An Extension of Throughput Drop Estimation Model for Three-Link Concurrent Communications under Partially Overlapping Channels and Channel Bonding in IEEE 802.11n WLAN

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Advances in Science, Technology and Engineering Systems Journal
Vol. 4, No. 4, 94-105 (2019)
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Special Issue on Advancement in Engineering and Computer Science
ASTES Journal
ISSN: 2415-6698
An Extension of Throughput Drop Estimation Model for Three-
Link Concurrent Communications under Partially Overlapping
Channels and Channel Bonding in IEEE 802.11n WLAN
Kwenga Ismael Munene
1
, Nobuo Funabiki
*,1
, Md. Manowarul Islam
1
, Minoru Kuribayashi
1
, Md. Selim Al Mamun
2
,
Wen-Chung Kao3
1Department of Electrical and Communication Engineering, Okayama University, Okayama, Japan.
2Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Bangladesh.
3Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan.
ARTICLEINFO ABSTRACT
Article history:
Received: 22 April, 2019
Accepted: 07 June, 2019
Online: 15 July, 2019
Keywords:
WLAN
Access point
IEEE 802.11n
Partially overlapping channel
Throughput estimation model
Three links
The IEEE 802.11n wireless local-area network (WLAN) has been exten-
sively deployed around the world due to the flexibility, lower cost, and the
high-speed data transmission capability at 2.4 GHz ISM band. However,
in the WLAN deployment, one key challenge is to optimize the channel
assignment of access-points (APs) under the small number of partially
overlapping channels (POCs) to reduce radio interference, particularly
for the channel bonding. In POCs, the frequency spectrums of adjacent
channels are partially overlapped with one another, which will result to
low throughput for concurrently communicating links using them. The
accurate throughput estimation of a link is critical in the optimal WLAN
deployment. Previously, we studied the throughput drop estimation model
using the receiving signal strength (RSS) from the interfered link for two
concurrently communicating links under POCs. In this paper, based on
measurement results, we have extended this model for three concurrently
communicating links. The accuracy of this model extension is verified by
comparing the estimated results with the measured ones.
1 Introduction
Nowadays, the IEEE 802.11 wireless local-area network
(WLAN) has been used across various sectors around the
world, due to the unlicensed frequency bands in wireless
medium and availability of low-cost devices [
1
]–[
3
]. It is
suggested that WLAN has become a crucial business and
the common asset [
4
]. For the access to the Internet using
WLAN, a host is connected to an access point (AP) wire-
lessly, where the AP is connected to the Internet with wires.
Therefore, a host can move randomly in the network area
with the current association. As the data transmission speed
increases due to technological advancements, WLAN has
proved popular even for static settings of personal computers
in offices and homes [5].
The IEEE 802.11 WLAN can function in two unlicensed
frequency bands [
6
,
7
]. One is the 2
.
4 GHz Industrial, Scien-
tific, and Medical (ISM) band, and the other is the 5 GHz Un-
licensed National Information Infrastructure (U-NII) band.
For either band, the IEEE 802.11 standards define the
small number of channels for use. Each channel has 22 MHz
width, conventionally called a 20 MHz channel. The fre-
quency gap between two adjacent channels is merely 5MHz.
Thus, the spectrums of adjacent channels are partially over-
lapped with one another, called the partially overlapping
channels (POCs). Figure 1 shows the 2
.
4 GHz spectrum with
40 MHz channels to illustrate the POCs in IEEE 802.11n.
1ch 2ch 3ch 4ch 5ch 6ch 7ch 8ch 9ch 10ch 11ch 12ch 13ch
20MHz
2412MHz 2432MHz 2452MHz 2472MHz
5MHz
Figure 1: 40MHz channels at 2.4 GHz band.
While the IEEE 802.11 WLANs referred to in this pa-
per use the unlicensed frequency bands, the Industrial, Sci-
*Corresponding Author: Nobuo Funabiki, Dep. of Elect. and Comm. Eng., Okayama Univ., Okayama, Japan, Email: funabiki@okayama-u.ac.jp
www.astesj.com
https://dx.doi.org/10.25046/aj040411
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K.I. Munene et al. /Advances in Science, Technology and Engineering Systems Journal Vol. 4, No. 4, 94-105 (2019)
entific, and Medical (ISM) 2.4 GHz band and the Unli-
censed National Information Infrastructure (U-NII) 5 GHz
band, the 802.11y is licensed to use [
8
]. The unlicensed
IEEE 802.11ad, which represents modifications of IEEE
802.11 physical layer (PHY) and medium access control
layer (MAC) [
9
], provides the very high speed, which is
equivalent to the fiber optic. However, its short range limita-
tion coupled with the expensive hardware makes the global
adoption of IEEE 802.11ad to be low [10].
Similarly, the IEEE 802.11af is advantageous due to its
long-range transmission and low power consumption. How-
ever, it requires expensive band-specific hardware, which
is not readily available in the global market where it is pri-
marily used in US and Canada. In addition, since it utilizes
the frequencies for unused TV channels, they may not be
available everywhere [11].
The proposed model in this paper targets WLANs on the
IEEE 802.11n unlicensed frequency band. Since this proto-
col has been widely adopted, the related devices are much
easily and cheaply available, compared to those of emerging
standards of the IEEE 802.11ad and IEEE 802.11af.
At the 2
.
4 GHz band, 13, 20MHz POCs are possible,
which indicates that the number of orthogonal channels
(OCs) is four at most. Since orthogonal channels do not
overlap with one another, APs assigned OCs are not inter-
fered for the medium access, if any pair of nearby APs do
not operate on the same OC.
Currently, the IEEE 802.11n is most used in WLAN due
to the high-speed data transmission capability at the 2
.
4 GHz
band using the channel bonding that combines two neighbor-
ing 20 MHz channels together to form one 40 MHz channel
as shown in Figure 1, [
12
]–[
14
]. Then, the number of OCs
is reduced to at most two, and the channel assignment using
OCs to the APs without interference cannot be avoided. [
15
]
has shown that channel bonding improves performance, al-
though it may increase frequency competition with adjacent
LANs.
To overcome this limitation of the OC assignment in
WLAN, it has been reported that the POCs will be beneficial
to enhance the performance by fully utilizing the available
spectrum [
16
,
17
]. Then, the throughput estimation model
under POCs is essential to identify the proper POC assign-
ment to the APs while evaluating the performance accurately.
Specifically, in a dense WLAN, the model must consider
the throughput drop caused by the interference that each AP
suffers from the neighboring APs on the adjacent POCs.
Previously, we have studied the throughput estimation
model for a single communicating link, which consists of the
log-distance path loss model and the sigmoid function [
18
].
Then, we move to estimate the throughput drop caused by
the interference for two concurrently communicating links
under POCs [
19
]. It is examined according to the receiv-
ing signal strength (RSS) from the interfering link and the
channel distance (chD) between the two links.
The proper use of POCs should be considered for the full
utilization of the limited frequencies, since there are limited
orthogonal channels available in the IEEE 802.11n protocol
with the channel bonding. Besides, the estimated throughput
should be directly used as the metric for selecting POCs by
estimating the network performance, since the interference
does not exhibit the exact performance of the network, which
has been observed in this paper.
Our model considers the channel bonding and POCs,
to directly estimate the throughput from the channel dis-
tance and the RSS of the interfering AP. From the estimated
throughput, the POC assignment can be optimized under the
channel bonding.
Besides, in our previous studies [
20
,
21
], we considered
the indoor network environment inside a building for the
target field, since WLANs are usually deployed there. A
lot of environmental factors, such as wall attenuations and
multipath effects, can affect the throughput performance. To
accurately estimate the throughput, we have developed the
throughput estimation model that consider the environmental
factors.
In this research work, we extend the throughput drop es-
timation model for three concurrently communicating links
under POCs. Firstly, we conduct extensive measurements of
receiving signal strength (RSS) and throughputs under three
links. Then, we extend the model based on the measurement
results.
To confirm the effectiveness of our proposal, we compare
the estimated throughputs by the model with the measured
ones, and through simulations. It has been demonstrated
that the proposal can estimate the throughput for three inter-
fered links under POCs with the considerably high degree
of accuracy.
The rest of this paper is organized as follows: Section 2
describes related works. Section 3 discusses the implemen-
tations of the auto-channel selection in vendor AP devices
and the significance of the throughput estimation model.
Section 4 reviews our previous studies. Section 5 presents
experiments with three concurrently communicating links
under POCs. Section 6 proposes the model extension for
them. Section 7 evaluates the proposal with experiments
and simulations. Section 8 introduces the application to the
channel assignment. Section 9 concludes this paper with
future directions.
2 Related Works
In this section, we discuss specific works related to our
proposal. A significant amount of research works have ad-
dressed the problem of interferences in WLAN to improve
the throughput performance by applying partially overlap-
ping channels.
In [
22
], Mishra et al. revealed that the orthogonal chan-
nel assignment to APs in WLAN is inefficient in a network
field, if a substantial number of access points are deployed
there. In such a case, the number of APs in the network field
is higher compared to that of orthogonal channels, hence
any AP could exist in the interference ranges of other APs
as indicated in [23].
In [
24
], Mishra et al. also noted that partially overlap-
ping channels have previously been avoided due to their
interferences. However, through their model and measure-
ments, it is demonstrated that the careful use of some POCs
does not only improve the spectrum utilization but also the
throughput performance.
In [
25
], Feng et al. proposed POCs as a means of reduc-
ing interference from the traditional use of OCs. Through
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testbed experiments, they reported that POCs offers better
flexibility in wireless frequency allocation, and can increase
overall network performance. Similarly, in [
26
], Zhang et
al. proposed the use of POCs for interference management
which outperforms the OCs.
In [
27
], Zhao et al. observed that with increased fre-
quency reuse which has been reported in POCs, the network
capacity can be scaled up through activating more APs in the
network field as the expected distance from the users to their
connected APs becomes smaller. However, it is reported that
since POCs are partially overlapped with each other, one
AP may suffer interference from multiple other APs. They
conclude by proposing a need to have a maximum and a
minimum bound for the number of APs based on the size of
the network field and the number of POCs available.
In [
28
], Mukherjee et al. explored the AP selection,
channel assignment, and the host association. They reported
that the three aspects are critical in maximizing the through-
put in WLAN, where the appropriate combination of both
non-overlapping channels and POCs can further improve the
overall throughput of the network.
In [
29
], Tewari et al. emphasized that multiple overlap-
ping transmissions cause low network performance due to
high interference from the limited non-overlapping channels.
They have proposed a combined power control and POC
assignment, where the former reduces the AP’s transmission
and the interference range while the latter improves spatial
reuse.
In [30], Zhao et al. demonstrated that the effect of inter-
ference on the network performance depends on the channel
separation and the degree of frequency overlap among the
interfered APs. In particular, it is noted that two interferes
cause higher performance deterioration than a single inter-
fere, but less than twice the interference caused by a single
interfere or the summation of interference from the two in-
terferes.
Furthermore, in [
31
], Su et al. measured the interference
among the APs when POCs are used from the perspective
of the physical characteristics of the communication. They
reported a clear distinction between in-range and hidden
terminal interferences with POCs, where the latter has worse
throughput performance due to high packet loss.
In [
28
], POC interference is evaluated by considering
the interference factor defined in [
32
]. Where more than
one APs interferes, [
27
] [
28
] and [
29
] considers summation
of interfering signal powers at the target node. However,
since the MAC protocol lowers the data transmission rate
of the target AP depending on the level of interference with
individual interfere, calculating the summation may fail to
identify a real value of interference.
At present, there is no research work that has proposed
the throughput drop estimation model for multiple interfer-
ing APs under POCs that considers the channel distance and
the interfering RSS.
3 Auto-channel Selection Implemen-
tations and Model Significance
In this section, we survey implementations of the auto-
channel selection in vendor AP devices, and discuss the
significance of the throughput estimation model under it.
The auto-channel selection is the dynamic adjustment
mechanism of the assigned channel to the AP, in order to
avoid radio interferences from other WiFi devices. Each AP
vendor has its own implementation approach.
In [
33
], Cisco describes their implementation of the auto-
channel selection, where the AP operating in the 2.4 GHz
band with 11 POCs can be set only to one of the three orthog-
onal channels of 1, 6, and 11 under the non-bonded channels
with the (20 MHz) width. It means that up to three APs can
be assigned the channels. The assignment for other POCs
must be done manually.
However, when the channel bonding with the (40 MHz)
width is applied, which is commonly used in IEEE 802.11n
WLANs to improve the throughput performance, the AP
can have only one choice of channel 3, since all the other
channels are partially overlapping with it. It means that the
auto-channel selection cannot work for the channel bonding.
On the other hand, our proposal can assign any POC to
the AP with the channel bonding by considering the through-
put drop from the other up to two interfered links.
Besides, from our experimental observations, the orthog-
onal channels can interfere with each other when the APs
are closely located together, which would further affect the
auto-channel selection in the Cisco Meraki APs.
Furthermore, Cisco’s approach scans for a possible chan-
nel change after every 15 minutes, in addition to the three
necessary situations: 1) a new AP joins the network, 2)
the network administrator manually runs the auto-channel),
and 3) the currently assigned channel fails to work. This
would lead to the performance overhead, since the services
of an AP must be stopped when channel change is applied.
To avoid the frequent service stops, our approach changes
channels of APs when an AP joins or leaves the network.
In [
34
], Fujitsu reports the auto-channel selection such
that the total interference is minimized, where the details of
the implementation are not given. However, the interference
index is not a good metric to evaluate the throughput perfor-
mance, because the interference and the throughput perfor-
mance are not proportional as our paper reported. Instead,
the throughput drop of each link from the other interfered
links should be evaluated to more accurately estimate the
network performance under a lot of interfered links.
In [
35
], Buffalo implements the auto-channel selection
that tries to find the non-interfered channel for a new AP. By
scanning all the channels, it selects a non-interfered channel
if it exists. However, they also reported that the channel
selection performs poorly, if no non-interfered channels ex-
ist. In real network environments, as demonstrated in our
paper, several APs coexist together where any channel is
interfered by an existing AP. On the other hand, our proposal
is applicable in such real cases.
Similarly, in [
36
], Vodafone limits the auto-channel se-
lection only to the non-interfered channels or the least inter-
fered channel. In [
37
], Google WiFi works on the similar
principle by selecting an orthogonal channel.
It should be pointed out that the proposed throughput
estimation model can be incorporated into an auto-channel
selection mechanism to improve the resulting performance
of WLAN. Our model considers the channel bonding, POCs,
and the direct throughput estimation. Therefore, we believe
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K.I. Munene et al. /Advances in Science, Technology and Engineering Systems Journal Vol. 4, No. 4, 94-105 (2019)
that this model can improve the auto-channel selection. In fu-
ture works, we will compare the performance by our model
with existing auto-channel selections.
4 Review of Throughput Estimation
Model
In this section, we review our previous works.
4.1 Motivation of Empirical Model
In [
5
], Reis et al. indicates that most of physical protocol
explorations with respect to interference may adopt simple
abstract models with multiple assumptions, including that
the signal propagation obeys a simple function of the dis-
tance, the radio coverage area forms a circle, and the interfer-
ence range is twice of the transmission range. Unfortunately,
experimental data using a real WLAN have shown that all
of these models appear to be largely inaccurate [38, 39].
In contrast to a physical model, an empirical model
is based on observations on actual network environments.
Thus, the empirical model for interference in WLAN is ex-
pected to be more descriptive and accurate compared to a
physical model. Therefore, we have developed the empirical
throughput drop estimation model for multiple-link concur-
rent communications based on experimental results.
4.2 Definitions of Three Distances
The channel distance, the physical distance, and the link
distance are defined to illustrate the throughput estimation
model under partially overlapping channels.
4.2.1 Channel Distance
The channel distance (
chD
) of the two links is defined as
the least channel difference between the channels of these
links. For instance, if the two links are activated with the
same channel, then
chD
is 0, and they will be entirely over-
lapped. When one link is assigned channel 3 and another
link is channel 5,
chD
is 2, in this case, these channels are
overlapped by 50% for 20MHz, and by 75% for 40MHz.
The largest
chD
is 12 for 20MHz and 8 for 40MHz, in which
any frequency overlapping does not exist theoretically.
4.2.2 Physical Distance
The physical distance (
phD
) is defined as the Euclidean
distance between the two links. As it represents the sepa-
ration distance among the interfering links, the farther the
two links are placed, that is, the higher the physical distance
is, the interference will become lesser. By increasing the
physical distance between the links, the signal interference
between them fades due to the path loss and the absorption
by obstacles on the path.
4.2.3 Link Distance
The Euclidean distance between the sender and the receiver
of the link is defined as the link distance. The longer link
distance generally reduces RSS at the receiver and degrades
the throughput.
4.3 Throughput Estimation Model
Generally, the throughput of a link will be affected by a
variety of factors like the modulation and coding scheme
(MCS), the transmission power, the transmission distance,
and the channel interference [
13
,
40
], which have made it
hard for the theoretical calculation of the throughput. In
[
18
], we proposed the throughput estimation model for the
single link communication based on empirical results. This
model first estimates the receiving signal strength (RSS) at
the host using the log-distance path loss model [
41
]. Next,
it estimates the throughput from the RSS using the sigmoid
function.
4.3.1 Received Signal Strength Estimation
Firstly, the Euclidean distance d (m) between each link
(AP/host pair) is calculated by:
d=q(APx−Hx)2+(APy−Hy)2(1)
Here,
APx
,
APy
and
Hx
,
Hy
denotes the
x
and
y
coordinates
for the AP and the host respectively.
Then, RSS at the host from the AP is estimated by:
Pd=P1−10αlog10 d−X
k
nkWk(2)
Here,
Pd
denotes RSS (
dBm
) at the host,
P1
does RSS at the
1
m
distance from the AP where no obstacle exists,
α
does
the path loss exponent, d (m) does the Euclidean distance
calculated between the AP and the host,
nk
does the number
of
type k
obstacles found on the path between the AP and
the host, and
Wk
represents the signal attenuation factor (
dB
)
for type k obstacle.
4.3.2 Throughput Estimation
From Pd, the throughput is estimated by;
tpij =a
1+e−((120+Pd)−b
c)(3)
where
tpij
denotes the estimated throughput (
Mb ps
) and,
a
,
b, and care constant coefficients.
4.4 Throughput Drop Estimation Model for
Two-Link Concurrent Communications
In [
19
], we proposed the throughput drop estimation model
to consider the frequency interference between concurrently
communicating adjacent two links using interfering chan-
nels. The throughput drop of the target link can be estimated
by the receiving signal strength at the receiver, called the
interfering RSS, (
RS S i
), from the interfered link, and the
channel distance chD between the two links:
tpD(RS S i,chD)=p(chD)×ln(q(chD)+RS S i)+r(chD).
(4)
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AP 1
AP 2 AP 3
0.5 m
lkD
0.5 m
lkD
0.5 m
lkD
Ethernet
1 Gbps
Ethernet
1 Gbps
Ethernet
1 Gbps Server PC 1
Server PC 2
Server PC 3
Host PC 1
Host PC 2
Host PC 3
phD
Link 1
Link 3
Link 2
Wireless Link
IEEE 802.11n, 40MHz
Wireless Link
IEEE 802.11n, 40MHz
Wireless Link
IEEE 802.11n, 40MHz
Figure 2: Measurement setup.
where
tpD
(
RS S i,chD
) represents the estimated throughput
drop (
Mb ps
), and
p
(
chD
),
q
(
chD
), and
r
(
chD
) represent
constants determined by the channel distance (chD).
The values of the three constants
p
,
q
, and
r
in the model
were obtained from the throughput drop measurement results
for every channel distance. Table 1 shows them.
Table 1: Constants for each channel distance.
channel distance p q r
0 27 88.17 -20
1 27 87.36 -20
2 27 89.00 -22
3 25 94.50 -22
4 33 92.00 -56
5 34 92.00 -57
6 45 91.00 -98
7 45 88.00 -100
8 40 75.50 -80
Then, the throughput estimation model under POCs for
two interfered links is modified by:
tpi
i j =t pi j −tpD(RS S i
j,chDi
j) (5)
where
tpi
i j
represents the throughput of the target link from
node
i
to node
j
under the interference from the interfered
link,
tpij
does the throughput of this link estimated by the
original model,
RS S i
j
does RSS from the interfered link at
node
j
, and
chDi
j
does the channel distance between the two
links.
5 Experiments in Three-Link Con-
current Communications
In this section, we present the experiment results to examine
the extension of the throughput drop estimation model for
three concurrently communicating links.
5.1 Experiment Setup
Table 2 indicates the necessary devices and software used
in the experiments. It is noticed that if different devices are
used, the parameter values of the model will vary accord-
ingly.
Table 2: Measurement devices and software.
Access Point (all links)
maker/type NEC WG2600HP
protocol IEEE 802.11n
operating band 2.4 GHz
frequency width 40 MHz
host PC (all links)
maker/type Toshiba dynabook R731/B
operating system Ubuntu 14.04 LTS
processor Intel Core i5-2520M 2.54 Ghz
network adapter Intel HM65 Express chipset
server PC (link1)
maker/type Toshiba dynabook R731/B
processor Intel Core i5-2520M 2.54 Ghz
operating system Ubuntu 14.04 LTS
network adapter Intel HM65 Express chipset
server PC (link2,link3)
maker/type Fujitsu lifebook S761/C
processor Intel Core i5-2520M 2.5GHz
operating system Ubuntu 14.04 LTS
network adapter Mobile Intel QM67 Express
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Figure 2 shows the topology of three links (
link1
,
link2
and
link3
) in experiments. The server PC connects to the AP
through the Gigabit Ethernet, and the host PC is connected
to the AP by the 11n wireless link. The host is 0.5m from
its connected AP, and each AP is 5
m
from the other AP. The
maximum transmission power is selected for any AP with
the equal antenna gain [
16
]. The minimax AP setup optimiza-
tion approach in [
18
] is applied to maximize the throughput
for each link. The iperf 2.05 [
42
] is used to generate the
TCP traffic between the server and the host.
5.2 Experiment Fields
The experiments were carried out in indoor fields on the 2nd
floor of Graduate School of Natural Science and Technology
Building and the 3rd floor of Engineering Building #2 in
Okayama University. Each field consists of several rooms,
walls, and floors, which can affect the throughput through the
multi-path effect.
RS S i
and the throughputs are measured
while increasing the physical distance,
phD
, between the two
APs and their channel distance,
chD
. By using Homedale
[
43
], the frequencies of the links under measurements and
the external interfering links are recorded. Figure 3 shows
the site while Figure 4 reveals the frequency utilization for
measurements, where several channels are highly utilized by
non-target APs.
Link_2 Link_3
Link_1
Figure 3: Experiment in Graduate School Building.
AP2
AP3
AP1
(a) AP1=ch11, AP2 =ch3, AP3
=ch3
AP2
AP1
AP3
(b) AP1=ch11, AP2 =ch3, AP3
=ch7
AP1
AP3
AP2
(c) AP1=ch3, AP2 =ch3, AP3 =
ch8
AP2
AP1
AP3
(d) AP1=ch3, AP2 =ch3, AP3 =
ch10
Figure 4: Frequency utilization in experiments.
5.3 Experimental Results
For
AP1
(and
AP2
for three links), the bonded channel 3 is
always assigned. For
AP2
(
AP3
for three links), the assigned
channel is changed from 3 to 11 one by one, so that the chan-
nel distance
chD
increases from 0 to 8. The throughput was
measured at the same time for all the links. The experiments
were conducted on weekends and at night on weekdays to
reduce the interference from other Wi-Fi devices.
Figures 5 and 6 show measured individual and average
throughput under concurrently communicating two links and
three links, respectively. The individual throughput fluc-
tuates, because the contention among the links is not well
resolved by the current carrier sense mechanism [
44
], which
may cause the unfair channel occupation among them. Thus,
in this paper, we use the average throughput among them for
the single link throughput.
0 1 2 3 4 5 6 7 8 9
0
10
20
30
40
50
60
70
80
90
100
Throughput (Mbps)
Channel Distance
AP_1 (Fixed bonded ch 3)
AP_2 (ch changing)
Averange
Figure 5: Throughput results for two links.
Figure 6 demonstrates that when the three APs are as-
signed the same channel, one AP takes the entire medium,
which makes the others have the lower throughput. As
chD
increases, the throughput of
AP3
will enhance gradually, due
to the reduced channel interference.
In this experimental setup, the measured maximum
throughput of one link is about 140
Mb ps
under no inter-
ference. Then, it drops significantly to about 40
Mb ps
under
two links, when the channel distance is smaller than seven.
Furthermore, it drops to about 20Mbps under three links.
3 4 5 6 7 8 9 10 11 12
0
10
20
30
40
50
60
70
80
90
Throughput (Mbps)
Bonded channel fo AP_3
AP_1 (Fixed ch 3)
AP_2 (Fixed ch 3)
AP_3
Averange
Figure 6: Throughput results for three links.
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5.4 Observations from Experiment Results
According to the evaluation, it is observed that for the target
link, the interference from the first interfering link causes the
larger throughput drop (from 140
Mb ps
to 40
Mb ps
), and the
interference from the second causes the smaller drop (from
40
Mb ps
to 20
Mb ps
). That is to say, when the target link is
interfered by the first link, the rate adaptation mechanism
lowers the transmission rate by adopting the robust MCS.
Then, the second interfering link further lowers the rate by
adopting the more robust MCS [
30
][
31
]. Here, the rate can
be lowered exponentially in MCS.
6 Model Extension for Three-Link
Concurrent Communications
In this section, we present the extension of the throughput
drop estimation model for three concurrently communicating
links based on experimental results.
6.1 Idea of Throughput Drop Estimation un-
der Three Links
As observed before, the throughput drop from multiple inter-
fering links can be estimated one by one through calculating
each drop using the model in Section 4.4 in descending order
of the interference. That is, the drop from the link with the
largest interference is first estimated, assuming the original
maximum throughput. Then, the drop from the link with the
second largest interference will be estimated, assuming the
maximum throughput has been reduced by the first drop.
6.2 Throughput Drop Estimation Procedure
The throughput of the target link
linki j
under three-link con-
current communications is evaluated by the following proce-
dure:
1.
Estimate the throughput of each of the three concur-
rently communicating links using the original model
in Section 4.3.
2.
Estimate the throughput drop tpD from each of the
two interfering links using Eq. (4) in Section 4.4.
3.
Sort them in the descending order: let
tpD1st
and
tpD2nd.
4.
For the largest interfering link, adjust
tpD1st
by the
maximum speed of the AP of the target link, because
different APs have different throughput performances:
tpD1st
ad j =t pD1st ×tpMAP
140 (6)
where
tpD1st
ad j
represents the adjusted throughput drop
by the largest interfering link, and
tpMAP
does the
maximum throughput for the AP of the target link.
Then, the throughput
tp1st
i j
of the target link after con-
sidering the drop by the first link interference is esti-
mated by:
tp1st
i j =t pi j −tpD1st
ad j (7)
5.
For the second largest interfering link, adjust the
tpD2nd by;
tpD2nd
ad j =tpD2nd ×
tpMAP −t pD1st
ad j
140 (8)
Then, the throughput
tp2nd
i j
of the target link after con-
sidering the drop by the second link interference can
be estimated by;
tp2nd
i j =tp1st
i j −tpD2nd
ad j (9)
In [
31
], Su et al. observed that the throughput drop by
the accumulated interference from two interfering links is
greater than that from a single interferer, but less than the
sum of the drops from the individual interference. The sec-
ond interfering link will cause a smaller drop than the first
one. This observation also supports our throughput drop es-
timation model for concurrently communicating three links.
7 Evaluations of Model Extension
In this section, we evaluate the throughput drop estimation
model for three concurrently communicating links under
POCs and the channel bonding through experiments and
simulations.
7.1 Experiment Results in One Room
First, the devices and one-room field in Sections 5.1 and 5.2
are used in experiments.
7.1.1 Channel Assignments
In experiments, the bonded channel 3 is always assigned to
AP1
. Then, the either channel of 3, 7, and 11 is assigned
to
AP2
. To
AP3
, the assigned channel is moved from 3 to
11 one by one so that the channel distance is changed. The
throughputs are measured at the same time for all the links.
7.1.2 Throughput Results
Figures 7, 8, and 9 show the measured average through-
put among the three APs when the channel 3, 7, and 11 is
assigned AP2, respectively.
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
Throughput (Mbps)
Bonded channel fo AP_3
Measured averange
Estimation
Figure 7: Throughput results in one room for AP1:ch3, AP2:ch3.
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K.I. Munene et al. /Advances in Science, Technology and Engineering Systems Journal Vol. 4, No. 4, 94-105 (2019)
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
Throughput (Mbps)
Bonded channel of AP_3
Measured averange
Estimation
Figure 8: Throughput results in one room for AP1:ch3, AP2:ch7.
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
Throughput (Mbps)
Bonded channel of AP_3
Measured averange
Estimation
Figure 9: Throughput results in one room for AP1:ch3, AP2:ch11.
When the three results are compared, Figure 8 indicates
the lowest throughput among them, because
AP2
is inter-
fered with both
AP1
and
AP3
. Figure 9 presents the highest
throughput in general, because
AP1
and
AP2
are not inter-
fered. On the other hand, Figure 7 shows that as the channel
distance increases, the throughput will raise as the reduction
of the interference between (AP1and AP2) with AP3.
Then, the estimated throughput is calculated by the pro-
posed throughput drop estimation model, and is compared
with the measured throughput. Figures 7- 9 demonstrate that
these throughput are similar in any case. Thus, the accuracy
of the proposed model for three concurrently communicating
links is confirmed.
7.2 Experiment Results in Three Rooms
Next, the experiments are conducted using three rooms on
the 3rd floor of Engineering Building #2 to examine the
accuracy of the model.
7.2.1 Experiment Field
The physical distance between the APs is changed by lo-
cating each AP in a different room from the previous ex-
periments, as revealed in Figure 10. By locating each AP
in a separate room, the overall interference between them
is reduced. The link distance between the AP and its asso-
ciated host is set 0
.
5
m
or 4
m
. The channel assignments in
Section 7.1.1 are adopted.
D308 D307 D306 D 305 D303 D301
EV
D304 D302 Refresh Corner
30m
7m
6m
3rd Floor, Engineering Building No.2, Okayama University.
Access Point Host
Figure 10: Three room topology.
7.2.2 Throughput Results with 0.5mLink Distance
Figures 11 - 13 show the measured and the estimated
throughput results when the link distance is 0
.
5
m
. These
throughput turns out to be similar at any channel distance,
which confirms the accuracy of the proposed model. At the
same time, they are higher than the ones in Figures 7- 9,
since they are less interfered here.
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
Throughput (Mbps)
Bonded Channel of AP_3
Measured averange
Estimated
Figure 11: Throughput results in three rooms with 0
.
5
m
link distance for
AP1:ch3, AP2:ch3.
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
65
Throughput (Mbps)
Bonded Channel of AP_3
Measured averange
Estimated
Figure 12: Throughput results in three rooms with 0
.
5
m
link distance for
AP1:ch3, AP2:ch7.
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K.I. Munene et al. /Advances in Science, Technology and Engineering Systems Journal Vol. 4, No. 4, 94-105 (2019)
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
Throughput (Mbps)
Bonded Channel of AP_3
Measured averange
Estimated
Figure 13: Throughput results in three rooms with 0
.
5
m
link distance for
AP1:ch3, AP2:ch11.
7.2.3 Throughput Results with 4mLink Distance
Figures 14- 16 show the measured and the estimated results
when the link distance is 4
m
. In Figures 15 and 16, they are
similar at any channel distance. Thus, the accuracy of the
model is verified in the larger link distance as well. How-
ever, in Figure 14, the measured throughput is lower than
the estimated one at each channel distance. This is because
the interference from non-target APs in the field is stronger
around the channel 3, as shown in Figure 4. It is a must to
conduct experiments in environments with less interference,
which will be included in future works.
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
Throughput (Mbps)
Bonded Channel of AP_3
Measured averange
Estimated averange
Figure 14: Throughput results in three rooms with 4
m
link distance for
AP1:ch3, AP2:ch3.
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
Throughput (Mbps)
Bonded Channel of AP_3
Measured averange
Estimation
Figure 15: Throughput results in three rooms with 4
m
link distance for
AP1:ch3, AP2:ch7.
3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
Throughput (Mbps)
Bonded Channel of AP_3
Measured averange
Estimation
Figure 16: Throughput results in three rooms with 4
m
link distance for
AP1:ch3, AP2:ch11.
8 Application to Channel Assign-
ment
In this section, we discuss the application of the proposed
throughput drop estimation model to the POC assignment to
the APs.
8.1 Modification of Channel Assignment
Phase
The channel assignment phase of the active AP configura-
tion algorithm in [
45
] is modified to assign POC using the
proposed model. Specifically, the formulations for this phase
are revised from the previous one in [45] as follows:
8.1.1 Modification of Input
The number of partially overlapping channels is adopted in
place of the number of orthogonal channels for C.
8.1.2 Modification of Output
The partially overlapping channel assigned to every active
AP is adopted rather than the orthogonal channel assigned
to every active AP.
8.1.3 Modification of Objective
The total interfered communication time
E3
is modified to
consider partially overlapping channels by:
E3=
N
X
i=1
[IT i
i] (10)
where
IT i
i
denotes the interfered communication time under
partially overlapping channels for APi.
Under POCs, the link speed drop by the interfered links
needs to be examined in the throughput estimation model.
Therefore, IT i
ican be simply given by:
IT i
i=X
j∈AHi
1
tp2nd
i j
(11)
where AHidenotes the set of hosts associated with APi.
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K.I. Munene et al. /Advances in Science, Technology and Engineering Systems Journal Vol. 4, No. 4, 94-105 (2019)
Our current throughput estimation model for partially
overlapping channels only examines the interference be-
tween three links. In this paper, we assume that the target
link is interfered by the two strongest interfering links, while
the other interfering links may have negligible effects. This
can be supported by the results in Section 5.4, where the
highest interfering link causes the large drop and the second
link causes the far small drop.
8.2 Evaluations by Simulations
First, we evaluate the performance of the POC assignment
via simulations.
8.2.1 Simulation Platform
The WIMNET simulator [
46
] is adopted for simulations. Ta-
ble 3 sums up the parameters for simulations [45].
Table 3: Simulation Parameters in WIMNET Simulator.
parameter value
packet size 2360 bytes
max. transmission rate 150 Mbit/s
propagation model log-distance path loss model
rate adaptation model sigmoid function
carrier sense threshold −85 dBm
transmission power 19 dBm
collision threshold 10
RTS/CTS yes
8.2.2 Results for Random Topology
To evaluate the performance in various network environ-
ments for WLAN, first, the random topology is considered.
As shown in Figure 17, in this topology we consider a net-
work field of size 800
m×
200
m
, where two rooms are located,
each of length 400
m
. Eight APs and 25 hosts are allocated
randomly.
Active AP Inactive AP Active Host
Figure 17: Random topology for channel assignment.
.
Then, the minimum host throughput and the overall
throughput are compared between the POC assignment and
the conventional OC assignment through simulations. Two
channels (3, 11) are used for the OC assignment all the time.
On the other hand, three channels (3, 7, 11), six channels (3,
5, 7, 8, 9, 11), and nine channels (3, 4, 5, 6, 7, 8, 9, 10, 11)
are used for the POC assignment. Table 4 shows the results.
Table 4: Throughput results for random topology.
channel assignment OC POC
# of channels 2 3 6 9
min. host throu. (Mbps) 7.12 9.16 8.42 8.63
overall throu. (Mbps) 174.56 196.74 197.04 198.46
8.2.3 Results for Regular Topology
Next, the regular topology in the third floor of Engineering
Building #2 at Okayama University is considered. The room
size is either 7
m×
6
m
or 3
.
5
m×
6
m
. Eight APs and 55 hosts
are regularly allocated, as signified in Figure 18.
Active AP Inactive AP Active Host
Figure 18: Regular topology.
The same two, three, six, and nine channels as for ran-
dom topology are considered. Table 5 shows the minimum
host throughput and the overall throughput for them.
It is noted that in both topologies, as the number of POCs
is increased, the overall throughput will enhance by reduc-
ing the interference while maintaining the minimum host
throughput.
Table 5: Throughput results for regular topology.
channel assignment OC POC
# of channels 2 3 6 9
min. host throu. (Mbps) 2.68 3.11 3.06 3.15
overall throu. (Mbps) 147.76 170.88 168.76 173.49
8.2.4 Results for Regular Topology with Large APs
Finally, in Figure 19 we evaluate the model by simulations
in a new instance by increasing the number of APs from 8
to 10 in the regular topology. The number of hosts remains
as 55.
The evaluation in a real environment using the testbed
is more important to confirm the effectiveness of the model.
Thus, it will be in our future studies.
Active AP Inactive AP Active Host
Figure 19: Regular topology for 10 APs.
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K.I. Munene et al. /Advances in Science, Technology and Engineering Systems Journal Vol. 4, No. 4, 94-105 (2019)
Table 6 shows the results. As the number of APs in-
creases, the total performance also increases, because more
hosts can be located nearer to the APs, which increase the
throughputs of them.
Table 6: Throughput results for regular topology on 10 APs.
channel assignment OC POC
# of channels 2 3 6 9
min. host throu. (Mbps) 3.97 4.66 4.49 4.55
overall throu. (Mbps) 218.24 250.72 247.29 251.24
8.3 Evaluations by Experiments
Lastly, the throughput of the POC assignment is evaluated
through experiments using the two-rooms topology in Fig-
ure 20. Two channels 3 and 11 are used for the OC assign-
ment, and three channels 3, 7, and 11 are for the POC.
Table 7 shows the simulation and measurement results.
This table indicates the following: 1) the POC assignment im-
proves the overall throughput, and 2) the estimated through-
put is well coincident with the measured one. The accuracy
of the throughput estimation model and the effectiveness of
the POC assignment are confirmed.
B
A
AP
Host
(a) Two-rooms topology
Figure 20: Two-rooms topology.
Table 7: Throughput results for Two-room topology.
channel assignment OC POC
# of channels 2 3
measurement (Mbps) 146.36 158.2
simulation (Mbps) 143.5 157.31
9 Conclusion
In this paper, we presented the throughput drop estimation
model extension for concurrently communicating three IEEE
802.11n links under partially overlapping channels (POCs)
and the channel bonding. Also, we proposed the model
application to the POC assignment to the access-points in
WLAN. Through extensive experiments and simulations, we
confirmed the accuracy of the model and the effectiveness of
the POC assignment. In future, we will upgrade this model
for four or more interfering links. Then, we will evaluate
it in a variety of network scenarios, such as for dense WiFi
environments.
Acknowledgments
This work is partially supported by JSPS KAKENHI
(16K00127).
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