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02240r0P802 15 SG3a Empirically Based UWB Channel

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Information about 02240r0P802 15 SG3a Empirically Based UWB Channel
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Published on January 10, 2008

Author: Bertrando

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Slide1:  Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs) Submission Title: Empirically Based Statistical Ultra-Wideband Channel Model Date Submitted: 24 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. Empirically Based Statistical Ultra-Wideband (UWB) Channel Model:  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 Executive Summary:  Executive Summary Important to characterize the wireless personal area network (WPAN) environment, £ 10 m, in both line of sight (LOS) and non line of sight (NLOS) cases for UWB. 429 channels soundings taken from 11 different home and office environments. Multipath channel parameters described statistically: RMS delay Distribution of multipath arrival times. Average power decay profile. Executive Summary (cont):  Executive Summary (cont) Modeling the UWB channel response as a sum of scaled and delayed versions of the channel input provides a relatively good fit to measurement data. Estimated signal distortion is less than 1 dB in the 3-5 GHz band. Exact parameter values for arrival times and decay profiles are dependent on the environment type. Executive Summary (cont):  Executive Summary (cont) Recommendations Models should utilize the statistics of occupancy probabilities and average power decay profile, rather than relying on a single parameter alone, such as RMS delay. Outline:  Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations Outline Slide7:  Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations Introduction:  Introduction Channel Impulse Response (CIR) modeling of radio-frequency channels necessary for system design, trades. Multipath channel effects will be a key determinant of system performance, reliability. Large literature on channel modeling available, including work on the UWB channel in particular. Important to characterize the wireless personal area network (WPAN) environment in both line of sight (LOS) and non line of sight (NLOS) cases. Models should be tuned to WPAN applications and environments. Approach:  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 model to data The D-K model. Assess goodness of fit Recommend models, parameters Overview of Results:  Overview of Results 429 channels soundings taken from 11 different home and office environments. Data will be made available to SG3a. Environmental signal distortion estimated. Multipath channel parameters described statistically: RMS delay Distribution of multipath arrival times. Average power decay profile. Ability of existing models to capture the phenomenology of the data assessed. Recommendations made for models and parameters. Slide11:  Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations Purpose:  Purpose Support statistical analysis WPAN propagation environments by obtaining a well-documented set of diverse measurements of the UWB channel. Short range (0-4 meters), and medium range (4 - 10 meters) LOS and NLOS channels office and residential environments Measurement Plan:  Measurement Plan NLOS and LOS measurements for WPAN multipath channel characterization. Metal stud and wooden stud environments. Metal studs typical of office environments; wooden studs more typical of residential environments. 11 different office and home locations Detailed documentation for each channel sounding X,Y,Z coordinates of transmit/receive antenna locations. Channel categorized as LOS or NLOS Test Setup Details:  Test Setup Details Summary: Approximately omni-directional transmit/receive antennas (roughly 3 dBi gain) PCS and ISM high pass rejection filter Effective noise figure: 4.8 dB at receive antenna terminals Gain: 19.8 dB Radiated power at approximately -10 dBm in the 3 to 5 GHz spectrum (close to FCC UWB limit) Test Setup Details:  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). Triggered sampling for accurate determination of effective LOS arrival time. Channel stimulus is UWB signal with 3 to 5 GHz 3 dB bandwidth, approximately 1.7 ns pulse duration. Channel Measurement Test Setup:  Channel Measurement Test Setup Measurement Issues:  Measurement Issues Received pulse distortion Need accurate received pulse templates for deconvolution analysis. Resolution: assessment of waveform distortion due to the angle of arrival of the incoming signal. Determination of line of sight delay time in NLOS channels. Accurate determination of multipath intensity profiles for NLOS channels requires knowing where the line of sight path would have arrived, had it not been obstructed. Resolution: careful design and characterization of test setup and parameters (group delays, NF, antenna pattern, etc.), along with periodic excitation of the environment. Utilize known delays of test equipment, known transmit/receive locations, and periodic triggering to estimate what the direct path arrival time would have been for a NLOS channel. Measurement Issue: Received Pulse Distortion:  Measurement Issue: Received Pulse Distortion Accurate received waveform template needed for effective deconvolution of channel impulse response. Sources of waveform distortion: environment (non-linear group delay, frequency-selective attenuation, etc.) interference (intermittent and steady state) antenna pattern Environmental distortion to be estimated in data analysis. Interference in minimized with appropriate filtering (PCS, ISM bands). Distortion due to non-ideal antenna pattern was assessed empirically. distortion as a function of elevation angle. Typical Normalized Antenna Azimuth and Elevation Patterns (omni-directional antennas):  Typical Normalized Antenna Azimuth and Elevation Patterns (omni-directional antennas) Received Pulse Distortion Test Setup:  Received Pulse Distortion Test Setup Pulse Distortion Test Results:  For angles of elevation between -70 degrees and +70 degrees, waveform distortion was found to be minimal. Significant distortion near ±90 degrees elevation; however, signal is severely attenuated in this region. Use of a single received pulse template was judged acceptable for deconvolution analysis. Pulse Distortion Test Results Normalized amplitudes Measurement Issue: Determination of LOS Delay:  In our test set-up, periodic excitation of the environment (non time-hopped) allowed for more accurate calculation of LOS delays. With periodic excitation the channel ring-down from previous pulse can add to the recorded response data if the record length is shorter than the ring-down time of the channel. Random excitation decorrelates the previous pulse’s ring-down from the recorded response through the DSO averaging process. Effect is most pronounced in channels with high RMS delay spread. Measurement Issue: Determination of LOS Delay Periodic Channel Stimulus Example:  Periodic Channel Stimulus Example Random Channel Stimulus Example:  Random Channel Stimulus Example Minimal Effect on RMS Delay:  Minimal Effect on RMS Delay Ability to accurately determine LOS delay was judged important enough to utilize periodic (non time-hopped) pulse trains. Channel Measurement Environments:  Channel Measurement Environments 11 different office and home environments Metal and wood stud constructions Distances less than or equal to 10 meters. 471 channel soundings taken in total. Complete documentation of measurement locations and environments. Slide27:  Example Measurement Locations A Typical Office Environment Slide28:  Example Measurement Locations Conference Room Slide29:  Example Measurement Locations Another Office Slide30:  Example Measurement Locations Residential Living Room Slide31:  Example Measurement Locations Residential Bedroom Measurement Database:  Measurement Database 471 channel soundings taken in total. Database consists of a subset of 429 of these channels: All measurements vertically polarized. Includes received waveform scans and extracted channel impulse responses. Includes calculated channel parameters, including RMS delay and path loss. Also includes various measurement meta-data, including locations of transmitter and receiver channel categorized as LOS or NLOS. calculated line of sight delay time environment type (wood stud, metal stud) polarization number of intervening walls between transmitter and receiver. Slide33:  Introduction Measurement Campaign Data Analysis Modeling the Channel Conclustions/Recommendations Analysis Goals:  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 parameters as a function of environment type. Major Analysis Assumptions:  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 “distortionless” impulse model: channel’s effect on signal is modeled as a series of amplitude scalings ak and time delays tk. (1) CLEAN Algorithm used to deconvolve CIR from channel record:  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 ak’s and tk’s of CIR independent of measurement system CLEAN Algorithm used to deconvolve CIR from channel record CLEAN Algorithm Compared to Frequency Domain De-Convolution:  CLEAN Algorithm Compared to Frequency Domain De-Convolution CLEAN Algorithm geometric interpretation:  CLEAN Algorithm geometric interpretation Energy Capture Ratio: Relative Error: Least Squares Condition: (2) CLEAN Algorithm estimation of signal distortion:  CLEAN Algorithm estimation of signal distortion CLEAN returns the CIR in precisely the desired form (1). Convolution of CIR with pulse template p(t) produces the “reconstructed” channel record r(t): When the least squares condition (2) holds, the residual difference between the CLEAN reconstruction and original channel record is a measure of the distortion introduced by the channel (i.e. the amount of signal energy that is not of the form (1)). CLEAN Residual Estimates of Signal Distortion:  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) Data Used for the Analysis:  Data Used for the Analysis 429 of the 471 channel records all vertically polarized measurements. duplicate measurements removed. General Remarks on the Data:  General Remarks on the Data Data collection SNRs varied from about 40 dB for 1-meter boresight scans to about 15 dB for some 10-meter NLOS scans. LOS and NLOS channels exhibit wide variations in path loss and RMS delay spread. Some NLOS channels have lower delay spreads than some LOS channels. The variations can be explained by grazing angles and destructive interference for LOS channels , and low attenuation through materials for NLOS channels. Scan #1: LOS 1m distance, Antenna Boresight 1/r2 Path Loss:  Scan #1: LOS 1m distance, Antenna Boresight 1/r2 Path Loss Scan #57: LOS 3.1m distance, office environment, approximately 1/r5.28 Path Loss:  Scan #57: LOS 3.1m distance, office environment, approximately 1/r5.28 Path Loss Scan #6: NLOS 1.3m distance, office environment, approximately 1/r26.5 path loss:  Scan #6: NLOS 1.3m distance, office environment, approximately 1/r26.5 path loss Estimation Scan #15: NLOS 2.7m distance, office environment approximately 1/r2.07 Path Loss:  Scan #15: NLOS 2.7m distance, office environment approximately 1/r2.07 Path Loss 0 2 4 6 8 10 12 14 16 18 20 -0.02 -0.015 -0.01 -0.005 0 0.005 0.01 0.015 0.02 Amplitude Time (ns) LOS Arrival time Estimation Descriptive Statistics of the Data:  Descriptive Statistics of the Data CIRs and channel parameters extracted for all 429 records. 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 cases: I. NLOS, 0 to 4 meters, metal stud. II. LOS, 0 to 4 meters, metal stud. III. NLOS, 4 to 10 meters, metal stud. Not enough LOS, 4 to 10 meter channels for analysis. I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Measurements per Meter I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Multipath Components Per Channel Average number of multipath components per channel: 36.1 I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution Graph of the probability that an excess delay bin contains a reflection. Probability of Occupancy I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean Log Relative Magnitude Mean + stdv. Mean - stdv. I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Mean RMS Delay vs. Distance Mean RMS Delay Mean + stdv. Mean - stdv. II. LOS, 0 to 4 meters, metal stud:  II. LOS, 0 to 4 meters, metal stud Histogram of Number of Measurements per Meter II. LOS, 0 to 4 meters, metal stud:  II. LOS, 0 to 4 meters, metal stud Histogram of Number of Multipath Components Per Channel Average number of multipath components per channel: 24.0 II. LOS, 0 to 4 meters, metal stud:  II. LOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution Probability of Occupancy II. LOS, 0 to 4 meters, metal stud:  II. LOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean Log Relative Magnitude Mean + stdv. Mean - stdv. II. LOS, 0 to 4 meters, metal stud:  II. LOS, 0 to 4 meters, metal stud Mean RMS Delay vs. Distance Mean RMS Delay Mean + stdv. Mean - stdv. III. NLOS, 4 to 10 meters, metal stud:  III. NLOS, 4 to 10 meters, metal stud Histogram of Number of Measurements per Meter III. NLOS, 4 to 10 meters, metal stud:  Histogram of Number of Multipath Components Per Channel III. NLOS, 4 to 10 meters, metal stud Average number of multipath components per channel: 61.6 III. NLOS, 4 to 10 meters, metal stud:  Multipath Arrival Time Distribution III. NLOS, 4 to 10 meters, metal stud Probability of Occupancy III. NLOS, 4 to 10 meters, metal stud:  Mean of Log Relative Magnitude vs. Excess Delay III. NLOS, 4 to 10 meters, metal stud Mean Log Relative Magnitude Mean + stdv. Mean - stdv. III. NLOS, 4 to 10 meters, metal stud:  Mean RMS Delay vs. Distance III. NLOS, 4 to 10 meters, metal stud Mean RMS Delay Mean + stdv. Mean - stdv. Slide63:  Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations D-K Model:  D-K Model time discretization unit = D = 0.5 ns for all cases. consistent with bandwidth of channel soundings. K determined from conditional occupancy probabilities in each case separately log-normal distribution of impulse magnitudes governed by statistics of data I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Linear Fit For Arrival Time Conditional Distributions Blue: negative conditionals Red: positive conditionals I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Ratio of Conditional Probabilities Provides K Estimate K = 0.85 I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud Linear Fit For Log Relative Magnitude Data I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud D-K Simulation Results: Multipath Arrival Time Distribution Blue: simulated channels Red: measured channels I. NLOS, 0 to 4 meters, metal stud:  I. NLOS, 0 to 4 meters, metal stud D-K Simulation Results: Log Relative Magnitude Distrubution Blue: simulated channels Red: measured channels Slide70:  Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations Conclusions:  Conclusions Modeling the UWB channel response as a sum of scaled and delayed versions of the channel input provides a relatively good fit to measurement data. Least squares estimates indicate that less than 1 dB of signal distortion in the 3-5 GHz band is introduced by the channels in this data set. Conclusions:  Conclusions Wide variety of channel characteristics, even within the same environment. LOS ¹ freespace. Multipath arrival times and average power decay profiles follow linear or piece-wise linear trends. Conclusions:  Conclusions 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. Recommendations:  Recommendations Models should utilize the statistics of occupancy probabilities and average power decay profile, rather than relying on a single parameter alone, such as RMS delay. References:  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 Slide76:  Definitions/Terminology Slide77:  LOS Line of Sight (transmit and receive antenna have a clear visible field of view relative to each other) NLOS Non-Line of Sight CIR Channel Impulse Response Waveform Template correlation template used in the correlation process (CLEAN Algorithm) LTI Linear Time Invariant Terminology Slide78:  Where: ak are the impulse amplitudes tk are the impulse delays CLEAN1 Variant of a serial correlation algorithm Channel Modeled as LTI filter, with impulse response h(t) of the form: Terminology Slide79:  RMS Delay Spread can be expressed as: Terminology Slide80:  Mean Excess Delay can be expressed as: Terminology Slide81:  Relative Magnitude can be expressed as: Where: Terminology Slide82:  Average Multipath Intensity Profile (MIP) (or Average Power Decay Profile (APDP) can be expressed as: Terminology

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