Fairness and throughput performance of infrastructure IEEE 802.11 networks with hidden-nodes

Physical Communication 1 (2008) 255–265

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Physical Communication journal homepage: www.elsevier.com/locate/phycom

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Fairness and throughput performance of infrastructure IEEE 802.11 networks with hidden-nodes Ozgur Ekici ∗ , Abbas Yongacoglu School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, Ontario, K1N 6N5, Canada

article Keywords: IEEE 802.11 WLAN Hidden-node Fairness Throughput

info

a b s t r a c t The fairness behavior and throughput performance of IEEE 802.11 distributed coordination function and request-to-send/clear-to-send channel access scheme in the presence of hidden nodes are investigated. A mathematical model which accurately predicts a user’s throughput performance and packet collision probability in non-saturated traffic and asymmetric hidden node environments is developed. The model allows us to see many interesting results in networks with hidden nodes. In an asymmetric hidden node network environment, the network fairness performance depends on the traffic load. In low traffic conditions, users get their fair share of the resources. However, in moderate-to-high traffic conditions, users that experience less number of hidden nodes dominate the network, causing badly located stations in a network to starve. In addition, the performance of request-to-send/clear-to-send channel access scheme, which is developed as a solution to hidden node problem, in networks with hidden nodes, is also estimated. It is shown that request-to-send/clear-to-send contention resolution scheme greatly improves the network fairness performance in hidden node scenarios. The developed model enables us to more accurately estimate the performance of practical wireless local area networks, where hidden node occurrence is common. Theoretical analysis presented in the paper is validated with simulation results. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Wireless local area networks (WLANs) are being integrated more and more into our daily lives. This widespread use of the technology, creating new challenges (i.e. congestion, load balancing) and exposing previously concealed problems (i.e. performance with hidden nodes) fuels the interest on the performance optimization of IEEE 802.11 systems. In [1] authors evaluated distributed coordination function (DCF) performance of IEEE 802.11 medium access control (MAC) in noisy channel conditions; an adaptive contention window selection algorithm is considered in [2]; a novel distributed association algorithm which targets the congestion problem in IEEE 802.11

∗

Corresponding author. Tel.: +1 6133018162. E-mail addresses: [email protected] (O. Ekici), [email protected] (A. Yongacoglu). 1874-4907/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.phycom.2008.12.003

networks is introduced in [3]; theoretical throughput and delay performance of DCF and a simple admission control algorithm considering a number of active stations (STAs) are presented [4,5]. A contemporary analytic performance analysis of the basic contention resolution method (DCF) of IEEE 802.11 MAC is presented in [6]. However, the model introduced in the mentioned paper has limiting assumptions such as; (i) STAs operate in saturated traffic conditions and (ii) all STAs in the network are in carrier sense range of each other, which is rarely the case in practical networks. An accurate non-saturated traffic analysis is presented in [7], where the authors extended the work presented in [6] by including the probability that a STA’s buffer has at least one packet awaiting transmission in their analysis. In real network environments, a STA may not be aware of the presence of all other STAs contending for channel access, and this may lead to hidden node scenarios.

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The increasing use of the technology, not only with laptops but also in cell-phones, along with limited operational frequency, escalates this hidden node probability. Most of the practical networks can be assumed as asymmetric hidden node environments where, depending on the orientation in the network, users might experience different number of hidden nodes in the same environment, which would bring up the fairness concern. In the literature, most of the papers evaluating DCF performance consider that a packet collision during contention can only happen when two or more users transmit in the same slot. Such an assumption ignores the possibility that a hidden node might commence transmission any time during other users’ whole packet transmission, that spans multiple slot durations, which is considerably more probable. In the literature, DCF performance under hidden node scenarios is mostly ignored. An analytic study with limited validation considering multi-hop networks under saturated traffic with request-to-send/clear-to-send (RTS/CTS) access mechanism is presented in [8]. In [9], a model focusing on solving the hidden node problem in DCF with reachability (connectivity) graph method is presented. In that work, limited simulation results are given and the analytical formulations are neither graphed nor compared against simulation results. The throughput performance of IEEE 802.11a networks with hidden nodes is calculated and verified with simulation results in [10]. However, the mentioned work is only applicable to symmetric networks where all the users in the communication environment share a common number of contending and hidden users (e.g. each user in the network has 1 contending and 2 hidden nodes). In this paper, the fairness and throughput performance, as well as packet collision probability of symmetric and asymmetric networks with hidden nodes, are evaluated analytically, taking into account IEEE 802.11 OFDM physical layer (PHY) characteristics, different traffic loads, network spatial set-ups and channel access mechanisms. The method developed in this work is IEEE 802.11 PHY agnostic. IEEE 802.11 OFDM PHY is chosen to illustrate the results obtained from the model. The rest of the paper is organized as follows. In Section 2 the relevant background information is summarized. Section 3 details the formulation of hidden node impact on symmetric and asymmetric network environments using basic channel access mechanism (DCF). Section 4 investigates the effect of RTS/CTS scheme on network fairness and throughput performance. The accuracy of the model is validated by numerical results presented in Section 5 and finally Section 6 concludes the paper. 2. Background An overview of Markov chain model for IEEE 802.11 MAC introduced in [6], and extended to unsaturated traffic conditions in [7] is presented here briefly as preliminary information. In [6], the author introduces a two-dimensional Markov chain analysis, describing IEEE 802.11 MAC operations as states and transitions between states. In the model, a MAC state is represented by two variables: the current retransmission stage and remaining backoff time in the considered stage. A frame is transmitted

when the backoff time counts to zero in any state. The transmitted frame suffers collisions with probability p, which is referred to as conditional collision probability. In case of a collision, STA’s retransmission stage is incremented by one and a new backoff time is uniformly chosen from the new contention window range. In [7], an additional parameter q is introduced to represent the probability that there is at least one packet waiting to be transmitted in a STA’s buffer. Let’s denote q¯ = 1 − q and p¯ = 1 − p — then the packet transmission probability τ in a generic slot time can be estimated as [7]:

τ =η

q2 W0



p¯ q¯ (1 − q¯ W0 )

−

q2 p¯



q¯

(1)

where W0 is the minimum window size and η can be found by: 1

η

= q¯ +

q2 W0 (W0 + 1) 2(1 − q¯ W0 )

q(W0 + 1) 2q¯



W0 1 − q¯ W0

+ ···

 2 pq 2 ¯ ¯ + p q − q p ··· 1 − q¯ W0 2q¯ p¯   p¯ − p(2p)m−1 − (1 − pp¯ ) 2W0 +1 1 − 2p 

q2 W0

(2) where m is the maximum back-off stage. Given q¯ and the constants W0 , m; τ would still depend on p, which is unknown as well. Under contention (i.e. when all users are in the carrier sense range of each other), the value of p can be calculated by noting that the probability of a transmitted packet encounters a collision, is the probability that, in a time slot, at least one of the n − 1 remaining STAs transmits. The fundamental independence assumption of conditional collision probability underpinning the Markov model implies that each transmission sees the system in the same (steady) state. Considering that there are n contending STAs and each STA transmits with τ probability, p can be calculated as: p = 1 − (1 − τ )n−1 .

(3)

Markov model gives us the probability measures of the events possible in a generic slot time. Basically a slot can be: (i) idle, (ii) used for successful packet transmission or (iii) in a collision. Transition from Markov model’s stationary probabilities to real-time can be accomplished by calculating the event probabilities in a generic slot time and taking into account the durations of the considered events. This would enable us to calculate the average slot duration. Then the network throughput (in Mbps) can easily be calculated by the number of bits successfully transmitted during average slot time (in µs): S sys = (Ps Ptr E [P ])/T

(4)

where E [P ] is the average payload size (in bits); Ptr is the probability that there is at least one frame transmission on the channel (Ptr = 1 − (1 − τ )n ); Ps is the probability of successful frame transmission (Ps = (nτ (1 − τ )(n−1) )/Ptr ) conditioned on the fact that there is at least

O. Ekici, A. Yongacoglu / Physical Communication 1 (2008) 255–265 Table 1 IEEE 802.11a MAC and PHY characteristics. Parameter

Definition

Value

ack cts c wmax c wmin fcs mach macpayload rts ser v

ACK frame size CTS frame size Max CW Min CW Frame control seq. MAC header MAC payload RTS frame size SERVICE field Slot time Propagation delay Tail bits EIFS time DIFS time SIFS time PLCP preamble SIGNAL field SYM interval

14 oct 14 oct 1024 slots 16 slots 4 oct 24–30 oct 0–2312 oct 20 oct 16 bits 9 µs 1 µs 6 bits 94 µs 34 µs 16 µs 16 µs 4 µs 4 µs

σ τch

tb Teifs Tdifs Tsifs Tpre Tsig Tsym

one transmission in the channel and T is the average slot time (in µs) that can be calculated as: T = (1 − Ptr )σ + Ptr [Ps Ts + (1 − Ps )Tc ]

(5)

where σ is the slot duration and Ts and Tc are the expected successful packet transmission and collision durations, respectively, which depend on OFDM PHY characteristics and packet exchange algorithm (i.e. two-way handshake [DCF] or four-way handshake [RTS/CTS]). The relative times for basic packet access (DCF) can be estimated as follows: Ts = Tdata + Tsifs + 2τch + Tack + Tdifs Tc = max(

1 Tdata

(6)

κ

, . . . , Tdata ) + τch + Teifs

where κ is the number of colliding frames in a collision scenario; τch is the propagation delay; Tsifs , Tdifs and Teifs are short, DCF and extended inter-frame durations, respectively, and finally Tdata and Tack are data and acknowledgment (ACK) frame transmission durations. All the parameters utilized in (6) can either be found explicitly in Table 1 or in [11]. The data and ACK frame transmission times (i.e. Tdata and Tack ) can easily be tracked, considering vertical data flow across IEEE 802.11 MAC and PHY layers: Tdata = Tpre + Tsig + Tsym · · ·   ser v + tb + 8(mac(h+payload) + fcs) Ndbps

 Tack = Tpre + Tsig + Tsym

ser v + tb + 8ack

(7)

257

that no packets arrive during an average slot time (i.e. q¯ ) can easily be calculated as P (t > T ) = e−λT . Different traffic conditions can be met by modifying the packet arrival rate and solving (1), (3) and (4) jointly for a range of q. For example, for a given q, T (average slot time in µs) and L (frame length in bytes), the requested load (in Mbps) can be found as:

λ = [− ln(¯q)/T ] × L × 8.

(8)

The effect of buffer is not considered in this study and the queue is set to 1 in simulations to reflect this assumption. A detailed analysis of buffering impact can be found in [12]. 3. DCF performance with hidden nodes The basic contention resolution algorithm in IEEE 802.11 (DCF) employs a discrete-time backoff scale. A STA that has a pending frame waits for the channel availability. In contention resolution, the time following an idle DIFS is slotted, and a STA is allowed to transmit only at the beginning of each slot time [13]. The assumption of perfect carrier sense by every STA in [7,6] implies that a collision may occur only when two or more packets are transmitted within the same slot time. For hidden node considerations, we need to extend this collision probability period to whole packet transmission duration of the considered STA (that will extend multiple slot durations). τ does not depend on the access mechanism, but the MAC structure. Therefore consideration of hidden node problem has no effect on the derivation of τ . p needs to be reworked to include hidden user interactions. In a hidden node scenario, all the users in the communication environment might have the same number of contending and hidden users (i.e. symmetric networks). The common assumption of papers [6,7] (where all the users are contending) is a subset of symmetric networks. In practical networks, however, it is more possible to see users having a different number of hidden and contending stations (i.e. asymmetric networks). Analytical formulation of the user performances differ for these two scenarios and therefore will be handled in two different subsections. At the same time, easy-to-track formulation of symmetric hidden-node networks provide a smoother transition to the more complex formulation of more common (and practically observed) asymmetric hidden-node network configurations.



Ndbps

where ser v is service field; Tpre is the preamble duration in PHY; Tsig is the SIGNAL field in the packet; tb is tail bits; fcs is the frame control sequence; Tsym is OFDM symbol duration; Ndbps is the number of data bits per OFDM symbol (i.e. it is 24 bits for 6 Mbps); mac and ack are frame payloads relayed from upper layers, and finally d.e is ceiling operation, where the given number is rounded up to the next integer. Regarding the traffic, assuming exponentially distributed independent and identically distributed interarrival times t of rate λ between packets, the probability

3.1. Symmetric networks In a considered network, we use U to denote the set of mobile users that reside in the network coverage area and let n = |U | denote the total number of STAs in U. In a symmetric network, from the considered STA point of view, a network of n STAs can be categorized as contending (c) if the STAs are in the carrier sense of the considered STA or hidden (h) if they are not (n = c + h). The considered user is assumed to be a part of contending users (i.e. If there is no other user in the carrier sense region of the own user, then c = 1). The considered STA would have a successful frame transmission if:

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(a) Scenario 1: All hidden.

(b) Scenario 2: 2 hidden & 1 contending.

(c) Scenario 3: All contending.

Fig. 1. Possible symmetric (hidden and contending) scenarios in a network with 1 AP and 4 STAs.

(1) In a generic slot time, none of the contending STAs transmit. The probability of such condition can be expressed as: (1 − τ )c −1 (2) During the duration of packet delivery (i.e. Tdata + Tsifs + σ ), none of the hidden STAs has interfering transmission and vice versa. (i.e. the considered user does not interfere with an ongoing hidden node transmission). This probability can be calculated by: [(1 − τ )h ]k where k indicates approximate number of average slot decrements in 2Ts : k = 2Ts /T .

(9)

In symmetric networks, all the users have the same contention characteristics (e.g. each user in the network has 2 contending users [in carrier sense region] and 1 hidden [outside of the carrier sense region] user affecting own transmission), therefore they share the same packet transmission, τ , and conditional collision, p, probabilities. Considering the fact that a STA that has a pending packet is allowed to proceed for transmission only at the beginning of a slot time (once in contending case and k times for hidden node case) p can be calculated as: p = 1 − (1 − τ )c −1 (1 − τ )h



k

.

(10)

Then, for symmetric hidden node scenario, packet transmission (Ptr ) and successful packet transmission (Ps ) probabilities become: Ptr = 1 − (1 − τ )n ,

Ps =

nτ (1 − τ )c −1+hk 1 − (1 − τ )n

.

(11)

In the formulation, k needs to be expressed in constants and known parameters in order to have a numerical solution. Considering the fact that Ts , Tc and σ are constants depending on IEEE 802.11 PHY, we can represent frame transmission (Ts ) and collision (Tc ) durations as a coefficient of (σ ) as follows: Ts = ασ ,

Tc = βσ

(12)

then k can be found by replacing Ts and Tc of (12) and (5) in (9): k=

2α

(1 − Ptr ) + Ptr [Ps α + (1 − Ps )β ]

.

(13)

Notice Ps Ptr = nτ p¯ and replace this in (13): k=

2α 1 + Ptr (β − 1) + nτ p¯ (α − β)

.

(14)

Finally rearranging (14) and replacing Ptr from (11) would give us: k=

2α 1 + (1 − (1 − τ )n )(β − 1) + nτ p¯ (α − β)

.

(15)

The k value given in (15) can be substituted to (10). A non-linear system represented by (1) and (10) can be solved using numerical techniques for the two unknowns τ and p. After estimating p along with τ , the average slot durations and finally the system throughput can be calculated by (5) and (4) sequentially. In the following, Example 1 demonstrates possible symmetric hidden node network scenarios with 4 users (n = 4). Example 1 Consider a wireless system of one access point (AP) and 4 STAs. From hidden-node perspective, STAs could have 5 different spatial configurations. All possible symmetric scenarios, where each and every user has the same number of contending and/or hidden STAs, in a 4 user network setup are illustrated in Fig. 1. In Fig. 1(a), all users are hidden to each other, and each user experiences 3 hidden STAs interfering with its transmission. Scenario 3 illustrated in Fig. 1(c) is the one where all the STAs are in the carrier sense region of each other, as considered in [6,7]. A hybrid scenario where a user experiences both hidden (2 STAs) and contending (2 STAs) users is shown in Fig. 1(b). Intuitively among symmetric network setups, Scenario 1 is expected to have the worst throughput performance whereas Scenario 3 would have the best. 3.2. Asymmetric networks In asymmetric networks, the users in the communication environment are naturally clustered into fairness groups (or sets), Gk ⊆ U. This clustering depends on the users’ orientation in the network. The list of groups are

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(a) Scenario 4.

259

(b) Scenario 5. Fig. 2. Possible asymmetric scenarios in a network with 1 AP and 4 STAs.

mutually exclusive and collectively exhaustive. Considering that there are N different fairness groups, it can easily be shown that G1 + G2 + G3 · · · + GN = U .

∀i ∈ 1, 2, . . . , N : n = ci +

hi,j .

(17)

For calculation of the packet transmission probability, a similar approach presented for symmetric networks will be followed. However, in asymmetric networks, the users in different fairness groups have different τ as well as p. Let’s denote τi and pi as the packet transmission and conditional collision probabilities of users in Gi . For the asymmetric case, the network wide probability that there is at least one transmission in the considered slot time, Ptr can be found as: N Y

(1 − τj )gj .

(18)

j =1

Successful frame transmission probability would be different for each fairness group. The probability that a transmission of a user in Gi is successful can be found by calculating the probability that exactly one STA in that group transmits, and all the hidden users to the considered user do not interrupt the transmission conditioned on the fact that there is a packet transmission on the channel:

" gi τi (1 − τi )

ci − 1

N Q

#i (1 − τj )

j =1

Psi =

N

1−

Q j =1

(1 − τj )gj

(1 − τi )

ci − 1

N Q

#i (1 − τj )

hi,j

j =1

p=1−

N Q

1−

.

(20)

(1 − τj )

gj

j =1

The corresponding average slot duration for asymmetric networks can be estimated by: T = (1 − Ptr )σ + · · ·

" Ptr

N X

! Psj

Ts +

j =1

j =1

Ptr = 1 −

"

(16)

Let’s denote the number of STAs in each group as gi = |Gi |. The number of contending STAs in Gi is denoted as ci whereas the number of hidden stations in Gj affecting STAs in group Gi is denoted as hi,j . It should be noted that the fact that STAs are in the same fairness group, does not necessarily mean that those STAs are co-located (i.e. in the carrier sense of each other). In each group, STAs can be either contending with or hidden to each other, gi = ci + hi,i . In a network of n users, it can easily be shown that N X

The conditional collision probability for a user in Gi can be calculated by:

1−

N X

! # Psj

Tc

.

(21)

j =1

Calculation of i value becomes more convoluted in asymmetric networks, if the steps of symmetric hidden node scenarios are followed. However, if we assume α = β 1 the formulation of the value of k is simplified to: k=

2α



1 + (β − 1) 1 −

N Q

(1 − τi )gi

.

(22)

i=1

Example 2 Asymmetric scenarios for a network configuration of 4 STAs and 1 AP are illustrated in Fig. 2. Different fairness groups in each orientation are also shown in the figure. Let’s focus on Scenario 4 to illustrate formulation parameters. In this spatial setup, there are a total of two fairness groups, N = 2 as G1 = {a, b} and G2 = {c , d}. If we consider the performance of users in G2 ; from a considered user perspective there is only one contending STA, c2 = 1 and STAs in G2 are hidden to each other, h2,2 = 1. All the users of G1 are also hidden to users in G2 ; therefore, h2,1 = 2. Intuitively, it is expected that the users of G2 in both scenarios 4 and 5 (who have higher number of hidden nodes), would have less of a network fair share.

hi,j

.

(19)

1 This assumption implies T = T . Considering the fact that T c s eifs is defined as Teifs = Tsifs + Tsifs + Tack in the standards, it is a safe assumption. In this work, this presumption is verified with simulations utilizing smallto-medium packet sizes.

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Fig. 3. Total network throughputs in all the scenarios with DCF channel access mechanism.

Fig. 4. Throughputs of different fairness groups in Scenario 4 with DCF channel access mechanism.

4. RTS/CTS performance with hidden nodes

(Tsrts ) and packet collision (Tcrts ) durations can be calculated as follows:

In IEEE 802.11 standards, RTS/CTS channel access scheme is developed as a solution to hidden-node problem. However, since the implementation of RTS/CTS scheme is costly from a radio resource perspective, its performance evaluation is mostly ignored in the literature. In RTS/CTS mode, the channel is reserved on both transmitter (RTS) and receiver (CTS) side to avoid any collision during packet transmission. In such a scheme, a single data frame transmission requires three control frames (fourway handshake), introducing further latency comparison to DCF mechanism. The developed model explained in this paper is also applicable to the RTS/CTS channel access scheme with a couple of modifications. Initially, the expected successful packet transmission and collision durations will be different than the values presented in (6). For RTS/CTS four-way handshake, the successful packet transmission

Tsrts = Trts + Tsifs + τch + Tcts + Tsifs + · · · τch + Tdata + Tsifs + τch + Tack + Tdifs + τch Tcrts

(23)

= Trts + Tdifs + τch .

In (23), RTS and CTS frame transmission durations are indicated as Trts and Tcts . With the help of the OFDM PHY and MAC parameters presented in Table 1, the additional frame transmission durations defined for RTS/CTS scheme can be calculated as follows:

 Trts = Tpre + Tsig + Tsym

 (24)

Ndbps

 Tcts = Tpre + Tsig + Tsym

ser v + tb + 8rts ser v + tb + 8cts Ndbps



.

The analytic formulation of the RTS/CTS throughput performance in a hidden node communication environment

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261

Fig. 5. Throughputs of different fairness groups in Scenario 4 with RTS/CTS channel access mechanism.

Fig. 6. Throughputs of different fairness groups in Scenario 5 with DCF channel access mechanism.

is similar to the study presented in Section 3. However, for hidden node collision cases, we will need to consider only RTS frame transmission duration rather than whole data frame transmission duration which was valid for DCF usage case. Therefore, in a hidden-node communication environment, the considered STA using RTS/CTS scheme would have a successful frame transmission if: (1) In a generic slot time, none of the contending STAs transmit RTS frame. The probability of such condition can be expressed as: (1 − τ )c −1 (2) During the duration of a RTS frame transmission (i.e. Trts + Tsifs +τch ), none of the hidden STAs has interfering transmission and vice versa. (i.e. the considered user does not interfere with an ongoing hidden node transmission). This probability can be calculated by: rts [(1 − τ )h ]k

where krts indicates the approximate number of average slot decrements in 2Trts : krts = 2Trts /T .

(25)

5. Numerical results The accuracy of the model presented in the previous sections is verified by NS-2 [14] simulation results. NS-2 parameters are configured for the IEEE 802.11a OFDM PHY and MAC values given in Section 17.5.2 of [11] and partially in Table 1 for convenience. Simulated network configurations are illustrated in Figs. 1 and 2. Performance of a more elaborate network configuration can be estimated by natural extension of the ideas presented in the paper. In the simulated network environment, STAs are transmitting packets to a single AP. In NS-2, the carrier sense range of an individual STA is configured and hidden nodes are simulated by tuning CSThresh_ parameter of wireless PHY.

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Fig. 7. Throughputs of different fairness groups in Scenario 5 with RTS/CTS channel access mechanism.

Fig. 8. Packet transmission and conditional collision probability of different fairness groups in Scenario 4 with DCF channel access mechanism.

UDP frames are generated according to NS-2 exponential on/off distribution. In the simulation campaign, the exponential on/off generator is configured to behave as a Poisson process by setting the variable burst_time_ to 0 and the variable rate_ to a very large value, making the packet transmission time negligible and setting idle_time_ parameter as packet inter-arrival time. In NS-2 performance figures, the packet payload is assumed to be 256 bytes (i.e. packetSize_ 256). AP and STA transmission power levels are assumed to be constant 200 mW (23 dBm) and located accordingly to simulate hidden or contending user scenarios, considering the Two-Ray ground reflection propagation model [14]. Considering that all the users in the communication environment are utilizing the DCF contention resolution technique, total network throughput of all the scenarios relative to changing traffic conditions is presented in Fig. 3. Mandatory and communication data rate of the

users are assumed to be 6 Mbps. Numerical results obtained from NS-2 show good agreement with the results obtained from analytical expressions. As expected, the all contending STAs scenario illustrated in Scenario-3 has the best throughput performance, whereas all hidden STAs presented in Scenario-1 has the worst performance. As it can be seen, presence of hidden nodes is minimal to the network performance in low traffic conditions. As mentioned in Section 3.2, users in different fairness groups illustrated in Scenario-4 of Fig. 2(a) are expected to have different network resource shares. This phenomenon is illustrated in Fig. 4 for DCF channel access scheme and in Fig. 5 for RTS/CTS channel access mechanism. As can be seen, RTS/CTS channel access scheme provides a better fairness performance in terms of throughput allocation in asymmetric network configurations. However, the RTS/CTS scheme is not immune to hidden node problem, but only has a better resistance. In higher load regions,

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263

Fig. 9. Packet transmission and conditional collision probability of different fairness groups in Scenario 4 with RTS/CTS channel access mechanism.

Fig. 10. Packet transmission and conditional collision probability of different fairness groups in Scenario 5 with DCF channel access mechanism.

RTS packet collision causes throughput performance differences among network users. Similar performance illustration for Scenario-5 is given in Fig. 6 for DCF and Fig. 7 for the RTS/CTS case. It can be seen that network fairness performance is a function of offered load. In low-traffic conditions, all the users of the network (independent of the number of hidden users affecting the transmission) have their fair share of the network. As the network load increases, users that have less numbers of hidden-nodes dominate the network, causing starvation of the locationally challenged users. If we investigate the performance of the configuration illustrated in Fig. 2(a) (Scenario-4), higher packet transmission probability and lower conditional collision probability of users in fairness group G1 illustrated in Fig. 8 clearly indicates that users in this group have an unfair advantage in this network configuration. RTS/CTS mechanism alleviates this fairness abnormality, as illustrated in Fig. 9. As it can

be seen from the figure, the packet transmission and conditional collision probabilities of different fairness groups do not present an extreme deviation, even in high load conditions. Similar performance analysis for the scenario illustrated in Fig. 2(b) (Scenario-5) is presented in Fig. 10 for DCF contention resolution and Fig. 11 for RTS/CTS contention resolution schemes. As expected, the performance difference of the fairness groups is bigger in this case compared to Scenario-4, because the user in G2 has three hidden users interfering with its transmission, whereas users in G1 have only one. Similarly, RTS/CTS successfully restores the channel access probabilities of different fairness classes. However, in Fig. 11 we can see that even the RTS/CTS scheme is not sufficient to provide fair performance in high load conditions. Besides individual STA throughputs, a network fairness index is also calculated and compared in asymmetric scenarios. Both RTS/CTS and DCF channel access schemes

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Fig. 11. Packet transmission and conditional collision probability of different fairness groups in Scenario 5 with RTS/CTS channel access mechanism.

Fig. 12. Jain’s index in asymmetric network Scenarios 4 and 5.

are considered in simulations. To quantify the network fair share of the asymmetric network configurations, the fairness index (also called The Jain’s index) introduced in [15,16] is used as a metric. Consider Si as the throughput of STA i in a multi-STA (n = |U |) network, then the fairness index F can be defined as:

 F=

n P i=0 n

P

2 Si

.

(26)

S2i

function of user load. For DCF channel access mechanism, in a lightly loaded network, each user obtains the fair share of the network resources. However, as the network load increases, the users that experience high numbers of hidden users, starve. This starvation can be prevented, and acceptable network fairness can be provided with RTS/CTS channel access scheme up to a certain point, and of course with a cost of reduced total network throughput. However, the RTS/CTS scheme is not 100% immune to the hidden node problem and, in an overloaded network, the reduced network fairness performance is observed.

i =0

The fairness index has the property that it is equal to 1 when all STAs have the fair share of the network, and it gets closer to 1/n when the STA throughputs are distinctly different. Fairness index results of asymmetric networks relative to offered user load are illustrated in Fig. 12. It can be seen that network fairness index is a decreasing

6. Conclusions The fairness performance of practical infrastructure wireless local area network environments considering multiple hidden nodes and unsaturated traffic condition is studied in this paper. Analytic performance of IEEE

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802.11 primary medium access algorithm (distributed coordination function) is presented for both symmetric and asymmetric networks. In addition, the performance of the request-to-send/clear-to-send channel access scheme, especially designed for hidden node environments, is also considered. It has been shown that the presence of hidden nodes barely affects the per user network fair share in low traffic conditions, but it causes considerable performance loss in moderate-to-high traffic scenarios. It has also been shown that per user performance highly depends on user location in the network. In an asymmetric network configuration, increasing traffic increases per user throughput, up to a certain point (that depends on overall network orientation). A further increase of traffic causes starvation of locationally challenged users that have more number of hidden nodes. We have shown that the request-to-send/clear-to-send channel access scheme greatly improves the network fairness performance for light and medium loaded network conditions. However, RTS frame collisions have a performance impact in highly loaded networks. The developed model’s predictions were validated against simulation results. References [1] V.M. Vishnevsky, A.I. Lyakhov, 802.11 LANs: Saturation throughput in the presence of noise, in: Proc. IFIP Networking 2002, pp. 1008–1019. [2] Q. Ni, I. Aad, C. Barakat, T. Turletti, Modeling and analysis of slow CW decrease for IEEE 802.11 WLAN, in: Proc. IEEE PIMRC 2003, vol. 2, pp. 1717–1721. [3] O. Ekici, A. Yongaoglu, A novel association algorithm for congestion relief in IEEE 802.11 WLANs, in: Proc. of the 2006 International Conference on Wireless Communications and Mobile Computing, July 2006, pp. 725–730. [4] E. Ziouva, T. Antonakopoulos, CSMA/CA performance under high traffic conditions: Throughput and delay analysis, Computer Communications 25 (2002) 313–321. [5] M. Ergen, P. Varaiya, Throughput analysis and admission control for IEEE 802.11a, Mobile Networks and Applications 10 (2005) 705–716. [6] G. Bianchi, Performance analysis of the IEEE 802.11 distributed coordination function, IEEE Journal on Selected Areas in Communications 1 (2000) 535–547. [7] D. Malone, K. Duffy, D.J. Leith, Modeling the 802.11 distributed coordination function in non-saturated heterogeneous conditions, IEEE/ACM Transactions on Networking 15 (1) (2007) 159–172. [8] T. Hou, L. Tsao, H. Liu, Analyzing the throughput of IEEE 802.11 DCF scheme with hidden nodes, in: Proc. VTC 2003-Fall, vol. 5, 2003, pp. 2870–2874.

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[9] S. Rahman, Throughput analysis of IEEE 802.11 distributed coordination function in presence of hidden stations, Technical Report, Stanford University, 2003. [10] O. Ekici, A. Yongaoglu, IEEE 802.11a throughput performance with hidden nodes, IEEE Communications Letters 12 (6) (2008) 465–467. [11] IEEE 802.11a WG and part 11: WLAN MAC and PHY Spec.: Highspeed PHY in the 5 GHz band, IEEE Standards, Aug. 1999. [12] K. Duffy, A.J. Ganesh, Modeling the impact of buffering on 802.11, IEEE Communications Letters 11 (2) (2007) 219–221. [13] IEEE 802.11 WG and part 11: WLAN MAC and PHY Spec., IEEE Standards, Aug. 1999. [14] The network simulator ns2, 2006. http://www.isi.edu/nsnam/ns/. [15] D. Chiu, R. Jain, Analysis of the increase and decrease algorithms for congestion avoidance in computer networks, Journal of Computer Networks and ISDN Systems 17 (1) (1989) 1–14. [16] A. Balachandran, P. Bahl, G.M. Voelker, Hot-spot congestion relief in public-area wireless networks, Mobile Computing Systems and Applications (2002) 70–80.

Ozgur Ekici received the B.Sc. degree from Bogaziçi University, Turkey, in 2001, the M.Eng. degree from the University of Ottawa in 2003, and currently he is enrolled in Ph.D. program in University of Ottawa, all in Electrical Engineering. His graduate research involves performance and fairness analysis of coordination functions in IEEE 802.11 systems. He is also working for Research In Motion Ltd. as a wireless system developer. His other research interests include optimization of WCDMA, GPRS/EDGE systems for packet switch operations.

Abbas Yongacoglu received the B.Sc. degree from Bogaziçi University, Turkey, in 1973, the M.Eng. degree from the University of Toronto in 1975, and the Ph.D. degree from the University of Ottawa in 1987, all in Electrical Engineering. He worked as a researcher and a system engineer at TUBITAK Marmara Research Institute in Turkey, Philips Research Labs in Holland and Miller Communications Systems in Ottawa. In 1987 he joined the University of Ottawa as an assistant professor. He became an associate professor in 1992, and a full professor in 1996. He spent sabbatical terms at Bogaziçi University, Istanbul, Turkey, École Nationale Superieure des Télécommunications, Paris, France, and Centre Nationale des Études deTélécommunications (CNET), Paris, France. His area of research is digital communications, with emphasis on modulation, coding, equalization and multiple access for wireless and high speed wireline communications. He collaborates closely with the local industry and research institutions. He has been active in organizing several workshops and international conferences on communications. He is a professional engineer in the province of Ontario and a senior member of IEEE.