TIME VARYING PATH SHADOWING MODEL

FOR INDOOR POPULATED

ENVIRONMENTSPresented by:

Sneha Rachel PhilipS1 MTechRoll No. 16

Seminar Guide:

Asst. Prof. Mohammed Salih K. K.

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INTRODUCTION• Path shadowing by human bodies is a major

cause for channel fluctuation in indoor environments

• In WLAN , sensor networks and similar systems using radio frequencies, where the transmitter and receiver elements are fixed, path shadowing effects need to be monitored

• Path shadowing has a significant effect on the channel Eigen values of MIMO channels

• A channel model that can simulate the shadowing effects is effective in performance estimation of such systems

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PROPAGATION MODELS• Multipath models for indoor and outdoor

environments based on experimental data , not taking in to account the moving obstacles around terminals were the earlier models

• Previous studies of human shadowing effects needed field measurements, probability measurements and consideration of routes in which human bodies move

• This seminar presents a path shadowing model for indoor populated environments based on computer simulations

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4PROPOSED MODEL • Propagation paths between the transmitting and

receiving points are determined using the ray tracing method

• Moving quasi-human bodies modeled as cylinders are generated in space

• Intersection of the path with bodies are counted

• The shadowing probabilities, durations, and intervals are evaluated for each path and this shadowing process is characterized as a Markov process

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PROPOSED MODEL • The seminar is outlined as follows:

i. Configuration of the computer simulationii. Results of simulationiii. New path shadowing modeliv. Experimental Results

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COMPUTER SIMULATION CONFIGURATION• Human body modeled as a perfectly absorbing

cylinder with height Hb = 1.7 m, diameter w=0.4 m

• An empty rectangular room is assumed to be the indoor propagation environment

• Reflections from ceiling/ floor surfaces are considered, max. reflections set to 18

• Transmitter and receiver heights both set to 0.7 m

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COMPUTER SIMULATION CONFIGURATION

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COMPUTER SIMULATION CONFIGURATION• Round(S x n) human bodies are generated and

where S is the area of the room and n the population density

• Each model is placed at a random location and is then moved every 0.1 m for each sampling instance up to the end of simulation

• Each propagation path is checked for intersection with the model

• Shadowing probability is calculated

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PATH SHADOWING PROCESS• Visibility given by:

• x is the length of the propagation path that exists in the area lower than the height of the human model

• n is the population density• w is the width of the human model

• Path shadowing probability will be:

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PATH SHADOWING PROCESS

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PATH SHADOWING PROCESS• Effective path length:

d is the path length C0 is the proportion factor

• C0 is approximated as:

Nr is the maximum no. of reflections on ceiling/floor Lz is the height of the room Hb is the height of the human model

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PATH SHADOWING PROCESS• Thus path shadowing probability for the jth path

will be:

• n is the population density• w is the width of the human model• dj is the path length of the jth path

• C0 is the proportion factor

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PATH SHADOWING PROCESSTwo State Markov Model To Characterize

Shadowing Duration And Interval

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PATH SHADOWING PROCESS• Multiple Path Shadowing Events:

• Considered to occur randomly and follow the Poisson distribution given by:

• n is the population density• w is the width of the human model• d is the path length

• C0 is the proportion factor

• Gives the probability that N persons exist inside the d x w rectangular space and block the path

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SIMULATION RESULTS

Path shadowing probabilities obtained from simulations(for varying n) Nr =6, Lz =2.7 m (a) n=0.02 (b) n=0.1 (c) 0.18 (d) n=0.26

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SIMULATION RESULTS

Path shadowing probabilities obtained from simulations(for varying Lz in m) Nr =6 (a) Lz=3.0 (b) Lz=5.0 (c) Lz=7.0 (d) Lz=9.0

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SIMULATION RESULTSTransition probabilitiesfrom simulation:• Total no. of evaluated

paths=22627

• Pbg can be regressed by a polynomial of degree 3 for path shadowing probability P:

• Based on the properties of Markov model:

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SIMULATION RESULTS• Transition probabilities are obtained with a

sampling step size of 0.1 m

• Sampling duration and interval cannot be taken accurately for step size>0.2 m

• For step size<0.1, lot of computing resources are required

• When sampling step size is set to s1< 0.1 m, an approximate transition probability can be obtained by multiplying 0.1/s1 to the relation for Pbg(P)

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SIMULATION RESULTSMultiple Shadowing

Event:• Figure shows simulation

results and the estimation curves

• N indicates N point multiple shadowing by human body models

• Results confirm that the Poisson distribution yields a good estimation for multiple shadowing events

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PATH SHADOWING MODELFrom the discussion so far:

Shadowing Probability for each path is modeled as:

Shadowing duration and interval as:

Multiple shadowing events as:

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PATH SHADOWING MODELSteps to generate the shadowing events on each propagation path:

1. Determine propagation paths using the ray tracing method and calculate the path length dj of the jth path

2. Calculate the path shadowing probability of the jth path pj using path length dj

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PATH SHADOWING MODEL3. Calculate the transition probabilities Pbg

(j) and Pgb

(j) using the path shadowing probability pj

4. Transit the states using the transition probabilities Pbg

(j) and Pgb(j) and simulate the

shadowing process. When a state transits to the shadowed

state, generate a random digit and use this number as a state bit. The number gives how many shadowed points exist on the jth path at this state.

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICSMeasurement Configuration

• Indoor measurements conducted using a 5.2 GHz high resolution channel sounder

• Transmitter antenna- vertically polarized omnidirectional half-wavelength sleeve dipole; transmitted power= 40 dBm

• Receiver antenna Stacked polarimetric uniform circular patch array

96 polarization sensitive single elements arranged on a cylinder in 4 rings

Each antenna has vertical and horizontal polarization ports

HPBW 120˚ azimuth 90 ˚ elevation

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICS• Channel characteristics measured using a periodic

multitone signal with a 100 MHz BW radiated from the transmitter

• Maximum delay=800 ns, one snapshot every 25.6 ms

Measurement Environment

• Room of dimension (6.4, 9.6, 2.7)m

• Height of receiver and transmitter 1.2 m

• 7 measurements taken for 3 population densities

• Velocity of each person controlled to maintain a constant velocity of 2.0 m/s

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICS

Figure shows the top view of the empty space with the expected propagation paths obtained by geometrical ray tracing

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICSMeasured Data Processing

• Measured data analyzed using the Joint Maximum Likelihood Algorithm called the RIMAX algorithm

• Path parameters solved by the algorithm are Directions of arrival(DoA)

Time Delay

Complex path weights of both horizontal and vertical polarizations

• Since the transmitter and receiver antennas are stationary, DoA and time delay may not significantly change for a path, and hence are used to identify the specular paths

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Figure shows data from RIMAX algorithm for measurement 002 on the azimuth delay plane

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICS• Group of data concentrated at almost the same

point, represents data from the same path

• Decrease in path weight may occur when there is path shadowing by human bodies

• If there is consecutive missing data for less than seven snap shots, this does not indicate shadowing, but as failures in the solution

• If there is consecutive missing data for more than 8 snapshots(>0.2 s), the event is assumed to have decreased path weight due to shadowing by human bodies

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICS• Figure shows the missing

data in the first graph compensated in the second one

• Shadowing events are detected when drop in received level becomes 3 dB less than the constant level

• The data is modified as shown in the third graph

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICS• Modified data is used to measure the shadowing

probability and transition probability

• In this measurement people moved approximately 0.051 m in every sampling instance<0.1

• Corrected transition probability can be obtained by multiplying 0.1/0.051 with the original transition probability from measurement

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MEASUREMENT OF PATH SHADOWING CHARACTERISTICS

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CONCLUSION• This paper has presented a new probability model to

estimate the shadowing effects on each propagation path due to moving human bodies in indoor environments.

• We have modelled the shadowing process as a two-state Markov model and proposed a method to individually and independently generate the shadowing effects on propagation paths using the transition probability.

• The propagation measurement using a high-resolution channel sounder has also been conducted to confirm the validity of the model presented in this paper.

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CONCLUSION• This model can be applied under the conditions

that the population density maintains approximately the same level and people move at constant velocities

• This model will be useful in accurately evaluating the signal level variation in the surrounding environments that contain movement while the transmitter and receiver terminals are fixed.

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REFERENCES[1] Ippei Kashiwagi, Member, IEEE, Tokio Taga, Senior

Member, IEEE, and Tetsuro Imai, Member, IEEE “Time-Varying Path-Shadowing Model for Indoor Populated Environments “IEEE Trans. on Vehicular Technology, vol. 59, no. 1, January 2010

[2] S. Obayashi and J. Zander, “A body-shadowing model for indoor radio communication environments, ” IEEE Trans. Antennas Propag., vol. 46, no. 6, pp. 920–926, Jun. 1998

[3] K. Sato and T. Manabe, “Estimation of propagation-path visibility for indoor wireless LAN systems under shadowing condition by human bodies,” in Proc. IEEE VTC, Ottawa, ON, Canada, May 1998, vol. 3, pp. 2109–2113

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REFERENCES[4] A. Richter and R. S. Thoma, “Joint maximum likelihood

estimation of specular paths and distributed diffuse scattering,” in Proc. IEEE VTC—Spring, Jun. 2005, vol. 1, pp. 11–15

[5] A. Richter, M. Landman, and R. S. Thoma, “RIMAX—A flexible algorithm for channel parameter estimation from channel sounding measurements,” COST273 TD(04)045, Athens, Greece, Jan. 26–28, 2004

[6] C. C. Chong, C.-M. Tan, D. I. Laurenson, S. McLaughlin, M. A. Beach, and A. R. Nix, “A novel wideband dynamic directional indoor channel model based on a Markov process,” IEEE Trans. Wireless Commun., vol. 4, no. 4, pp. 1539–1552, Jul. 2005

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REFERENCESTextbooks:

• Wireless Communications –Principles and practice-2nd Edition, T. S. Rappaport

PPT

• Channel Sounding - PG Course in Radio Communications -Fabio Belloni

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THANK YOU

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QUESTIONS???