1. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 1 Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs) Submission Title: Empirically Based Statistical Ultra-Wideband Channel Model Date Submitted: 08 June, 2002 Source: Marcus Pendergrass, Time Domain Corporation 7057 Old Madison Pike, Huntsville, AL 35806 Voice:256-428-6344 FAX: [256-922-0387], E-Mail: marcus.pendergrass@timedomain.com Re: Ultra-wideband Channel Models IEEE P802.15-02/208r0-SG3a, 17 April, 2002, Abstract:An ultra-wideband (UWB) channel measurement and modeling effort, targeted towards the short-range, high data rate wireless personal area network (WPAN) application space, is described. Results of this project include a measurement database of 429 UWB channel soundings, including both line of sight and non line of sight channels, a statistical description of this database, and recommended models and modeling parameters for several UWB WPAN scenarios of interest. Purpose:The information provided in this document is for consideration in the selection of a UWB channel model to be used for evaluating the performance of a high rate UWB PHY for WPANs. Notice:This document has been prepared to assist the IEEE P802.15. 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 P802.15.

2. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 2 Marcus Pendergrass and William C. Beeler 24 June 2002 with thanks to Laurie Foss, Joy Kelly, James Mann, Alan Petroff, Alex Petroff, Mitchell Williams, and Scott Yano for assistance and support. Empirically Based Statistical Ultra-Wideband (UWB) Channel Model

3. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 3 Executive Summary Important to characterize the Wireless Personal area network (WPAN) environment. 429 channel soundings taken in residential and office environments. Statistical multipath models for 3 environments described: LOS 0-4 meters, NLOS 0-4 meters, NLOS 4 - 10 meters. Channel response modeled as a sum of scaled and delayed versions template waveform. Good fit to measurement data. Distortion <1dB. Recommendations offered

4. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 4 Outline Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations

5. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 5 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations

6. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 6 Approach Measurement Campaign Channel soundings taken in a variety of WPAN-type environments. Data Analysis Deconvolution of channel impulse response (CIR) from measurements. Assessment of channel distortion. Statistical analysis of UWB channel parameters as a function of environment type. Fit existing models to data IEEE 802.11 model. The  -K model. Assess goodness of fit Recommend models, parameters

7. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 7 Overview of Results 429 channels soundings taken from 11 different home and office environments. –Data and documentation will be made available to SG3a. Environmental signal distortion estimated. Multipath channel described statistically: Number of multipath components. Distribution of multipath arrival times. Average power decay profile Distribution of RMS delay vs. distance Distribution of mean excess delay vs. distance Ability of existing models to capture the phenomenology of the data assessed. Recommendations made.

8. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 8 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations

9. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 9 Purpose: Obtaining diverse set of measurements of the UWB channel. –11 Different office and home environments –LOS and NLOS channels –Wood & Metal Studs construction –Distances up to 10 meters –Documentation: Methodology, location, environments

10. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 10 Test Setup Details Data recorded : –100 ns channel record. –4096 data points per record. –Effective sampling time is 24.14 ps (20 GHz Nyquist frequency). –350 averages per data point per channel record (for high SNR).

11. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 11 Channel Measurement Test Setup LNA Delay Line Preamp Filter f (GHz) 3  Floppy HP54750A Ch.1 Ch.2 Trig DSO 5 Tx: -10dBm TDC SG Attenuator 37dB 0-80dB Rx: 20dB Gain 4.8dB NF 3dBi 30dB 2.2NF Channel

12. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 12 Measurement Location Example NLOS LOS

13. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 13 Measurement Database 429 included in delivered data base. Database includes: –Received waveform –Extracted channel impulse responses. –Calculated channel parameters (RMS delay and path loss). –Various measurement meta-data locations of transmitter and receiver channel categorized as LOS or NLOS. calculated line of sight delay time environment type (wood stud, metal stud) number of intervening walls between transmitter and receiver.

14. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 14 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations

15. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 15 Analysis Goals Extract a description of the channel that is independent of the channel stimulus. Estimate “distortion” caused by the propagation environments. Produce a statistical description of channel as a function of environment type.

16. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 16 Major Analysis Assumptions Channel modeled as a linear time-invariant (LTI) filter. –assume that there are negligible changes to the channel on the time scale of a communications packet. Impulse response for the channel is assumed to be of the form –channel’s effect on signal is modeled as a series of amplitude scalings a k and time delays  k. (1)

17. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 17  CLEAN is a variation of a serial correlation algorithm  Uses a template received waveform to sift through an arbitrary received waveform  Cross-correlation with template suppresses non-coherent signals and noise  Result is  k ’s and  k ’s of CIR independent of measurement system CLEAN Algorithm used to deconvolve CIR from channel record

18. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 18 CLEAN Algorithm Compared to Frequency Domain De-Convolution

19. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 19 CLEAN Algorithm geometric interpretation s r s-r Original scan Error vector Linear space of all possible reconstructed scans CLEAN approximation to original scan (reconstructed scan) Energy Capture Ratio: Relative Error: Least Squares Condition: (2)

20. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 20 CLEAN Residual Estimates of Signal Distortion Least squares condition met at 85% energy capture ratio, on average. Estimated signal distortion: –NLOS, 0 to 4 meters, metal stud case: 15.5% (0.7 dB) –LOS, 0 to 4 meters, metal stud case:16.6% (0.7 dB) –NLOS, 4 to 10 meters, metal stud case:17.0% (0.8 dB)

21. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 21 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations

22. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 22 Explanation of Channel Statistics Channels characterized in terms of the following statistical parameters –Number of multipath components per channel. –Occupancy probabilities as a function of excess delay. –Mean log relative magnitudes as a function of excess delay. –RMS delay as a function of distance. –Mean excess delay as a function of distance.

23. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 23 delays amplitudes LOS delay k th excess delay:  k –  0 00 11 kk a0a0 a1a1 akak a max Channel Statistics Mean excess delay is a weighted average of the excess delays in the CIR. CIR square amplitudes provide the weights RMS delay is the standard deviation of the excess delays. again the CIR square amplitudes provide the weights. k th relative magnitude: time multipath component

24. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 24 excess delay relative magnitude Mean relative magnitude at a given excess delay value over a collection of CIRs Channel Statistics excess delay probability of occupancy Probability that there is a multipath component at a given excess delay offset

25. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 25 Dependence of Channel Statistics on CLEAN Algorithm Stopping Condition Channel statistics computed from channel impulse response as calculated by CLEAN algorithm. Dependence of channel statistics on stopping criteria assessed. The following energy capture stopping criteria were evaluated: 80%, 85%, 90%, 95%

26. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 26 80% Energy Capture (notional) amplitudes time

27. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 27 amplitudes time 85% Energy Capture (notional)

28. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 28 90% Energy Capture (notional) amplitudes time

29. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 29 95% Energy Capture (notional) amplitudes time What is the effect on channel statistics?

30. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 30 Comparison of Statistics Across Energy Capture Ratios II. LOS, 0 to 4 meters, metal stud 85% energy capture95% energy capture Avg. RMS Delay Mean Number of Components per Channel Avg. Mean Excess Delay 6.36 ns5.27 ns 5.17 ns4.95 ns 24.042.3

31. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 31 85% Energy Capture Ratio Used for Statistical Analysis Number of multipath components per channel is the statistic that is most sensitive to changes in the stopping criteria. Large change in number of multipath components causes only small changes in other statistics in going from 85% to 95% energy capture ratio. 85% stopping criteria also good from a least squares point of view.

32. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 32 Statistical Environmental Models Each environment characterized by statistical profile of channels collected from that environment. Statistical analysis and model fitting done only for metal stud measurements. –369 metal stud measurements. –60 wood stud measurements not enough for statistical breakdown. –Three scenarios considered: I.NLOS, 0 to 4 meters, metal stud (120 channels). II.LOS, 0 to 4 meters, metal stud (xxx channels). III.NLOS, 4 to 10 meters, metal stud (xxx channels). –Not enough LOS, 4 to 10 meter channels for analysis.

33. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 33 I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Measurements per Meter Total Number of Measured Channels: 120

34. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 34 I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Multipath Components Per Channel Mean Number of Components Per Channel: 36.1

35. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 35 I. NLOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution Graph of the probability that an excess delay bin contains a reflection.

36. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 36 I. NLOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean Log Relative Magnitude Mean + stdv. Mean - stdv.

37. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 37 I. NLOS, 0 to 4 meters, metal stud Mean RMS Delay vs. Distance Mean RMS Delay Mean + stdv. Mean - stdv. Mean RMS Delay: 8.78 ns Standard Deviation of RMS Delay: 4.34 ns

38. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 38 I. NLOS, 0 to 4 meters, metal stud Average Mean Excess Delay vs. Distance Average Mean Excess Delay: 10.04 ns Standard Deviation of Mean Excess Delay : 6.26 ns Avg. Mean Excess Delay Mean + stdv. Mean - stdv.

39. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 39 NLOS LOS 4 – 10 m 0 – 4 m Number of Components Per Channel comparison across scenarios NLOS 0 – 4 m

40. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 40 Distribution of Multipath Arrival Times comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m

41. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 41 Mean of Log Relative Magnitude comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m

42. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 42 RMS Delay vs. Distance comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m

43. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 43 Mean Excess Delay vs. Distance comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m

44. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 44 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations

45. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 45 Modeling Approach Attempted to fit two analytical models to the data –A modified IEEE 802.11 channel model –Modified  - K model Models evaluated on how well they reproduced the statistic distributions of the data –Bhattacharyya distance calculated between simulated and measured distributions.

46. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 46 Modified IEEE 802.11 model Regularly spaced impulses –modified for UWB to allow for random placement of impulses in each time bin Raleigh-distributed magnitudes Exponential decay profile input parameters –T RMS : RMS delay parameter –T S : time discretization unit Unable to match both RMS delay and multipath intensity profile simultaneously.

47. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 47 I. NLOS, 0 to 4 meters, metal stud Distribution of RMS Delay measured: 8.85 (ns) Mean RMS Delay simulated: 8.58 (ns)

48. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 48 I. NLOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay

49. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 49  - K Model Arrival time model –Model “clumping” of multipath arrival times by making the probability of an arrival in a given excess delay bin dependent on whether there was an arrival in the previous bin. –“ K ” value is the ratio of these conditional probabilities. Modeling assumption is that K is constant. –“  ” value is the time discretization unit. positive conditional negative conditional

50. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 50  - K Model Amplitude model –Log-normal model for multipath amplitudes –Mean and standard deviation as functions of excess delay given by the statistics of the data.

51. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 51 Multipath arrival times governed by statistics of data –Probability of a multipath arrival in a given time bin depends on whether previous bin was occupied. –Positive and negative conditional probabilities derived from statistics of data. –No assumption that ratio of conditional probabilities is constant. Modified  -K Model

52. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 52 Simulation Results Time discretization unit  =  = 0.1 ns for all cases. Empirical probabilities of occupancy and log relative magnitude data used as inputs to model. –A  - K simulation would use approximations to these quantities as its inputs, and hence could perform no better.

53. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 53 II. LOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution measured simulated

54. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 54 II. LOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay

55. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 55 II. LOS, 0 to 4 meters, metal stud Distribution of Number of Multipath Components Per Channel measured: 42.3 Mean Number of Components Per Channel simulated: 43.9

56. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 56 II. LOS, 0 to 4 meters, metal stud Distribution of RMS Delay measured: 6.36 (ns) Mean RMS Delay simulated: 11.70 (ns)

57. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 57 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations

58. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 58 Conclusion Modeling channel response as a sum of scaled/delayed versions of channel input provides a good fit to data. Wide variety of channel characteristics, even within the same environment. Multipath arrival times and average power decay profiles follow linear or piece-wise linear trends. Exact parameter values for arrival times and decay profiles are dependent on the environment type. Occupancy probabilities and decay profiles do not completely characterize the channel data, since two models can have the same statistics for these quantities, and yet differ in the statistics of RMS delay.

59. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 59 Recommendations IEEE 802.11 and  -K model should not be used, because they do not provide good fits to the statistical models of the environments. Selected SG3A model should fit the collected data. –Number of multipath components per channel –Probability of occupancy –Average power decay profile –Distribution of RMS delay vs. distance –Distribution of mean excess delay vs. distance

60. doc.: IEEE 802.15-02/294SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 60 R.A. Scholtz, Notes on CLEAN and Related Algorithms, Technical Report to Time Domain Corporation, April 20, 2001 Homayoun Hashemi, “Impulse Response Modeling of Indoor Radio Propagation Channels”, IEEE Jornal on Slected Areas in Communications, VOL. 11, No. 7, September 1993 Theodore S. Rappaport, “Wireless Communications Principles and Practice”, 1996 Intelligent Automation, Inc., “Channel Impulse Response Modeling: Comparison Analysis of CLEAN algorithm and FT-based Deconvolution Techniques, Technical Report to Time Domain Corporation, November 21, 2001 Bob O’Hara and Al Petrick, “IEEE 802.11 Handbook A Designer’s Companion”, 1999 References