Performance Enhancement of Multi-Rate IEEE 802.11 ...

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Performance Enhancement of Multi-Rate IEEE 802.11 WLANs with Geographically-Scattered Stations Duck-Yong Yang, Tae-Jin Lee, Member, IEEE, Kyunghun Jang, Jin-Bong Chang and Sunghyun Choi, Member, IEEE

Abstract In today’s IEEE 802.11 Wireless LANs (WLANs), e.g., the popular IEEE 802.11b, stations support multiple transmission rates, and use them adaptively depending on the underlying channel condition via link adaptation. It has been known that when some stations use low transmission rates due to bad channel conditions, the performance of the stations using high rates is heavily degraded, and this phenomenon is often referred to as performance anomaly. In this paper, we model the WLAN incorporating stations with multiple transmission rates in order to demonstrate the performance anomaly analytically. Note that all the previously-proposed models of the IEEE 802.11 assume a single transmission rate. We also develop possible remedies to improve the performance. Our solution is basically to control the access parameters such as the initial backoff window, the frame size, and the maximum backoff stage, depending on the employed transmission rate. Throughout simulations, we demonstrate that our analytical model is accurate, and the proposed mechanism can indeed provide the remedies to the performance anomaly by increasing the aggregate throughput up to six times. Index Terms CSMA/CA, DCF, Performance Anomaly, Link Adaptation, Backoff Algorithm, Throughput, IEEE 802.11a/b/g.

Duck-Yong Yang and Tae-Jin Lee are with the School of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, KOREA. (email: {ducky, tjlee}@ece.skku.ac.kr). Kyunghun Jang and Jin-Bong Chang are with i-Networking Lab., Samsung Advanced Institute of Technology, Yongin 449-712, KOREA. (email: {khjang, jinb.chang}@samsung.com). Sunghyun Choi is with the School of Electrical Engineering, Seoul National University, Seoul 151-744, KOREA. (email: [email protected]).

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I. I NTRODUCTION Since IEEE 802.11 standard was originally developed in 1997 [1], wireless LANs (WLANs) have been more and more accepted gradually, and recently they are being deployed widely in many different environments including campuses, offices, homes, and hot-spots. The number of Access Points (APs) in public hot-spots are expected to exceed 350,000 worldwide in 2007 [2]. The technology now starts being embedded into notebook PCs and PDAs so that no external adapters are required any more. This wide acceptance of WLANs may introduce some side-effects, which have not been conceived as serious problems so far. Today’s IEEE 802.11 stations support multiple transmission rates, which are used for frame transmissions in an adaptive manner depending on the underlying channel condition. For example, the popular IEEE 802.11b [3] supports four transmission rates, namely, 1, 2, 5.5, and 11 Mbps, and the new highspeed 802.11a [4] supports eight transmission rates from 6 to 54 Mbps. In nomadic systems such as WLANs without high mobility, the channel condition is often dominated by the distance between the transmitting and receiving stations. In such a case, the farther the distance between two stations, the lower the transmission rate employed typically. For example, in IEEE 802.11b WLAN, as a station moves away from its AP, it decreases its transmission rate used for the transmissions to the AP from 11 Mbps to 1 Mbps. A mechanism to select one out of multiple available transmission rates is referred to as “link adaptation.” Link adaptation algorithms for IEEE 802.11 WLAN have been studied in [11], [9], [5], [6], [10], [7]. Our main interest in this work is not the link adaptation algorithm itself, but the impact of the link adaptation on the overall performance of multi-rate IEEE 802.11 WLANs with geographically scattered stations. As pointed out in [12], the throughput performance of high-rate1 stations are heavily affected by low-rate stations, and they suffer from throughput degradation although they are near the AP. This is basically caused by the fact that the 802.11 Medium Access Control (MAC) provides a fair channel access among contending stations by giving the same channel access opportunities. Then, assuming that the same amount of data is to be transmitted over the wireless channel, the stations with lower rates, i.e., stations far from the AP, tend to grab the wireless channel longer than those with higher rates, i.e., stations near from the AP. Accordingly, the stations do not get their throughput linearly proportional to their transmission rate. This phenomenon is often referred to as “performance anomaly” of IEEE 802.11 1

In this paper, a high-rate (low-rate) station is referred to as a station using relatively higher (lower) transmission rates compared to other

stations in the same WLAN.

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showing unexpected performance degradation of the stations using high-rates, or near the AP. Therefore, the overall system performance is dominated by the stations with the lowest rate. This, of course, causes a serious resource leakage problem that needs to be avoided or mitigated. Considering that hot-spot areas with a lot of stations are being deployed at an ever increasing pace, this problem becomes even more important. Although the problem was discovered experimentally and studied via a simple analysis in [12], neither mathematical formulation of the problem nor remedies to improve the performance was considered. The authors of [8] also looked at the performance anomaly problem in the IEEE 802.11b WLAN. They also proposed a scheme to handle the problem at the AP by controlling the number of frames to stations with bad link quality. However, it considers only the downlink transmissions. Note that the uplink transmissions cannot be handled by this kind of approach. There has been also an approach to reduce the impact of bad link by the transmission opportunity (TXOP) of IEEE 802.11e [27]. In this paper, we formulate the performance anomaly problem using Markov chains and propose possible remedies to improve the performance. IEEE 802.11 WLAN uses the Distributed Coordination Function (DCF) mechanism to control stations to access the radio resources. Many research on the performance analysis of IEEE 802.11 Medium Access Control (MAC) have been conducted in [13]-[20] and [23], considering only a single transmission rate. Stations, however, typically use different transmission rates in practice, as studied in this paper. Analysis of IEEE 802.11 WLAN using Markov chains has been shown to provide rather accurate saturation throughput [13], [14], [15], [20], [23]. Based on the model of [13] and [14], an improved model has been presented in [20] by considering the maximum retransmission count. In [21], [22], and [26], an analytical model for a simple priority scheme providing a differentiated service in IEEE 802.11 is proposed. They also assume a single transmission rate. Saturation throughput and delay of different priority classes are derived analytically using a Markov chain model. They define three control parameters: initial contention window size, window-increasing factor, and maximum backoff stage. By appropriately selecting control parameters, they show service differentiation can be achieved. We show that one can combat the aforementioned performance anomaly via an appropriate control of MAC parameters. Among those MAC parameters, initial contention window size is shown to be the most effective parameter for the control of stations’ channel access. We present how much adjustment affects the system performance via both analysis and simulations. Our mechanism can easily be incorporated into existing WLAN systems, and help boosting the radio resource utilization.

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The organization of the paper is as follows. We first present an analytical model to demonstrate the performance anomaly in Section II, and then propose an approach to improve the overall system performance, i.e., how to mitigate the performance anomaly using the initial contention window or size of frames in Section IV. We then illustrate examples and evaluate the performance in Section V. Simulation results are presented in Section V. Finally, we conclude in Section VI. II. M ODELING OF S CATTERED S TATIONS IN WLAN S Let us consider IEEE 802.11b WLAN supporting four transmission rates, namely, 1, 2, 5.5, and 11 Mbps. Markov chain models for saturation throughput and delay of conventional and prioritized services in IEEE 802.11 have been studied in [13], [14], [20], [21], [22], [23] and [26]. In the pioneering models [14], [20], [21], they have built embedded Markov chains in which a state transition of the backoff counter occurs only when a slot is idle. Ziouva et al. [23] and Xiao [21], [22] improve the models so that the backoff counter stops when the channel is sensed busy. However, under their models, stations can transmit a new frame without entering a backoff stage if the channel is idle after each successful transmission. It is inconsistent with IEEE 802.11 standard since the IEEE 802.11 DCF requires a station to enter a backoff stage after each frame transmission irrespective of the channel status as well as the frame transmission success/failure. Based on this observation, Xiao constructs a new model [26], in which a successful transmission is followed by a backoff automatically. In addition, it imposes a frame retransmission limit in order to obtain the frame dropping probability. We note that all the previous models did not consider the link adaptation or multirate transmission which is inherent in today’s IEEE 802.11 WLANs. That is, all stations are assumed to have the same channel quality so that their transmission rates are identical. In order to incorporate the link adaption, we construct a new Markov chain and model the behavior of high-rate and low-rate stations in a Basic Service Set (BSS)2 of IEEE 802.11b, and possibly IEEE 802.11a. We assume that there is always another frame waiting for transmission once a frame is successfully transmitted, and it is referred to as “saturated condition.” To model the IEEE 802.11b WLANs consisting of stations with various transmission rates, we group the stations in a BSS according to their transmission rates, i.e., 11, 5.5, 2 and 1 Mbps, and assign group ID i = 1, 2, 3 and 4 to each group of stations, respectively (see Fig. 1). Now, we have four groups, i.e., the number of groups, N = 4. With these heterogeneous stations, Fig. 2 shows that the time durations to transmit a frame by a station 2

A BSS is a unit of an IEEE 802.11 WLAN, which is composed of a single AP and multiple associated stations.

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: Wireless stations group4 group3 1 Mbps 2 Mbps 5.5 Mbps

group2 group1 11 Mbps AP

Fig. 1.

50m

70m

90m

115m

Example of IEEE 802.11b WLAN with geographically-scattered stations.

in different groups are different, i.e., the lower the transmission rate, the longer the time to transmit a frame. Let wi,k be the backoff window for a station in group i at backoff stage k. In the conventional DCF mechanism using Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), all the stations in any group i have the same initial (at backoff stage 0) backoff window wi,0 , for all i = 1, 2, 3, and 4. When the channel is idle during DCF interframe space (DIFS), each station selects a random backoff counter among [0, wi,0 −1] slots and wait for the corresponding number of slots before attempting to access the wireless medium. The value of the backoff counter is decremented by one whenever a slot is sensed idle. If there are transmissions from other stations during this period, the backoff counter countdown is suspended. Then, when the channel becomes idle again, the station resumes its backoff process after a DIFS idle period. When the backoff counter value reaches zero, the station transmits the pending frame. If the frame collides with another frame in a transmission attempt, the colliding station increases its backoff window to minimize the collision probability, i.e., it increases the backoff stage by one. This increase occurs at every additional collision until the maximum backoff stage is reached. After that, the backoff window size remains constant until the backoff stage becomes equal to the retry limit. If the last transmission attempt in the retry limit phase fails, the frame is dropped. For backoff stage k, the backoff window wi,k becomes if Lretry,i > ui , wi,k

  2k wi,0 , =  2ui w , i,0

0 ≤ k ≤ ui − 1, ui ≤ k ≤ Lretry,i ,

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B B P M

Station1 (Group1) Station2 (Group1)

P M

Station3 (Group4)

P

PHY Header

M

MAC Header

Payload 1500bytes 11Mbps


B Ack

AP

Backoff

P

M


Ack

Ack Time

SIFS DIFS

Fig. 2.

SIFS

DIFS

SIFS

Example of different transmission times by stations in different groups of IEEE 802.11b WLAN; stations 1 and 2 are in group 1

corresponding to the transmission rate of 11 Mbps, while station 3 is in group 4 corresponding to 1 Mbps.

if Lretry,i ≤ ui , wi,k = 2k wi,0 ,

0 ≤ k ≤ Lretry,i ,

where Lretry,i and ui denote the retry limit of group i and the maximum backoff stage of a station in group i, respectively. That is, each station selects a random backoff time among [0, wi,k − 1] slots at backoff stage k. Let U (i, t) be the random process representing the backoff stage of a station in group i, and C(i, t) be the random process representing the value of the backoff counter of a station in group i. A new random process X(i, t) = {U (i, t), C(i, t)} can be defined. Note that C(i, t) at slot time t is a uniform random variable in the range of [0, wi,k − 1]. Assuming the probability that a transmitted frame collides with another frame in a slot and the probability that the channel is busy in a slot are independent to the backoff mechanism, one can consider X(i, t) as a discrete time Markov chain. The state of the Markov chain is denoted as (i, k, `), 1 ≤ i ≤ N , 0 ≤ k ≤ Lretry,i , 0 ≤ ` ≤ wi,k − 1, where i, k and ` represent group i, backoff stage k, and backoff counter value `, respectively. Let P {i, k, `|i, k0 , `0 } = P {U (i, t + 1) = k, C(i, t + 1) = `|U (i, t) = k0 , C(i, t) = `0 }. Then, the state transition probabilities of the Markov chain shown in Fig. 3 are obtained as follows:

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XV $ OXT PV $ SWSW

XT

SWSX

XT

ˎ UUU

SWSY

w

XT

w

w

w

UUU

UUU

UUU

UUU

UUU

U

U

U

U

V STXSW V XT SSW

SSX

XT

ˎ UUU

SSY

w

w

w

w

ˎ UUU ˎ

w

UUU

UUU

UUU

UUU

UUU

V %&'(&)*+ XT XT Ss SW Ss SX Ss SY

Fig. 3.

V

XT

V %&'(&)+ *

" %&'(&)! *+

w

XT

# %&'(&)! *+

w

w

The state transition diagram of the stations in group i for the Markov chain model.

P {i, k, `|i, k, ` + 1} = 1 − pb,i ,

0 ≤ ` ≤ wi,k − 2

(1)

P {i, k, `|i, k, `} = pb,i ,

1 ≤ ` ≤ wi,k − 1

(2)

P {i, k, `|i, k − 1, 0} = pc,i /wi,k , 1 ≤ k ≤ Lretry,i , 0 ≤ ` ≤ wi,k−1

(3)

P {i, 0, `|i, k, 0} = (1 − pc,i )/wi,0 , 0 ≤ k ≤ Lretry,i , 0 ≤ ` ≤ wi,0−1, P {i, 0, `|i, Lretry,i , 0} = 1/wi,0 ,

0 ≤ ` ≤ wi,0

(4) (5)

where pb,i and pc,i denote the probability that a station in group i senses the channel busy and the probability that a transmitted frame of a station in group i collides, respectively. The transition probabilities can be found by the followings: (1) the backoff counter decreases by one when a station senses that the channel is idle during a slot, (2) the backoff counter freezes when a station senses that the channel is busy, (3) a station chooses a backoff delay of the next backoff stage k after an unsuccessful transmission at backoff stage k − 1, (4) a station chooses a backoff counter value using the backoff window of backoff stage 0 if its current frame was successfully transmitted, and it attempts to transmit a new frame 3 , and (5) a station reaches the retry limit, and it attempts to transmit a new frame 3

A backoff procedure is always followed once a frame is successfully transmitted, which is often referred to as post backoff.

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regardless of success or failure of the current transmission. Let’s define the steady state distribution bi,k,` of the Markov chain for a station in group i, with backoff stage k and backoff counter value `, i.e., bi,k,` = limt→∞ P {U (i, t) = k, C(i, t) = `}. One can derive the following relations regarding the steady state distributions. bi,k,0 = pkc,i bi,0,0 ,

bi,k,`

0 ≤ k ≤ Lretry,i wi,k − ` 1 = bi,k,0 , wi,k 1 − pb,i 0 ≤ k ≤ Lretry,i , 1 ≤ ` ≤ wi,k − 1

(6)

(7)

Lretry,i wi,k −1

X k=0

X

bi,k,` = 1.

(8)

`=0

Eqs.(6) and (7) can be derived from the balance equations in steady state, i.e., the sum of the transition probabilities out of a state times the probability in the state is equal to the sum of the transition probabilities from other states into the state times the probabilities in the states. Then, the stationary probability bi,0,0 can be found from the above equations.  Ã !−1 wi,k −1 Lretry,i X X 1 wi,k − `  pkc,i 1 + bi,0,0 =  1 − pb,i `=1 wi,k k=0   µ ¶ −1 Lretry,i X wi,k − 1  . =  pkc,i 1 + 2 (1 − p b,i ) k=0

(9)

All the stationary distributions bi,k,` can be computed using Eqs. (6)–(9). In order to find the distributions, the probabilities pb,i and pc,i have to be known. They can be derived from the distributions bi,k,` as follows: First, the probability that a station in group i transmits a frame during a slot time is given by Lretry,i

pt,i =

X k=0

L

bi,k,0

1 − pc,iretry,i = 1 − pc,i

+1

bi,0,0 ,

(10)

since a station can send a frame when its backoff counter value reaches zero. In addition, the medium is sensed busy when at least one station other than the observing (sensing) station transmits. Therefore, one can further find pb,i as ni −1

pb,i = 1 − (1 − pt,i )

N Y

(1 − pt,k )nk ,

(11)

k=1 k6=i

where ni denotes the number of stations in group i. On the other hand, a collision occurs when at least one station other than the current transmitting station transmits. Accordingly, pc,i is derived in

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i Backoff

DIFS

DIFS

STAG1

i

STAG1

STAG2

i

STAG2

STAG3

i

STAG3

STAG4

DIFS STAG1

i

i

STAG4

STAG2

i

STAG3

i

STAG4

(a)

Fig. 4.

PHY Protocol Data Unit (PPDU)

(b)

(c)

Examples of upper 3-heterogeneous collisions: (a) collision among a station in group 3, one in group 1 and one in group

2, (b) collision between a station in group 3 and one in group 1, (c) collision between a station in group 3 and one in group 2. The collision time spent by a collision in (a), (b) and (c) is dominated by the station with the lowest rate. That is, it is determined by the transmission time of the station in group 3. STAGi denotes a station in group i.

a similar manner as pc,i = 1 − (1 − pt,i )ni −1

N Y

(1 − pt,k )nk .

(12)

k=1 k6=i

Note that there is a difference between pb,i and pc,i , even though they look the same. In pb,i , the observing station is not transmitting but sensing, and in pc,i , it is transmitting. Using Eqs. (6)–(10), (11) and (12), one can solve pt,i numerically. Let pb denote the probability that the channel is busy in a slot. Then, we have pb = 1 −

N Y

(1 − pt,k )nk .

(13)

k=1

The probability ps,i that a station in group i has successfully transmitted in a slot is given by ni −1

ps,i = ni pt,i (1 − pt,i )

N Y

(1 − pt,k )nk .

(14)

k=1 k6=i

Then, the probability that the transmission is successful in a slot is ps =

N X

ps,i .

(15)

i=1

The probabilities pb and ps are computed from Eqs. (13) and (15) for which pt,i in Eq. (10) is required. The above numerical expressions are based on the analysis for a single transmission rate in [26], and are further extended to consider multi-rate stations. Note also that if the number of stations in a group is given in a probabilistic manner, e.g., the numbers of stations in 4 groups are uniformly distributed, previous models [14], [23], [26] can be easily modified to obtain the saturation throughput.

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upper 2-heterogeneous collision

Group 1

lower 2-heterogeneous collision

Group 2

Group 3

Group 4

upper 3-heterogeneous collision upper 4-heterogeneous collision (a) upper 3-heterogeneous collision

Group 1

Group 2

lower 3-heterogeneous collision

Group 3

Group 4

upper 4-heterogeneous collision (b)

Fig. 5.

Upper i- and lower i-heterogeneous collisions: (a) lower 2-heterogeneous collisions are included in upper 3- and 4-heterogeneous

collisions; (b) lower 3-heterogeneous collisions are included in upper 4-heterogeneous collisions.

Our model, however, can capture the behavior of the exact number of stations in each multi-rate group. Next, we need to find the collision probability in a multi-rate environment, which is distinct from the previous studies. When a collision occurs, the collision time is dominated by the longest transmission time (lowest transmission rate) among the transmission time involved in the collision as illustrated in Fig. 4. (It is assumed that all stations transmit the frames with the same size.) Note that the collision caused by the stations in group i, and the collision caused by the stations in group i and those in group j (i 6= j) have different characteristics. To formulate the collision time of multi-rate stations, i.e., that of groups, we refer the former type of collision to as an i-homogeneous collision and the latter to as an i-heterogeneous collision, respectively. An i-heterogeneous collision could be either an upper iheterogeneous collision or a lower i-heterogeneous collision, which are defined as a collision between the stations in group i and those in group j (j < i, i ≥ 2), and a collision between the stations in group i and those in group j (j > i, i ≥ 2), respectively. Since the lower i-heterogeneous collisions are included in the upper k-heterogeneous collisions (k > i), we need to consider only the upper i-heterogeneous collisions. For example, when the number of groups N = 4, the 2-heterogeneous collisions consist of the upper 2-heterogeneous collisions (i.e., collisions between group 2 and group 1) and the lower 2-heterogeneous

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collisions (i.e., collisions between group 2 and group 3 or 4). Then, the upper 4-heterogeneous collisions (i.e., collisions between group 4 and group 1, 2 or 3) and the upper 3-heterogeneous collisions (collisions between group 3 and group 1 or 2) include the lower 2-heterogeneous collisions (see Fig. 5 (a)). Similarly, the upper 4-heterogeneous collisions include the lower 3-heterogeneous collisions (see Fig. 5 (b)). Let phmcoll,i be the probability of the i-homogeneous collisions and puhcoll,i be the probability of the upper i-heterogeneous collisions, respectively. Then, the probability phmcoll,i is given by the probability that at least two stations in group i collide. That is, phmcoll,i =

¡ ¡ ¢¢ 1 − (1 − pt,i )ni + ni (1 − pt,i )ni −1 pt,i N Y × (1 − pt,k )nk , k=1 k6=i

ni ≥ 2, 1 ≤ i ≤ N.

(16)

Now, the probability puhcoll,i is given by the probability that at least one station in group i and at least one station in group j (where j can be any value smaller than i) collide, i.e., ! Ã i−1 Y nk (1 − pt,k ) (1 − (1 − pt,i )ni ) puhcoll,i = 1− ×

k=1 N Y

(1 − pt,k )nk ,

k=i+1

puhcoll,N

n ≥ 1, 2 ≤ i ≤ N − 1, ! Ãi N −1 Y nk (1 − pt,k ) = 1−

(17)

k=1

× (1 − (1 − pt,N )nN ) .

(18)

Then, the probability pcoll,i of the i-homogeneous or the upper i-heterogeneous collisions caused by the stations in group i or those in group i and j (j < i, i ≥ 2) is given by pcoll,1 = phmcoll,1

(19)

pcoll,i = phmcoll,i + puhcoll,i , 2 ≤ i ≤ N.

(20)

Now, we can determine the saturation throughput of the stations in group i. The normalized saturation throughput of the stations in group i can be computed as follows: Si =

ps,i TP,i TE + TS + TC

(21)

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where TP,i = SM SDU,i × 8/ri

(22)

TE = (1 − pb ) × Tslot N X TS = ps,i Tf rame,i

(23) (24)

i=1

TC =

N X

pcoll,i Tcoll,i

(25)

i=1

and Tf rame,i = TH,i + TP,i + TSIF S + Tprop +TACK,i + TDIF S + Tprop Tcoll,i = TH,i + TP,i + TDIF S + Tprop ,

(26) (27)

where SM SDU,i is the size of MAC Service Data Unit (MSDU) or payload of a MAC frame transmitted by the stations in group i in bytes, ri is the transmission rate of the stations in group i, Tslot is the duration of a slot time, Tf rame,i is the average time duration during which the channel is sensed busy by the stations in group i because of a successful transmission, and Tcoll,i is the average time duration during which the channel is busy by collision, in which the transmission rate of group i is the lowest. And TSIF S is the duration of Short Interframe Space (SIFS), TDIF S is the duration of DIFS, Tprop is the propagation delay, TACK,i is the time spent to transmit an ACK frame by the stations in group i, TH,i is the time spent to transmit the PHY and MAC header by the stations in group i, and TP,i is the time spent to transmit an MSDU by the stations in group i. Assuming that the transmission rate of ACK frames is 1 Mbps in the case of IEEE 802.11b, TACK,i and TH,i are given as follows: TH,i = 192bits/1M bps + 28bytes × 8/ri TACK,i = 192bits/1M bps + 14bytes × 8/1M bps. Finally, the aggregate throughput S of the WLAN system is given by N X S= (Si × ri ).

(28)

i=1

III. P ROPOSED R EMEDIES TO P ERFORMANCE A NOMALY When many stations are associated with the AP in a BSS, some stations are far away from the AP. These stations have a bad channel quality and the received signal strength is weak. In such a case, it is

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more efficient to modulate the signal with a lower rate, which is often referred to as the link adaptation. However, if the rate becomes low, it takes more time to transmit the same amount of data. Therefore, lowrate stations can degrade the performance of the BSS significantly as stated above. We focus on WLANs where many stations are scattered, and propose methods that can improve the aggregate throughput of the network without adding any new type of management frames. To reduce the performance degradation by low-rate stations, we propose to control some MAC parameters of IEEE 802.11. Since each station can know the current data rate which is determined by its own link adaptation algorithm, the amount of performance degradation by performance anomaly can be found from Eqs. (21)–(28). It is influenced by such factors as the number of the stations in each group, the traffic load of each station, interference and noise in the channel. Since we have the equations associated with the performance degradation, it is possible to observe how the adjustment of the MAC parameters can ameliorate the performance degradation. We propose several mechanisms, and evaluate their effect. The parameters and control mechanisms that we consider are as follows: 1) different initial backoff window size (wi,0 ) for different group i. 2) different frame size (SM SDU,i ) for different group i. 3) different maximum backoff stage (ui ) for different group i. 4) combination of 1) and 2). The rationale to select the above parameters is as follows. The initial backoff window size (wi,0 ) determines the channel access probability by a station since a station is allowed to access the channel by the parameter in a probabilistic manner. The frame size (SM SDU,i ) determines the channel access time by a station since a station can occupy the channel during a frame transmission once the access is allowed. Similarly, the maximum backoff stage (ui ) affects performance by deciding how many times of channel access chances are permitted on collisions. Among them, the initial backoff window size and the frame size directly control channel access probability and time, and they are believed to be the most dominant performance factors. First, we consider a mechanism in which each group uses its own initial backoff window size. CSMA/CA tends to give all stations fairness from the viewpoint of the channel access probability. However, each group has a different transmission time for the same payload size. In order to improve this unfairness, the opportunity that the stations in a low-rate group access the wireless channel must be reduced. So we propose that the stations in low-rate groups have larger initial backoff window size than those in

14

high-rate groups. It was presented in [14] that the initial backoff window size affects the performance of a station significantly. Controlling the initial backoff windows can have another advantage. As the number of stations increases, the collision probability also increases. In such a case, increasing the initial backoff window of low-rate stations can reduce the collision probability as well [14]. Second, we note that the wireless medium is occupied longer when the frames of the low-rate stations are transmitted than those of high-rate stations are. In addition, every station can transmit frames of different size. This is not fair from the viewpoint of channel occupation time, and it also causes the performance degradation. To minimize this effect, the frame size of the low-rate stations could be reduced. In order to do so, the fragmentation threshold parameter can be controlled. We, however, evaluate this mechanism using fixed frame size, i.e., payload size, for the sake of convenience. Note that how to reduce the frame size of low-rate stations needs to be treated with care since there is a close relation between system utilization and frame size. That is, reducing the frame size might decrease the transmission efficiency of IEEE 802.11 MAC as will be demonstrated later. Next, we consider a mechanism allowing differentiated maximum backoff stages for different groups. The more stations there are, the more important the maximum backoff stage becomes due to increasing collisions. When a collision occurs, the frame is retransmitted with an increased backoff stage. If the maximum backoff stage is allowed to grow up, the maximum backoff window may become larger than 1024, the default value. The maximum backoff stage ui can specify the size of the backoff window, e.g., when ui = 0 and initial backoff window wi,0 = 32, the maximum backoff window is limited to 32 even if many collisions occur due to many contending stations. If a station has a small backoff window, it is likely to select a small backoff counter value. Therefore, the maximum backoff window can affect the probability that stations access the medium. In order to differentiate the maximum backoff window, one can use smaller maximum backoff stage ui for high-rate stations or larger one for low-rate stations. Although the maximum backoff stage of low-rate stations is increased above the default value (ui ≥ 5, wi,0 = 32), it is expected to influence the performance insignificantly because the probability of successive collisions is very low. Instead, when there are a few high-rate stations and many low-rate stations coexist, a smaller maximum backoff stage for high-rate stations are expected to help increasing resource utilization. We, therefore, propose to reduce the maximum backoff stage of high-rate stations. Note that if there are many high-rate stations, this mechanism may not be effective. Finally, we consider a combination of two control mechanisms to reduce the performance anomaly, i.e., differentiated initial backoff windows and frame sizes for high-rate and low-rate stations. This integrated

15

control mechanism can minimize the chances and durations of hogging the wireless medium by low-rate stations, and compensates the performance degradation experienced by high-rate stations. Note that we should not kill out low-rate stations completely to increase the overall network performance. The value of the parameters used for our mechanism can be determined adaptively as required by the stations. For example, in the case of a few high-rate stations with real-time traffic, the initial backoff window of the low-rate stations can be adjusted to at most 64. When the channel is idle for a long time, low-rate stations can set their parameters to the default value, e.g., wi,0 = 32 and ui = 5. In the next section, we present the effect of the proposed mechanisms, e.g., wi,0 = 32 and 128 for i = 1 and 4, respectively. In order to implement our mechanism, we consider a single BSS and further assume that all the stations in a BSS do not have hidden terminals so that stations can hear one another. Then, the following method can be possibly considered. All stations can monitor frame exchanges between other stations by observing the “signal” field in the PHY header of every frame, since the field contains the rate information. Note that the PHY header is always modulated using the most reliable 1 Mbps rate irrespective of the rate used for the payload, and hence observe the signal field of each frame should be feasible. Each station then estimate how much amount of the channel time each group use. Based on this information, each station except the stations with the highest rate can decide how much they need to concede by controlling the MAC parameters. For example, when stations of 1 Mbps consumes a large amount of channel, they can change their initial contention window size from 32 to 256. As a result, stations of 2, 5.5 or 11 Mbps achieve more opportunities to access the channel. This method may incur some overhead to measure channel time of other stations, and control the parameters accordingly. However, it should not be a big overhead since 802.11 stations continue to sense the channel and receive any incoming frames due to the CSMA/CA operation. IV. P ERFORMANCE E VALUATION In order to evaluate the performance, we consider a network topology composed of two stations in group 1 (11 Mbps) and varying number of stations in group 4 (1 Mbps) as shown in Fig. 6. The number of stations in group 4 ranges from 2 to 14. The stations in group 1 and those in group 4 can transmit data at rate r1 = 11 and r4 = 1 Mbps, respectively. We note that the throughput of the system with CSMA/CA decreases as the number of stations increases because the probability of collision becomes larger [13][14]. When there are many stations, it is likely that more than one stations select the same

16

Station in high-rate group

3

Station in low-rate group

4

2 AP

5

16

1 6 8

7

Fig. 6.

A network topology for performance evaluation. TABLE I PARAMETERS USED IN THE PERFORMANCE EVALUATION . Parameters

Value

Unit

TH,i

192bits/1Mbps + 28 × 8bits/ri

[sec]

TACK,i

192bits/1Mbps + 14 × 8bits/1Mbps

[sec]

ri

11, 5.5, 2, 1 for i=1,2,3,4

[Mbps]

TSIF S

10

[µsec]

TDIF S

50

[µsec]

Tslot

20

[µsec]

Tprop

1

[µsec]

backoff value. The parameters of IEEE 802.11b used in the performance evaluation are summarized in Table I. Control response (ACK) frames in IEEE 802.11 should be transmitted at the highest rate in the BSS basic rate set that is less than or equal to the rate of the immediately previous frame in the frame exchange sequence [3]. We assume that ACK frames are transmitted at the rate of 1Mbps assuming that 1Mbps is the only rate in the BSS basic rate set. We also assume the long PHY Layer Convergence Procedure (PLCP) preamble (see Fig.7) [24]. If one wants to use the short PLCP preamble, one can change parameters in Table II. In order to have a better understanding on how the CSMA/CA protocol behaves and how the backoff procedure affects the system performance, we assume that the network is overloaded, i.e., each station has infinite number of frames to transmit. This corresponds to a saturated condition. We first look into improving the aggregate throughput through the differentiated initial backoff window. The highest-rate (i.e., 11 Mbps) stations use the default initial backoff window, e.g., w1,0 = 32, but low-

17

TABLE II PARAMETERS FOR SHORT PLCP PREAMBLE OPTION OF IEEE 802.11 B . Parameters

Value

Unit

TH,i

72bits/1Mbps + 48bits/2Mbps + 28 × 8bits/ri

TACK,i

[sec]

72bits/1Mbps + 48bits/2Mbps + 14 × 8bits/2Mbps

Long PLCP Sync 128(bits)

PLCP frame

[sec]

1, 2, 5.5 or 11Mbps

1Mbps DBPSK SFD 16

Signal 8

PLCP Preamble

Service 8

Length 16

PLCP Header

CRC 16

PSDU(MPDU) Variable

PSDU (MAC frame)

Short PLCP Sync 56(bits)

SFD 16

1Mbps DBPSK

Fig. 7.

Signal 8

Service 8

Length 16

CRC 16

2Mbps DQPSK

PSDU(MPDU) Variable

2, 5.5 or 11Mbps

PLCP frame in IEEE 802.11b.

rate stations use larger initial backoff windows, e.g., w4,0 = 32 ∼ 512. Fig. 8 shows the total saturation throughput of the stations in group 1 (S1 × r1 from Eq. (21)) as well as that of the stations in group 4 (S4 × r4 from Eq. (21)) as the initial backoff window for group 4, w4,0 varies. The payload size of all stations is 2304 bytes, which corresponds to the maximum payload size of a MAC frame without using the security option in IEEE 802.11. When the initial backoff windows are the same for both groups, i.e., wi,0 = 32, i = 1, 4, and there are two stations in group 1 and six stations in group 4, the throughputs of groups 1 and 4 are around 0.4 Mbps and 0.8 Mbps, respectively. It exhibits the exact behavior of the performance anomaly. Therefore, the saturation throughput of each station in group 1 becomes only 0.2 Mbps although the stations in group 1 have the good channel quality and use the transmission rate of 11 Mbps. This analytical result is consistent with the simulation result in [12]. Moreover, the throughput decreases even more as the number of stations in group 4 increases. However, as we increase the initial backoff window of the stations in group 4 from w4,0 = 32 to 64, 128, 256, and 512, while keeping the maximum backoff window size at 1024, the performance anomaly phenomenon starts diminishing as shown in Fig. 8. Therefore, when w1,0 = 32 and w4,0 = 512 and the

18 6

6

x 10

Group 1, w = 32 1,0 Group 4, w4,0 = 32 Group 1, w = 32 1,0 Group 4, w = 64 4,0 Group 1, w1,0 = 32 Group 4, w4,0 = 128 Group 1, w = 32 1,0 Group 4, w = 256 4,0 Group 1, w1,0 = 32 Group 4, w4,0 = 512

5

Throughput [bps]

4

3

2

1

0 4

Fig. 8.

6

8

10 Number of stations

12

14

16

Saturation throughput of each group with different initial backoff window wi,0 for different groups (n1 = 2, n4 = 2 ∼ 14). 6

5.5

x 10

w = i,0 wi,0 = wi,0 = w = i,0 w =

5 4.5

i,0

32, 32 32, 64 32,128 32,256 32,512

Total throughput [bps]

4 3.5 3 2.5 2 1.5 1 0.5 4

Fig. 9.

6

8


12

14

16

Total saturation throughput with different initial backoff window wi,0 for different groups. (n1 = 2, n4 = 2 ∼ 14).

number of stations in group 4 is four (n1 = 2, n4 = 4), the performance of group 1 grows dramatically from 0.8 Mbps to 4 Mbps. The performance of group 4, of course, is a little sacrificed to around 0.5 Mbps. The overall performance gain, i.e., total throughput, is observed in Fig. 9. When there is no control over the performance anomaly, the aggregate saturation throughputs of both group 1 and 4 are around 1.5 Mbps or below. As the different initial backoff windows are used for different groups, one can achieve the total throughput of up to 5.3 Mbps, which is more than three-fold improvement in link utilization. As the number of stations increases, the throughput is shown to decrease since the number of low-rate stations increases while that of high-rate stations is fixed thus resulting in more collisions. Next, we observe the impact of frame size. While we fix the size of the payload, i.e., MSDU for the high-rate stations, to SM SDU,1 = 2304 bytes, the maximum payload size, we vary the payload size of low-

19 6

2.5

x 10

Group 1, SMSDU,1 = 2304 Group 4, SMSDU,4 = 2304 Group 1, SMSDU,1 = 2304 Group 4, SMSDU,4 = 2000 Group 1, SMSDU,1 = 2304 Group 4, SMSDU,4 = 1500 Group 1, SMSDU,1 = 2304 Group 4, SMSDU,4 = 1000 Group 1, SMSDU,1 = 2304 Group 4, SMSDU,4 = 500

Throughput [bps]

2

1.5

1

0.5

0 4

Fig. 10.

6

8


12

14

16

Saturation throughput of each group with different frame size SM SDU,i for different groups (n1 = 2, n4 = 2 ∼ 14). 6

3

x 10

S = 2304,2304 MSDU,i SMSDU,i = 2304,2000 SMSDU,i = 2304,1500 S = 2304,1000 MSDU,i S = 2304, 500

2.5


MSDU,i

2

1.5

1

0.5 4

Fig. 11.

6

8


12

14

16

Total saturation throughput with different frame size SM SDU,i for different groups (n1 = 2, n4 = 2 ∼ 14).

rate stations from SM SDU,4 = 2304 to 500 bytes. In order to observe the impact of frame size, the same initial backoff windows for both groups are used, i.e., w1,0 = w4,0 = 32. We can see that the throughput of high-rate stations slightly increases as the size of the frames for the low-rate stations decreases as shown in Fig. 10. The total throughput is slightly increased from 1.2 Mbps to 2 Mbps when the number of low-rate stations is four, i.e., n4 = 4 (see Fig. 11). Therefore, the throughput gain is not significant when we reduce the size of the frames for the far or low-rate stations. In addition, we have investigated the effect of the maximum backoff stage. The maximum backoff stage of high-rate stations varies from u1 = 5 to 0 while that of low-rate stations is fixed at u4 = 5. The default initial backoff window is 32 for both groups, i.e., w1,0 = w4,0 = 32. Fig. 12 depicts throughputs of group 1 and group 4, respectively. Fig. 13 shows that the aggregate throughput is slightly increased

20 5

10

x 10

9 8

Throughput [bps]

7

Group 1, u1 = Group 4, u4 = Group 1, u1 = Group 4, u4 = Group 1, u1 = Group 4, u4 = Group 1, u1 = Group 4, u4 = Group 1, u1 = Group 4, u4 =

6 5 4 3

5 5 3 5 2 5 1 5 0 5

2 1 4

Fig. 12.

6

8


12

14

16

Saturation throughput of each group with different maximum backoff stage ui for different groups (n1 = 2, n4 = 2 ∼ 14). 6

1.7

x 10

u = i ui = ui = u = i u =

1.6


1.5

i

5, 5 5, 3 5, 2 5, 1 5, 0

1.4 1.3 1.2 1.1 1 0.9 0.8 4

Fig. 13.

6

8


12

14

16

Total saturation throughput with different maximum backoff stage ui for different groups (n1 = 2, n4 = 2 ∼ 14).

by differentiating the maximum backoff stage. We observe that different maximum backoff stages for different groups have very little impact on differentiating high-rate groups from low-rate groups. The reason is that the nominal backoff stage tends to stay at lower value than 5, the default value of the maximum backoff stage, with high probability. For example, when two high-rate stations and 14 low-rate stations have the parameters of u1 = 0, w1,0 = 32 and u4 = 5, w4,0 = 32, respectively, high-rate stations have the stationary distribution of b1,k,0 = 0.0426, 0.0101, 0.0027, and 0.00075 for k = 0, 1, 2, and 3, and b4,k,0 = 0.0156, 0.0051, 0.0016, and 0.00053 for k = 0, 1, 2, and 3. In the case of both differentiated backoff windows and differentiated frame sizes, the impact on improving performance is shown to be the greatest. In Fig 14, the dramatic performance improvement is observed as a larger initial backoff window and a smaller frame size for the low-rate stations are used.

21 6

7

x 10

Group 1, SMSDU,1 = 2304, w1,0 = 32 Group 4, SMSDU,4 = 2304, w4,0 = 32 Group 1, SMSDU,1 = 2304, w1,0 = 32 Group 4, SMSDU,4 = 2000, w4,0 = 64 Group 1, SMSDU,1 = 2304, w1,0 = 32 Group 4, SMSDU,4 = 1500, w4,0 = 128 Group 1, SMSDU,1 = 2304, w1,0 = 32 Group 4, SMSDU,4 = 1000, w4,0 = 256 Group 1, SMSDU,1 = 2304, w1,0 = 32 Group 4, SMSDU,4 = 500, w4,0 = 512

6

Throughput [bps]

5

4

3

2

1

0 4

Fig. 14.

6

8


12

14

16

Saturation throughput of each group with different initial backoff window wi,0 and frame size SM SDU,i for different groups

(n1 = 2, n4 = 2 ∼ 14). 6

7

x 10

S = 2304,2304, w = 32, 32 MSDU,i i,0 SMSDU,i = 2304,2000, wi,0 = 32, 64 SMSDU,i = 2304,1500, wi,0 = 32,128 S = 2304,1000, w = 32,256 MSDU,i i,0 S = 2304, 500, w = 32,512

6

MSDU,i

i,0


5

4

3

2

1

0 4

Fig. 15.

6

8


12

14

16

Total saturation throughput with different initial backoff window wi,0 and frame size SM SDU,i for different groups (n1 = 2,

n4 = 2 ∼ 14).

When the MAC payload size SM SDU,4 = 500 bytes and the initial backoff window w4,0 = 512 for lowrate stations (SM SDU,1 = 2304 bytes and w1,0 = 32 for high-rate stations), the performance of high-rate stations is recovered from far less than 1 Mbps to more than 6 Mbps (when n1 = 2, n2 = 4), which is a noticeable performance gain as shown in Fig. 15. It exhibits about six-fold link utilization increase achieved by the proposed integrated control mechanism. V. S IMULATION R ESULTS In order to validate our analytical model, we conduct simulations, and compared analytical results with those from simulations using OPNET 10.0A [25]. Then, we have additionally simulated under non-

22

TABLE III PARAMETERS USED IN THE SIMULATION . Parameters

Value

Unit

PHY header size

24

[bytes]

MAC header + FCS size

28

[bytes]

MSDU (payload) size

500 ∼ 2304

[bytes]

ACK size

14 + PHY header size

[bytes]

RTS threshold

3000

[bytes]

Fragmentation threshold

2346

[bytes]

window size

1024

[slots]

Retry limit

7

Maximum backoff

saturated traffic conditions in order to investigate practical operating conditions since our analytical model assumes the saturated traffic condition. The same network configuration as the one in the analytical performance evaluation is used for simulations. The values of the parameters used for our simulation of an IEEE 802.11b WLAN are summarized in Table III, in which FCS represents Frame Check Sequence based on CRC-32. In our simulation and analysis model, RTS threshold is set to 3000 bytes while the maximum payload size is 2304 bytes (resulting in the MAC frame size of 2332 bytes) so that RTS/CTS exchange is never used and the retry limit Lretry,i is set to 7 which is the default value of the short retry limit. The use of RTS/CTS frames can reduce the collision time [14], and may improve the network performance when hidden terminals exist. We, however, do not consider the RTS/CTS mechanism because the effect of our remedies to performance degradation is expected to be similar. It is assumed that there is no fragmentation. In our simulations, the mechanism controlling the initial backoff window among our remedies to improve resource utilization is considered since the mechanism is simple and shows reasonable performance gain. Fig. 16 shows that the proposed analytical model is accurate. That is, the simulation results closely follows the analytic results. We also observe that when no control is applied, our analytical throughput matches with that of [12], e.g., when the number of stations equals to 6 (two high-rate stations and four low-rate stations), the throughput of group 1 (high-rate stations), is about 0.4 Mbps and that of group 4 (low-rate stations) is about 0.8 Mbps. It exhibits that the throughput of each station in group 1 is 0.2 Mbps (0.4 Mbps/2 stations) and that in group 4 is 0.2 Mbps (0.8 Mbps/4 stations).

23 6

6

x 10

Group 1, w = 32 1,0 Group 4, w4,0 = 32 Group 1, w = 32 1,0 Group 4, w = 64 4,0 Group 1, w1,0 = 32 Group 4, w4,0 = 128 Group 1, w = 32 1,0 Group 4, w = 256 4,0 Group 1, w1,0 = 32 Group 4, w4,0 = 512

5

Throughput [bps]

4

3

2

1

0 4

6

8


12

14

16

Fig. 16. Saturation throughput of each group: analysis (dotted line with symbols) and simulation results (solid line) (n1 = 2, n4 = 2 ∼ 14).

Station in high-rate group Station in low-rate group 11 Mbps

1

Fig. 17.

1 Mbps

2 5

AP

3

5.5 Mbps 2 Mbps

5

4

BSS with one station moving away from the AP and four stationary stations.

Next, we simulate the environment where there are five stations composed of one moving station and four stationary high-rate stations near the AP (see Fig. 17). As station 5 moves away from the AP, i.e., the data rate of the station is decreased by the link adaptation, the throughputs of the high-rate stations are decreased significantly. Figs. 18 and 19 show the simulation results of the throughput performance for each group and aggregate throughput, respectively. Group 1 represents four stationary high-rate stations transmitting at 11Mbps and group 2 represents the single station (i.e., station 5) moving away from the AP for notational convenience. When every station transmits frames at 11 Mbps, we can achieve the maximum throughput. When there is no control over the performance anomaly (w1,0 = w2,0 = 32), the total saturation throughput of the high-rate group decreases dramatically as station 5 moves away from the AP (by adapting the transmission rate from 11 to 1 Mbps). If our proposed mechanism using differentiated wi,0 is used, we can reduce the performance anomaly effect. That is, we could increase the throughput of four high-rate stations from 2.2 Mbps to 6.4 Mbps when the low-rate station’s transmission rate is 1 Mbps

24 6

8

x 10

7

Throughput (bps)

6

5

Group 1, w1,0 = 32 Group 2, w2,0 = 32 Group 1, w1,0 = 32 Group 2, w2,0 = 64 Group 1, w1,0 = 32 Group 2, w2,0 = 128 Group 1, w1,0 = 32 Group 2, w2,0 = 256 Group 1, w1,0 = 32 Group 2, w2,0 = 512

4

3

2

1

0 1

Fig. 18.

2

5.5 Data rate of low−rate group (Mbps)

11

Saturation throughput of each group when there are one moving low-rate station (group 2) and four stationary high-rate stations

(group 1) in a BSS. 6

7.5

x 10

7 6.5

Total throughput (bps)

6 5.5 5 4.5 Wi,0 = Wi,0 = Wi,0 = W = i,0 Wi,0 =

4 3.5 3 2.5 1

2

5.5 Data rate of low−rate group (Mbps)

32, 32 32, 64 32, 128 32, 256 32, 512

11

Fig. 19. Total saturation throughput of stations when there are one moving low-rate station (group 2) and four stationary high-rate stations (group 1) in a BSS.

in our simulation. When the rate of station is 5.5 Mbps, the throughput of high-rate stations increase from 4.9 Mbps to 7 Mbps. In addition to the simulations under the saturated condition, we simulate the proposed mechanism in unsaturated environments. The network consists of one low-rate (1 Mbps) station and four high-rate (11 Mbps) stations as shown in Fig. 17. We assume that all stations have the same traffic load and it varies from low load to high load. Therefore, each station has the same traffic load varying from 0.5 Mbps to 4 Mbps, and the payload size is 2304 bytes as used above. Note that group 2 represents the low-rate station (1 Mbps) for notational convenience. Our proposed mechanism, which controls the initial backoff window, is still able to reduce the performance anomaly effect very well.

25 6

7

x 10

6

Throughput (bps)

5

4

Group 1, w1,0 = 32 Group 2, w2,0 = 32 Group 1, w1,0 = 32 Group 2, w2,0 = 64 Group 1, w1,0 = 32 Group 2, w2,0 = 128 Group 1, w1,0 = 32 Group 2, w2,0 = 256 Group 1, w1,0 = 32 Group 2, w = 512

3

2

1

2,0

0 0.5

Fig. 20.

1

1.5

2 2.5 Traffic load of each node (Mbps)

3

3.5

4

Non-saturation throughput of each group as the traffic load of each station increases from 0.5 Mbps to 4 Mbps. 6

7

x 10

6.5 6

wi,0 = w = i,0 w = i,0 w = i,0 wi,0 =

32, 32 32, 64 32, 128 32, 256 32, 512

Total throughput (bps)

5.5 5 4.5 4 3.5 3 2.5 2 0.5

Fig. 21.

1

1.5

2 2.5 Traffic load of each node (Mbps)

3

3.5

4

Total non-saturation throughput as the traffic load of each station increases from 0.5 Mbps to 4 Mbps.

Fig. 20 shows the throughput of each group as the traffic load of each station increases. The traffic load of 4 Mbps per station is close to the saturated condition. Fig. 21 shows the aggregate throughput of five stations. When traffic load is low, i.e., an unsaturated condition, the aggregate throughput is low. As the load increases, our mechanism improves throughput considerably while the legacy mechanism shows very little increment. When the legacy uncontrolled mechanism is used, the total throughput increases from 2.5 Mbps to 2.7 Mbps. However, when our control mechanism is employed, it increases from 2.5 Mbps to 6.4 Mbps in our non-saturated traffic simulation. Finally, we conduct the performance evaluation and comparison between IEEE 802.11b with our remedy and IEEE 802.11e with TXOP [28]. IEEE 802.11e is proposed to provide Quality of Service (QoS) and enhance legacy IEEE 802.11, and is being finalized. In an attempt to increase channel

26 15 Group4, w4,0=512 to Group1, w1,0=32 Group4, w4,0=256 to Group1, w1,0=32 Group4, w4,0=128 to Group1, w1,0=32 Group4, w4,0=64 to Group1, w1,0=32 Group4, w4,0=32 to Group1, w1,0=32 Group4, TXOP4 to Group1, TXOP4 Group4, TXOP40 to Group1, TXOP40

Throughput Ratio

10

5

0

Fig. 22.

1

2

3

4

5 6 7 Number of low−rate stations

8

9

10

Throughput ratio of IEEE 802.11b with our remedy and that of IEEE 802.11e with TXOP (n1 = 10, n4 = 1 ∼ 10).

utilization it allows frame burst in a TXOP. Note that while legacy stations have to wait for a backoff interval as well as DIFS to transmit a frame, IEEE 802.11e stations have to wait for only SIFS (