linear approximations could be implementedwith IEEE 802.11e WLANs as a solution to optimize thenetwork performance with our CAC mechanism.

V. CONCLUSIONS AND FUTURE WORK

In this paper, we have shown by simulation that the admis-sion region of a 802.11 WLAN in DCF mode is linear formixed voice and data traffic. We also proposed a new CACalgorithm based on the EB of a traffic source in terms ofpackets/sec. As future work, we plan on generalizing theseresults to networks with more than two traffic classes, carry outsimilar experiments for forthcoming standards and improvingthe theoretical model.

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