International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015), pp. 49-56
http://dx.doi.org/10.14257/ijseia.2015.9.3.06
ISSN: 1738-9984 IJSEIA
Copyright ⓒ 2015 SERSC
Utilizing CSI to Improve Distance Estimation Precision in the
Indoor Environment
Jeong-Woo Yang and Gi-hwan Cho*
Div. of Computer Science and Engineering, Chonbuk National University, Korea
{reewr, ghcho}@chonbuk.ac.kr
Abstract
To localize mobile devices in the indoor environment, it is considered as a natural
approach to make use of a commodity device. One popular work utilizes the physical
layer information of WLAN devices. The information includes a beneficial characteristic,
that is, the possibility of eliminating some degree of multipath fading effects. This paper
deals with some experiments to improve the indoor distance estimation precision by
taking advantage of the new RF channel property, CSI(Channel Status Information). It
mainly aims to show how much the multipath effect can be mitigated with CSI rather than
RSSI, then how it influences to the distance estimation.
Keywords: indoor; multipath; CSI; distance estimation
1. Introduction
For a mobile device such as navigator and smart phone, the most prominent
application can be considered as the location based services. Here, the location
information of user and/or device is the critical factor to enable these services to
work properly. GPS(Global Positioning System) is a public and popular localization
system. However, it does not work in indoor environments where it is not possible
to receive its positioning signal. Recently it is getting to be interested in making use
of a commodity device for indoor localization in terms of easy-to-use and cost
effective.
The distance estimation has been traditionally performed with RSSI(Received
Signal Strength Indication) which measures RF signal strength by applying a radio
propagation model[1]. However, RSSI simply capture the total power received at the
receiver. So, in indoor environment, it shows relatively big errors from the actual
distance[2]. Because of irregular signal attenuation is caused by multipath fading.
Therefore, it is strongly required to mitigate the effect of multipath fading.
Recently, IEEE 802.11 standardization based on OFDM(Orthogonal Frequency
Division Multiplexing) technology provide RF channel properties, named as
CSI(Channel State Information). It contains information about the channel at the
level of individual subcarriers, for each pair of transmit and receive antennas [3].
CSI consists of amplitude and phase per subcarrier. It measures the channel status
from receiver and feedbacks to sender to optimize its transmission rate. By
processing the CSI, it is possible to cut down the some degree of multipath effect
[4]. This is the prominent feature from RSSI As a result, it is highly expected to
contribute more accurate distance estimation in indoor environment. That is why we
have examined this issue.
Initially, CSI value is a frequency domain representation. It is converted to the
time domain one with IFFT(Inverse Fast Fourier Transform). To mitigate the
multipath effect, only the low powered signal components are eliminated to filter
* Corresponding Author
International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015)
50 Copyright ⓒ 2015 SERSC
)sin(
)()(
Hj
kkefHfH
out LoS(Line of Sight) signal. The remained signal components are again changed
to the frequency domain one, and utilized for distance estimation. This paper deals
with some experiments to improve the indoor distance estimation precision by
taking advantage of the new RF channel property, CSI. It mainly aims to show how
much the multipath effect can be mitigated with CSI rather than RSSI, then how it
influences to the distance estimation.
The paper is organized as follows. In section 2, we briefly summarize the related
works. Section 3 examines the CSI features, and Section 4 describes the mitigation
process of multipath fading effect. In Section 5, we show experiments results, then
finally, Section 6 concludes our work.
2. Related Works
In the distance estimation with RF properties, RSSI was a very popular feature
that does not usually required a professional hardware because it is available in most
of the wireless devices. Simply the distance can be estimated from the measured
RSSI at fixed receiver, by making use of the path loss signal propagation model.
However, it is considerably fluctuated in the indoor environment, because it leads to
complex multipath effect. Even, RSSI value changes over time at same location. As
a result, it brings about a large error in distance estimation. Recently, CSI based
new approaches were concentrated to resolve this problem with RSSI.
FILA[5] extracts only LoS factor among the signals transmitted through
multipath using commercialized NIC(Network Interface Card). It determines the
LoS signal components based on a threshold value; signals having amplitude of
lower than 50% of the maximum amplitude are removed in time domain
representation. The remained signal components are used to estimate the sender’s
distance. Based on the estimated distance, it determines a mobile dev ices’ location
with the triangulation technique. With constituting a radio map with CSI on
different location, it is possible to improve the localization precision can be
improved. Experimental result shows localization errors around 2m and 3m with
probabilities of 80% and 90%.
CUPID[6] determines the LoS signal component that is the first arriving one by
making use of the time domain representation of CSI. However, this signal has low
amplitude than following multipath signals when LoS path was blocked by obstacles.
To solve this problem, the path loss fading exponent is selected considering lfactor
that is a ratio between the EDP and the RSSI. lfactor and the path loss fading
exponent have a linearly decreasing relationship. The experimental result shows
that its localization error is around 3m and 4m with probabilities of 80% and 90%
when five APs are take part in.
This work mainly refers the FILA's approach to process CSI information to
mitigate the multipath effect. Then, we concentrate on evaluating which the distance
estimation factors, RSSI and CSI, are much appropriated in the indoor localization.
3. CSI Features
On receiving signal from Intel 5300agn NIC, CSI information can be obtained by
making use of an open CSI tool program[7]. CSI information gathered from AP is
stored as a unit of packet transmitted by a mobile device. A CSI value is represented
with amplitude and phase in terms of subcarriers[8]. For a subcarrier having a center
frequency fk, CSI is described with phase and amplitude as follows.
International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015)
Copyright ⓒ 2015 SERSC 51
50 100 150 200
5
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sequence
inde
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sub
carr
ier
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sequence
inde
x of
sub
carr
ier
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(a) antenna A (b) antenna B (c) antenna C
Figure 2. Amplitude of CSI on Frequency Domain each Antena in Dynamic
Environment
50 100 150 200
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sequence
inde
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carr
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sequence
inde
x of
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carr
ier
15
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30
(a) antenna A (b) antenna B (c) antenna C
Figure 1. Amplitude of CSI on Frequency Domain each Antenna in Static
Environment
where H is the phase and )(k
fH is the amplitude of subcarrier.
In 20MHz WiFi bandwidth, 30 groups of subcarriers are reported from the Intel
5300agn NIC, which consists of 3 x 3 antennas. Three CSI values are obtained for
one signal using three antennas. But, this study utilizes CSI values of one antenna.
So, we choose one of them. Figure 1 shows amplitudes of CSI in the frequency
domain representation from one signal which is measured at the same location in
static environment. Each figure represents the different amplitudes for each antenna.
As we can see, CSI amplitudes are showed in stable on consecutive time. Figure 2 shows amplitudes of CSI in the frequency domain from one signal which
is measured at the same location in dynamic environment. The CSI for antenna A
and antenna B show unstable patterns on consecutive time, while antenna C is
relatively stable than the others. Among the 3 different CSIs, we choose one which
is relatively stable and high amplitude in the frequency domain representation for
the distance estimation process.
4. Distance Estimation with RF Channel Property
4.1. Multipath Effect Mitigation
Initially, CSI values are collected from a commodity network device in a
frequency domain representation. It can be converted into the time domain one with
IFFT. Then, the time domain CSI is represented in the different multipath
components which are separated by time. With this CSI feature, it is possible to
differentiate LoS component(s) from multipath components.
Signal components except for the LoS component(s) might be delivered with
some degree of reflection from wall, floor and/or other objects, so its original signal
strength has been attenuated on the path. It is natural to consider that multipath
components have relatively low amplitude. Here, we will filter out these signal
components which are lower than 50% of peak amplitude to mitigate the multipath
International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015)
52 Copyright ⓒ 2015 SERSC
agcRSSIPrssi
44 (1)
0 0.5 1 1.50
10
Delay (microsecond)
Am
plit
ude
0 0.5 1 1.50
10
Delay (microsecond)
Am
plit
ude
(a) Amplitude of CSI in Time Domain
5 10 15 20 25 300
5
10
15
20
25
30
subcarrierA
mpl
itude
Original value
Filtered value
(b) Amplitude of CSI in Frequency Domain
Figure 3. An Example of Mutipath Effect Mitigation
K
k
k
k
effH
f
f
KCSI
1 0
1 (1)
fading effect. Then, with applying FFT(Fast Fourier Transform), the time domain
representation would be converted into the frequency domain one.
Figure 3 shows an example of mitigate the multipath fading effect process. Figure
3(a) shows the time domain representations of CSI; the above shows the one before
filtering of multipath components while the below shows the one after filtering.
Figure 3(b) shows the frequency domain representations corresponding to Figure 3
(a) respectively.
4.2. Quantization of CSI
Here, it is well known that different frequency domain components of a signal
have an uncorrelated fading, so it is called as frequency selective fading. This means
30 subcarrier groups are occurred different fading effect. If we use only the
maximum amplitude of subcarrier groups, distance estimation errors will be taken
place irregularly. In our study, we average the 30 subcarrier groups to reduce the
error caused by the frequency selective fading, that is, CSI quantization. We give a
different weight to each other sub-carrier because signal attenuation effect is
frequency-related. For CSI value of the high frequency group is much weighted that
that of the low one.
The quantization of CSI is calculated as an equation below.
where 0
f is the central frequency, k
f and k
H are the frequency and amplitude
of the th
f subcarrier of CSI.
4.3. Scaling of CSI
The measured CSI from a commodity NIC is provided in a normalized form to be
directly utilized for channel control. This means there is no relationship between the
distance and the measured CSI value. So, the measured one should be compensated
by AGC(Automatic Gain Control) which is also obtained from the NIC. AGC is the
gain to maintain a constant range of the amplitude of the signal input to the A-D
converter. Usually formula to obtain the RSSI is provided with the below equation.
International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015)
Copyright ⓒ 2015 SERSC 53
0
0log10
d
dnPP
r (1)
1010
agc
eff
csi
CSIP (1)
where rssi
P is the received signal strength in dBm, RSSI is received signal
strength which is relative to an internal reference in dB and agc is an indicator for
the compensation.
The quantized CSI and agc are applied to scale the measured CSI in order to
reflect the receiving signal strength as below equation; it is a conversion from the
normalized one to the radio propagation dependent one. For two RF properties,
RSSI and scaled CSI, a performance comparison of distance estimation is described
in Section 5.
where csi
P is the CSI strength in dBm and agc is an indicator for the
compensation.
4.4. Path Loss Propagation Model
Radio frequency delivery is modeled with a free-space loss which is the signal
strength attenuation in proportion to the propagation distance. The path loss
propagation model is a formula that shows how the signal strength is decreased on
increasing the distance. This formula provides a basement to estimate the distance
with RF properties as RSSI or CSI. The most common model is represented as
follow.
where 0
P is the signal strength at distance of unit distance 0
d , n is the path loss
fading exponent, and d is the measured distance from the transmitter.
Path loss fading exponent is a quantification of environmental factors such as RF
gain, antenna gain, refraction, shadowing and propagation loss that affect the
propagation of electromagnetic wave. It is known that an environment having
complicated structures; such as office, has a value of about 4 or more. On the other
hand, it has a value of about 2 in simple environment; such as corridor.
5. Experimental Results
The performance was measured using the LG Optimus LTE terminals and
notebook PC which are equipped with Intel 5300agn chipset with running of an
open CSI tool program[7]. The laptop equipped with three antennas acts as the AP,
and collects packets transmitted from the mobile device.
The experiment was conducted in only the situation where LoS is guaranteed.
Estimated distance is directly reflected by the multipath mitigation effect and the
scaling scheme. Therefore, the selection of suitable scaling scheme is also important.
Figure 4 shows the distances estimation errors for two scaling schemes, that is,
RSSI scaling (above) and the proposed one, in comparatively.
International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015)
54 Copyright ⓒ 2015 SERSC
0 5 10 15 20 25 30 350
0.05
0.1
0.15
0.2
prob
abili
ty
distance error
RSSI scaling
0 5 10 15 20 25 30 350
0.05
0.1
0.15
0.2
prob
abili
ty
distance error
Propose scaling
Figure 4. Distance Error of two Different Scaling Schemes
0 0.5 1 1.5 2
x 10-3
0
5
10
15
20
25
30
dist
ance
(m
eter
s)
RSSI
RSSI amplitude
Exponential Fitting
(a) Relation between RSSI and distance (path loss fading exponent is 3.8710)
0 0.2 0.4 0.6 0.8 1 1.20
5
10
15
20
25
30
dist
ance
(m
eter
s)
CSI
CSI amplitude
Exponential Fitting
(b) Relation between CSI and distance (path loss fading exponent is 2.1860)
0 2 4 6 8 10 12 140
0.2
0.4
0.6
0.8
1
CD
F
distance error
RSSI
CSI
(c) CDF of Distance Errors for CSI and RSSI
Figure 5. Distance Errors for CSI based System and RSSI based One
Figure 5 shows the distances estimation errors for two different RF properties,
that is, CSI and RSSI. Figure 5(a) and Figure 5(b) show the exponential fitting
graphs respectively; Figure 5(a) shows a drastic reduction of the RSSI signal.
However, CSI shows an ideal path loss fading exponent as the free space in Figure
5(b). Figure 5(c) depicts that CSI based distance estimation shows more lower errors
than that of RSSI. Distance estimation error with RSSI is ranged from 4m to 10m
with probabilities of 60% and 90% while the error with CSI shows 5m distance error
with 90% probabilities.
With our experimental examination, CSI based approach is much superior than
RSSI based one. Mitigating multipath effects with CSI contributes to eliminate the
multipath effect in distance estimation, but it is not possible with RSSI.
International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015)
Copyright ⓒ 2015 SERSC 55
6. Conclusion
Indoor localization is known to be one of the most important applications in the
future. RSSI based distance estimation approach has been widely used, however, it
has some degree of drawbacks, in which it does not reflect the distance with the
multipath fading occurred by various obstacles in the indoor environment.
To resolve this problem, this work provides an experimental examination with
CSI characteristics, calibration of CSI and distance estimation in detail. Then it
shows that the CSI based multipath mitigation process great contribute to reduce the
distance estimation error; distance estimation error with CSI can be reduced up to
5m for 90% possibility. The future work is to refine the multipath mitigation method,
and a novel method the separate LoS component from the multipath components.
Acknowledgements
This research was supported by Basic Science Research Program through the National
Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and
Technology (2012R1A1A2042035).
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Authors
Jeon-woo Yang, he received B.S. degree in computer science and
engineering from Chonbuk National University, Jeonju, Korea, in
2013. Currently, he is a master course student at Chonbuk National
University, Jeonju, Korea. His current research interests include
network security, mobile internet and sensor networks.
Gi-hwan Cho, he received B.S. degree from Chonnam National
University, Gwangju, Korea, in 1985, and his M.S. degree from
Seoul National University, Seoul, Korea, in 1987, both in computer
science and statistics. He received his Ph.D. degree in computer
science from the University of Newcastle, Newcastle Upon Tyne,
England, in 1996. He worked for the Electronics and
Telecommunications Research Institute (ETRI), Daejeon, Korea, as a
senior member of the technical staff from Sep. 1987 to Aug. 1997, for
International Journal of Software Engineering and Its Applications
Vol. 9, No. 3 (2015)
56 Copyright ⓒ 2015 SERSC
the department of computer science at Mokpo National University,
Mokpo, Korea, as a fulltime lecturer from Sep. 1997 to Feb. 1999. In
Mar. 1999, he joined the division of computer science and
engineering at Chonbuk National University, Jeonju, Korea, and he is
currently serving as a professor. His current research interests include
mobile computing, computer communication, security framework,
wireless security, sensor networks, and distributed computing
systems.