An Enhanced Auto-Rate Algorithm for Wireless Local Area Networks ...

Pang, Leung & Liew,

“An Enhanced Auto-Rate Algorithm for WLANs Employing Loss Differentiation”

An Enhanced Auto-Rate Algorithm for Wireless Local Area Networks Employing Loss Differentiation (Revised Version) 1

Qixiang Pang ,

Victor C. M. Leung1,

Soung C. Liew2

1

Department of Electrical and Computer Engineering The University of British Columbia 2

Department of Information Engineering The Chinese University of Hong Kong

Submitted on Sep. 6, 2005; Revised on Sep. 16, 2006 Abstract — Channel conditions in wireless local area networks (WLANs) are variable due to mobility and interference. Auto-rate algorithms are commonly used in WLANs to maximize the data rate over the variable physical channel. However, the widely used automatic rate fallback (ARF) algorithm does not work properly in the presence of multi-access collisions due to its inability to differentiate frame losses caused by link errors from those caused by collisions. This paper proposes an improved ARF algorithm that is capable of differentiating between these two types of losses with minimal modification to the existing medium access control protocol. Performance evaluations show substantial throughput improvements over ARF and other existing algorithms. Keywords — WLAN, DCF, protocol design, loss differentiation, collision loss, link error loss, rate adaptation

Corresponding author: Victor C.M. Leung, Email: [email protected]; Tel: +1-604-8226932. This paper is based in part on our conference paper appeared in IEEE BroadNets 2005, Boston, MA, USA, October 3-7, 2005. This work was sponsored by the Canadian Natural Sciences and Engineering Research Council under grant STPGP 257684-02 and the AoE scheme under the University Grant Committee of Hong Kong (AoE/E-01/99).

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I. INTRODUCTION In recent years, wireless local area networks (WLANs) based on the IEEE 802.11 (Wi-Fi) standards have been widely used in homes, offices, and public areas [1][2]. WLAN technologies are also increasingly employed for communications within vehicles and airplanes, between vehicles, and to enable vehicular passengers to access the Internet through hot-spots or road-side gateways [3][4]. Most IEEE 802.11 WLAN devices employ the Distributed Coordination Function (DCF) [2] to coordinate channel access by means of carrier-sense multiple access with collision avoidance (CSMA/CA), which requires a device (station) to sense for an idle wireless medium before transmitting a data frame. To resolve collisions, the stations employ a binary slotted exponential backoff algorithm and retransmission scheme. Current WLAN standards support more than one data rates using different modulation schemes/parameters. IEEE 802.11b supports 4 distinct data rates [5] while 802.11a/g support 8 different data rates [6][7]. A higher data rate is counter-acted by a higher bit-error rate (BER) and does not necessarily yield a higher throughput. Only when the channel condition is good, does a higher data rate give a higher throughput. In practice, the channel condition of a WLAN can vary dynamically due to mobility of stations and other objects, multipath interference, interference from other WLANs, etc. Auto-rate (or rate adaptation) algorithms are commonly employed to adapt the transmission data rate to different channel conditions. Automatic Rate Fallback (ARF) is the original auto-rate algorithm developed by Lucent for its WaveLAN-II WLAN product [8]. ARF and similar algorithms have been widely implemented in many WLAN products although it is not included in the IEEE 802.11 standard [2]. In ARF, the sender deduces the channel condition by counting the numbers of consecutively successful and failed transmissions, and adjusts its modulation mode and data rate accordingly. However, the design of ARF has not taken into account possible frame losses due to collisions. It assumes that all frame losses are due to link errors (referred as link error losses). ARF lowers the data transmission rate whenever consecutive frame losses occur. This is appropriate when there is only 2

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one station. However, when a WLAN has a number of active stations, frequent collisions may occur [9][10] and ARF may lose its effectiveness. In fact, if losses are due to collisions (referred as collision losses), reducing the data transmission rate will certainly reduce throughput. In such cases, not only the transmission time of a data frame, but also the collision period, become longer. Worse, if a station using a higher data rate collides with another one using a lower data rate, both stations will experience the same collision period determined by the lower data rate. In this paper, we show that the above problem could lead to performance degradations, and propose an effective auto-rate algorithm called loss-differentiating-ARF (LD-ARF) that is suitable for a realistic WLAN environment where both collision losses and link error losses can occur. The organization of the remainder of this paper is as follows. Section 2 reviews the problems in existing auto-rate algorithms and highlights the invalidity of the popular ARF algorithm when there are multiple stations contending for channel access. In Section 3, we present the proposed LD-ARF algorithm, and describe how the new algorithm modifies the 802.11 MAC layer function and the ARF algorithm. Section 4 describes several simulation scenarios designed to evaluate the performance of LD-ARF and other auto-rate algorithms. Simulation results are presented to show that the LD-ARF algorithm improves throughput significantly. Section 5 concludes this paper.

II. SHORTCOMING OF EXISTING AUTO-RATE ALGORITHMS A.

Why Auto-Rate IEEE 802.11b provides 4 while 802.11a/g provides 8 physical data rates. Figure 1 shows the

throughput between a single pair of transmit and receive stations at different data rates under different signal-to-noise ratio (SNR) values, obtained by simulations of the basic access procedure of 802.11 WLAN using NS-2 [11]. In the simulations, the single transmit station is saturated with data traffic, i.e., it always has data frames to send. The MAC layer payload size is 500 bytes. The values of other parameter settings are listed in TABLE 1. The measured performance metrics is 3

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the MAC layer payload throughput. The channel models used in the simulations are explained as follows. For 802.11b, we use the experimental model measured in Roofnet constructed by Prism 802.11b WLAN cards [12]. For 802.11a/g, we use the analytical model derived from [15][26]. 802.11a/g employs BPSK modulation at 6 Mbps and 9 Mbps, for which BER is given by ⎛ 2 SNR ⎞ ⎟ BER ≈ Q⎜⎜ ⎟ c ⎝ ⎠

(1)

where c is the code rate (½ for 6 Mbps and ¾ for 9 Mbps). For 802.11a/g employs QPSK or QAM modulation at other data rates, for which BER is given by

1 ⎞ ⎛ 3 log 2 ( M ) SNR ⎞ ⎛ ⎟ BER ≈ 4⎜1 − ⎟Q⎜ ( M − 1)c ⎟⎠ M ⎠ ⎜⎝ ⎝

(2)

In (2) the values of M and c are respectively: 4 and ½ for 12 Mbps, 4 and ¾ for 18 Mbps, 16 and ½ for 24 Mbps, 16 and ¾ for 36 Mbps, 64 and ½ for 48 bps, and, 64 and ¾ for 64 Mbps. SNR in (1) and (2) is replaced by the signal-to-noise-plus-interference ratio, SINR, in the presence of interference over the wireless channel. It can be seen from Figure 1 that a higher data rate does not necessarily yield a higher throughput. This is because in general for a given SINR, with the increase of data rate, BER increases as well. An auto-rate algorithm can dynamically adjust the data rate to achieve optimized performance under variable channel conditions. However, when there are multiple contending stations in one WLAN, the existing auto-rate algorithms may lose their effectiveness. B.

Problem with ARF

In the ARF algorithm [8], if the acknowledgment (ACK) frames for Nd (= 2 in [8]) consecutive data frames are not received by the sender, then the sender drops the transmission rate to the next lower data rate and a timer (referred as the rate-up timer here) is started. If Nu (= 10 in [8]) consecutive ACK frames are received or the rate-up timer expires, then the transmission rate is raised to the next higher data rate. When the rate is increased, the first transmission (also called 4

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probing frame) after the rate increase must be successful, or else the rate is immediately decreased and the rate-up timer is started again. Despite the wide application of ARF, it can be shown that rate adjustments by ARF can be misguided when multiple stations contend for channel access. Consider a WLAN in which a number of active stations are sending data frames. The following statement can be proven. Statement 1: Given a set of control parameters (Nd, Nu, and rate-up timer) for ARF, when the number of active stations is large, the average performance with ARF is worse than the case where each station uses a fixed rate that has been randomly selected. Without loss of generality, we assume that there are n active stations in the WLAN and the stations can transmit at two data rates, R1 and R2 with R1 ≤ R2. Let ER1 be the effective rate of R1 defined by ER1 = R1 × (1 − p e1 ) , where p e1 is the frame loss probability of a data frame transmission at rate R1 due to link errors. Correspondingly we have ER2 for rate R2. Consider the case when the channel condition is good enough such that ER2 > ER1. Let {1, i} ( 0 ≤ i ≤ N u - 1 ) be the state that the station is at rate R1 and the past i packets were transmitted successfully. Let {2, t} be the state that the station’s transmit data rate has just changed from R1 to R2. In this state, the next transmission must be successful in order to keep the rate at R2. Let {2, j} ( 0 ≤ j ≤ N d - 1 ) be the state that the station is at rate R2 and the past j packets were not successfully transmitted. Let us first ignore the effect of the rate-up timer. Let p f 1 and p f 2 be the failure probabilities of data frames transmitted at rate R1 and R2, respectively. Denote the collision probability of a data frame transmission by pc . Then we have

p f 1 = p c + p e1 − p c × p e1

(3)

p f 2 = pc + pe2 − pc × pe2

(4)

The states, {1, i} ( 0 ≤ i ≤ N u - 1 ), {2, t} and {2, j} ( 0 ≤ j ≤ N d - 1 ), constitute a Markov

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chain as shown in Figure 2. The one-step state transition probabilities of the chain are as follows. ⎧ P(1, i + 1 | 1, i ) = 1 − p f 1 0 ≤ i < N u - 1 ⎪ ⎪ P(1,0 | 1, i ) = p f 1 0 ≤ i ≤ N u - 1 ⎪ P( 2, t | 1, N - 1) = 1 − p u f1 ⎪ ⎪⎪ P( 2, j + 1 | 2, j ) = p f 2 0 ≤ j < N d - 1 ⎨ ⎪ P( 2,0 | 2, j ) = 1 − p f 2 0 ≤ j ≤ N d - 1 ⎪ P(1,0 | 2, N − 1) = p d f2 ⎪ ⎪ P( 2,0 | 2, t ) = 1 − p f 2 ⎪ ⎪⎩ P(1,0 | 2, t ) = p f 2

(5)

From (5) and Figure 2, we have P2,t = P1,0 (1 − p f 1 ) N u

P2,t + N u −1

∑P i =0

1, i

(

N d −1

∑P j =0

(6)

2, j

⎡ (1 − p f 2 ) 1 + p f 2 + p f 2 2 + ... + p f 2 N d −1 = P2,t ⎢1 + N −1 1 − (1 − p f 2 ) − ... − p f 2 d (1 − p f 2 ) ⎢⎣

[

= P1, 0 1 + (1 − p f 1 ) + (1 − p f 1 ) 2 + ... + (1 − p f 1 ) N u −1

)⎤⎥ = P ⎥⎦

2 ,t

⎛ 1 ⎜ ⎜ p Nd ⎝ f2

⎞ ⎟ ⎟ ⎠

]

(7)

(8)

In order to prove Statement 1, we only need to show that even if ER2 > ER1, when n is large enough, the average number of stations transmitting at rate R1 is larger than n/2 when ARF is applied, i.e., N u −1

N d −1

i =0

j =0

∑ P1,i > P2,t +

∑P

(9)

2, j

or equivalently,

1 + (1 − p f 1 ) + (1 − p f 1 ) 2 + ... + (1 − p f 1 ) N u −1 (1 − p f 1 ) It can be shown that

Nu

1 pf2

Nd

>

1 pf2

is monotone decreasing with p c , from

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(10)

Nd

1 pe2

Nd

(when p c = 0 )

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to 1 (when p c = 1 ), and


1 + (1 − p f 1 ) + (1 − p f 1 ) 2 + ... + (1 − p f 1 ) N u −1 (1 − p f 1 ) N u

is monotone increasing to

∞ (when p c = 1 ) with p c . For n active stations in a WLAN, pc is given by

p c = 1 − (1 − τ ) n −1

(11)

where τ is the probability that a station transmits in a randomly chosen slot time. The value of τ depends on the average contention window size. In practice, for stations following the 802.11 WLAN standard, there are two parameters which can limit the maximum value of the contention window: CWmax and the transmission retry limit, m. When either of the two limits has been reached, the increase of the contention window stops. Therefore, we have

τ ≥ τ min ≡

2 min CW max, CW min× 2 m + 1

{

(12)

}

and

p c ≥ 1 − (1 − τ min ) n −1

(13)

From (13), it is easy to see that a large enough n will yield a large enough pc for (10) to be true. The above analysis assumes a homogeneous wireless channel condition (same p e1 and p e 2 for all stations). When different stations experience different channel conditions (say there are C types of channel conditions), the stations can be classified into C classes accordingly. From the above analysis, for each class k ( 1 ≤ k ≤ C ), there is an N k such that when the number of stations, n > N k , Statement 1 is true for Class k stations. When the total number of stations is large enough (larger than the maximum N k ), Statement 1 is true for all stations. Now let us consider the rate-up timer. The role of the rate-up timer is to increase the transition probability from R1 to R2. We consider the practical case where the expiry time is longer than the time of at least one data frame transmission. For a pre-specified value of the timer

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(usually in the order of seconds or minutes [8][15]), the increase in transition probability is quite limited. Based on (8) and (13), as long as the number of stations is sufficiently large, the effect of the rate-up timer cannot compensate for the fact that collisions cause more stations to transmit at rate R1 than R2. For simplicity, the analysis above considers only two data rates, but we believe that it can be extended to a system with a larger number of data rates. The analysis tells us that under a good channel condition that should favor a higher data rate, when the number of stations is large enough, collisions would cause most stations to stay at the lower data rate. Therefore, using ARF can give worse performance than not using ARF, let alone achieving optimal or near optimal performance under variable channel conditions. The existing ARF design is unable to adapt to both the changes of channel condition and collisions between a large number of active stations. By simply adjusting ARF control parameters the problem cannot be solved. This will be verified further by simulations in Section 4. C.

Other Existing Auto-Rate Algorithms

Besides the widely-used ARF, there are other auto-rate algorithms presented in literature [13]-[24]. However, to the best of our knowledge, they have not been implemented in WLAN products. In order to estimate the channel quality for the purpose of rate adaptation, an auto-rate algorithm can use one of the following 4 methods: 1) Deduce the channel status from consecutive successful transmissions (good) or consecutive failures (bad), as in ARF; 2) FER-based: frame error rate (FER) is measured over a time period and compared with predefined thresholds; 3) RSS-based: the measured received signal strength (RSS) is compared with specific thresholds calculated from a predefined channel model; 4) SNR-based: the measured SNR is compared with specific thresholds calculated from a 8

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predefined channel model. In terms of implementation complexity, the first method is the easiest and the last one is the most difficult as most WLAN products do not have ability to measure SNR. The FER- and RSSbased methods have intermediate complexity. So far, only the first method has been implemented with ARF in practical WLAN products. The others have been considered only in the literature. As the purpose of this paper is to improve the ARF scheme with minimal modifications, we shall consider the first method in conjunction with our proposed loss differentiation method. However, the proposed improvement can also be used with the other channel estimation methods. In the following, we give a brief yet complete review of existing auto-rate algorithms employing different channel estimation methods. In [13], a FER based approach was proposed for rate adaptation. The FER is estimated by the sender through counting the number of received ACK frames and the number of transmitted data frames during a rather short time window. The FER information is used for two types of operations: (1) downscaling - if the FER exceeds some threshold, then switch to the next lower rate; and, (2) upscaling - if the FER falls below a second threshold, probe the link at the next higher rate with a few frames and if all of them get acknowledged, stay in that higher rate. Since the frame errors can also be caused by collisions, in case of multiple stations causing many collisions, the algorithm may experience the same problem as ARF. The Receiver-Based Auto Rate (RBAR) scheme [14] performs rate adaptation at the receiver instead of at the sender. RBAR mandates the use of RTS/CTS mechanism. The receiver of an RTS frame calculates the transmission rate to be used by the sender based on the SNR of the received RTS frame and on a set of SNR thresholds calculated from a predefined wireless channel model. The rate to be used to send the data frame is then returned to the sender in the CTS frame. The RTS, CTS and data frames are modified to include information on the size and rate of the data frame transmission to allow all the nodes within the transmission range to correctly update their NAV. First, the RTS/CTS access procedure is always required, which incurs extra overhead. 9

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Secondly, it requires a priori knowledge of the channel model to calculate the SNR thresholds and the measurement of SNR may not be easy to realize in low cost WLAN devices. Similar to RBAR, the method proposed in [15] makes use of a predefined wireless channel model and SNR measurements, but performs rate adaptation using a static PHY mode table indexed by the data frame length and channel SNR, which is pre-established offline according to the channel model. Unlike RBAR, RTS/CTS is not used. The sender dynamically monitors the wireless channel condition and estimates the SNR value of the frame when it reaches the receiver. The channel condition estimation can be destroyed by frequent frame collisions. Since some inexpensive receivers do not have the ability to measure SNR, in [16], an approach using RSS was studied. It is also a channel model based method, but the channel condition is measured by the RSS. It is easier to implement than the SNR based methods. But the major problem of this method is that it does not provide a proper information feedback mechanism and it estimates the receiver’s channel condition (RSS level) at the sender side. To do that, fixed power level of the sender must be assumed, which limits its wide applications. The data traffic must be bi-directional and frequent enough for the current channel condition to be estimated promptly. In [17], the authors proposed a hybrid rate control scheme in which the existing techniques are carefully combined. Both statistical information (FER, throughput) and measured SNR (or RSS) information are collected and utilized. By using a more complex control mechanism, it is possible to overcome some of the problems in other existing methods as described above. However, not withstanding its complexity, this method still does not account for the coexistence of multiple contending stations, since it is just a combination of the existing algorithms. Unlike ARF, the Adaptive ARF (AARF) algorithm [18] continuously changes the Nu threshold value at runtime. When the transmission of the probing frame fails, the data rate is switched back immediately and the Nu threshold is doubled. The threshold is reset to its initial

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value of 10 when the rate is decreased because of 2 consecutive failed transmissions. In this way, the authors claimed that it works better in a high latency system. However, the AARF still cannot solve the collision problem. To make matter even worse than ARF, when a failed transmission is caused by collision, a doubled Nu can further reduce the chance of using an appropriate rate. The Opportunistic Auto Rate (OAR) [19] improved RBAR [14] by observing that when a receiver’s channel condition is good, more than one packet can be opportunistically sent to the same receiver thus improving overall throughput. The Medium Access Diversity (MAD) scheme [20] further improved OAR by using a receiver scheduling algorithm and a packet concatenation scheme. The Full Auto Rate (FAR) algorithm [21] considered the rate adaptation of control frames (i.e., RTS, CTS, and ACK). None of OAR, MAD and FAR has the ability to deal with collisions. Only until recently has the collision issue in ARF algorithm been addressed in [22][23][24]. In this paper, we expand upon the loss-differentiating ARF (LD-ARF) algorithm originally proposed in [22], which can solve the collision issue for both RTS/CTS and basic access methods in 802.11 WLANs. In [23], the proposed Closed-Loop Adaptive Rate Allocation (CLARA) scheme enhanced ARF and mandates the use of RTS/CTS to differentiate losses. If an RTS/CTS exchange is successful, the consequent data loss is very likely due to link error. Otherwise, if RTS/CTS exchange fails, very likely a collision occurs. We follow the same approach for the RTS/CTS version of LD-ARF in [22] and this paper. The drawback with RTS/CTS loss differentiation is its overhead, as pointed out by [22][23][24]. For example, consider 802.11b and assume that the data frame size is 500 bytes, data rate is 11 Mbps and basic rate is 2 Mbps. The RTS/CTS exchange time is 540 µs while the transmission time of a data frame is 884 µs. It is easy to see that the relative overhead due to RTS/CTS becomes smaller for bigger data frames. However, traffic measurements [25] reveal that the data traffic over WLANs and the Internet typically has an average packet size of several hundred bytes. Consequently, the RTS/CTS overhead is significant 11

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in most cases. Therefore in CLARA there is a tradeoff between the overhead loss and the potential gain offered by loss differentiation with RTS/CTS. A frame size threshold is considered in [23] to decide whether or not to invoke RTS/CTS. However, as shown in [23], the threshold is quite sensitive to the number of contending stations and channel condition, which may be highly variable in practice, and a practical way to find the threshold has not been provided in [23]. Moreover, even if the threshold is known, when the data frame size is shorter than the threshold the ability to differentiate losses using RTS/CTS is bypassed. Thus the collision issue in the rate adaptation is not solved thoroughly by CLARA. Collision-Aware Rate Adaptation (CARA) [24] also employs RTS/CTS for loss differentiation as the basis of ARF. Aimed at reducing the RTS/CTS overhead, a selective RTS/CTS exchange is used in CARA. By default, a data frame is sent without exchanging RTS/CTS before it. If the data frame is lost, a RTS/CTS exchange is executed. The selective RTS/CTS scheme can significantly reduce the overhead as shown in our simulations in Section III. CARA also proposes an auxiliary CCA (clear channel assessment) detection method to the RTS/CTS loss differentiation, but the differentiation effect of the CCA method is limited. Our work initially published in [22] and extended in this paper are similar to CLARA [23] and CARA [24] for RTS/CTS access. However, neither CLARA nor CARA provides collision differentiation for basic access. In contrast, the work in [22] and this paper is the first to provide a complete collision differentiation solution for both basic and RTS/CTS access procedures. D. Contributions of This Paper

The main contributions of this paper are the proposal of a LD-ARF algorithm for both basic and RTS/CTS access, and the performance evaluations to show that LD-ARF effectively adapts to combinations of collisions and link errors that occur in practice. Our proposed method involves only minimal modifications to the MAC layer. Compared with other ARF enhancements, our method shows higher performance improvements.

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This paper substantially extends with our preliminary work in [22] in the following aspects: 1) a complete survey and discussion of existing auto rate algorithms; 2) a formal analysis of the invalidity of ARF; 3) a refined LD-ARF algorithm; 4) compatibility and implementation analysis of LD-ARF; 5) using more realistic/accurate 802.11 WLAN channel models in performance evaluation; 6) more simulation cases and discussions; and, 7) performance comparisons with the two recently proposed auto rate algorithms, CLARA [23] and CARA [24].

III. DESIGN OF LOSS-DIFFERENTIATING ARF ALGORITHM The proposed LD-ARF algorithm employs a loss-differentiating MAC (LD-MAC) protocol. The main concept of LD-MAC has been described in our preliminary work [27], which however lacks a detailed performance evaluation, nor has its application in rate adaptation been discussed. A.

Description of Loss-Differentiating MAC Protocol

The proposed LD-MAC protocol is intended as an enhancement of the 802.11 DCF and incorporates both the basic and RTS/CTS access procedures specified in the standard. RTS/CTS is an optional 4-way handshake access procedure. Since CTS and ACK frames provide feedback from the intended receiver, the sender can utilize them for LD purposes as follows: (i) If both the CTS and then the subsequent ACK frames are received, the data frame transmission is successful. (ii) If the CTS frame is received but the ACK frame is not, the data frame transmission has failed, most likely due to a link error loss. (iii) If the CTS frame is not received, most likely a collision has occurred. The 4-way exchange sequence in RTS/CTS access is not changed. Because RTS and CTS frames are short and usually transmitted at a low rate, the loss differentiation can be effective. CLARA [23] and CARA [24] also employ a similar method. The default basic access method employing two-way handshakes is more efficient than RTS/CTS access in most cases. However LD for basic access is not straightforward as only ACK frames provide feedback from receivers. The following describes a LD method for basic access

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which requires minimal modifications to the standard to provide additional feedback. The LD method for basic access is based on the following observation. The MAC data frame can be functionally partitioned into two parts: header and body. The MAC header contains information such as frame type, sender and receiver addresses, and comes before the MAC body, which contains the data payload. In a WLAN with multiple stations sharing a common channel, a collision occurs when two or more stations start transmitting in the same time slot, which will likely corrupt the whole frame (header plus body) at the receiver. On the other hand, a frame transmission not affected by a collision may still be corrupted by link errors. In this case, the noise or interference may result in a few bit-errors that cause a frame check sequence (FCS) error in the decoded data frame, which is then discarded by the receiver. As the MAC header is typically much shorter than the MAC body, when FCS fails, it is much more likely caused by bit errors in the body than the header. The MAC frame, including the MAC header, is sent at a data rate specified in the “Signal” field of the Physical Layer Convergence Protocol (PLCP) header. The MAC header can be made even more reliable by employing a lower order modulation or forward error correction (FEC) coding (e.g., as in Bluetooth) if the current standard is modified. If the MAC header is correctly received even though the body is corrupted, the receiver can decode the sender and receiver addresses from the MAC header and verify that it is the intended receiver. To verify that the MAC header is not corrupted, a short Header Error Check (HEC) field can be added at the end of the header (Figure 1) to provide error checking over the header, while the FCS at the end of the frame provides error checking over the entire MAC frame. Note that the use of HEC in the header is not a new concept as it has been adopted in many other communication systems, such as asynchronous transfer mode (ATM) [28], Bluetooth [29] and WiMedia [30], all of which include a 1-byte or 2-byte HEC or header check sequence (HCS) field in their headers. With the HEC, when a data frame is received and FCS fails, the HEC can be verified to see if the header is free of error, and if the header is correctly received, proper feedback can then be returned to the sender identified by the source address in the MAC header. 14

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As discussed above, FCS failure but correct HEC in a frame reception is a good indication that the frame has been corrupted by link errors rather than a collision. Because in the original basic access, only ACK frames are available to provide positive feedback, a new control frame – NAK needs to be introduced to inform the sender that the data frame transmission has failed and the failure is due to link errors; i.e., the data frame has suffered a link error loss. On the other hand, if the sender receives neither a NAK nor an ACK after sending a data frame, it is a good indication that the frame transmission has suffered a collision loss. The NAK frame can be implemented with exactly the same structure as the ACK frame except for a one-bit difference in the frame type field in the header, and is sent at the same data rate as an ACK frame. The transmission of a NAK does not consume more bandwidth or collide with other frames because it is transmitted SIFS after the end of the data frame transmission and occupies the time that would have been used by the transmission of an ACK. The HEC field is a necessary modification to the standard because without it, when the FCS fails, the receiver would not be able to determine if the header is in error and would not be able to trust the sender/receiver addresses in the header for returning the NAK. In fact, this modification is necessary if proper feedback is to be returned to the sender when corrupted data frames are received, regardless of the use of more accurate channel quality estimation techniques such as RSS, SNR, or FER measurements at the receiver. The HEC field (which can be 1 or 2 bytes) costs an extra overhead, but it can be shown that this extra overhead is much less than 1% of the total transmission time [27]. Therefore the extra overhead is negligible. The effectiveness of the loss differentiating MAC protocol depends on the correct reception of header information and control frames (ACK, NAK, RTS, CTS), and hence the link error rate. With link errors, the correctness of loss differentiation is probabilistic, but analysis shows that the correct loss differentiation probabilities are higher than 95% for 1000-byte payloads and BER < 10-4 [27]. Therefore the proposed LD-MAC is quite effective.

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B.


Description of LD-ARF Algorithm

We have explained in Section II that when a collision occurs, the data rate should not be reduced. Therefore the straightforward modification to the ARF algorithm is that the data rate is reduced only when multiple data frame losses are caused by link errors. Figure 4 describes the new LD-ARF algorithm employing this idea. In the new LD-ARF (Figure 4), the rate increase procedure is exactly the same as that in ARF, i.e., if there are Nu consecutive successful transmissions, the data rate is promoted to the next higher level. The rate decrease procedure in LD-ARF is, however, different in comparison with ARF. LD-ARF employs the link error detection method enabled by the LD-MAC protocol described in the previous section. When a sequence of consecutive frame losses include Nd link error losses, the data rate is dropped to the next lower level at the sending station. As discussed, the effectiveness of loss differentiation depends on the correct reception of header information and correct delivery of control frames (RTS, CTS, ACK, and NAK). Although header and control frames are much shorter than the data frame payload, an extreme situation may occur where the channel condition is so deteriorated that even the error rates of the data frame headers and control frames are very high. In such cases, the sender will not be able to receive timely and accurate feedback from the receiver to properly adjust its data rate. If this situation persists for a long time, the performance of LD-ARF will deteriorate. To address this problem, as we have mentioned, we can apply lower rate to the control frames and/or the header part to enhance their robustness. It is easy to set up a lower value to BasicRate for the control frames. However, using different modulation levels and rates in the headers and bodies of data frames will increase the implementation complexity. To overcome the above problem, the following enhancement can be added to the LD-ARF described in Figure 4, without using lower data rates in the headers or control frames. In RTS/CTS access, if no CTS has been received for Nf consecutive RTS transmissions, or in basic

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access, if neither NAK nor ACK has been received for Nf consecutive data frame transmissions, the sender will conclude that the channel is experiencing a persistent degraded condition, and decrease its data rate to the next lower rate. Nf is set to a value larger than Nd, e.g., 20 in our simulations in Section IV. With such a value, the probability of misinterpreting collisions as poor channel condition is very low; e.g., at a collision probability of 0.5, the contention intensity is usually regarded as high [9]-[10], but the probability of Nf consecutive collisions is less than 10-6. We also note that when an auto-rate algorithm is applied in an infrastructure WLAN, the auto-rate adaptation for down link traffic in an AP should be performed separately on a per flow or per destination station basis since different stations may experience different channel conditions. This has not been considered in the original ARF algorithm [8]. C.

Backward Compatibility

We explain how a station enhanced with LD-MAC and LD-ARF communicates with an existing 802.11 station, where the different MAC versions are indicated using the 2 protocol version bits in the frame control field of the MAC header as follows: 00 ⇒ v0: standard 802.11 MAC, 01 ⇒ v1: 802.11 MAC with LD-MAC enhancement. For proper interoperability, a v1 station always uses v0 MAC and ARF when it sends the first data frame to its recipient but identifies the MAC version as v1 in the frame header. The recipient then replies with its own MAC version in the reply ACK frame as v0 or v1. The sending station then switches to the LD-MAC protocol if both sender and recipient are v1 stations. If the recipient indicates v0, then the sending station continues to follow the v0 MAC and ARF. The proposed LD-ARF and LD-MAC have the following advantages: (i) simple implementation that requires no modification to the PHY layer and only minimal modification to the MAC layer; (ii) negligible overhead; and, (iii) as shown in the Section IV, substantial improvements in system throughput in the presence of multiple contending stations.

17

Pang, Leung & Liew,


IV. PERFORMANCE IMPROVEMENT BY LOSS-DIFFERENTIATING ARF ALGORITHM A.

Simulation Models

We evaluate and compare the performance of the auto-rate algorithms by simulations using ns-2 [11]. Systems with IEEE 802.11b and 802.11a/11g PHY parameters, as shown in TABLE 1, have been simulated. The Gilbert-Elliot channel model [31][32] and its K-state extensions [33]-[35] have been widely used to model wireless channels with variable conditions. Channel measurements recently conducted on an IEEE 802.11 WLAN show that this type of model also gives good predictions of WLAN performance [36]. To evaluate the performance of the proposed LD-ARF algorithm and compare with that of the ARF algorithm under variable channel conditions, we apply the K-state Markov channel model shown in Figure 5. This channel model has also been used in [15] to study the rate adaptation algorithms in WLANs. In this model, the wireless channel can be in one of the K states: {S0, S1, …, SK-1}. In state Si, each received frame has an SINR value uniformly taken from the range of [xi, xi+1) dB, where xi+1>xi. The sojourn time in each state follows an exponential distribution (or if discrete time is used, the equivalent geometric distribution). In the simulations, we let K=10. For each state, Si (0 ≤ i ≤ 9) , the average time duration is 1 second to reflect a moderately changing channel condition. The xi (0 ≤ i ≤ 10) values (in dB) of the channel model are given by

for 802.11b ⎧i xi = ⎨ ⎩3i for 802.11a/g

(14)

The difference in (14) is due to the fact that the higher level modulations employed by 802.11a/g require higher SNRs than 802.11b to perform normally.

18

Pang, Leung & Liew,


Both TCP and UDP traffic were employed in the simulations. For UDP traffic, the data frame payload is 500 bytes. For TCP traffic, TCP Reno with DATA segment size of 480 bytes, including a 20-byte TCP header, is applied. The IP header is an additional 20 bytes. In the simulations, the data frame error rate, the data frame header error rate, and the control frame error rates are determined respectively based on their sizes, data rates, and receive SNR values generated from the 10-state channel model. The MAC header and payload are transmitted at the same data rate (i.e., no FEC or lower header rate is applied). The value of BasicRate for the control frames follows the rules given below: a RTS frame is sent at the same rate used by the sending station to send data frames; ACK, NAK and CTS frames are sent at the same rate at that of the corresponding received data frame or RTS frame. Note that if BasicRate or the header data rate is set lower than the payload data rate, the loss differentiation can be made more effective and LD-ARF can yield better performance than our simulation results indicate. Even without these enhancements, our simulation results show that LD-ARF can achieve significant performance gains. A recommended value for the default expiry time of the rate-up timer in ARF was not given in [8]. As shown in [15], comparable throughput is obtained over a wide range of rate-up timer values (from several seconds to minutes). In the simulations, we set the timer value to 10 seconds. We have run simulations for numerous scenarios including different PHY standards, channel state parameters, traffic types, accesses, and control parameters, all yielding consistent results. Due to space limitation, we present the results of the following selected scenarios.

B.

Performance of LD-ARF Algorithm In Figure 7 to Figure 9, we provide the performance comparison of the six schemes: 1)

ARF with basic access (arf-b); 2) ARF with RTS/CTS access (arf-r); 3) LD-ARF with basic access (ldarf-b); 4) LD-ARF with RTS/CTS access (ldarf-r); 5) CLARA (clara); and, 6) CARA (cara). Note that both CLARA and CARA rely on RTS/CTS access for loss differentiation. 19

Pang, Leung & Liew,


(1) Performance comparisons with saturated UDP traffic We first consider 802.11b and 802.11a/g stations saturated with UDP traffic. Each link (or traffic flow) shown in Figure 6 experiences a channel condition that is determined independently from the 10-state model. Default auto-rate control parameters, Nd=2 and Nu=10, are used. The simulation results for the six schemes are compared in Figure 7 (for 802.11b) and Figure 8 (for 802.11a/g). Among the six schemes, LD-ARF with basic access is the best due to its both loss differentiation ability and low overhead. ARF performs well when there is only one station. However, when there are multiple stations, the performance of ARF degrades dramatically; the stations work mostly at a low data rate even when the channel condition is good due to the misunderstanding of collisions to be link error losses. In contrast, the other schemes (LD-ARF, CLARA and CARA) continue to work well even when there are many stations due to their loss differentiation capability. In our simulations, we empirically set the data frame size threshold that enable RTS/CTS exchange in CLARA for optimal performance. However, as shown in [23], a practical method to set this threshold in a systematic manner does not exist. When the threshold is not used in CLARA, it performs the same as LD-ARF with RTS/CTS access. While CARA can significantly reduce the RTS/CTS overhead by using a selective RTS/CTS scheme, its performance is still inferior to LD-ARF with basic access, by about 18% on average. Both collision losses and link error losses can contribute to the inefficiency of CARA.

(2) Performance comparisons with TCP traffic We next evaluate the performance of TCP traffic over 802.11b WLANs when the six autorate schemes are applied. Note that the topology and TCP traffic in our simulations (Figure 6) are different from those in [37][38] where the TCP data traffic is in the downlink direction. Different from the UDP traffic studied above, TCP traffic is two-way. However, the similar observations as those given for UDP traffic can be made on the results shown in Figure 9. ARF performs poorly when there are multiple stations, while the loss differentiating auto-rate 20

Pang, Leung & Liew,


algorithms yield substantially higher TCP throughputs. LD-ARF with basic access produces the best overall performance, not only when there are multiple TCP connections sharing the WLAN. Even when there is only one TCP connection, the throughput of LD-ARF is slightly higher than that of ARF due to the possible collisions of TCP data and TCP ACK sent over opposite links.

(3) Effect of adjusting ARF parameters The simulation results presented above reveal that the original ARF algorithm cannot perform well in the presence of multiple stations, mainly due to the misunderstanding of collision losses to be link error losses resulting in the data rate being inappropriately decreased. Here, we investigate whether it is possible to eliminate the problem by setting different threshold values of Nd and Nu. We use 802.11b as an example and consider the saturated UDP data traffic.

In Figure 10, we increase the value of Nd used in the six schemes to 10 and keep the value of Nu (which is 10) unchanged. In Figure 11, we decrease the value of Nu to 2 and keep the value of Nd (which is 2) unchanged. By either increasing Nd or decreasing Nu, we hope that stations

employing ARF could have more chances to use the higher data rates. However, simulation results show that the effect of tuning the control parameters in ARF is very limited. When the number of stations becomes large, the performance of ARF is still unacceptable. These results demonstrate the correctness of Statement-1 in Section II. In contrast, the other auto-rate algorithms (LD-ARF, CLARA, and CARA) work well in case of multiple stations and yield significant throughput improvements over ARF. Again, we stress that LD-ARF with basic access, which is one of the major contributions of this paper, performs the best among the six methods. By comparing Figure 10 and Figure 11 with Figure 7, it can be seen that although the new parameter settings for ARF, < Nd =2, Nu =2> and < Nd =10, Nu =10>, produce higher throughput than the default settings, < Nd =2, Nu =10>, when there are many stations, the contrary is true when there are few stations. It again shows the invalidity of ARF algorithm.

21

Pang, Leung & Liew,


A summary comparing the auto-rate schemes studied in this paper is given as follows. In terms of implementation, among the six schemes, LD-ARF with RTS/CTS access is the simplest. If CLARA does not impose a RTS threshold, it is equivalent to LD-ARF with RTS/CTS access. In any case, an appropriate threshold is difficult to find. Therefore CLARA is relatively difficult to implement. Both LD-ARF with basic access and CARA need some modifications to the standard. CARA needs to change the protocol state transition diagram and message exchange sequence. LD-ARF with basic needs the LD-MAC protocol incorporating a modified MAC header with a HEC field and a new NAK frame. However, both HEC field and NAK are very basic protocol elements and have been implemented in many low-cost communication products. The implementation complexity of LD-ARF should be low and comparable to that of CARA. In terms of performance, from numerous simulations (for which typical results have been shown above), LD-ARF with basic access demonstrates the best performance overall.

V. CONCLUSION In this paper, the problem of collision losses being mistaken as link error losses in existing auto-rate algorithms for IEEE 802.11 WLANs has been investigated. A novel LD-ARF algorithm that can more accurately perform rate adaptation has been proposed. As LD-ARF enhances ARF with loss differentiation ability through minimal changes to the MAC layer only, it is easy to implement. We have evaluated the performance of LD-ARF, ARF, CLARA and CARA over numerous simulation scenarios. Comparisons with ARF show that more than 100% improvement in throughput can be achieved by LD-ARF for both 802.11b and 802.11a/g when there are multiple competing stations. Results also show that LD-ARF with basic access outperforms CLARA and CARA substantially.

REFERENCES [1] M.S. Gast, 802.11 Wireless Networks: The Definitive Guides, O’Reilly, 2002.

22

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[2] IEEE Std 802.11-1999, Wireless Lan Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Jun. 2003. [3] J. Ott and D. Kutscher, “Drive-thru Internet: IEEE 802.11b for ‘automobile’ users,” Proc. IEEE INFOCOM 2004, pp. 362-373, Mar. 2004 [4] H. Alshaer and E. Horlait, “Emerging client-server and ad-hoc approach in inter-vehicle communication platform,” Proc. IEEE VTC 2004-Fall, pp. 3955-3959, Sep. 2004. [5] IEEE Std 802.11b-1999, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Higher-speed Physical Layer Extension In The 2.4 GHz Band, Sep. 1999. [6] IEEE Std 802.11a-1999, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: High-speed Physical Layer in the 5 GHz Band, Sep. 1999. [7] IEEE Std 802.11g-2003, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Further Higher Data Rate Extension in the 2.4 GHz Band, June 2003. [8] A. Kamerman and L. Monteban, “WaveLAN-II: a high-performance wireless LAN for the unlicensed band,” Bell Labs Technical Journal, vol. 2, pp. 118-133, Aug. 1997. [9] G. Bianchi, “Performance analysis of the IEEE 802.11 distributed coordination function,” IEEE Journal on Selected Areas in Communications, vol. 18, pp.535-547, Mar. 2000. [10] L. Bononi, M. Conti, and E. Gregori, “Runtime optimization of IEEE 802.11 wireless LANs performance,” IEEE Transactions on Parallel and Distributed Systems, vol. 15, pp. 66-80, Jan. 2004. [11] NS-2, URL http://www.isi.edu/nsnam/ns. [12] D. Aguayo et al., “Link-level measurements from an 802.11b mesh networks,” Proc. SIGCOMM 2004, pp. 121-131, Aug.-Sep. 2004. [13] B.E. Braswell and J.C. McEachen, “Modeling data rate agility in the IEEE 802.11a WLAN protocol,” Proc. OPNETWORK 2001, March 2001. [14] G. Holland, N. Vaidya, and P. Bahl, “A rate-adaptive MAC protocol for multi-hop wireless networks,” Proc. ACM MOBICOM 2001, pp. 236-250, July 2001. [15] D. Qiao, S. Choi, and K.G. Shin, “Goodput analysis and link adaptation for IEEE 802.11a wireless LANs,” IEEE Transactions on Mobile Computing, vol. 1, pp. 278-292, Oct.-Dec. 2002. [16] J.P. Pavon and S. Choi, “Link adaptation strategy for IEEE 802.11 WLAN via received signal strength measurement,” Proc. IEEE ICC 2003, pp. 1108-1113, May 2003.

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[17] Hatatcherev, K. Langendoen, R. Lagendijk, and H. Sips, “Hybrid rate control for IEEE 802.11,” Proc. ACM MobiWac 2004, pp. 10-18, Oct. 2004. [18] M. Lacage, M. H. Manshaei, and T. Turletti, “IEEE 802.11 rate adaptation: a practical approach,” Proc. ACM MSWiM 2004, pp. 126-134, Oct. 2004. [19] B. Sadeghi et al., “Oppotunistic media access for multirate ad hoc networks,” Proc. ACM MOBICOM 2002, pp.24-35, Sep. 2002. [20] Z. Ji et al., “Exploiting medium access dilivery in rate adaptive wireless LANs,” Proc. ACM MOBICOM 2004, pp.345-359, Sep. 2004. [21] Z. Li, A. Das, A.K. Gupta, and S.Nandi, “Full auto rate MAC protocol for wireless ad hoc networks,” IEE Proceedings - Communications, pp.311-319, June 2005. [22] Q. Pang, V.C.M. Leung, and S.C. Liew, “A rate adaptation algorithm for IEEE 802.11 WLANs based on MAC-layer loss differentiation,” Proc. IEEE BROADNETS 2005, Oct. 2005. [23] C. Hoffmann, M. H. Manshaei, and T. Turletti, “CLARA: closed-loop adaptive rate allocation for IEEE 802.11 wireless LANs,” Proc. IEEE WIRELESSCOM 2005, pp.668673, June 2005. [24] J. Kim, S. Kim, S. Choi, and D. Qiao, “CARA: collision-aware rate adaptation for IEEE 802.11 WLANs,” Proc. IEEE INFOCOM 2006, April 2006. [25] C. Na, J. K. Chen, and T. S. Rappaport, “Hotspot traffic statistics and throughput models for several applications,” IEEE GLOBECOM 2004, pp.3257-3263, Nov. 2004. [26] T.S. Rappaport, Wireless Communications: Principles and Practices, Prentice Hall, 1996. [27] Q. Pang, S.C. Liew, and V. C. M. Leung, “Design of an Effective Loss-Distinguishable MAC Protocol for 802.11 WLAN,” IEEE Communications Letters, vol. 9, no. 9, pp. 781783, Sep. 2005. [28] ATM Forum, “ATM User-Network Interface Specification V3.0,” Sep. 1993. [29] IEEE 802.15.1, “Wireless medium access control (MAC) and physical layer (PHY) specifications for wireless personal area networks (WPANs),” Jun. 2002. [30] WiMedia Alliance, URL http://www.wimedia.org. [31] E.N. Gilbert, “Capacity of a burst-noise channel,” Bell Systems Technical Journal, vol. 39, pp. 1253-1265, Sep. 1960. [32] E.O. Elliot, “Estimates of error rates for codes on burst-noise channels,” Bell Systems Technical Journal, vol. 42, pp.1977-1997, Sep. 1963.

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[33] H. S. Wang and N. Moayeri, “Finite-state Markov channel – a uselful model for radio communication channels,” IEEE Transactions on Vehicular Technology, vol. 44, pp. 163171, Feb. 1995. [34] Q. Zhang and S.A. Kassam, “Finite-state Markov model for Rayleigh fading channels,” IEEE Transactions on Communications, vol. 47, pp. 1688-1692, Nov.1999. [35] H.P. Lin and M.J. Tseng, “Two-layer multistate Markov model for modeling a 1.8 GHz narrow-band wireless propagation channel in urban Taipei city,” IEEE Transactions on Vehicular Technology, vol. 54, pp. 435-446, Mar. 2005. [36] A. Willig, M. Kubisch, C. Hoene, and A. Wolisz, “Measurements of a wireless link in an industrial environment using an IEEE 802.11-compliant physical layer,” IEEE Transactions on Industrial Electronics, vol. 43, pp. 1265-1282, Dec. 2002. [37] S. Choi, K. Park, and C. Kim, "On the performance characteristics of WLANs: revisited," Proc. ACM SIGMETRICS'05, Jun. 2005. [38] Q. Pang, S.C. Liew, and V.C.M. Leung, “Performance improvement of 802.11 wireless network with TCP ACK agent and auto-zoom backoff algorithm,” Proc. IEE VTC 2005Spring, pp.2046-2050, May-June 2005.

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TABLE 1. NOTATIONS AND PARAMETER VALUES OF 802.11 WLAN Descriptions Min contention window Max contention window MAC overhead Slot time the time of DIFS the time of SIFS ACK frame size RTS frame size CTS frame size overhead in physical layer physical layer rate for data frame (Mbps) physical layer rate for control frame (Mbps)

throughput (Mbps)

Notations CWmin CWmax MAC slot DIFS SIFS ACK RTS CTS PHY DataRate BasicRate

802.11b 32 1024 224 bits 20 µs 50 µs 10 µs 112 bits 160 bits 112 bits 192 µs 1, 2, 5.5, 11 1, 2, 5.5, 11

4 3.5 3 2.5 2 1.5 1 0.5 0

802.11a/g 16 1024 224 bits 9 µs 34 µs 16 µs 112 bits 160 bits 112 bits 20 µs 6,9,12,18,24,36,48,54 6,9,12,18,24,36,48,54

1 Mbps 2 Mbps 5.5 Mbps 11 Mbps

0

2

4

6

8

10

Signal-to-Noise Ratio (dB)

throughput (Mbps)

(a) 802.11b 18 16 14 12 10 8 6 4 2 0

6 Mbps 9 Mbps 12 Mbps 18 Mbps 24 Mbps 36 Mbps 48 Mbps 54 Mbps 5

7

10

15

20

25

Signal-to-Noise Ratio (dB)

(b) 802.11a/g

Figure 1. Throughput versus SNR and data rates with a single transmit station

26

Pang, Leung & Liew,


pf1

pf1

1,1

1,Nu-1

pf2 1-pf1

2,t

1-pf1

1- pf2 1- pf2

2,0

1,0 pf2

pf2 pf1

2,Nd-1

2,2

pf2

2,1

1- pf2

Figure 2. State transition diagram of ARF

Bytes

18 ~ 30

Frame control/Duration/Addresses

1~2

0 ~ 2312

HEC

Frame Body

4 FCS

Header

Figure 3. Enhanced data frame format for loss differentiation in basic access

27

Pang, Leung & Liew,


Parameters: Nu: the number of consecutive successful transmissions needed to increase rate; by

default it is 10. Nd: the number of consecutive failed transmissions needed to decrease rate; by default it is 2.

•

If an ACK is received (the transmission is successful) or rate-up timer expires, then, counter_downrate = 0; counter_uprate ++; If (counter_uprate ≥ Nu) { physical rate is increased; counter_uprate = 0; rate-up timer is stopped. }

•

For basic access, if a NAK is received; For RTS/CTS access, if CTS is received but ACK is not; then a link error loss is detected, counter_uprate = 0; counter_downrate ++; If (counter_downrate ≥ Nd) or the rate was just increased physical rate is reduced; counter_downrate = 0; rate-up timer is started. }

{

Figure 4. LD-ARF algorithm

[x0, x1 )

t0,1

[xK-2, xK-1 )

S1

S0

t0,0

[x1, x2 )

t1,0

tK-2,K-1

SK-1

SK-2

t1,1

tK-2,K-2

[xK-1, xK )

tK-1,K-2

tK-1,K-1

Figure 5. A K-state Markov chain to model the wireless channel variations

28

Pang, Leung & Liew,


1 2 erroneous channels

n

(a) ad hoc

Data traffic flow

1 2 erroneous channels

n

(b) infrastructure

Figure 6. Two WLAN topologies used in the simulations with equivalent performance

arf-b

arf-r

ldarf-b

ldarf-r

clara

cara

2

total throughput (Mbps)

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1

3

5 10 15 # of competing stations

20

Figure 7. Performance comparisons of auto-rate algorithms – 802.11b 29

Pang, Leung & Liew,


arf-b

arf-r

ldarf-b

ldarf-r

clara

cara

10


9 8 7 6 5 4 3 2 1 0 1

3


20

Figure 8. Performance comparisons of auto-rate algorithms – 802.11a/g

arf-b

arf-r

ldarf-b

ldarf-r

clara

cara

1.4


1.2 1 0.8 0.6 0.4 0.2 0 1

3


20

Figure 9. Comparisons of TCP performance between auto-rate algorithms

30

Pang, Leung & Liew,


arf-b

arf-r

ldarf-b

ldarf-r

clara

cara

2


1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1

3


20

Figure 10. Performance comparisons under different control parameters (Nd=10, Nu=10)

arf-b

arf-r

ldarf-b

ldarf-r

clara

cara

2


1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1

3


20

Figure 11. Performance comparisons under different control parameters (Nd=2, Nu=2)

31

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