doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 1 Project: IEEE P 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 Voice: FAX: [ ], Re: Ultra-wideband Channel Models IEEE P /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 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
doc.: IEEE /240SG3a 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
doc.: IEEE /240SG3a 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 meters. Channel response modeled as a sum of scaled and delayed versions template waveform. Good fit to measurement data. Distortion <1dB. Recommendations offered
doc.: IEEE /240SG3a 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
doc.: IEEE /240SG3a 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 6 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 7 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 model. The -K model. Assess goodness of fit Recommend models, parameters
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 8 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 9 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 10 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 ( meters) –LOS and NLOS channels –office and residential environments
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 11 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 12 Test Setup Details Summary : –Approximately omni-directional transmit/receive antennas (roughly 3 dBi gain) –PCS and ISM band 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)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 13 Test Setup Details Data recorded : –100 ns channel record. –4096 data points per record. –Effective sampling time is 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 14 Channel Measurement Test Setup
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 15 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 16 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 17 Typical Normalized Antenna Azimuth and Elevation Patterns (omni-directional antennas) 0 TDC SG
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 18 Received Pulse Distortion Test Setup
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 19 –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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 20 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 21 Periodic Channel Stimulus Example
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 22 Random Channel Stimulus Example
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 23 Minimal Effect on RMS Delay –Ability to accurately determine LOS delay was judged important enough to utilize periodic (non time-hopped) pulse trains.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 24 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 25 Example Measurement Locations A Typical Office Environment
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 26 Example Measurement Locations Conference Room
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 27 Example Measurement Locations Residential Living Room
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 28 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 29 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 30 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 31 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)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 32 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 33 CLEAN Algorithm Compared to Frequency Domain De-Convolution
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 34 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)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 35 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)).
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 36 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)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 37 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 38 Data Used for the Analysis 429 of the 471 channel records –all vertically polarized measurements. –duplicate measurements removed.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 39 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 40 Scan #1: LOS 1m distance, Antenna Boresight 1/r 2 Path Loss Amplitude Time (ns)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 41 Scan #57: LOS 3.1m distance, office environment, approximately 1/r 5.28 Path Loss x Amplitude Time (ns) Check this one!
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 42 Scan #6: NLOS 1.3m distance, office environment, approximately 1/r 26.5 path loss x Amplitude Time (ns)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 43 Scan #15: NLOS 2.7m distance, office environment approximately 1/r 2.07 Path Loss Amplitude Time (ns)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 44 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 scenarios considered: 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 45 Explanation of Channel Statistics Channels characterized in terms of the following statistical parameters –RMS delay as a function of distance. –Mean excess delay as a function of distance. –Number of multipath components per channel. –Occupancy probabilities as a function of excess delay. –Mean log relative magnitudes as a function of excess delay.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 46 delays amplitudes LOS delay k th excess delay: k – 0 00 11 kk a0a0 a1a1 akak a max Channel Statistics Mean excess delay is a weighted average of the excess delays in the CIR. CIR amplitudes are the weights RMS delay is the standard deviation of the excess delays. again using the CIR amplitudes as the weights. k th relative magnitude: time multipath component
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 47 excess delay relative magnitude Mean relative magnitude 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 48 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%
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 49 80% Energy Capture (notional) amplitudes time
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 50 amplitudes time 85% Energy Capture (notional)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 51 90% Energy Capture (notional) amplitudes time
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 52 95% Energy Capture (notional) amplitudes time What is the effect on channel statistics?
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 53 Comparison of Statistics Across Energy Capture Ratios 85% energy capture95% energy capture Avg. RMS Delay Mean Number of Components per Channel Avg. Mean Excess Delay ns8.78 ns ns10.04 ns I. NLOS, 0 to 4 meters, metal stud
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 54 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 55 Comparison of Statistics Across Energy Capture Ratios III. NLOS, 4 to 10 meters, metal stud 85% energy capture95% energy capture Avg. RMS Delay Mean Number of Components per Channel Avg. Mean Excess Delay ns16.80 ns ns14.24 ns
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 56 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 57 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 58 I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Measurements per Meter Total Number of Measured Channels: 120
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 59 I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Multipath Components Per Channel Mean Number of Components Per Channel: 36.1
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 60 I. NLOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution Graph of the probability that an excess delay bin contains a reflection.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 61 I. NLOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean Log Relative Magnitude Mean + stdv. Mean - stdv.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 62 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 63 I. NLOS, 0 to 4 meters, metal stud Average Mean Excess Delay vs. Distance Average Mean Excess Delay: ns Standard Deviation of Mean Excess Delay : 6.26 ns Avg. Mean Excess Delay Mean + stdv. Mean - stdv.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 64 II. LOS, 0 to 4 meters, metal stud Histogram of Number of Measurements per Meter Total Number of Measured Channels: 79
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 65 II. LOS, 0 to 4 meters, metal stud Histogram of Number of Multipath Components Per Channel Mean Number of Components Per Channel: 24.0
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 66 II. LOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 67 II. LOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean Log Relative Magnitude Mean + stdv. Mean - stdv.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 68 II. LOS, 0 to 4 meters, metal stud Mean RMS Delay vs. Distance Mean RMS Delay Mean + stdv. Mean - stdv. Mean RMS Delay: 5.27 ns Standard Deviation of RMS Delay: 3.37 ns
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 69 II. LOS, 0 to 4 meters, metal stud Average Mean Excess Delay vs. Distance Average Mean Excess Delay: 4.95 ns Standard Deviation of Mean Excess Delay: 4.14 ns Avg. Mean Excess Delay Mean + stdv. Mean - stdv.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 70 III. NLOS, 4 to 10 meters, metal stud Histogram of Number of Measurements per Meter Total Number of Measured Channels: 119
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 71 Histogram of Number of Multipath Components Per Channel III. NLOS, 4 to 10 meters, metal stud Mean Number of Components Per Channel: 61.6
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 72 Multipath Arrival Time Distribution III. NLOS, 4 to 10 meters, metal stud
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 73 Mean of Log Relative Magnitude vs. Excess Delay III. NLOS, 4 to 10 meters, metal stud Mean Log Relative Magnitude Mean + stdv. Mean - stdv.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 74 Mean RMS Delay vs. Distance III. NLOS, 4 to 10 meters, metal stud Mean RMS Delay Mean + stdv. Mean - stdv. Mean RMS Delay: ns Standard Deviation of RMS Delay: 3.41 ns
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 75 Average Mean Excess Delay vs. Distance Average Mean Excess Delay: ns Standard Deviation of Mean Excess Delay: 5.97 ns III. NLOS, 4 to 10 meters, metal stud Avg. Mean Excess Delay Mean + stdv. Mean - stdv.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 76 NLOS LOS 4 – 10 m 0 – 4 m Number of Components Per Channel comparison across scenarios NLOS 0 – 4 m
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 77 Distribution of Multipath Arrival Times comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 78 Mean of Log Relative Magnitude comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 79 RMS Delay vs. Distance comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 80 RMS Delay vs. Distance comparison across scenarios NLOS LOS 4 – 10 m 0 – 4 m NLOS 0 – 4 m
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 81 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 82 Modeling Approach Attempted to fit two different models to the data –A modified IEEE 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 83 Modified IEEE model Regularly spaced impulses –modified for UWB to allow for random placement of impulses in each time bin Raleigh-distributed magnitudes input parameters –T RMS : RMS delay parameter –T S : time discretization unit Was not able to match both RMS delay and multipath intensity profile simultaneously.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 84 I. NLOS, 0 to 4 meters, metal stud Distribution of RMS Delay measured: 8.85 (ns) Mean RMS Delay simulated: 8.58 (ns)
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 85 I. NLOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 86 - 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 87 - 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 88 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 89 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 90 II. LOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution 95% Energy Capture data used.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 91 II. LOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay 95% Energy Capture data used.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 92 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: % Energy Capture data used.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 93 II. LOS, 0 to 4 meters, metal stud Distribution of RMS Delay measured: 6.36 (ns) Mean RMS Delay simulated: (ns) 95% Energy Capture data used.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 94 Introduction Measurement Campaign Data Analysis Statistical Environmental Models Analytical Models Conclusions/Recommendations
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 95 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.
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 96 Recommendations IEEE 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 97 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 Handbook A Designer’s Companion”, 1999 References
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 98 Definitions/Terminology
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 99 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
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 100 Where:a k are the impulse amplitudes k are the impulse delays CLEAN 1 –Variant of a serial correlation algorithm Channel Modeled as LTI filter, with impulse response h(t) of the form: Terminology
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 101 RMS Delay Spread can be expressed as: Terminology
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 102 Mean Excess Delay can be expressed as: Terminology
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 103 Relative Magnitude can be expressed as: Where: Terminology
doc.: IEEE /240SG3a Submission July 2002 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) Slide 104 Average Multipath Intensity Profile (MIP) (or Average Power Decay Profile (APDP) can be expressed as: Terminology