Project: IEEE P Working Group for Wireless Personal Area Networks N

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1 Project: IEEE P82.15 Working Group for Wireless Personal Area Networks N (WPANs( WPANs) Title: [UWB Channel Model for Indoor Residential Environment] Date Submitted: [2 September, 24] Source: [Chia-Chin Chong, Youngeil Kim, SeongSoo Lee] Company [Samsung Advanced Institute of Technology (SAIT)] Address [RF Technology Group, Comm. & Networking Lab., P. O. Box 111, Suwon 44-6, Korea.] Voice:[ ], FAX: [ ], [chiachin.chong@samsung.com] Re: [Response to Call for Contributions on IEEE a Channel Models] Abstract: [This contribution describes the UWB channel measurement results in indoor residential environment based in several types of high-rise apartments. It consists of detailed characterization of the frequency-domain parameters, temporal-domain parameters, small-scale amplitude statistics and S-V clustering multipath channel parameters of the UWB channel with bandwidth from 3 to 1 GHz.] Purpose: [Contribution towards the IEEE a Channel Modeling Subgroup.] 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 Slide 1

2 UWB Channel Model for Indoor Residential Environment Chia-Chin Chong, Youngeil Kim, SeongSoo Lee Samsung Advanced Institute of Technology (SAIT), Korea Slide 2

3 Outline Measurement Setup & Environment Data Analysis & Post-Processing Measurement Results Large-Scale Parameters Small-Scale Parameters Conclusion Slide 3

4 Measurement Setup (1) Frequency domain technique using VNA Center frequency, f c : 6.5GHz Bandwidth, B : 7GHz (i.e. 3-1GHz) Delay resolution, τ : 142.9ps (i.e. τ=1/b) No. frequency points, N : 161 Frequency step, f : 4.375MHz (i.e. f=b/(n-1)) Max. excess delay, τ max : 229.6ns (i.e. τ max =1/ f) Sweeping time, t sw : 8ms Max. Doppler shift, f d,max : 1.25Hz (i.e. f d,max =1/t sw ) Slide 4

5 Measurement Setup (2) UWB wideband planar dipole antennas Measurement controlled by laptop with LabVIEW via GPIB interface Calibration performed in an anechoic chamber with 1m reference separation Static environment during recording Both large-scale & small-scale measurements Large-scale: different RX positions local point Small-scale: 25 (5x5) grid-measurements around each local point spatial point At each spatial point, 3 time-snapshots of the channel complex frequency responses are recorded Slide 5

6 Measurement Setup (3) TX antenna RX antenna Propagation Channel Coaxial Cables Vector network analyzer (Agilent 8722ES) Low Noise Amplifier (Miteq AFS5) Power Amplifier (Agilent 832A) Laptop with LabVIEW GPIB Interface Attenuator (Agilent 8496B) Slide 6

7 UWB Planar Dipole Antenna Slide 7

8 Measurement Environment Measurements in various types of high-rise apartments based on several cities in Korea typical types in Asia countries like Korea, Japan, Singapore, Hong Kong, etc. 3-bedrooms (Apart1) 4-bedrooms (Apart2) Both LOS and NLOS configurations TX-RX antennas: Separations: up to 25m Height: 1.25m (with ceiling height of 2.5m) TX antenna: always fixed in the center of the living room RX antenna: moved around the apartment (i.e. 8-1 locations) 12, channel complex frequency responses are collected (i.e. 2 apartments x 8 RX local points x 25 spatial points x 3 time snapshots 2x8x25x3=12,) Slide 8

9 3-Bedroom Apartment Grid-Measurement Slide 9

10 4-Bedroom Apartment Slide 1

11 Data Analysis & Post-Processing All measurement data are calibrated with the calibration data measured in anechoic chamber to remove effect of measurement system Perform frequency domain windowing to reduce the leakage problem Complex passband IFFT is deployed to transform the complex frequency response to complex impulse response Perform temporal domain binning before extract channel parameters Slide 11

12 Complex Passband IFFT Slide 12

13 Channel Model Description Large-Scale Parameters: Path loss and Shadowing Frequency Decaying Factor Small-Scale Parameters: Temporal Domain Parameters S-V Multipath Channel Parameters Small-Scale Amplitude Statistics Slide 13

14 Path Loss and Shadowing Path loss (PL) vs. Distance (d): d PL ( d) = PL + 1 n log 1 + S; d d d d = 1m PL : intercept n : path loss exponent S : Shadowing fading parameter Perform linear regression to the above equation with measured data to extract the required parameters Slide 14

15 Path Loss vs. Distance LOS 6 Path Loss under LOS Scenario in 3-Bedroom Apartment Data Linear Regression Path Loss (db) Path Loss under LOS Scenario in 4-Bedroom Apartment log (Distance) (m) 1 Path Loss (db) Data Linear Regression log (Distance) (m) 1 Slide 15

16 Path Loss vs. Distance NLOS 65 Path Loss under NLOS Scenario in 3-Bedroom Apartment Data Linear Regression 6 Path Loss (db) log (Distance) (m) Path Loss under NLOS Scenario in 4-Bedroom Apartment Data Linear Regression Path Loss (db) log (Distance) (m) 1 Slide 16

17 Frequency Decaying Factor Path loss (PL) vs. Frequency (f): PL f ( ) exp( δ f ) 1 (Method 1) or δ2 PL ( f ) f (Method 2) Slide 17

18 Frequency Decaying Factor LOS September 24 Frequency Dependence Path Loss under LOS Scenario (3-Bedroom Apartment Measurement Data Method 1-1 Method 2 Path Loss [db] Frequency [GHz] Frequency Dependence Path Loss under LOS Scenario (4-Bedroom Apartment Measurement Data -5 Method 1 Method 2-1 Path Loss [db] Slide Frequency [GHz]

19 Frequency Decaying Factor NLOS September 24 requency Dependence Path Loss under NLOS Scenario - (3-Bedroom Apartme Measurement Data Method 1-1 Method 2 Path Loss [db] Frequency Dependence Path Loss under NLOS Scenario (4-Bedroom Apartmen Frequency [GHz] Path Loss [db] Measurement Data -45 Method 1 Method Frequency [GHz] Slide 19

20 Large-Scale Parameters Slide 2

21 Temporal Domain Parameters These parameters were obtained after taking frequency domain Hamming windowing, passband IFFT & temporal domain binning Slide 21

22 S-V Multipath Channel Parameters Saleh-Valenzuela model described by the following parameters: Γ : cluster decay factor γ : ray decay factor Λ : mean cluster arrival rate λ : mean ray arrival rate σ a : standard deviation of lognormal distributed path powers (db) L mean : Mean number of clusters µ Kl : Mean of the exponential distributed number of MPCs per cluster Slide 22

23 Normalized Cluster Relative Amplitude September 24 Cluster Decay Factor, Γ LOS Normalized Cluster Relative Power vs. Cluster Relative Delay (Apart1-LOS) Cluster amplitude Linear least-squares fit Γ = 22.1 ns Cluster Relative Delay, [ns] Normalized Cluster Relative Amplitude Normalized 1 Cluster Relative Power vs. Cluster Relative Delay (Apart2-LOS) Cluster amplitude Linear least-squares fit Γ = ns Cluster Relative Delay, [ns] Slide 23

24 Cluster Decay Factor, Γ NLOS September 24 Normalized 1 2 Cluster Relative Power vs. Cluster Relative Delay (Apart1-NLOS) Normalized Cluster Relative Power Cluster power Linear least-squares fit Γ = ns Cluster Relative Delay, [ns] Normalized 1 1 Cluster Relative Power vs. Cluster Relative Delay (Apart2-NLOS) Normalized Cluster Relative Power Cluster power Linear least-squares fit Γ = ns Cluster Relative Delay, [ns] Slide 24

25 Ray Decay Factor, γ LOS Slide 25

26 Ray Decay Factor, γ NLOS Slide 26

27 Cluster Arrival Rate, Λ LOS September 24 Cluster Inter-Arrival Times CCDF (Apart1-LOS) -1-2 ln(ccdf) /Λ = 8.69 ns Cluster Inter-Arrival Times CCDF (Apart2-LOS) -5 Cluster inter-arrival times Linear least-squares fit Clsuter Inter-Arrival Times, T [ns] ln(ccdf) /Λ = ns -5 Cluster inter-arrival times Linear least-squares fit Clsuter Inter-Arrival Times, T [ns] Slide 27

28 Cluster Arrival Rate, Λ NLOS September 24 Cluster Inter-Arrival Times CCDF (Apart1-NLOS) -1-2 ln(ccdf) /Λ = ns Cluster Inter-Arrival Times CCDF (Apart2-NLOS) -6 Cluster inter-arrival times Linear least-squares fit Clsuter Inter-Arrival Times, T [ns] ln(ccdf) /Λ = ns -5 Cluster inter-arrival times Linear least-squares fit Clsuter Inter-Arrival Times, T [ns] Slide 28

29 Ray Arrival Rate, λ LOS Ray Intra-Arrival Times CCDF (Apart1-LOS) -5 ln(ccdf) /λ =.51 ns Ray Intra-Arrival Times CCDF (Apart2-LOS) -2 Ray intra-arrival times Linear least-squares fit Ray Intra-Arrival Times, τ [ns] ln(ccdf) /λ =.86 ns -16 Ray intra-arrival times Linear least-squares fit Ray Intra-Arrival Times, τ [ns] Slide 29

30 Ray Arrival Rate, λ NLOS Ray Intra-Arrival Times CCDF (Apart1-NLOS) ln(ccdf) /λ =.72 ns -45 Ray intra-arrival times Linear least-squares fit Ray Intra-Arrival Times, τ [ns] ln(ccdf) Ray Intra-Arrival Times CCDF (Apart2-NLOS) 1/λ =.56 ns Slide 3-25 Ray intra-arrival times Linear least-squares fit Ray Intra-Arrival Times, τ [ns]

31 Mixture Poisson Distribution Fitting the ray arrival times to a mixture of 2 Poisson distributions similar to [1]: ( ) = exp[ ( )] p τ τ βλ λ τ τ kl, ( k 1), l 1 1 kl, ( k 1), l ( β 1) λ exp[ λ ( τ τ )] kl, ( k 1), l β: mixture probability λ 1 & λ 2 : ray arrival rates Slide 31

32 Mixture Poisson Distributions LOS September 24 Slide 32

33 Mixture Poisson Distributions NLOS September 24 Slide 33

34 Number of Clusters.4 Apart1-LOS.4 Apart1-NLOS Probability Probability No. of Clusters Apart2-LOS No. of Clusters Apart2-NLOS Probability Probability No. of Clusters No. of Clusters Slide 34

35 Number of MPCs per Cluster 1 Apart1-LOS 1 Apart1-NLOS µ Kl = 24.1 µ Kl = CDF.5 Empirical Data Exponential Fit CDF.5 Empirical Data Exponential Fit No. of MPCs per Cluster Apart2-LOS No. of MPCs per Cluster Apart2-NLOS µ Kl = 3.47 µ Kl = CDF.5 Empirical Data Exponential Fit CDF.5 Empirical Data Exponential Fit No. of MPCs per Cluster No. of MPCs per Cluster Slide 35

36 S-V Multipath Channel Parameters Slide 36

37 Small-Scale Amplitude Statistics Comparison of empirical path amplitude distribution with the following four commonly used theoretical distributions: Lognormal Nakagami Rayleigh Ricean Weibull The goodness-of-fit of the received signal amplitudes is evaluated using Kolmogorov-Smirnov (K-S) test & Chi-Square (χ 2 ) test with 5% and 1% significance level, respectively. Slide 37

38 Goodness-of-Test: LOS Slide 38

39 Goodness-of-Test: NLOS Slide 39

40 CDF of Path Amplitude LOS Small-scale amplitude CDFs at different exces delays (3-bedroom apartment, LOS) Empirical Lognormal Nakagami Rayleigh Weibull.6 CDF Excess delay 1ns Excess delay 5ns.2.1 Excess delay 5ns Amplitude in db Small-scale amplitude CDFs at different exces delays (4-bedroom apartment, LOS) Empirical Lognormal Nakagami Rayleigh Weibull CDF.6.5 Excess delay 5ns.4.3 Excess delay 1ns.2.1 Excess delay 5ns Slide Amplitude in db

41 CDF of Path Amplitude NLOS Small-scale amplitude CDFs at different exces delays (3-bedroom apartment, NLOS) Empirical Lognormal Nakagami Rayleigh Weibull CDF Excess delay 5ns.2.1 Excess delay 1ns Excess delay 5ns Amplitude in db Small-scale amplitude CDFs at different exces delays (4-bedroom apartment, NLOS) Empirical Lognormal Nakagami Rayleigh Weibull CDF Excess delay 5ns.2.1 Excess delay 1ns Excess delay 5ns Amplitude in db Slide 41

42 Small-Scale Amplitude Statistics Parameters The results demonstrate that lognormal, Nakagami and Weibull fit the measurement data well. Parameters of these distributions (i.e. standard deviation of lognormal PDF, m-parameter of Nakagami PDF and b-parameter of Weibull PDF) can be modeled by a lognormal distribution These parameters are almost constant across the excess delay Slide 42

43 Standard Deviation of Lognormal PDF LOS September 24 1 Lognormal Standard Deviation CDF (Apart1-LOS) CDF Empirical CDF Lognormal CDF 1.9 Lognormal Standard Deviation CDF (Apart2-LOS) Lognormal Standard Deviation CDF Empirical CDF Lognormal CDF Lognormal Standard Deviation Slide 43

44 Standard Deviation of Lognormal PDF NLOS September 24 1 Lognormal Standard Deviation CDF (Apart1-NLOS) Empirical CDF Lognormal CDF CDF Lognormal Standard Deviation CDF Lognormal Standard Deviation CDF (Apart2-NLOS) Empirical CDF Lognormal CDF Lognormal Standard Deviation Slide 44

45 m-nakagami Parameter LOS 1 Nakagami-m Values CDF (Apart1-LOS) Empirical CDF Lognormal CDF CDF Nakagami-m Values CDF Nakagami-m Values CDF (Apart2-LOS) Empirical CDF Lognormal CDF Slide Nakagami-m Values

46 m-nakagami Parameter NLOS 1 Nakagami-m Values CDF (Apart1-NLOS) Empirical CDF Lognormal CDF CDF Nakagami-m Values CDF Nakagami-m Values CDF (Apart2-NLOS) Empirical CDF Lognormal CDF Slide Nakagami-m Values

47 b-weibull Parameter LOS 1 Weibull-b Values CDF (Apart1-LOS) Empirical CDF Lognormal CDF CDF Weibull-b Values CDF Weibull-b Values CDF (Apart2-LOS) Empirical CDF Lognormal CDF Slide Weibull-b Values

48 b-weibull Parameter NLOS 1 Weibull-b Values CDF (Apart1-NLOS) Empirical CDF Lognormal CDF CDF Weibull-b Values CDF Weibull-b Values CDF (Apart2-NLOS) Empirical CDF Lognormal CDF Weibull-b Values Slide 48

49 Variations of Lognormal-σ with Delay LOS September 24 Lognormal Standard Deviation vs. Excess Delay (Apart1-LOS) 2.5 Lognormal Standard Deviation Lognormal Standard Deviation vs. Excess Delay (Apart2-LOS) Excess Delay, τ [ns] Lognormal Standard Deviation Excess Delay, τ [ns] Slide 49

50 Variations of Lognormal-σ with Delay NLOS September 24 Slide 5

51 Variations of Nakagami-m with Delay LOS September 24 2 Nakagami-m Values vs. Excess Delay (Apart1-LOS) Nakagami-m Values Nakagami-m Values vs. Excess Delay (Apart2-LOS) Excess Delay, τ [ns] Nakagami-m Values Excess Delay, τ [ns] Slide 51

52 Variations of Nakagami-m with Delay NLOS September 24 Slide 52

53 Variations of Weibull-b with Delay LOS September 24 3 Weibull-b Values vs. Excess Delay (Apart1-LOS) 2.5 Weibull-b Values Weibull-b Values vs. Excess Delay (Apart2-LOS) Excess Delay, τ [ns] Weibull-b Values Excess Delay, τ [ns] Slide 53

54 Variations of Weibull-b with Delay NLOS September 24 Slide 54

55 Small-Scale Amplitude Statistics Parameters Slide 55

56 Conclusion Frequency domain technique UWB measurement campaign has been carried out in indoor residential environment (high-rise apartments) covering frequencies from 3-1 GHz. Measurement covered both LOS & NLOS scenarios. Channel measurement results which characterize both the large-scale and small-scale parameters of the channel are reported. Slide 56

57 Reference 1. B. Kannan et. al., Characterization of UWB Channels: Small- Scale Parameters for Indoor and Outdoor Office Environment, IEEE a, July 24. Slide 57