A NEW DISTRIBUTED CONTENTION CONTROL PROTOCOL
FOR THE IEEE 802.11 MAC LAYER
Project of ATcrc Application Programms
Jiankun Hu, Konrad Eriksson
School of Computer Science & Information Technology, RMIT University, Melbourne
3001, Victoria, Australia.
Keywords: TCP protocol, MAC protocol, wireless networks
Abstract: The famous binary backoff algorithm in IEEE 8
02.11 MAC layer can forget the contention level between
each successfully transmitted data frame and hence suffers significant performance degradation when the
contention level is high. In IEEE 802.11 standard, a distributed contention control (DCC) algorithm is
proposed to address this problem by observing how many of slots in the last backoff period were busy, i.e.
slot utilization. The introduction of slot utilization can provide good estimation of the most recent
congestion dynamics, i.e. transient fluctuations of the traffic. However, it is inaccurate to estimate the
overall traffic pattern as one backoff period is too short to obtain accurate stationary statistics. In this paper,
a new DCC algorithm is proposed that can combine transient and stationary characteristics, which provides
better estimation of congestion level of the medium. Extensive simulation by using NS-2 simulator has
shown that our scheme has better throughput and low collisions compared with original binary backoff
MAC protocol and slot utilization algorithm (Nononi, Conti and Donatiello, 1998).
1 INTRODUCTION
Research on the wireless medium access protocol is
an ongoing effort (Bononi, Conti and Donatiello,
1998; Weinmiller & Woesner, 1996; Weinmiller,
Woesner & Wolisz, 1996; . Bianchi, Fratta &
Oliveri,1996; Cali, Conti & Gregori,1998; Natkaniec
& Pach, 2000). Most of the modifications to the
backoff algorithm are done to make it adapt better to
the current activity on the medium. Better adaptation
makes the algorithm schedule transmissions in a way
so that the medium is utilized to its maximum,
realizing a throughput close to the theoretical limit.
Because the original backoff algorithm in the IEEE
802.11 MAC layer is “forgetting” the contention
level between each successfully transmitted data
frame it suffers from performance degradation much
below the theoretical limit when the contention level
is high. Much of the degradation is due to the high
number of collisions needed to increase the
contention window (CW) size when the contention
level is high in the medium.
One way to reduce the contention level, even if there
are m
any stations competing for transmission, was
introduced by H. Kim and J.C. Hou (Kim & Hou,
2003). They proposed to insert a delay before a STA
attempts transmission of its pending frame. The
length of the delay changes depending on how the
medium is utilized, where the utilization level is
estimated from the numbers of collisions registered
between two consecutive successful data frame
transmissions. Based on Goodman’s work
(Goodman, Greenberg, Madras & March, 1998), an
analytical model for the medium access is
established (Kim & Hou, 2003). The model defines
two analytical components (the fluid chunk and the
MAC fluid) that are used to characterize data
transmission activities in the wireless medium.
A flu
id chunk determines the number of
currently active STAs with the number of collisions
observed during the fluid chunk. The estimated
number of STAs is then used to determine network
utilization that is subsequently used for computing
the scheduling delay. The normal access mechanism,
defined in IEEE 802.11, is then used after passing
the calculated delay. Simulation has demonstrated a
substantial improvement in throughput by just
adding this delay (Kim & Hou, 2003). This scheme
still suffers the same problem, to a certain extent, by
using number of collisions as this indicator lacks of
temporal distribution information.
393
Hu J. and Eriksson K. (2004).
A NEW DISTRIBUTED CONTENTION CONTROL PROTOCOL FOR THE IEEE 802.11 MAC LAYER - Project of ATcrc Application Programms.
In Proceedings of the First International Conference on E-Business and Telecommunication Networks, pages 393-401
DOI: 10.5220/0001402903930401
Copyright
c
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L. Bononi et al (Bononi, Conti and Donatiello,
1998) introduced another way, called DCC, to
improve the binary backoff scheme by adding an
additional stage between the original scheduling part
and the physical access. In this additional stage the
scheduled transmission is evaluated again based on
an estimated medium utilization value. The extended
mechanism may defer the transmission at this stage
with a probability based on how high the medium
utilization is estimated. The deferring of
transmission in the additional stage is fed back to the
original transmission scheduler in the form of a
virtual collision notification. The medium utilization
is estimated every time the original mechanism
backs off by counting how many of the slots during
the backoff period were busy. The probability to
transmit when the normal mechanism indicates
transmission is then a probability function P_T that
is based on the slot utilization (Slot_Utilization) and
the number of attempts already performed
(Num_Att). This function takes into account the
number of attempts already performed to give STAs
that have attempted and failed a higher priority to
transmit. The more failed retransmissions the higher
the probability becomes. This will generally reduce
the maximum time for a transmission.
This mechanism does not modify the actual
backoff algorithm proposed in IEEE
802.11(P802.11, 1997) but rather extends the
original access mechanism. J. Weinmiller et al
(Weinmiller & Woesner, 1996) examined the slot
selection probability in the original backoff
mechanism defined in IEEE 802.11(P802.11, 1997).
They discovered that because every individual STA
selects a slot within the contention window (CW)
size with the same probability it turns out that early
slots are more likely to be selected. The original
mechanism says that STAs losing the competition
have to pause their backoff countdown while the
medium is busy. This results in that the slots close to
transmission (early slots) are more crowded than
slots in the end of the CW size, because they may
contain both STAs that lost the last competition and
new STAs that randomly selected at an early slot.
Based on this observation, Weinmiller & Woesner,
1996) proposed a weighted slot selection to be used
by each STA, where new STAs arriving in the
competition have a higher probability to select a
higher (later) slot. This spreads out the probability of
a slot being busy equally over all slots in the CW.
The problem is that every STA may have a different
CW size and if they should communicate the size it
would require additional bandwidth and complexity.
Each STA is still resetting the CW size after a
successfully transmitted data frame as the original
algorithm does. This creates a high number of
collisions if the contention level is high because of
the consecutive collisions needed to increase the CW
to the appropriate size.
F. Cali, M. Conti and E. Gregori model the
dynamics of IEEE 802.11 MAC layer with
Markovian chains (Cali, Conti & Gregori, 2000).
They show that if a STA has exact knowledge of the
medium status, it is possible to tune the backoff
algorithm to achieve a protocol capacity very close
to the theoretical maximum. Because exact
knowledge of the medium status cannot be realized
in real cases, they propose a way of adapting the
contention window (CW) by observing the length of
both the last idle period and the last transmission
attempt. They use these two values to estimate the
collision probability. The collision probability in
combination with other calculated values ends up
controlling the CW size. Both the length of the last
idle period and the last transmission attempt are easy
to calculate when modeling the behavior. However,
the calculations introduced for estimating the
number of STAs and medium load can be very
heavy for devices with limited computing power.
One way to solve the problem of dynamically
estimating the unknown number of STAs is to keep
it at a fixed value derived offline (Bruno, Conti &
Gregori, 2002).
Up to now, almost all MAC protocols are based
on the estimation of either transient network traffic
dynamics or stationary statistics. In this paper, a
new MAC protocol is proposed that can utilize both
transient and stationary statistics, which provides
better estimation of the network state, and hence
improve network throughput. Extensive
experiments have validated our scheme.
2 A NEW DISTRIBUTED
CONTENTION CONTROL
PROTOCOL
2.1 Collisions Average
To get a good estimation of the contention level of
the medium we looked at the number of collisions
that occur on the medium. Some collisions are
unable to be avoided due to the randomness in
selecting backoff slots at each STA, but too many
collisions means that the contention level is higher
than what the current contention window (CW) size
Is optimal for. To get a good estimation of the
congestion level, an average (colAvg) of the number
collisions (colCount) that occurred during the last
time interval (colWindow) was calculated. This is
expressed as
)/( colWindowcolCountcolAvg
=
(1)
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To count the number of collisions that occurred
during this time interval, a history of collisions has
to be maintained. The history of collisions is
produced by having a buffer where new collision.
This history window, shown in Fig.1 can be
implemented as a circular buffer. Whenever a
collision occurs it is added to the buffer and the
buffer is scanned for collisions that are outside the
history window (colWindow). By using a circular
buffer the old collisions are always at the end of the
buffer making it easy to delete these by removing
collisions at the end of the buffer until we encounter
a collision event in the buffer with a timestamp that
is inside the history time interval again. This saves
scanning the complete history buffer and makes this
history maintaining process a relatively low CPU
intensive task.
By counting collisions and making the CW size
depend on the number of collisions, the
information about the contention level is also
maintained between successfully transmitted frames
avoiding letting the algorithm “try out” the
contention from scratch again for each new packet.
This is especially useful when the contention level is
high and the normal algorithm would require several
collisions and backoffs to occur before reaching the
effective CW size.
2.2 Slot Utilization
This method was first introduced by L. Bononi
(Bononi, Conti and Donatiello, 1998) and its basic
idea is to sense the medium while backing off and
therefore get a idea of the contention level before
doing the next medium access.
According to the IEEE 802.11
specification(P802.11, 1997) a STA should always
be able to sense the medium to detect if it is busy or
idle. Further it also says that if the medium is
detected busy during a slot while a STA is backing
off the STA should not decrease its backoff counter
at the end of that slot.
The slot utilization represents a measurement of the
contention level of the medium during this backoff
period. When the backoff period has ended, the
normalized slotUtilization value is calculate as
follows
SlotUtilization =NumBusySlots/InitBackoff (2).
InitBackoff is the randomly selected length of the
backoff period and NumBusySlots is how many of
the backed off slots were sensed busy. The
SlotUtilization value is a measurement of how many
of the slots that the STA spent on backing off . The
value is normalized by dividing it with the total
number of slots (InitBackoff) of the backoff period.
This normalization changes the range to a decimal
value between 0.0 and 1.0 where 0.0 means no slots
were busy and 1.0 means that all slots were busy.
This gives us a good indication of the contention
level of the medium with little additional processing
cost. The SlotUtilization value is a fast changing
value because of the limited time period it samples
over (one backoff period), which will gives us a
value that represents a very current status of the
system.
Current
time
colWindow
Collision
Time
Figure 1: Collision history window over time
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2.3 Combined Algorithm
To get the main benefits from both methods
discussed, we propose the following formula to
adapt the window
(
)
KationSlotUtilizcolAvgcolAvgCW +×+= (3).
The collision average (colAvg, explained in section
2.1) is the slow changing average of the system
representing a good overall picture of the contention
level while the slot utilization (SlotUtilization,
explained in section 2.2) gives the latest state of the
medium making our algorithm adapt faster to sudden
changes of the contention level.
This parameter K works as an offset for the second
part of the equation, giving us the opportunity to
decrease the CW size by setting K a negative value.
The parameter K works as a threshold level where a
SlotUtilization value under –K results in the CW size
being decreased. The choice of K gives us the
ability to adjust the CW size between 0.5 and 1.5 of
the colAvg. How to select the optimal K is
discussed in the subsequent sections. Our new
algorithm can provide an accurate estimation of the
overall congestion level while at the same time have
the ability to quickly adapt to sudden changes in
form of increased and decreased contention levels.
3 SIMULATION
3.1 Setup
The physical layer (PHY) was running as Direct
Sequence Spread Spectrum (DSSS) and the
attributes are given in Table 1. These attributes are
taken directly from the recommendation in the IEEE
802.11 specification (P802.11, 1997). These values
are used in all simulations unless otherwise stated.
Table 1: Attributes for IEEE 802.11 PHY in DSSS
Attribute Value
CWMin* 31
CWMax 1023
SlotTime 20 us
CCATime 15 us
RxTxTurnaroundTime 5 us
SIFSTime 10us
PreambleLength 144
bits
PLCPHeaderLength 48
bits
*) The CWMin was set to 31 for all algorithms
except the one proposed in this paper. The CWMin
in our algorithm has a lower threshold, which the
CW size can not go below even when the collision
average and the slot utilization say otherwise. This is
done to avoid a zero CW size.
The physical setup was that every node could
hear each other (fully meshed network). The
bandwidth of the physical medium is set to 2 Mbits.
The layout of the data flows between the STAs is
shown in Fig. 2. Each STA has two outgoing flows
and two incoming flows in a ring fashion. This
layout was chosen because it gives each STA the
same amount of incoming and outgoing data. The
ring layout could have been exchanged for a random
destination model and give the same results. Once a
STA has access to the medium it has the same
probability of a successful transmission as any of the
other STAs because they all share the medium.
STA 3 STA 4
STA 1
STA 2
STA 5
STA n STA 6
STA 7
Figure 2: Data streams layout of simulations
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3.2 Simulation Results
In Fig. 3 the accumulated data successfully
transferred between all STAs are plotted against
time. In this scenario 32 nodes were used and the
simulation ran for 10 seconds for each algorithm.
The average throughput for each algorithm is shown
in Table 3.
Table 2: Legend
Original original backoff algorithm proposed in
the IEEE 802.11 standard. Using an
exponential increase of CW
DCC the Distributed Contention Control
algorithm proposed by L. Bononi 0
where transmission may be deferred
depending on slot utilization level during
the last backoff.
Konrad the combined algorithm proposed in this
paper. The CW size is computed from
the collision average and utilization level
during the last backoff.
Figure 3: Data stream layout
Table 3: average throughput comparisons
Method Average
throughput
(KB/sec)
Performance
increase
compared to
Original (%)
Original 104 -
DCC 111 6.7
Konrad 137 31.7
In the scenario with 32 STAs trying to transmit as
fast as possible, our proposed algorithm increased
the throughput by roughly 30% compared to the
original binary backoff algorithm specified by IEEE
802.11. This 30% increase in throughput is
substantial for wireless networks due to their much
lower bandwidth and higher protocol overheads
compared to wired networks.
Table 3 shows the average throughput of one
scenario in a congested system. To get a view of
how the different algorithms works under different
loads, we ran each algorithm for 4 to 50 STAs in the
system observing the average throughput during 60
seconds for each case. The results can be seen in
Fig. 4.
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The three algorithms perform similarly when the
requested bandwidth is less than what the medium
can provide. However, when the requested
bandwidth rises above that level, the difference
between the algorithms becomes visible. It is at this
level that the peak throughput will occur, because
heavier usage will introduce more overhead in the
form of backoffs and collisions, and less usage
leaving the medium underutilized. The peak level in
our simulations can be seen around 8 nodes in Fig.
4. The algorithm proposed in this paper keep
outperforming the other algorithms even when the
number of STAs in the system increases up to 50.
This feature makes the algorithm ideal to be
deployed in systems with a large number of active
STAs within one BSS, such as dense office
buildings and public access spots.
Collisions Analysis
Another way of measuring how efficient a
contention algorithm is to observe the number of
collisions generated on the medium. A lower
number of collisions means that the algorithm
distributes the available bandwidth better between
the STAs. Each collision wastes bandwidth and
increases the mean delay for the data, which impacts
negatively on the throughput of the system. Fig. 5
shows the collision average over time in a system
with 32 STAs. All three algorithms have an initial
transient peak of collisions before they stabilize,
seen at the first 2-3 seconds in Fig. 5. That is
because when the algorithms start they do not know
Figure 4: Overall throughput versus number of STAs in the syste
m
the contention level of the medium until they have
gained some feedback in the form of collisions
and/or slot utilization. Once they have stabilized the
collision rate it can be seen that the binary backoff
algorithm has the highest collision rate, explaining
the lower throughput observed in Fig. 3.
It is also interesting to see the collision rate for
different medium loads. Fig. 6 shows the collisions
average for the three algorithms with 4 to 50 STAs
in the BSS. The collision average is calculated
between 5 seconds after start and to the end in 60
seconds. The jump of 5 seconds in the beginning is
to avoid the initial transient collision rate seen in
Fig. 5.
The fast increase of the collision rate for the
binary backoff algorithm seen in Fig. 6 is due to
that the algorithm lacks of ability to remember
contention levels between successfully transmitted
frames. The binary backoff algorithm needs to
experience a certain number of collisions before it
has adapted its contention window (CW) size
appropriately for each new frame to transmit. The
binary backoff algorithm has to experience a lot of
collisions if the contention level is high, causing a
high average collision rate. The collision rate
average for the DCC and Konrad algorithms
increases in a similar slow fashion with a slightly
lower favorable rate for Konrad, making it utilize the
medium better.
Now it may look strange that even though the
DCC and Konrad algorithms have a similar collision
average, the Konrad algorithm gives an additional
20% increase in throughput compared to DCC as
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seen in Fig. 6. The reason for the difference in
throughput despite similar collision rate is because
that the DCC algorithm underutilizes the medium
while the Konrad algorithm uses the collisions in
better way for upcoming transmissions. Because of
the way that the DCC algorithm works, it generates
collision notification for the backoff scheme not
only for missing ACKs but also for when the slot
utilization was high during the last backoff time.
However, such collision notification based on slot
utilization is not based on real collisions on the
medium but rather virtual collisions within the
algorithm. Because the virtual collision is triggered
by a probability based on the fast changing slot
utilization value, it may falsely tell the backoff
scheme to backoff, hence causing the
underutilization of the medium.
Selecting optimal K
How the CW depends on the collision average
(colAvg) for different values on K is shown in Table
4. Slot utilization equal to zero means that the
medium was not accessed at all during the last
backoff time, indicating an idle medium. Slot
utilization of 0.5 means that half of the backoff slots
were busy and 1.0 means that all of the backoff slots
were busy.
Table 4: CW size for different slot utilization and K
CW size
SlotUtilization =
K
0.0 0.5 1.0
- 1.0 0 colAvg/2 colAvg
- 0.5 colAvg/2 colAvg colAvg *
1.5
0.0 colAvg colAvg * 1.5 colAvg *
2.0
1.0 colAvg * 2.0 colAvg * 2.5 colAvg *
3.0
For a K equal to –1.0 the CW size can only be
decreased, which gives a maximum CW size of
colAvg when the slot utilization is 1.0 (100% of the
slots where busy last backoff time). A higher K
gives the CW size a higher base value even if the
slot utilization is low. The CW size cannot be
decreased under colAvg if K equals 0.0 or higher,
removing the ability to temporarily decrease the CW
size even if the slot utilization value states that the
medium is underutilized. To find the optimal K, a
series of simulations was performed as shown in
Fig. 7. The peak throughput is just below a K of –0.5
in Fig. 7. The simulation results have illustrated the
role of adding the parameter K in the Eq. 3. The
parameter K can give low slot utilization a chance
Figure 5: Collision average over time in a system with 32 STAs
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to decrease the CW size, which renders the
algorithm able to adapt fast on both increased and
decreased contention levels.
Table 4 shows that a K of –0.5 moves the offset of
the slot utilization as expressed in in Eq.3. So a slot
utilization value below 0.5 decreases the CW size
while a slot utilization above 0.5 increases the CW
size. Slot utilization below 0.5 can be seen as an
indication that the medium is not fully utilized
because less than half of the slots were busy and a
adjustment of the CW size to a lower value would
benefit the throughput by giving the STA a smaller
range to select its next backoff time from.
Fi
g
ure 6: Collision avera
g
e for different number of STAs STAs
The shorter backoff time selected at the next backoff
brings the competing STAs retransmission attempts
closer to each other, minimizing the mean data delay
and maximizing the medium utilization. The
increased medium utilization will result in the slot
utilization value calculated next time being higher
thus not decreasing the CW any further. This
feedback behavior makes the algorithm converge to
a CW size that creates the maximum utilization.
Figure 7: Th
r
oughput for different values on
K
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4 CONCLUSIONS
In this paper a new MAC access protocol was
proposed. The double feedback introduced can
combine transient medium traffic activities and
long-term stationary traffic statistics. Extensive
simulation has been conducted to compare this new
algorithm with the popular binary backoff algorithm
proposed by IEEE 802.11 and the DCC algorithm
introduced by L. Bononi 0. The throughput gain of
our new algorithm was around 20-30% compared to
the other algorithms and the gain is increasing when
the number of nodes increases. It will be interesting
to see how the proposed algorithm affects higher-
level protocols like TCP protocols. This is our future
research topic.
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