ad Biaz and Shaoen Wu
Dept. of Computer Science and Software Engineering, Dustan hall 107, Auburn University, Al 36849, USA
Beacon, Rate Adaption.
The availability of multiple rates in IEEE802.11 WLANs and instability of wireless channel conditions call
for data rate adaption algorithms to optimize network performance. Rate adaption is the process of assessing
instantaneous channel conditions and determining the most appropriate data rate. This paper presents a relaxed
probing rate adaptation scheme to determine the most appropriate instantaneous data rate for both downlink
and uplink channels, especially in the case where control frames such as RTS/CTS are not available. For this
goal, the proposed scheme exploits the mandatory management beacon frame, thus without requiring probing
frames like RTS/CTS necessary for other so far proposed schemes. Ns-2 simulations with IEEE802.11b of the
proposed scheme yield more than 100% throughput improvement in high density networks. Also, simulations
show that the proposed scheme is insensitive to beacon interval.
Wireless signals are by nature instable and sensitive to
distance and disturbances (e.g. other wireless source,
temperature and humidity, etc.). Therefore, multiple
rates (modulation schemes) are necessary to enhance
wireless communications. When a wireless channel
is in good condition, it can support high data rates,
otherwise it requires more robust modulation schemes
yielding low data rates. Rate adaptation is the pro-
cess of estimating the instantaneous wireless channel
conditions and then determining the most appropriate
data rate the channel could support.
Generally, rate adaptation strategies can be cate-
gorized into probing or non probing channel schemes.
Non probing rate adaptation algorithms usually are
driven by some metrics such as frame loss rate that
indirectly reflects the channel conditions. These adap-
tation schemes do not use control frames such as
RTS/CTS to probe wireless channel conditions. Rep-
resentatives of this category include Auto Rate Fall-
back (Kamerman and Monteban, 1997), and Adaptive
Auto Rate Fallback (M. Lacage, M. Manshaei, and T.
Turletti, 2004). These schemes increase the data rate
if multiple consecutive data frames are successfully
transmitted. If the data frame transmission fails for
several times, then the data rate is decreased. These
schemes are appealing because they do not require
control frames to probe the channel conditions.
For probing rate adaptation schemes, the sender
sends out a probing frame such as RTS, and then
gets a feedback frame from a receiver to complete
the negotiation of an appropriate data rate. Propos-
als Sadeghi, et al (B. Sadeghi, V. Kanodia, A. Sabhar-
wal, and E. Knightly, 2002), Qiao, et al (D. Qiao, and
S. Choi, and K. Shin, 2002), Pavon and Choi (Pavon
and Choi, 2003), and Holland et al (Holland et al.,
2001) are typical probing schemes. Probing data
rate schemes estimate better the channel state than
non probing schemes because non probing schemes
get an implicit information on the channel condition.
Moreover, the implicit information may be mislead-
ing. For example, the schemes that monitor frame loss
rate conclude that channel conditions are poor when
the frame loss rate increases. This may be wrong if
frames are lost due to congestion. The disadvantage
of probing schemes is that probing frames incur some
communications overhead.
This paper proposes a relaxed probing data rate
adaptation scheme. The key difference between
Biaz S. and Wu S. (2007).
In Proceedings of the Second International Conference on Wireless Information Networks and Systems, pages 93-98
DOI: 10.5220/0002150600930098
the proposed scheme and other probing schemes is
the channel probing mechanism used. The pro-
posed strategy does not use probing frames such as
RTS/CTS that are required by the other schemes,
but rather exploits the mandatory broadcast beacon
frame. Note that RTS/CTS control frames are op-
tional in IEEE 802.11 standard and are not always
used, especially for short data frames. The contri-
butions of this work are: 1) a beacon based probing
data rate scheme. This scheme is characterized as “re-
laxed” because it does not require control frame over-
head such as RTS/CTS; 2) rate estimation for the very
first frame of a communication session ; 3:) this work
evaluates the impact of beacon interval on the accu-
racy of the proposed data rate adaptation scheme.
The remaining of this paper is organized as fol-
lows: Section 2 outlines the related work in data rate
adaptation. Section 3 discusses the motivation. Sec-
tion 4 details the proposed scheme. The simulation
results are presented in Section 5. Section 6 concludes
this paper.
Data rate adaptation has been extensively studied in
last several years in wireless networks from advanced
CDMA network (Bender and et al, 2000) to wireless
local area network like IEEE802.11 (IEEE802.11,
1999). Non probing data rate adaptation schemes esti-
mate the data rate without using probing frames such
as RTS/CTS frames. For example, Auto Rate Fall-
back (ARF) (Kamerman and Monteban, 1997) was
proposed for Lucent WaveLan-II WLAN product. In
this protocol, the data rate adaptation is completed in-
dependently at the sender side without any informa-
tion from the receiver. When the sender fails twice
to transmit a data packet, it automatically decreases
(falls back) its data rate to the next lower level, (for
instance from 5.5 Mbps to 2 Mbps). If the transmis-
sion succeeds for ten consecutive times at the same
data rate, the sender infers that the channel condition
is good enough to support higher data rates and thus
increases its data rate to a higher level. ARF defi-
nitely suffers from data rate fluctuations. For instance,
when the channel condition is good for 5.5 Mbps, but
not good enough for 11 Mbps, the sender will suc-
cessfully transmit ten consecutive times at 5.5 Mbps.
Therefore, the sender will increase the data rate to 11
Mbps and will likely fail transmissions at that rate,
leading to a decrease to 5.5 Mbps. This scenario will
likely repeat itself leading to fluctuations.
In Receiver Based Rate Adaptation (RBRA) (Hol-
land et al., 2001), the authors propose a probing al-
gorithm to adapt the data rate with the cooperation
from the receiver. This protocol requires the exchange
of RTS/CTS control frames between sender and re-
ceiver stations before data/acknowledgement packets
are transmitted. The RTS and CTS control frames are
transmitted at the lowest rate so that they are acces-
sible to all stations in carrier sense range. When the
receiver gets the RTS, it determines the best data rate
that it can support under current wireless channel con-
ditions, based on physical layer measurements. Then
it feeds back the selected data rate embedded in the
CTS frame. This proposal can work in both WLAN
and Ad Hoc networks as long as control (probe)
frames such as RTS/CTS precede the data frames.
A hybrid algorithm named Full Auto Rate
(FAR) (Z. Li, and A. Das, and A.K. Gupta and S.
Nandi, 2005) integrates probing and non probing con-
cepts to achieve full data rate adaptation. The au-
thors of FAR argue that receiver based protocols like
RBRA (Holland et al., 2001). can dramatically be im-
proved if the RTS/CTS can be transmitted at an appro-
priate data rate that the wireless channel can support
rather than at some heuristic or lowest rate. Thus, it
determines the rate for RTS/CTS control frames trans-
mission using a non probing scheme.
In a previous work (Wu and Biaz, 2007), we pro-
pose to estimate the initial rate by sniffing the peri-
odic beacon frame for the first time. This work dif-
fers from that one in three aspects: first, we consider
the data rate estimation for both downlink and uplink
of the wireless channel; Second, we evaluate the im-
pact of beacon period on the rate estimation accuracy;
Third, we compare the role of different coefficients in
the adaptive estimation of the data rate.
Although the rate adaptation is well studied, there still
exists some unaddressed problems.
3.1 Problem #1: How to Probe without
RTS/CTS Control Frames?
In wireless network, when two nodes that can not
sense each other transmit to the same receiver, the
transmissions get collided at the receiving station.
This is called hidden terminal problem. To mitigate
such collision, In IEEE802.11 networks, RTS/CTS is
introduced to clear the channel before the data frame.
However, RTS/CTS control frames are pure overhead
for data frames. These control frames are optional
and are recommended only for large data frames. If a
data frame is small, (which is highly possible in real
WINSYS 2007 - International Conference on Wireless Information Networks and Systems
time applications such as VoIP, video and so on,) the
use of RTS/CTS dramatically increases the overhead.
According to Garg and Kappes (Garg and Kappes,
2003), if a unique 802.11b station transmits VoIP traf-
fic with 160 byte frames, the data packet efficiency
drops to about 12% in 802.11b (IEEE802.11b, 1999)
networks at 11 Mbps with RTS/CTS control frames.
Therefore, RTS/CTS frames are recommended only
when the size of data packet exceeds a certain thresh-
old (2347 recommended in IEEE 802.11). For com-
munications not using RTS/CTS frames, most prob-
ing data rate adaptation schemes can not work as they
require RTS/CTS frames. However, probing schemes
can estimate the data rate more promptly and accu-
rately than non probing schemes. Therefore, commu-
nications not using RTS/CTS frames call for a new
probing scheme with minimal overhead.
3.2 Problem #2: Rate Adaptation for
RTS/CTS Frames
In multi-rate IEEE802.11, the recommended rate for
the control frames RTS/CTS is the lowest data rate
so that they can be captured by every station in trans-
mission range. Full Auto Rate (Z. Li, and A. Das,
and A.K. Gupta and S. Nandi, 2005) scheme has an-
alyzed and concluded that throughput improvement
can be achieved if the RTS/CTS frames are transmit-
ted at an appropriate data rate, rather than at the low-
est data rate. Thus, a well-predicted instantaneous
data rate for RTS/CTS benefits the entire network per-
3.3 Problem #3: Rate Adaptation For
First Transmission
None of the data rate adaptation schemes addresses
the estimation of the data rate at the very beginning
of a communication. Suppose station X wants to ini-
tiate packet transmission to station Y. If station Y is
inactive for a long time (e.g. several minutes), the
sender station X is unaware of the link status to sta-
tion Y. It does not know which data rate is suitable to
restart transmissions. Thus, the initial data rate must
be somehow estimated.
To address the above problems, this work pro-
poses a relaxed probing data rate adaptation scheme
based on the periodic Beacon frame. Note that al-
though the simulation is based on IEEE 802.11 stan-
dard, this strategy is also applicable to all other wire-
less networks with periodic broadcast frames.
This section details the procedure of the proposed
data rate adaptation with beacon frame. The adap-
tation of initial data rate consists of uplink and down-
link rate adaptation depending on the location where
rate adaptation occurs (at access point or client sta-
tion). Uplink is denoted as the wireless link from a
client station to its access point. And the downlink
refers to the opposite direction. After the initial trans-
mission, either the access point or the client station
can adapt the data rate based on received data or ac-
knowledgement frames.
4.1 Uplink Initial Rate Adaptation
When a client station needs to initiate a commu-
nication to its access point, it has to adopt an ap-
propriate data rate for the initial transmission. In a
802.11 WLAN, the beacon is a mandatory manage-
ment frame. It is broadcast periodically by an ac-
cess point for synchronization and association pur-
poses. Therefore, this periodic frame offers an oppor-
tunity for the wireless client stations to estimate the
uplink data rate. Since the beacon frame is manda-
tory, it does not introduce any overhead for channel
condition probing. When a client station receives the
beacon frame, it retrieves the statistics of the channel
conditions (e.g. signal to noise ratio, signal strength,
and error rate) from the physical adapter. Based on
such wireless channel information, the client station
can determine the best data rate and record it for im-
mediate or future use. The recorded information in-
cludes the modulation level or data rate and the time
when the last beacon was received. When this station
needs to transmit a data frame, it looks up for the data
rate it can use. Then it instructs the physical layer to
transmit the data packet with the appropriate modula-
tion. This scheme is especially accurate for slow fad-
ing channels. Although such estimation might be out
of date for fast fading channels in case of long beacon
interval, it is still better than a random selected initial
4.2 Downlink Initial Rate Adaptation
While there are beacon frames from an access point
to the client stations there are none towards the access
point. If the access point needs to initiate a transmis-
sion to a particular client station, two options are pos-
sible: the first is the lowest data rate, and the second is
the rate most recently used. Furthermore, if the low-
est rate is selected, we propose to fragment the first
data frame so that the length of the short fragment is
equal to the length of an RTS frame. This short frag-
ment functions as a channel reservation and probing
as RTS does. Since the the frame is short, even it is
transmitted at the lowest rate, the transmission time is
negligible and does not impact much the network per-
formance. Then, the new data rate for the remaining
large fragment can be estimated from the acknowl-
edgment frame.
4.3 Adaptive Rate Adaptation After
Initial Rate
After initial rate is adopted, both the access point and
its client stations can adapt their transmission rate
from the exchanged frames. Also, the client station
still can adapt its data rate from the periodic beacon.
We propose an adaptive algorithm for the ongoing
For instantaneous data rate adaptation, if the rate
is estimated only from the last received frame, the
data rate will fluctuate due to instantaneous channel
condition variations. The performance suffers espe-
cially when a mobile station is experiencing the ping-
pong effect in handoff, where the data rate might
fluctuate between two levels of rates. This results
in costly (in power) retransmissions. Multiple spuri-
ous retransmissions definitely hurt the network per-
formance. Therefore, to solve the above data rate
fluctuations problem, the data rate should be adjusted
from the latest frame, but adaptively from multiple
past frames information.
Compared to the available discrete data rate lev-
els(e.g. 2, 5.5, 11.), channel statistics like signal
strength has better granularity for adaptive algorithm.
Thus, signal strength is employed in the following for-
mula. An adaptive low pass filter coefficient is intro-
duced to smooth signal strength impacted by the wire-
less channel upon history signal strength information.
= (1 α) RSSI
+ α rssi (1)
where 0 < α < 1.
Here, the RSSI
is the adopted signal strength. It
predicts the channel variation trend. rssi is the in-
stantaneous signal strength obtained from a received
α is further dynamically updated as follows:
α =
rssi Threshold
and Threshold
are the signal
strength thresholds for the current rate.
3.4 3.45 3.5 3.55 3.6 3.65 3.7
Data Rate
ARF Data Rate Fluctuation
Figure 1: Auto Rate Fallback rate fluctuation.
Simulation configuration: Simulations are per-
formed on ns-2 (NS2, 2006) version 2.30. Three lev-
els of data rates are used: 11 Mbps, 5.5 Mbps and
2 Mbps. The data rate 2 Mbps is defined as the
basic rate. Channel fading is configured as Ricean
model. These simulations compare the proposed
scheme against the ARF proposal and evaluate the
impact of the beacon interval and the low pass filter
parameters used in formula 1. The IEEE 802.11 cell
is a 500mx500m square with the access point located
at the center. The radio range is 250m. In all ex-
periments, each client sends UDP CBR traffic to the
access point at 4 Mbps.
Data Rate Smoothing: Figure 1 and Figure 2
show the data rate adaptation resulted from ARF and
our scheme respectively. The channel condition is
stable for 5.5 Mbps in the experiment with ARF.
From Figure 1, data rate in ARF fluctuates heavily be-
cause it adapts the data rate based on frame loss that
is vaguely reflect channel conditions. With dynamic
channel conditions, the proposed scheme is tested and
depicted in Figure 2. As expected, its adaptive ability
reduces the sensitivity to channel variations and over-
comes rapid fluctuations.
Throughput performance: Figure 3 illustrates the
improvement of network upon network density (dif-
ferent number of client stations). The X-axis repre-
sents the network density and Y-axis stands for im-
provement over ARF. In this scenario, all nodes are
stationary at their specified positions, which are uni-
formly distributed along the four diagonal lines to the
center of square. One example of the layout with
8 client stations is shown in Figure 4 (access point
is at the center). It can be observed from Figure 3
that as the density increases, our scheme can improve
WINSYS 2007 - International Conference on Wireless Information Networks and Systems
30 32 34 36 38 40
Data Rate (bps)
Time (s)
Data Rate in Sender Adaptive Auto Rate
Figure 2: Data Rate Adaptation.
25 16 9 4
The Number of Nodes
Figure 3: throughput improvement with stationary location.
throughput sharply and eventually achieve more than
100% improvement over ARF in such nodes layout.
This can be explained by the robustness of our scheme
to frame loss. When network density increases, the
frame loss rate will increase due to more collisions.
ARF will decrease its rate in case of frame loss re-
gardless of the cause of the losses. Our scheme is in-
sensitive to frame loss rate, and reacts only to received
signal strength variations. Thus, more throughput im-
provement can be achieved in denser scenarios.
In the second throughput experiment, all client
stations are roaming in the square ground with speed
from 2 m/s to 20 m/s randomly. We still test the
improvement upon network density. The result is
demonstrated in Figure 5. Generally, our strategy
can achieve more than 50% improvement over ARF.
In some density, the improvement is even more than
100%. As might be observed, when the network den-
sity becomes too high, the improvement decreases
dramatically. This falling is caused by the increased
frame loss from the collision, which can not be dis-
criminated from channel fading by this strategy.
Figure 4: network layout with 8 client nodes.
4 8 12 16 20 24 28 32 36 40
The Number of Nodes
Figure 5: throughput improvement with roaming nodes.
Then, we evaluate the beacon interval impact
on the effect of the performance of our adaptation
scheme. Figure 6 depicts the result. In this figure,
the X-axis denotes throughput and the Y-axis repre-
sents the beacon interval varying from 20 ms to 200
ms. The result are promising and surprising: the per-
formance is insensitive to the beacon interval. This
observation suggests that even if the rate estimation
interval is not short, the estimation is still pretty up-
to-date on slow fading channel.
Figure 7 depicts the throughput improvement with
the adaptive low pass filter and different constant low
pass filters with formula 1. The X-axis denotes the
number of nodes in the network area of 500x500 and
the Y-axis represents the throughput improvement.
The different lines in Figure 7 show the improvement
results for different coefficients used. From this fig-
ure, we can observe that, the adaptation scheme using
a constant low pass filter is outperformed by that of
the adaptive filter in formula 2. Also, note that differ-
ent fixed coefficients do impact on the improvement.
20 40 60 80 100 120 140 160 180 200
Throughput (KBps)
Beacon Interval (ms)
Figure 6: throughput of different beacon intervals.
4 8 12 16 20 24 28
Throughput Improvement(%)
Adaptive Coef
Figure 7: Impact on improvement by coefficients.
This work proposes a relaxed probing rate adaptation
to exploit the broadcast management beacon frame in
802.11 networks. The scheme includes also an adap-
tive algorithm designed to smooth out the data rate
during temporary variations of channel conditions.
The simulations yield promising throughput improve-
ment over ARF and reveal that the beacon interval has
little impact on the performance improvement on slow
fading channel.
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WINSYS 2007 - International Conference on Wireless Information Networks and Systems