This paper proposes a new backoff algorithm called the Estimation-based Backoff Algorithm (EBA) to enhance the performance of the Distributed Coordination Function (DCF) in IEEE 802.11 wireless networks. The EBA observes the number of idle slots during the backoff period to estimate the number of active nodes. It then determines a more appropriate contention window size based on this estimation. Simulation results show that the EBA outperforms other backoff algorithms by increasing throughput by around 25% and decreasing mean packet delay by about 50% compared to the binary exponential backoff algorithm when there are 70 nodes.

1. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
A Novel Estimation-Based Backoff Algorithm in the IEEE
802.11 Based Wireless Network
Seok-Won Kang, Jae-Ryong Cha and Jae-Hyun Kim
School of Electrical Engineering,
Ajou University
San 5 Woncheon-Dong, Youngtong-Gu, Suwon 443-749, Korea
Abstract — This paper proposes a new backoff algorithm to the wireless channel. In the PCB, the countdown procedure in
enhance both the delay and the throughput of the Distributed the backoff mode is paused when a node uses the wireless
Coordination Function (DCF) in the IEEE 802.11 based wireless channel. However, the PCB could not apply the network load,
networks. The proposed algorithm, which is named as the which dramatically changes, because the PCB could not
Estimation-based Backoff Algorithm (EBA), observes the estimate the number of active nodes. For this reason, this paper
number of the idle slots during the backoff period in order to proposes a new backoff algorithm, which is known as the EBA,
estimate the number of active nodes in the network. Especially, in order to apply the CW based on the network load. The EBA
when the number of nodes or the amount of traffic dramatically estimates the number of active nodes by using the number of
varies, the proposed algorithm determines a more appropriate
the idle slots in the backoff period.
contention window based on the estimation algorithm. This paper
evaluates the performance of the proposed EBA by using This paper is organized as follows. In Section II, we
simulation, and it compares the EBA’s performance with other describe the function of the DCF, and how the related works
backoff algorithms such as the binary exponential back-off review the DCF’s functionality within a wireless channel. In
(BEB), the exponential increase exponential decrease (EIED), the section III, we describe the proposed EBA. The simulation
exponential increase linear decrease (EILD), the pause count results and the performance analysis of the proposed EBA are
backoff (PCB) and the history based adaptive backoff (HBAB). discussed in section IV. Finally, section V concludes the paper
The simulation results show that the EBA outperforms the other and presents future works.
backoff algorithms because it has better adaptability to the
network load variation. By comparing the performance of the
EBA to that of the BEB, which is defined in the IEEE 802.11, the II. THE CONVENTIONAL ALGORITHMS
EBA increases the network throughput by around 25 %, and it
decreases the mean packet delay by about 50 % when the A. The IEEE 802.11 DCF with the BEB
number of nodes is 70. In the DCF, when a node has to transmit a data frame, it
first senses the wireless link and waits until the link becomes
Keywords— DCF, Backoff Algorithm, EBA
idle. When the node finds out that the wireless link is idle
during a DCF interframe space (DIFS) period, the random
I. INTRODUCTION backoff procedure starts [1].
The DCF is the fundamental access mechanism in the IEEE A node generates a random backoff interval before the
802.11 medium access control (MAC) protocol. In the DCF, transmission, and it decreases the backoff interval counter
the BEB algorithm is used as a contention resolution scheme while the wireless link is idle. However, the backoff counter is
[1]. However, the performance of the BEB can deteriorate paused when a transmission is detected, and it is reactivated
when the network is heavily loaded because the collision rate when the wireless link is sensed as being idle during the DIFS
increases due to the aggressive reduction in the backoff period period. If the backoff counter reaches zero, then the node starts
after a successful transmission is completed. In order to to transmit the frame. The random integer follows a uniform
overcome this problem, several algorithms such as the EIED distribution on [0, CW]. The CW is initially set to be CWmin. If
and the EILD [2-3], which adopt the slow reduction in the the transmission fails n times, then the CW is increased by 2n
backoff period, have been proposed. The EIED doubles the size times.
of the contention window (CW) after a collision occurs, and it
cut in half the CW after a successful transmission is completed If the node exceeds the maximum retransmission, then the
[2]. The EILD also doubles the backoff period after a collision, frame is dropped. If a frame is dropped or if it is successfully
and it linearly decreases the backoff period after a successful transmitted, then the CW is reset to be CWmin. The BEB
transmission is completed [3]. Unfortunately, these algorithms backoff algorithm can be expressed as follows:
cannot cope with the dramatic variation of the network load.
For this reason, the PCB has been proposed [5]. The algorithm ⎛Transmission success : CW = CWmin ⎞
⎟.
⎜
⎜ ⎟ (1)
estimates the CW by using the number of pauses while sensing ⎜Transmission fail : ⎟
⎟
CW = CWold × rI ⎠
⎝
978-1-4244-5176-0/10/$26.00 ©2010 IEEE

2. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
ㆍSense channel during DIFS Backoff Backoff Backoff
counter resume counter resume counterp
DIFS Contention Window paused paused aused
DIFS PIFS
Backoff- Data
Busy Medium SIFS Next Frame
Window transmission
Node A
Slot time
Defer Access
ㆍBackoff slot reduced when channel is idle Node B
Figure 1. Illustration of the IEEE 802.11 DCF mechanism Node C
The BEB degrades the performance of the network when Node D
the network is heavily loaded because each new packet starts time
with the minimum CW. This resetting behavior becomes very
unstable when numerous nodes are contending within the Figure 2. Estimating the number of active nodes with the PCB
same wireless channel. This can cause more collisions and it
decreases the whole system’s utilization. Fig. 1 shows how the that is using the wireless channel, and so the traffic load of the
DCF works. network is determined by the number of pauses. The PCB
counts the pauses during the countdown procedure and it sets
B. The EIED and the EILD an appropriate CW size for the current traffic load of the
In the EIED [2], the CW exponentially increases by a network. Fig. 2 describes the PCB.
backoff factor of rI whenever a collision occurs, and it
exponentially decreases by a backoff factor of rD if a node D. The HBAB
successfully transmits a packet. The EIED can be given as. The HBAB algorithm checks the last N states of the
medium (N=2 in this implementation), and it determines
⎛Transmission success : CW = CWold / rD ⎞
⎟ whether to increment or decrement the CW value based on the
⎜
⎜ ⎟
⎜Transmission fail : CW = CWold × rI ⎟
⎟ (2) channel's tendency to being free or busy [8]. The HBAB
⎝ ⎠
algorithm fixes two parameters, α and β, which are used to
, ( rI > 1 and rD > 1). increase or decrease the new CW based on the old CW value.
The EILD linearly decreases by a backoff factor of rD. The TABLE 1 shows the suggested CW values per state check (0
EILD can be expressed as follows: indicates both a busy channel and 1 indicates a free channel.
⎛Transmission success : CW = CWold − rD ⎞
⎟. III. THE PROPOSED BACKOFF ALGORITHM
⎜
⎜ ⎟ (3)
⎜Transmission fail : CW = CWold × rI ⎟
⎟
⎝ ⎠ The proposed algorithm has two main functions: The
estimation scheme for the number of active nodes and the
The EIED and the EILD methods are based on partial optimal CW allocation scheme are shown in TABLE 2. The
observations, such as that each node uses its own results of estimation scheme exploits the number of idle slots in the
transmissions to represent the whole system. The results of backoff period in order to derive the exact number of active
both the transmissions and the system load may have a positive nodes. The optimal CW allocation scheme uses the estimated
correlation, but they are not sufficient to precisely set the CW number of active users in order to enhance the system
value. performance. The detailed description is as follows.
C. The PCB A. Estimating the number of active nodes
The PCB monitors the traffic load of the network, and the In step 1 in Table 2, each node obtains the average number
PCB sets an appropriate CW to match the traffic load of the of both the idle slots and the busy slots during the backoff
network [4]. The countdown procedure in the backoff period period. Given N slots in the total backoff period and n nodes,
pauses when other nodes simultaneously use the wireless the probability that r out of n nodes transmit their data during a
channel. Therefore, each pause represents more than one node slot is given by
⎛ n⎞⎛ 1 ⎞ ⎛
r n-r
TABLE I. THE CW ESTIMATION ALGORITHM IN THE HBAB ⎞
1
P( X = r ) = ⎜ ⎟ ⎜ ⎟ ⎜1 − ⎟ . (4)
Ex: CW value ⎝ r ⎠⎝ N ⎠ ⎝ N⎠
State CW value
(with α=1 β=2) The number r in a particular slot is called the occupancy
number of the slot [7]. The expected number of slots, with the
00 CW=CWold × (α β) 2 CWold
occupancy number r, is given by
01 CW=CWold × (α / β) 1/2 CWold
⎛ n⎞⎛ 1 ⎞ ⎛
r n−r
1⎞
10 CW=CWold × (β / α) 2 CWold
E[ X = r ] = N ⎜ ⎟ ⎜ ⎟ ⎜1 − ⎟ . (5)
⎝ r ⎠⎝ N ⎠ ⎝ N⎠
11 CW=CWold × (1/ α β) 1/2 CWold To estimate the number of nodes (nest), this paper defines
the average number of idle slots a0(N, n), which means the ratio

3. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
of the number of the idle slots to the number of slots in the 1 ⎛ 1⎞
n −1
backoff period[5] is given by Psucc , N = ×⎜1 − ⎟
⎜ ⎟ ×N
N ⎜
⎝ N⎟
⎠ (10)
n n −1
⎛ 1⎞ ⎛ 1⎞
= ⎜1 − ⎟
a0 ( N , n) = N × E[ X = 0] = N × ⎜ 1 − ⎟ . (6) ⎜ ⎟ .
⎝ N⎠
⎜
⎝ N⎟
⎠
By using (6), the number of users can be derived as Let Psucc(k) be the probability that a node successfully
transmits a frame in the kth retransmission. Then Psucc(k) is
log(a0 ( N , n)) − log( N )
nest = . (7) Psucc (k ) = Psucc , N (1− Psucc , N )k −1 . (11)
log( N −1) − log( N )
Thus, the average number of retransmissions is
After the end of the backoff period, a node can calculate the
total backoff period N and the estimated number of active users, ∞
1
as shown in TABLE 2. E ( X = k ) = ∑ kPsucc ( k ) = n −1
.
⎛ 1⎟⎞ (12)
k =1
⎜1 − ⎟
⎜
⎜
⎝ N⎟⎠
B. Deciding the optional CW
This paper derives the optimal CW based on the average Therefore, D(N, n) can be obtained from (8) and (12) as
access delay D(N, n) which refers to the time that is needed to
N
transmit a packet from one node to the other. D(N, n) can be D ( N , n) = n −1
.
⎛ ⎞ (13)
obtained as follow [6]. ⎜1 − 1 ⎟
⎜ ⎟
⎜
⎝ N⎟⎠
D( N , n) = number of retransmission × total backoff size. (8)
In (13), D(N, n) depends on N and n. Since N is the
The probability that a node successfully transmits its data system’s parameter, this paper drives the optimal N to
during a slot is given by minimize D(N, n). Since D(N, n) is a concave function with
n−1
respect to N, the optimal N can be obtained by differentiating
1 ⎛ 1⎞ D(N, n) with respect to N as
Psucc = ×⎜1− ⎟
⎜ ⎟ , (9)
N ⎜ N⎟
⎝ ⎠
∂ ∂ N
D( N , n) = n −1
= 0.
where 1/N is the probability that a node transmits its data at the ∂N ∂N ⎛ ⎞
⎜1 − 1 ⎟
⎜ ⎟
(14)
particular slot in a backoff slot. Based on (9), the probability ⎜
⎝ N⎟⎠
that a node successfully transmits a frame during the total
backoff period is given by From (14), the optimal CW can be obtained as
CWoptimal = n . (15)
TABLE II. THE EBA ALGORITHM
IV. THE SIMULATION RESULTS
Step1: Estimating the number of active nodes This section evaluates the system performance in terms of
the throughput and the average access delay. This paper
When a channel is busy during the backoff period simulates the IEEE 802.11b based WLAN setup module as
-. busy_count = busy_count +1 defined in the OPNET. The range of the number of nodes is
Backoff period end within 30 ~ 70 and the simulation time is 300 seconds. All
nodes are within one hop distance and they select a random
Calculate the parameters
-. busy_slot_count=busy_count * α, destination. The parameters that were used in the simulation are
listed in Table 3. The parameters rI and rD in the EIED are set
⎛ data _ packet _ size 1 ⎞
⎟ to 2, as suggested in [2].
⎜
⎜α = × ⎟
⎜ ⎟
⎟
⎝ transmission _ data _ rate slot _ size ⎠
TABLE III. THE IEEE 802.11B MAC AND THE NETWORK
-. total_backoff_period PARAMETERS THAT ARE USED IN THE SIMULATION
= idle_slot_count + busy_slot_count
Section Value
-. a0(N,n)= idle_slot_count
Data rate 11 Mbits/s
Obtain the estimated number of active nodes Slot_time 20 μs
log(a0 ( N, n)) −log(total _ backoff _ period ) SIFS 10 μs
nest = DIFS 50 μs
log(total _ backoff _ period −1) −log(total _ backoff _ period )
CWmin 31
Step 2: Deciding the optimal CW
CWmax 1023
Packet size exponential(1024) bytes
Obtain the optimal CW
Packet inter-arrival time exponential(0.1) sec
-. CWoptimal= nest

4. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
6
x 10 A. The network throughput
5
Fig. 3 indicates the throughput according to various backoff
BEB
EIED
algorithms in the IEEE 802.11 WLAN. The efficiency standard
4.5
EILD of the DCF performs worse (as expected) when more stations
PCB contend for the channel. Although the EIED algorithm takes an
EBA
4
exponential decrease in the CW policy instead of resetting to
CWmin when there is a successful transmission, the curve
Throughput(bits/sec)
decreases when there are more active stations in the system.
3.5
This means that the stations that are applying the EIED and the
DCF algorithms make decisions with an unclear system status
3 and they quickly adjust the CW from the result of a single
transmission. In contrast to the PCB, the EIED and the DCF,
the throughput of the EILD and the EBA algorithms remains
2.5
high with respect to various system loads. These improvements
mean that the stations that are using both the EILD and the
2 EBA algorithms adjust the CW value appropriately according
30 35 40 45 50
Number of nodes
55 60 65 70
to the load variation within the network. In the cases of both
light and heavy loads, the EBA successfully determined the
optimal backoff slot because the traffic measurement is
Figure 3. The throughput vs. the number of nodes accurate. Overall, the EBA algorithm obtains high efficiency
when it is compared with the other backoff algorithms in
4.5 various network conditions.
4 BEB
EIED
B. The average access delay
3.5 EILD The variation of the end to end packet delay according to
Average access delay(sec)
3
PCB the number of active nodes is presented in Fig. 4. As expected,
EBA the delay increases as the number of nodes increases. The
2.5 objective of the EBA algorithm is estimating the actual
network status and setting the corresponding optimal CW to
2
precisely minimize the overheads in the system. In Fig. 4, the
1.5 EBA shows the advantage of overhead reduction and the EBA
obtains the lowest delay among these backoff algorithms. The
1
delay of the EBA is around 50% less than that of a standard
0.5 DCF when n = 70.
0
30 35 40 45 50 55 60 65 70 C. The fairness
Number of nodes Fairness among stations is an important problem in the BEB
Figure 4. The average access delay vs. the number of nodes study, and it has been discussed by many research projects.
The Fairness index can show if a resource is fairly allocated to
each station.
We use Jain’s fairness index formula. Jain’s fairness index
0.6
is calculated as
⎛ n ⎟2⎞
0.5 ⎜ y⎟
⎜∑ i ⎟
⎜ i=1 ⎟
⎝ ⎠
Fairness Index
g ( y1 , y2 ,..., yn ) = n
. (16)
n ⋅ ∑ yi 2
0.4
i =1
0.3 BEB
PCB
Jain’s fairness index always lies between 0 and 1. A
EIED fairness index of 1 indicates a throughput-fair algorithm [9]. In
0.2 EILD Fig. 5, we present the fairness index of each backoff algorithm
EBA
among the stations. By using the simulation setup that was
0.1
30 35 40 45 50 55 60 65 70 described in the previous section, we executed the simulation
Number of nodes for 10 iterations, and we calculated the average of the results.
From Fig. 5, the proposed EBA algorithm has the most stability
Figure 5. The Fairness index vs. the number of nodes when it is compared with the other contention algorithms. We
also observe that the fairness index of the BEB, the EILD, the
EIED and the PCB are both low and oscillatory. This

5. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE CCNC 2010 proceedings
phenomenon means that some stations occupy more channel
capacity than do other stations due to the different
understanding of the system status among stations.
V. CONCLUSION
The proposed EBA algorithm estimates the system status
by using the idle slot counts for the backoff duration, and it
determines a proper contention window size that accurately
matches the current network conditions. We compared the
performance of the proposed EBA with that of the conventional
algorithms such as the IEEE 802.11 the DCF, the EIED, the
EILD and the PCB. Our simulation results show that the EBA
outperforms the previously proposed algorithms for various
performance metrics, and that the EBA dynamically adapts to
the variations of the amount of data traffics in the network.
Based on the simulation results, we can use the proposed
algorithm in the future transportation information system
named as Telematics. The Telematics is a system where the
information such as traffic jam, living, and emergency rescue,
and etc. is exchanged between the vehicles. The Telematics
needs more efficiency backoff algorithm because the variation
of data traffics may be large due to the many vehicles’
existence in the heart of city. Therefore the proposed EBA may
improve the performance of Telematics system.
In the future, we plan to explore how to implement our
algorithm in the Telematics system.
ACKNOWLEDGMENT
"This research was supported by the MKE(The Ministry of
Knowledge Economy), Korea, under the ITRC(Information
Technology Research Center) support program supervised by
the NIPA(National IT Industry Promotion Agency" (NIPA-
2009-C1090-0902-0003)
REFERENCES
[1] IEEE 802 Part 11: Wireless LAN Medium Access Control (MAC) and
Physical Layer (PHY) specifications, IEEE Std., 1999.
[2] N. Song, B. Kwak, J. Song and M.E. Miller, “Enhancement of IEEE
802.11 distributed coordination function with exponential increase
exponential decrease backoff algorithm,” in Proc. VTC 2003, Vol. 4, pp.
2775 – 2778, Orlando, USA, 22-25 April 2003.
[3] V. Bharghavan, A. Demers, S. Shenker, and L. Zhang, “MACAW: A
Media Access Protocol for Wireless LAN’s,” in Proc. SIGCOMM’94,
pp. 212-225, London, England, 1994.
[4] H. Liang, S. Zeadally, N. K. Chilamkurti and C. Shieh, “A Novel Pause
Count Backoff Algorithm for Channel access in IEEE 802.11 based
Wireless LANs,” in Proc. CSA '08 International Symposium, pp. 163 –
168, Hobart, Australia, 13-15 Oct. 2008.
[5] J. Lee, W. Kim and H. Kim. “Estimation of Number of Tags in
ALOHA-based RFID Systems,” KICS, ’07-7 Vol. 32, No.7, 2007.
[6] J. R. Cha and J. H. Kim, "Dynamic Framed Slotted ALOHA Algorithm
using Fast Tag Estimation method for RFID System," in Proc.
CCNC2006, Las Vegas, USA, 8-10, Jan. 2006.
[7] Normal Lloyd Johnson and Samuel Kotz, Urn Models and Their
Applications, Wiley, 1977.
[8] Q. Nasir and M. Albalt, “History Based Adaptive Backoff (HBAB)
IEEE 802.11 MAC Protocol”, in Proc. CNSRC’08, pp. 533 – 538, Nova
Sotia, Canada, 5-8, May. 2008.
[9] R. Jain, The art of computer systems performance analysis: techniques
for Experimental Design, Measurement, Simulation, and Modeling,
Wiley, New York, 1991.