Utilizing CSI to Improve Distance Estimation Precision in the Indoor ...

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


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.


Vol. 9, No. 3 (2015)

Copyright ⓒ 2015 SERSC 51

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sequence

inde

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carr

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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

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inde

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carr

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15

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(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


Vol. 9, No. 3 (2015)


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

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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.


Vol. 9, No. 3 (2015)


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.


Vol. 9, No. 3 (2015)


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.


Vol. 9, No. 3 (2015)


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).

References

[1] K. Wu, H. Tan, H. Ngan and L. Ni, “Chip Error Pattern Analysis in IEEE 802.15.4”, IEEE Transactions

on Mobile Computing, vol. 11, no. 4, (2012) April, pp. 543-552.

[2] S. Sen, B. Radunovic, R. R. Choudhury and T. Minka, “Spot Localization using PHY Layer

Information”, Proc. on ACM MobiSys’12, Lake District, UK, (2012) June 25-29, pp. 183-196.

[3] P. Bahl and V. N. Padmanabhan, “RADAR: An In-building RF-Based User Location and Tracking

System”, Proc. on IEEE INFOCOM, Tel Aviv, Israel, (2000) March 26-30, pp. 775-784.

[4] K. Wu, J. Xiao, Y. Yi and D. Chen, “CSI-Based Indoor Localization”, IEEE Transactions on Parallel

and Distributed System, vol. 24, no. 7, (2013) July, pp. 1300-1309.

[5] K. Wu, J. Xiao, Y. Yi, M. Gao and L. M. Ni, “FILA: Fine-grained Indoor Localization”, Proc. on IEEE

INFOCOM, Orlando, Florida, (2012) March 25-30, pp. 2210-2218.

[6] S. Sen, J. Lee. K. Han and P. Congdon, “Avoiding Multipath to Revive Inbuilding WiFi Localization”,

Proc. on ACM MobiSys’13, Taipei, Taiwan, (2013) June 25-28, pp. 249-262.

[7] D. Halperin, W. Hu, A. Sheth and D. Wetherall, “Tool Release: Gathering 802.11n Traces with Channel

State Information”, Proc. on ACM SIGCOMM, vol. 41, no. 1, (2011) January, pp. 53.

[8] K. Chintalapudi, A. P. Iyer and V. N. Padmanabhan, “Indoor Localization without the Pain”, Proc. on

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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


Vol. 9, No. 3 (2015)


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.

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