1. <month year>
doc: IEEE c
July 2006
Project: IEEE P Working Group for Wireless Personal Area Networks (WPANs)
Submission Title: [Saleh-Valenzuela Channel Model Parameters for Library Environment]
Date Submitted: [July 2006]
Source: [Alexei Davydov, Alexander Maltsev, Ali Sadri]
Company: [Intel Corporation]
Address: [Intel Corporation, Turgeneva 30, Nizhny Novgorod, Russia],
Abstract: [This contribution contains the parameters for Saleh-Valenzuela channel model with direction-of-arrival extension extracted from IMST data for library environment]
Purpose: [Contribution to TG3c at July 2006 meeting in San-Diego, USA]
Notice: This document has been prepared to assist the IEEE P It is offered as a basis for discussion and is not binding on the contributing individual(s) or organization(s). The material in this document is subject to change in form and content after further study. The contributor(s) reserve(s) the right to add, amend or withdraw material contained herein.
Release: The contributor acknowledges and accepts that this contribution becomes the property of IEEE and may be made publicly available by P
Alexei Davydov (Intel Corporation)
<author>, <company>

2. July 2006
Goals
Present estimated parameters for Saleh-Valenzuela model with direction-of-arrival extension (currently considered by c channel model subgroup) for library environment
Minimize the impact of TX/RX antenna patterns in IMST measurements data on extracted parameters of the channel model
Alexei Davydov (Intel Corporation)

3. Measurements Scenarios
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doc: IEEE c
July 2006
Measurements Scenarios
Library environment with tables, chairs and metal bookshelves with books
3 main types of measurement scenarios
LOS: unobstructed line of sight conditions
Edge: partially obstructed line of sight by the edge of a metal bookshelf
NLOS: non line of sight obstructed by a densely filled bookshelf
3 types of RX antennas (horn, wideband dipole array antenna, biconical)
Fixed TX lens antenna position at the suspended ceiling, RX measurements range ~2-5m
Time resolution is 1/960MHz ≈ 1ns
Two types of virtual uniform antenna arrays for direction of arrival analysis
501x1 uniform linear array with 1mm antenna spacing (los scenarios)
301x51 uniform planar antenna array with 1mm antenna spacing (edge scenario)
The blue colored measurement scenarios were considered for model development / parameters extraction
Alexei Davydov (Intel Corporation)
<author>, <company>

4. Measurements Scenarios Plan
July 2006
Measurements Scenarios Plan
LOS
NLOS
Edge
Measurements data for biconical RX antenna in pure NLOS scenario is not available
Alexei Davydov (Intel Corporation)

5. Saleh-Valenzuela Channel Model
<month year>
doc: IEEE c
July 2006
Saleh-Valenzuela Channel Model
L – Number of clusters
Kl – Number of MPC in the lth cluster
KLOS – LOS K factor
KMP – Cluster K factor
αk,l – MPC complex amplitude
Tl – Time of arrival of lth cluster
τk,l – Relative time of arrival for kth MPC within lth cluster
θk,l – Relative direction of arrival for kth MPC within lth cluster
Θl – Direction of arrival of lth cluster
δ(·) – Delta function 10 20 30 40 50 60 70 80 5 15 25 35
Delay, [ns]
Relative Power, [dB]
KLOS
KMP
Alexei Davydov (Intel Corporation)
<author>, <company>

6. Inter-Cluster Parameters (1)
July 2006
Inter-Cluster Parameters (1)
-150
-100
-50 50
100
150
0.005
0.01
0.015
Cluster DoA, [deg]
Probability Density
Empirical pdf
Uniform pdf
-150
-100
-50 50
100
150
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9 1
Cluster DoA, [deg]
Cumulative Density
Empirical cdf
Uniform cdf
00 – reference direction: RX antenna pointed in the TX antenna direction. Uniform distribution may be used for cluster DoA modeling.
Alexei Davydov (Intel Corporation)

7. Inter-Cluster Parameters (2)
July 2006
Inter-Cluster Parameters (2) 5 10 15 20 25 30
0.05
0.1
0.15
0.2
0.25
Probability Density
Cluster inter-arrival time, [ns]
Empirical cdf
Exponetial cdf 5 10 15 20 25 30
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9 1
Cumulative Density
Cluster inter-arrival time, [ns]
Empirical cdf
Exponetial cdf
Poisson process (Λ = 4, [ns-1]) may be used to model cluster arrival times
Alexei Davydov (Intel Corporation)

8. Inter-Cluster Parameters (3)10 20 30 40 50 60 70 80 90
-50
-40
-30
-20
-10
Cluster arrival time, [ns]
Relative Power, [dB]
Empirical data
Linear LS fit 10 20 30 40 50 60 70 80 90
100
-50
-40
-30
-20
-10
Cluster arrival time, [ns]
Relative Power, [dB]
Empirical data
Linear LS fit
<month year>
doc: IEEE c
July 2006
Inter-Cluster Parameters (3)
Flat-Exp aprx of cluster PDP
Exp aprx of cluster PDP
KLOS = 8, [dB]
KLOS = 8, [dB]
Δ = 11, [dB]
τ = 30, [ns]
Cluster decay (Γ = 12 [ns]) was estimated from clusters arriving with delays > 30 ns
Cluster amplitude is defined as maximum amplitude ray in the cluster. Cluster amplitudes were normalized to the amplitude of the LOS component.
Note: Amplitudes of cluster arriving with delay < 30 [ns] are affected by TX beam shaped antenna. Two approximations of cluster PDP are possible.
Alexei Davydov (Intel Corporation)
<author>, <company>

9. Intra-Cluster MPC parameters (1)
<month year>
doc: IEEE c
July 2006
Intra-Cluster MPC parameters (1)
Probability Density
MPC normalized DoA
Empirical pdf
Normal pdf
Laplacian pdf
Cumulative Density
MPC normalized DoA
Empirical cdf
Normal cdf
Laplacian cdf
Various empirical pdf’s for MPC DoA estimated from the measurements data may be approximately described by common Gaussian distribution with fixed angle spread (σAS = 100).
Alexei Davydov (Intel Corporation)
<author>, <company>

10. Intra-Cluster MPC parameters (2)
July 2006
Intra-Cluster MPC parameters (2) 1 2 3 4 5 6 7 8
0.5
1.5
2.5
3.5
4.5
Probability Density
MPC inter-arrival time, [ns]
Empirical pdf
Exponetial pdf 1 2 3 4 5 6 7 8
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Cumulative Density
MPC inter-arrival time, [ns]
Poisson process (λ = 4, [ns-1]) may be used to model intra-cluster MPC arrival times
Alexei Davydov (Intel Corporation)

11. Intra-Cluster MPC parameters (3)
<month year>
doc: IEEE c
July 2006
Intra-Cluster MPC parameters (3) 2 4 6 8 10 12 14 16 18 20
-35
-30
-25
-20
-15
-10 -5 5
MPC relative arrival time, [ns]
Relative Power, [dB]
Empirical data
Linear LS fit 2 4 6 8 10 12 14 16 18 20
-35
-30
-25
-20
-15
-10 -5 5
MPC relative arrival time, [ns]
Relative Power, [dB]
Empirical data
Linear LS fit 2 4 6 8 10 12 14 16 18 20
-35
-30
-25
-20
-15
-10 -5 5
MPC relative arrival time, [ns]
Relative Power, [dB]
Empirical data
Linear LS fit
Intra-cluster MPC amplitudes were normalized to the amplitude of the maximum ray (cluster amplitude).
Clusters were divided in three group (arriving in delay intervals [0..30] ns , [30..60] ns, [60..90] ns). Estimation of ray decay constant and cluster K factor were made separately for each group.
Cluster ToA interval from 0 ns to 30 ns
Cluster ToA interval from 30 ns to 60 ns
Cluster ToA interval from 60 ns to 90 ns
Different ray decay constant (γ) and cluster K-factor (KMP) were observed in the measurements data for different cluster groups
Alexei Davydov (Intel Corporation)
<author>, <company>

12. Intra-Cluster MPC parameters (4)
<month year>
doc: IEEE c
July 2006
Intra-Cluster MPC parameters (4) 10 20 30 40 50 60 70 80 90 4 6 8 12 14 16
Cluster Delay, [ns]
Ray Decay, [ns]
Empirical data
Linear LS fit 10 20 30 40 50 60 70 80 90
-18
-16
-14
-12
-10 -8 -6 -4 -2
Cluster Delay, [ns]
Cluster K factor, K M P
[dB]
Empirical data
Linear LS fit
Linear function of intra-cluster ray decay constant from cluster delay γ gives good approximation to empirical data
Linear function with saturation of cluster K factor (KMP, [dB]) from cluster delay gives good approximation to empirical data
Alexei Davydov (Intel Corporation)
<author>, <company>

13. Intra-Cluster MPC parameters (5)
July 2006
Intra-Cluster MPC parameters (5) 1 2 3 4 5 6 7 8 9 10
0.2
0.4
0.6
0.8
1.2
1.4
Probability Density
MPC amplitude
Empirical pdf
Log-normal pdf
Rayleigh pdf 1 2 3 4 5 6 7 8 9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Cumulative Density
MPC amplitude
Empirical cdf
Log-normal cdf
Rayeigh cdf
Log-normal distribution (σ2 = 6 [dB]) may be used for intra-cluster MPC amplitude modeling. Rayeligh distribution gives worse approximation of empirical data.
Alexei Davydov (Intel Corporation)

14. Shadow region with blocked LOS component
July 2006
Edge Scenario
100
200
300
400
500
-130
-125
-120
-115
-110
-105
-100
-95
-90
Spacing, [mm]
Relative Power, [dB]
Shadow region with blocked LOS component
LOS region
The scenario with blocked LOS component (“partial NLOS”) can covered with LOS model by dropping the direct path by dB and keeping the remaining parameters unchanged.
Alexei Davydov (Intel Corporation)

15. Extracted channel model parameters
<month year>
doc: IEEE c
July 2006
Extracted channel model parameters
Inter-cluster Power Delay Profile parameters
Exponential PDP: K factor KLOS = 8 [dB], cluster decay Γ = 12 [ns]
Flat-Exponential PDP: K factor KLOS = 8 [dB], cluster decay Γ = 12 [ns], Δ = 11 [dB], τ = 30 [ns]
Inter-cluster DoA – Uniform distribution
Intra-cluster DoA – Gaussian (Angles Spread (AS) σAS = 10 [0])
Inter-cluster ToA / Intra-cluster ToA – Poisson (Λ = 0.25 [ns-1] / λ = 4 [ns-1])
Cluster* / Ray amplitude – Log-normal (σ1 = 5 [dB] / σ2 = 6 [dB])
Intra-cluster K factor KMP = min(μ·Tl + η, 0) [dB] (μ = 0.16 [dB/ns], η = [dB])
Exponential ray decay γ = ρ·Tl + ν [ns] (ρ = 0.12, ν = 4.5 [ns])
Summary of the model parameters for LOS scenario. For partial NLOS the LOS component shall be dropped by [dB] keeping remaining parameters of the channel unchanged.
Note: Two approximations of cluster PDP are proposed:
Exponential PDP - for TX omni-directional antenna mounted at the suspended ceiling.
Flat-exponential PDP - for TX beam-shaped antenna mounted at the suspended ceiling.
Alexei Davydov (Intel Corporation)
<author>, <company>

16. July 2006
Summary
Based on IMST measurements data the parameters of Sale-Valenzuela channel models with DoA extension were extracted for the LOS scenario in library environment
Exponential PDP - for TX omni-directional antenna
Flat-exponential PDP - for TX beam-shaped antenna
NLOS scenario (with blocked direct path) may be covered by LOS model by dropping the direct path by 10-15dB
Different RX antenna patterns may be taken into account in the framework of the proposed channel models
Alexei Davydov (Intel Corporation)

17. Back up Channel generation procedure Matlab code Examples July 2006
Alexei Davydov (Intel Corporation)

18. Channel Generation Procedure
July 2006
Channel Generation Procedure
Initialize the channel parameters
Generate cluster’s parameters
Cluster time of arrival / angle of arrival / amplitude
For each cluster generate MPC’s parameters
Ray decay / Cluster K factor / time of arrival / angle of arrival / amplitude
Alexei Davydov (Intel Corporation)

19. Example of impulse response generation routine
<month year>
doc: IEEE c
July 2006
Matlab code
This code should be considered just as an example
Example of impulse response generation routine
Alexei Davydov (Intel Corporation)
<author>, <company>

20. Modeled channel impulse responses
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doc: IEEE c
July 2006
Modeled channel impulse responses 50
100
150 5 10 15 20 25 30 35 40 45
Delay, [ns]
Relative Power, [dB] 50
100
150
200
250
300
350 20 40
Delay, [ns]
Direction of Arrival, [deg]
Relative Power, [dB]
1-D Instantaneous impulse response
2-D Instantaneous impulse response
Alexei Davydov (Intel Corporation)
<author>, <company>