Optimum Parameters for VoIP in IEEE e Wireless LAN

Optimum Parameters for VoIP in IEEE 802.11e

Wireless LAN

Ryo Kitahara

NTT DoCoMo 3-5 Hikari-no-oka, Yokosuka City Kanagawa Prefecture, Japan

[email protected]

Koichiro Doi

Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo, JAPAN

[email protected].

waseda.ac.jp

Tomoya Iimura

Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo, JAPAN

[email protected].

waseda.ac.jp

Shingo Morita

Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo, JAPAN

[email protected].

waseda.ac.jp

Shigeki Goto

Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo, JAPAN

[email protected].

waseda.ac.jp

ABSTRACT

IEEE 802.11e is an enhanced standard in wireless LAN which has QoS mechanisms, EDCA and HCCA. This paper an-alyzes the performance of IEEE 802.11e EDCA functions through working testbeds and a large-scale simulation. EDCA has three parameters: AIFS, CW and TXOP to dif-ferentiate packets. This paper shows the effectiveness of EDCA by simple experiments. Then, we try to find the op-timum values for EDCA parameters. It is interesting that default parameter values do not ensure good communication quality for VoIP communications.

This paper also illustrates the results of a simulation which covering a large number of nodes. Again, it is shown that the default values are not ideal for VoIP communications on a larger scale.

The results of this paper are meaningful when VoIP commu-nications have priority over other traffic in a wireless LAN environment.

Categories and Subject Descriptors

C.2.1 [Computer - Communication Networks]: Net-work Architecture and Design—Wireless communication

General Terms

Measurement, Performance, Experimentation

Keywords

IEEE 802.11e, EDCA

1. INTRODUCTION

IEEE 802.11 standards are widely employed to access the In-ternet. Almost every laptop computer shipped today has an IEEE 802.11 a/b/g-enabled Network Interface Card (NIC) embedded. Access points (AP) are installed into many of-fices and homes. Some companies provide fare-paying wire-less LAN service in public places like airports, railway sta-tions and coffee shops. In addition, there also exist free-of-charge services. FON has started an AP sharing service over 9 countries since 2005 and already has more than 5 million users worldwide [1].

People use wireless Internet access for many services such as the World Wide Web, E-mail or short messages. Wireless Internet access today is also used for video streaming and IP telephony, which requires good Quality of Service (QoS) in terms of i) low packet loss rate; ii) minimum delay time of packet arrival; iii) small jitter of delay. Traditional Me-dia Access Control (MAC) is enough when the load is not heavy. With a large amount of traffic, however, it cannot guarantee the quality of such services because it does not classify traffic which requires high QoS from normal traffic. The main goal of simple CDMA/CA-based traditional MAC is to share the radio medium with other nodes. Traditional MAC has a single queue for outgoing frames and cannot distinguish what service the traffic is delivering.

The IEEE 802.11e standard aims to provide appropriate QoS functions for traffic in accordance with their require-ments. The QoS enhancement of 802.11e is called the Hy-brid Coordination Function (HCF). HCF has two different MACs: HCF-controlled channel access (HCCA) and En-hanced Distributed Channel Access (EDCA). Almost all the APs labeled “802.11e enabled” only implement EDCA and few implement HCCA. This paper mainly discusses EDCA. It will show the performance of VoIP traffic over a small EDCA-enabled network by experimental comparison with traditional MAC mechanism. We emphasize the fact that default EDCA parameter values are not appropriate for VoIP traffic.

This paper further illustrates the results of simulation for a large number of nodes which have VoIP traffic over 802.11e networks. It is hard to make a large-scale working testbed; we use simulation to show the results. Our simulation is based on DESMO-J, which is a general-purpose simulation library [2]. The result shows again that the default values are not appropriate for large-scale networks.

The rest of this paper is organized as follows: In section 2, we will briefly describe the 802.11e standard and VoIP. It also introduces some related work. The experiments and the results are illustrated in section 3 followed by the conclusion in section 4.

2. BACKGROUND

The legacy Media Access Control (MAC) mechanism in-cluded in IEEE 802.11 a/b/g has two medium-sharing func-tions: the distributed coordination function (DCF) and the point coordination function (PCF). IEEE 802.11e is intended to add QoS functionality to legacy 802.11. 802.11e provides two MAC layer protocols: the enhanced distributed channel access (EDCA) and the hybrid coordination function (HCF) controlled channel access (HCCA).

The performance of 802.11e varies depending on the appli-cation. This paper mainly deals with Voice over IP (VoIP) communications over the 802.11e network.

2.1 802.11 Legacy

Two MAC layer protocols are defined in the 802.11 legacy: DCF and PCF. DCF is a contention-based medium-sharing function. On the other hand, PCF is a polling-based medium-sharing function. An Access Point (AP) decides which node should use the radio resource at every moment.

DCF has a contention-based mechanism called carrier sense multiple access with collision detection (CSMA/CD), which is well-known. With DCF, wireless nodes have to wait for a fixed time (DIFS, Distributed Inter-Frame Space) before submitting frames to see if any other node is transmitting a frame to avoid collision. When collisions occur, all the nodes should stop to submit frames and wait for a random time. This mechanism is called backoff [3]. Backoff time is calculated by a formula:

Backof f time = Random × slottime, (1) where Random is between 0 and Contention Window (CW ). CW is defined as follows.

CW = (CWmin+ 1) × 2n− 1, (2)

where n represents the number of resubmission.

DCF has two parameters: CWmin and CWmax, which are the minimum value of CW and the maximum value, respec-tively.

PCF is a direct extension of DCF. PCF is not popular among AP products yet. PCF is not used in most EDCA functions. This paper does not deal with the details of PCF.

2.2 802.11e

Since IEEE 802.11e is a MAC layer extension to the 802.11 legacy, 802.11e is not intended to replace 802.11a/b/g. 802.11e

Previous

Fram e Next Fram e C W

AIFS SIFS

Slot Tim e DIFS

Figure 1: Parameters in EDCA: AIFS, CW and SIFS

doesn’t specify any physical (PHY) layer protocol. A QoS mechanism is one of the new features of 802.11e.

As mentioned earlier, 802.11e has two MAC mechanisms: HCCA and EDCA. HCCA is a polling-based medium access protocol, which extends the PCF of the 802.11 legacy. APs allocate time slots to each node for transmission according to demand. This is called Dynamic Time Division Multiple Access. EDCA is a contention-based medium access proto-col, which inherits the DCF of the 802.11 legacy. Almost all the APs products today claiming to be 802.11e-enabled im-plement only EDCA and do not have HCCA functionality. This is the reason why we mainly discuss EDCA.

The largest difference of EDCA from DCF is that EDCA has four traffic queues. It can assign as access category (AC), thus QoS settings, to each queue independently from

the other queues. The four ACs are: Voice (ACV O), Video

(ACV I), Best Effort (ACBE) and Background (ACBK). The

judgment of AC is done by checking the type of service (TOS) field in an IP packet header.

EDCA has the same two parameters as DCF to control the quality of traffic: CWmin and CWmax. EDCA has ad-ditional parameters: Arbitration Inter Frame Space (AIFS) and the Transmission Opportunity limit (TXOPlimit). AIFS determines the fixed time before a node sends a frame. An Access Point (AC) with a short AIFS value can submit frames before another AC submits, thus it can have better QoS. The TXOPlimit is time that a node can use to submit the frames exclusively without waiting for other node. A large TXOPlimit value reduces overhead and can increase throughput of the AC.

The relationship between AIFS, CW, and SIFS is shown in Fig. 1. (SIFS is the Shortest Inter-Frame Space for the APs control usage.) Table 1 shows the default parameters for

each access category [4]. Two T XOPlimit values are given

for ACV Oand ACV Idepending on the physical (PHY) layer.

The values shown in Table 1 are those of the PHY layer of 11b [5], which is used in our experiments.

2.3 VoIP

VoIP is an IP-based telephony application. VoIP software or hardware exchanges digitally coded voice data between nodes.

VoIP has faced a QoS problem since the beginning. Ordi-nary IP networks employed by VoIP services are, to some extent, more fragile than closed Plain Standard Telephone Networks (PSTNs). Traffic may be heavy in some IP net-works. In addition, LANs at each end may be overwhelmed when great numbers of people use the network simultane-ously.

Table 1: Default EDCA parameters

Access Category CWmin CWmax AIFSN T XOPlimit

AC VO (aCWmin+ 1)/4 − 1 (aCWmin+ 1)/2 − 1 2 3.264 msec

AC VI (aCWmin+ 1)/2 − 1 aCWmin 2 6.016 msec

AC BE aCWmin aCWmax 3 0 msec

AC BK aCWmin aCWmax 7 0 msec

Table 2: IP telephony quality standard in Japan

class R value delay time (ms)

A > 80 < 100 B > 70 < 150 C > 50 < 400

This paper only focuses on the QoS issue of VoIP commu-nications over wireless LANs. We do not discuss QoS issues in general.

The quality of voice exchanged over VoIP can be evaluated by scales such as: the Mean Opinion Score (MOS) and the R value. MOS is originally calculated using subjective scores obtained from human evaluators [6]. The R value is ob-tained by twenty quality indicators such as echo, delay and distortion [7].

The R value is more versatile than the MOS, because there is a function given by ITU-T as G.107 to convert R values to MOS. Moreover, R values can reflect some packet level quality indicators. We adopt the R value to evaluate the quality of VoIP traffic in this paper.

In Japan, the Ministry of Internal Affairs and Communica-tions (MIC) [8] defines the quality class standard (Table 2) for the R value together with the mean delay time as a ref-erence for assign telephone numbers for telephone services. Telephone services with public telephone numbers must have at least C class quality, according to the standard. Read-ers can undRead-erstand the results of this paper in terms of real VoIP service.

2.4 Related works

There have been many research projects on the performance of the IEEE 802.11 legacy [9][10][11], and some work has been done on 802.11e [12][13][14][15][16][17]. These papers verify that IEEE 802.11e gives better QoS to traffic

catego-rized as ACV O and ACV I than ACBK and ACBE.

Alonso-Gonzalez [14] showed by using an ns-2 simulator that the quality of high-prioritized traffic can be maintained de-spite background traffic saturating the channel capacity. We will confirm this result with a real testbed using the R value instead of packet level indicators, which do not directly rep-resent the quality of voice.

It is known that the quality varies greatly depending on the content of the MPEG-4 movies transmitted over an 802.11e network [13]. The result [13] suggests the same performance tendency in VoIP. Some adaptive method for tuning param-eters is desirable to get a truly optimal set of paramparam-eters. We will give optimal parameter sets for a large number of VoIP nodes.

Figure 2: Network for the first experiment

Although there have been many research projects on the per-formance of UDP applications including VoIP and MPEG-4 video over 802.11e, the performance of TCP applications has not been investigated in detail. Thottan [15] evaluates the quality of TCP traffic with a variety of traffic patterns. The paper tries to give the best quality of service.

Not only experimental researches but also analytical works have been done. Bellalta et al.[12] made an analytical model of EDCA functions. They verified that their analysis matches well the discrete simulation.

3. EXPERIMENTS

We have conducted a series of experiments. It consists of three experiments, which are explained in this section.

3.1 EDCA Effectiveness

We first conducted an experiment to measure the quality of VoIP packets in EDCA enabled networks [18]. It verifies the effect of EDCA for VoIP in a working network.

VoIP applications require high quality for the traffic. VoIP packets should not be delayed and the jitter of the delay should be kept small.

3.1.1 Configuration

We set up a network consisting of two broadcast domains (Fig. 2): the one connected by a wired switching hub and the other connected by a 802.11e-enabled Access Point (AP). The two networks are joined by a PC (software) router, which can change the TOS field value of VoIP packets in an IP header using iptables. Node VS transmits VoIP traf-fic to VC. We use ITU-T recommendation G723.1 [19] for the VoIP CODEC system. Nodes S0 and S1 submit 6 Mbps of best-effort traffic to node C0 and C1, respectively, using iperf [20] software.

We also measured the VoIP quality in a more realistic

sit-uation where a video traffic ACV I coexists alongside voice

Table 3: R value

TOS not set TOS set

23.5 77

one part, of the best-effort traffic, we classified it into ACV I

at the AP. The size of the video traffic ranges from 1 Mbps to 5 Mbps.

We used ASTEC Eyes for VoIP software[21] installed on VS to submit pseudo voice traffic and also on VC to receive the voice traffic from VS. ASTEC Eyes can measure voice traffic R values.

3.1.2 Results

As Table 3 shows, EDCA improved the voice quality over a wireless connection. With the TOS field off, the R value of 23.5 fell below the range of the Japanese IP telephony standard R=50 (Table 2). When the TOS field is on, the value of 77 almost reached A class quality 80.

Without Classification.

First, we disabled the iptables at the router so that all traffic is classified as background traffic. The R value of the VoIP traffic was 24.8 with EDCA disabled and 23.5 with EDCA enabled.

This result simply confirmed that without setting the TOS field value, EDCA is just meaningless in terms of QoS clas-sification, or even worse: It introduces some overhead to all the packets going through the AP.

With Classification.

The R value of the VoIP traffic dra-matically improved (Fig. 3) when the TOS field of VoIP

packets is set so that they are classified in the voice ACV O

access category.

Second, we replaced one part of the best-effort traffic, ACBK,

with video streaming traffic categorized as ACV I, which has

higher priority than ACBK traffic and is considered to

in-troduce much more load on voice ACV O. Until the video

bandwidth exceeds 3 Mbps, the voice traffic ACV O keeps

the R value as high as that when there is no other priori-tized traffic other than itself. What is more, although the

R value of voice ACV O starts to decrease when video ACV I

increases and exceeds 3 Mbps, it still keeps a high value compared with the VoIP which are not prioritized.

3.2 Default Settings Performance

The effect of EDCA depends on applications because a single set of parameters may not be able to meet all the require-ments for various applications. We measured the R value for VoIP traffic over the 802.11e network with EDCA with default parameters and with alternative settings [22].

3.2.1 Configuration

We use the same network configuration shown in Fig. 2. The tools and CODEC are the same as those of the first experiment, except that we eliminated s1 and c1 from the network to simplify it.

75 75.5 76 76.5 77 77.5 78 78.5 79 1 2 3 4 5 R value AC_VI traffic R value

Figure 3: R value with ACV I traffic

10 20 30 40 50 60 70 80 1 2 3 4 5 6 7 8 9 10 R value

Back ground traffic [Mbps] EDCA on EDCA off

Figure 4: Effect of EDCA on background traffic

3.2.2 Results

First, we simply measured the R value of VoIP traffic against background traffic while changing the size. As a result (Fig. 4), EDCA has a practical effect on VoIP quality only when the bandwidth of background traffic exceeds 6 Mbps. There-fore, we will focus on the performance of EDCA for back-ground traffic of 6–10 Mbps in this experiment.

Then we performed the same experiment but changing AIF S

(Fig. 5), CW (Fig. 6) and T XOPlimit(Fig. 7), respectively.

Each parameter set is shown in Tables 4 and 5. AIFS values shown in Fig. 5 are represented by the number of time slots, e.g., AIFS 0, AIFS 1.

According to the results, we picked up values which appar-ently have good quality. We define an alternative parameter set as shown in Table 6. The R values of selected settings are shown in bold lines in Fig. 5 – 7.

Finally, we applied the new alternative setting to the AP and measured the performance against the default setting. As shown in Fig. 8, the alternative setting performing basi-cally better than the default parameters. This result implies that there would be optimum parameter sets for various sit-uations other than the default setting.

Table 4: CW parameter sets CWmin CWmax CW 0 0 0 CW 1 1 1 CW 2 1 3 CW 3 3 3 CW 4 1 7 CW 5 3 7 CW 6 7 7 CW 7 1 15 CW 8 3 15 CW 9 7 15

Table 5: TXOP parameter sets

TXOP 0 752 ms TXOP 1 1504 ms TXOP 2 3008 ms TXOP 3 6016 ms TXOP 4 12032 ms TXOP 5 24064 ms 40 45 50 55 60 65 70 75 6 7 8 9 10 R value

Back ground traffic [Mbps]

AIFS 0 AIFS 1 AIFS 2 AIFS 3 AIFS 4 AIFS 5 AIFS 6 AIFS 7

Figure 5: R values for different AIFS values

40 45 50 55 60 65 70 75 6 7 8 9 10 R value

Back ground traffic [Mbps]

CW 0 CW 1 CW 2 CW 3 CW 4 CW 5 CW 6 CW 7 CW 8 CW 9

Figure 6: R values for different CW values

40 45 50 55 60 65 70 75 6 7 8 9 10 R value

Back ground traffic [Mbps]

TXOP 0 TXOP 1 TXOP 2 TXOP 3 TXOP 4 TXOP 5

Figure 7: R values for different TXOP values

Table 6: Alternative parameter set AIF S CWmin CWmax T XOPlimit

2 1 15 3008 [ms] 45 50 55 60 65 70 75 80 85 1 2 3 4 5 6 7 8 9 10 R value

Back ground traffic [Mbps] altered default

3.3 Simulation for Large Scale Networks

The results of the two previous experiments show that the optimum parameters of EDCA depend on the applications and the condition of the network. In this section, we con-duct a simulation for a large number of nodes to seek the optimum set of parameters for large-scale networks. In this simulation, the EDCA parameters are adjusted not only at the Access Point (AP) but also at wireless nodes. In this section, up means the direction of traffic from wireless nodes to AP and down means the opposite direction.

3.3.1 Configuration

The structure of the simulated network is virtually the same as the one shown in Fig. 2, which is a combination of a wired LAN and wireless LAN with an AP bridging them. The wired nodes communicate with the wireless nodes across the AP.

We dynamically change the number of nodes at both sides of the AP. The numbers of the nodes are 20, 22 and 24. With less than 20 nodes, the R value is so high that IEEE EDCA is not necessary, and with more than 24 nodes, it is difficult to achieve R value 90, which is required to deliver good quality of service.

The physical (PHY) layer of the wireless LAN of this net-work is IEEE 802.11b. The size of the VoIP traffic is fixed at 64 kbps, and the payload size is 160 bytes [23]. The length of the queues at the AP and nodes are set to 200. The simulation runs for 10 seconds for each parameter.

The simulator is constructed by using the DESMO-J library. DESMO-J is a Java-based simulation framework developed at the University of Hamburg, Germany [2].

We measure the performance of VoIP traffic over the EDCA-enabled network with various parameter settings. In this paper, we explain some of the meaningful results from which we can learn some instructive lessons.

3.3.2 Results

In Fig. 9–22, the value from 0 to 100 in the map stands for an R value. Each R value will be shown as the average of all the values collected from the nodes. The white arrow on the map indicates the direction toward higher R values, and we define optimum sets as those achieving R value larger than 88.5.

CW

size.

Figure 9 shows the optimum CW sizes while the number of VoIP nodes communicating through the wireless network increases.

The optimum CW size increases as the number of communi-cating nodes increases. In general, a small CW value leads to a high possibility of collision with other nodes. If a col-lision happens, a node retransmits frames after waiting for only a small period of time, indicated by small CW size. This result is meaningful in designing a large-scale wireless network. If there is no other prioritized node in the network, the performance of the single prioritized node improves with smaller CW . Apparently, giving fixed parameters is inap-propriate to achieving the best performance. The default

0 50 100 150 200 250 300 0 2 4 6 8 10 12 14 16 18 20 22 24 optimum CW size

The number of nodes CWmin

CWmax

Figure 9: The optimum CWmax and CWmin values

0 10000 20000 30000 40000 50000 60000 70000 0 2 4 6 8 10 12 14 16 18 20 22 24 TXOPlimit

The number of nodes TXOPlimit down

TXOPlimit up

Figure 10: The optimum T XOP limit values

value showed best performance only when less than four nodes coexist.

T XOPlimit

.

The result is shown in Fig. 10. This result

ver-ifies again that the default fixed parameters are only

suit-able for a certain network condition. T XOPlimit must be

increased as the number of nodes increases. T XOPlimitfor

the AP must be greater than for client nodes because the AP communicates with many nodes while each single node only communicates with the AP alone.

CW

(up - down).

Two trends are discovered from the re-sult shown in Fig. 11 – 13. First, the larger the CW size for AP (down) is, the better the performance gets. Secondly, CW size for client nodes must be smaller than that of AP.

T XOPlimit

(up - down).

We adjust T XOPlimit values of

the AP and client nodes independently; the results are shown in Fig. 14 – 19. From these results, it is shown that to

sup-port less than 24 nodes, the T XOPlimit value of the nodes

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 3_7 3_15 3_31 7_31 7_63 15_63 15_127 31_127 63_127 63_255 127_255 255_511 511_1023 1023_2047 C wM in_C wM ax(Down) C w M in _ C w M a x (U p ) _90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10 90 - 100 0 - 10 80 - 90 70 - 80 60 - 70 40 - 50 10 - 20

Figure 11: CW size (up and down) (n = 20)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 3_7 3_15 3_31 7_31 7_63 15_63 15_127 31_127 63_127 63_255 127_255 255_511 511_1023 1023_2047 CwM in_CwM ax(Down) C w M in _ C w M a x (U p ) _90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10 90 - 100 80 - 90 70 - 80 50 - 60 40 - 50 50 - 60 0 - 10

Figure 12: CW size (up and down) (n = 22)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 3_7 3_15 3_31 7_31 7_63 15_63 15_127 31_127 63_127 63_255 127_255 255_511 511_1023 1023_2047 CwM in_CwM ax(Down) C w M in _ C w M a x (U p ) 85-90 80-85 75-80 70-75 65-70 60-65 55-60 50-55 45-50 40-45 35-40 30-35 25-30 20-25 15-20 10-15 5-10 0-5 85 - 90 80 - 85 75 - 80 50 - 55 45 - 50 5 - 10 30 - 35

Figure 13: CW size (up and down) (n = 24)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7

0_65535

816_63335

3264_65535

8160_65535

13056_65535

20400_65535

32640_65535

48960_65535

65535_65535

CwM in_CwM ax

T x o p (D o w n ) _T x o p (U p )

90-100

80-90

70-80

60-70

50-60

40-50

30-40

20-30

10-20

0-10

90 - 100 80 - 90 70 - 80 60 - 70 50 - 60 0 - 10

Figure 14: T XOPlimit down (n = 20)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7

0_65535

816_63335

3264_65535

8160_65535

13056_65535

20400_65535

32640_65535

48960_65535

65535_65535

CwM in_CwM ax

T x o p (D o w n ) _T x o p (U p )

90-100

80-90

70-80

60-70

50-60

40-50

30-40

20-30

10-20

0-10

90 - 100 80 - 90 50 -60 40 - 50 30 - 40 10 - 20

Figure 15: T XOPlimit down (n = 22)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 0_65535 816_63335 3264_65535 8160_65535 13056_65535 20400_65535 32640_65535 48960_65535 65535_65535 CwM in_CwM ax T x o p (D o w n ) _T x o p (U p ) 85-90 80-85 75-80 70-75 65-70 60-65 55-60 50-55 45-50 40-45 35-40 30-35 25-30 20-25 15-20 10-15 5-10 0-5 0- 10 70 - 75 55 - 60 50 - 55 45 - 50 5 - 10 30 - 35

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7

65535_0

65535_816

65535_3264

65535_8160

65535_13056

65535_20400

65535_32640

65535_48960

65535_65535

CwM in_CwM ax

T x o p (D o w n ) _T x o p (U p )

90-100

80-90

70-80

60-70

50-60

40-50

30-40

20-30

10-20

0-10

90 - 100 80 - 90 30 - 40 50 -60

Figure 17: T XOPlimit up (n = 20)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7

65535_0

65535_816

65535_3264

65535_8160

65535_13056

65535_20400

65535_32640

65535_48960

65535_65535

CwM in_CwM ax

T x o p (D o w n ) _T x o p (U p )

90-100

80-90

70-80

60-70

50-60

40-50

30-40

20-30

10-20

0-10

90 - 100 80 - 90 60 - 70 40 - 50 20 - 30

Figure 18: T XOPlimit up (n = 22)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 65535_0 65535_816 65535_3264 65535_8160 65535_13056 65535_20400 65535_32640 65535_48960 65535_65535 CwM in_CwM ax T x o p (D o w n ) _T x o p (U p ) 85-90 80-85 75-80 70-75 65-70 60-65 55-60 50-55 45-50 40-45 35-40 30-35 25-30 20-25 15-20 10-15 85 - 90 80 - 85 75 - 80 65 - 70 55 - 60 50 - 55 55 - 60 45 - 50 35 - 40 20 - 25

Figure 19: T XOPlimit up (n = 24)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 0_0 816_816 3264_3264 8160_8160 13056_13056 20400_20400 32640_32640 48960_48960 65535_65535 CwM in_CwM ax T x o p (D o w n ) _T x o p (U p ) 90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10 90 - 100 80 - 90 70 - 80 50 - 60 30 - 40 0 - 10

Figure 20: CW size and T XOPlimit (n = 20)

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 0_0 816_816 3264_3264 8160_8160 13056_13056 20400_20400 32640_32640 48960_48960 65535_65535 CwM in_CwM ax T x o p (D o w n ) _T x o p (U p ) 90-100 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10 90 - 100 80 - 90 50 - 60 0 -10 30 - 40 40 - 50 10 - 20

Figure 21: CW size and T XOPlimit (n = 22)

have the largest T XOPlimitpossible.

CW

size and

T XOPlimit

.

In Fig. 20 – 22, which

repre-sent configurations with 16 nodes, 20 nodes and 24 nodes, respectively. R values are shown for various CW sizes and T XOPlimit when T XOPlimitfor up and down is the same

value.

The result shows that T XOPlimit should be maximized as

much as possible, and that the pair of (127, 255) for the CW size gives the best performance.

4. CONCLUSION

There have been many related studies on the performance of EDCA and their effects on VoIP packets. They have proven that EDCA improves packet-level QoS. However, two networks both having the same QoS condition do not nec-essarily give the same quality from the users’ perspective. We used well-defined R value to evaluate the quality of VoIP traffic instead of simple packet-level indicators such as packet delay time, jitter and packet loss rate.

3 _7 3 _1 5 3 _3 1 7 _3 1 7 _6 3 1 5 _6 3 1 5 _1 2 7 3 1 _1 2 7 6 3 _1 2 7 6 3 _2 5 5 1 2 7 _2 5 5 2 5 5 _5 1 1 5 1 1 _1 0 2 3 1 0 2 3 _2 0 4 7 0_0 816_816 3264_3264 8160_8160 13056_13056 20400_20400 32640_32640 48960_48960 65535_65535 CwM in_CwM ax T x o p (D o w n ) _T x o p (U p ) 80-90 70-80 60-70 50-60 40-50 30-40 20-30 10-20 0-10 80 - 90 70 - 80 60 - 70 50 - 60 40 - 50 30 - 40 0 - 10 10 - 20

Figure 22: CW size and T XOPlimit(n = 24)

EDCA mechanism of IEEE 802.11e which can prioritize the VoIP traffic against the background traffic.

Secondly, we measured the R value of VoIP traffic over var-ious sizes of background traffic. Then, we picked up a set of parameters and compared the performance with the default parameters set. The new set showed better performance, regardless of the size of the background traffic. This result implies that the default set of parameters is not appropriate to various network conditions.

To seek for the optimum parameter set for a large-scale net-work, we conducted the third experiment using DESMO-J simulation and acquired many optimum parameter sets. In addition, we got results useful for modifying the EDCA pa-rameters. We plan to add background traffic in the network simulation to get further practical results.

Our results indicate some algorithmic or parameter-tuning method should be developed to adaptively change the EDCA parameters to improve Quality of Service for VoIP traffic.

5. REFERENCES

[1] FON.

http://www.fon.com/. [2] DESMO-J.

http://www.desmoj.de/.

[3] IEEE. 802.11-1999 (R2003). IEEE, New York, NY, 2003.

[4] IEEE. 802.11e. IEEE, New York, NY, 2005.

[5] IEEE. 802.11b-1999 (R2003). IEEE, New York, NY, 2003.

[6] Methods for subjective determination of transmission quality.

http://www.itu.int/rec/T-REC-P.80/.

[7] Telecommunication Technology Committee. A Method for Speech Quality Assessment of IP Telephony (JJ-201.01). TTC, Minato-ku Tokyo, Japan, 2007. [8] The Ministry of Internal Affairs and Communications.

http://www.soumu.go.jp/.

[9] Miroslaw Narbutt and Mark Davis. Gauging voip call quality from 802.11 wlan resource usage. In

International Symposium on a World of Wireless,

Mobile and Multimedia Networks, 2006.

[10] Rovert A. Malaney, Ernesto Exposito, and Xun Wei. Seeking voip qos in physical space. In the 3rd ACM international workshop on Wireless mobile

applications and services on WLAN hotspots, 2005. [11] Sangki Yun, Hyogon Kim, and Inhye Kang. Squeezing

100+ voip calls out of 802.11b wlans. In International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2006.

[12] Boris Bellalta, Cristina Cano, Miquel Oliver, and Michela Meo. Modeling the ieee 802.11e edca for mac parameter optimization. In Heterogenius Networks, Sep 2006.

[13] Deyun Gao, Jianfei Cai, Paul Bao, and Zhihai He. Mpeg-4 video streaming quality evaluation in ieee 802.11e wlans. IEEE International Conference on Image Processing, 1:187–200, Sept 2005.

[14] I. Alonso-Gonzalez, C. Ley-Bosch, and C. C. Ojeda-Guerra. Experimental evaluation of ieee 802.11e. In Proceedings of the 24th IASTED International Multi-Conference, pages 70–75, Feb 2006.

[15] Marina Thottan and Michele C. Weigle. Impact of 802.11e edca on mixed tcp-based applications. In Proceedings of the ACM WiCon 2006, Aug 2006. [16] Yusuke Natsume, Duoyi Yan, Koichiro Doi, Ryo

Kitahara, and Shigeki Goto. Voip quality measurement in ieee 802.11e wlan. In Forum on Information Technology, 2006.

[17] Atsunori Noguchi, Takahiro Suzuki, and Shuji Tasaka. Effect of ieee 802.11e edca parameters on

application-level qos. Technical report of IEICE. Multimedia and virtual environment, 104(635):7–12, Jan 2005.

[18] Shingo Morita. VoIP Quality Measurement in IEEE 802.11e Wireless LAN. Graduation Thesis of Waseda University, Shinjuku-ku Tokyo, Japan, 2007.

[19] ITU-T, Mar 1996. G.723.1 : Dual rate speech coder for multimedia communications transmitting at 5.3 and 6.3 kbit/s.

http://www.itu.int/rec/T-REC-G.723.1/en. [20] Iperf - TCP/UDP Bandwidth Measurement Tool.

http://dast.nlanr.net/Projects/Iperf/. [21] ASTEC Eyes for VoIP.

http://www.asteceyes.com/.

[22] Tomoya Iimura. Designing of VoIP network

considering load traffic in IEEE 802.11e. Graduation Thesis of Waseda University, Shinjuku-ku Tokyo, Japan, 2007.

[23] ITU-T, Feb 2004. G.711 : Pulse code modulation (PCM) of voice frequencies. Apppendix II http://www.itu.int/rec/T-REC-G.711/en.