This document discusses the importance of line of sight (LOS) and non-line of sight (NLOS) identification for improving communication systems, particularly through the use of channel state information (CSI) for indoor environments. It outlines challenges in accurately identifying LOS conditions, describes measurement techniques, and evaluates performance metrics like skewness and kurtosis. The future scope includes applications such as target tracking and mobile radio systems, with combined features yielding high identification rates for LOS and NLOS conditions.

1. By:
Nidhi Kumari

2. LINE OF SIGHT & NON-LINE
OF SIGHT

3. IMPORTANCE OF LOS
IDENTIFICATION
IMPORTANCE
Increasing the
performance
Ensuring tight
electromagnetic
coupling
Reducing the error
Optimizing our
communication
system

4. CHALLENGES IN LOS
IDENTIFICATION
 Physical layer information is yet unexplored and wifi
devices only report single-valued MAC layer RSS to upper
layers.
Real-world evaluation of LOS identification is still a
difficult task.
 It is a labor intensive process as information need to be
extracted from MAC layer RSS.

5. INTRODUCTION
In this indoor LOS identification technique we exploit
channel state information (CSI) from the PHY layer to
identify the availability of LOS component indoors .
 Use of natural mobility to magnify the distinction
between LOS and NLOS conditions.
 Use of frequency diversity to reveal the spatial
disturbance of NLOS propagation.

6. MEASUREMENT AND
INFORMATION EXTRACTION
 Channel State Information (CSI) : In this LOS identification
scheme we explore the recently available PHY layer
information. The sampled version of channel frequency
response (CFR) is revealed to upper layers in form of
channel state information (CSI).
 Measurements with CSI:
(a) Shape-Based Features with CSI:

7. (b) Statistics-Based Features with CSI: signals travelling
alog NLOS paths tend to behave more randomly compared
with those along a clear LOS path
Rician-k Factor
Channel Statistics with Mobility:
Shape based features are
infeasible due to insufficient
bandwidth of WiFi.The main
hurdle is short transmission
distance which makes the NLOS
path not adequately random,thus
increasing difficulty.

8. LOS IDENTIFICATION
 Preprocessing: Since lack of time and frequency
synchronization induces phase noise when measuring
channel response we revise the phase to reduce error.
 Normalization: To make LOS identification independent
of power attenuation we normalize CIR samples and CSI
amplitudes by dividing them by average amplitude.

9.  Identification: For a set of normalized CIR and CSI
amplitude from N packets,
skewness feature hypothesis test is: H0 : s < sth
H1 : s > sth
kurtosis feature hypothesis test is: H0 : κ > κth
H1 : κ < κth
sth and κth are the identification threshold for the skewness and
kurtosis features, respectively which are pre-calibrated.
HO & H1 represents hypothesis test with LOS condition and NLOS
condition resp.

10. OVERALL PERFORMANCE
 Static vs Mobile Links: The graph indicates that the mobility
increases spatial disturbance of NLOS paths.
 Skewness vs Kurtosis: The kurtosis feature performs slightly
better because skewness feature relies on extracting dominant paths.
 Combining Skewness and Kurtosis: Combining the two feature
and plotting the linear separator gives the marginal performance gain
with LOS and NLOS detection rates of 94.36% and 95.98%

11. Fig : Overall LOS identification performance of the skewness and kurtosis features
and their combination. (a) ROC curve of skewness and kurtosis. (b) ROC
curve of kurtosis.

12. Fig : Impact of propagation distances: Both features perform better for medium
propagation ranges. (a) Skewness. (b) Kurtosis.
Impact of propagation distances

13. Fig : Impact of moving speed: Both features retain detection accuracy of above
82% for moving speeds of 0.5 m/s to 2.0 m/s. (a) Skewness. (b) Kurtosis.
Impact of moving speed

14. FUTURE SCOPE
 Target tracking

15.  Wireless Energy Harvesting

16.  LED – ID System

17.  Mobile Radio Systems

18.  PHY layer information to identify LOS conditions with
wifi.
 Explore skewness and kurtosis features for LOS
identification.
 Combination of skewness and kurtosis feature gives LOS
and NLOS identification rates around 95%.
CONCLUSION

19. REFERENCES
 “WiFi-Based Indoor Line-of-Sight Identification” IEEE Journel
published in November 2015 – base paper
J. Borras, P. Hatrack, and N. Mandayam, “Decision theoretic
framework for NLOS identification,” in Proc. IEEE Veh. Technol.
Conf., 1998.
 J. Lin, “Wireless power transfer for mobile applications, and health
effects,” IEEE Antennas Propag. Mag., vol. 55, no. 2.
 https://en.wikipedia.org/wiki/OSI_model