doc.: IEEE <doc#>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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 35806 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 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Empirically Based Statistical Ultra-Wideband (UWB) Channel Model
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

Executive Summary (cont)
July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

Executive Summary (cont)
July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Outline Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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 We also will include results on the fit provided by the legacy channel model. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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 The short range, non line of sight case is of particular interest. It is not hard to imagine WPAN scenarios with very short range channels that are nevertheless severely obstructed. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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) The 19.8 dB gain figure spans pre-amp to DSO. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Channel Measurement Test Setup
<month year> doc.: IEEE <doc#> July 2002 Channel Measurement Test Setup Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Measurement Issue: Received Pulse Distortion
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Typical Normalized Antenna Azimuth and Elevation Patterns (omni-directional antennas) TDC SG Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Received Pulse Distortion Test Setup
<month year> doc.: IEEE <doc#> July 2002 Received Pulse Distortion Test Setup Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Pulse Distortion Test Results
<month year> doc.: IEEE <doc#> July 2002 Pulse Distortion Test Results Normalized amplitudes Amplitudes are normalized to 1 to show effects of signal distortion only. The 90-degree waveform is actually attenuated by approximately 17 dB due to the antenna null. 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Measurement Issue: Determination of LOS Delay
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Periodic Channel Stimulus Example
<month year> doc.: IEEE <doc#> July 2002 Periodic Channel Stimulus Example Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Random Channel Stimulus Example
<month year> doc.: IEEE <doc#> July 2002 Random Channel Stimulus Example Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Minimal Effect on RMS Delay
<month year> doc.: IEEE <doc#> July 2002 Minimal Effect on RMS Delay Note: the channel deconvolution process uses the calculated LOS delay information to reject any multipath ring-down prior to the LOS delay. Ability to accurately determine LOS delay was judged important enough to utilize periodic (non time-hopped) pulse trains. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Channel Measurement Environments
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Example Measurement Locations A Typical Office Environment Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Example Measurement Locations Conference Room Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Example Measurement Locations Another Office Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Example Measurement Locations Residential Living Room Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Example Measurement Locations Residential Bedroom Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Introduction Measurement Campaign Data Analysis Modeling the Channel Conclustions/Recommendations Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Major Analysis Assumptions
<month year> doc.: IEEE <doc#> July 2002 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) Equation (1) states that the UWB channel response is a sum of delayed and scaled versions of the channel stimulus. So when we say that a channel is “distortionless”, we mean that it only affects the signal in these ways: it can attenuate the signal in amplitude (evenly across all frequencies), and it can delay the signal in time. Naturally, real channels are not distortionless, in this sense. But the model is nevertheless useful. For one thing, if we can quantify the extent to which the data does not fit our model, we will have quantified the amount of signal distortion, in the sense just defined, produced by the channel. It should be noted that the amplitudes ak and delays tk that appear in equation (1) are functions of the environment, and are generally considered to be statistically random. This applies to the number N of multipath reflections in the channel as well. A complete statistical characterization of a channel model of the form (1) would entail the specification of the all dependencies among these random variables (i.e. all the finite-dimensional distributions). Such a characterization is almost always outside the realm of feasibility, unless simplifying modeling assumptions are made. For example, many models assume that the squares of the amplitudes ak are log-normally distributed. The applicability of such assumption to a particular data set is subject to verification, however. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

CLEAN Algorithm used to deconvolve CIR from channel record
<month year> doc.: IEEE <doc#> July 2002 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 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

CLEAN Algorithm Compared to Frequency Domain De-Convolution
<month year> doc.: IEEE <doc#> July 2002 CLEAN Algorithm Compared to Frequency Domain De-Convolution Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

CLEAN Algorithm geometric interpretation
<month year> doc.: IEEE <doc#> July 2002 CLEAN Algorithm geometric interpretation Energy Capture Ratio: s r s-r Original scan Error vector Linear space of all possible reconstructed scans CLEAN approximation to original scan (reconstructed scan) Relative Error: Least Squares Condition: (2) Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

CLEAN Algorithm estimation of signal distortion
<month year> doc.: IEEE <doc#> July 2002 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)). Again, distortion here means any action of the channel on the signal that is not a simple scaling or delay. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

CLEAN Residual Estimates of Signal Distortion
<month year> doc.: IEEE <doc#> July 2002 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: % (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) Note: experiments in which the CLEAN algorithm extracts CIRs from channels with known CIRs that have negligible distortion have shown that inaccuracies in the CLEAN algorithm itself account for about 3% of this distortion. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Data Used for the Analysis
<month year> doc.: IEEE <doc#> July 2002 Data Used for the Analysis 429 of the 471 channel records all vertically polarized measurements. duplicate measurements removed. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

General Remarks on the Data
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Scan #1: LOS 1m distance, Antenna Boresight 1/r2 Path Loss
<month year> doc.: IEEE <doc#> July 2002 Scan #1: LOS 1m distance, Antenna Boresight 1/r2 Path Loss 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -0.06 -0.04 -0.02 0.02 0.04 0.06 Amplitude Time (ns) LOS Arrival Time Estimation Path loss calculations on this and subsequent slides are done on the basis of peak instantaneous power ratios. Path loss exponents are calculated by comparing these ratios to reference 1-meter freespace propagation, assuming a power law of the form 1/rn. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Scan #57: LOS 3.1m distance, office environment, approximately 1/r5.28 Path Loss Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Scan #6: NLOS 1.3m distance, office environment, approximately 1/r26.5 path loss Estimation Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Scan #15: NLOS 2.7m distance, office environment approximately 1/r2.07 Path Loss 0.02 0.015 0.01 0.005 Amplitude -0.005 Note: need to define LOS and NLOS explicitly. -0.01 -0.015 LOS Arrival time Estimation -0.02 2 4 6 8 10 12 14 16 18 20 Time (ns) Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Descriptive Statistics of the Data
<month year> doc.: IEEE <doc#> July 2002 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. Time discretization unit for all statistical analyses was 0.5 ns, consistent with the bandwidth of the channel soundings. Also, the 0.5 ns time discretization provided enough samples in each time bin to make the statistics of occupancy probability and mean log relative amplitude meaningful.. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

I. NLOS, 0 to 4 meters, metal stud
<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Measurements per Meter This is probably the strongest group of measurements in the current data set. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Histogram of Number of Multipath Components Per Channel Time discretization unit was 0.5 ns Average number of multipath components per channel: 36.1 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution Probability of Occupancy Thus, for instance, there is about a 40% probability that a channel in this data set has a reflection between 16.0 ns and 16.5 ns. Time discretization unit: 0.5 ns This plot shows how the probability of a multipath arrival in a time bin of size 0.5 ns varies with excess delay. Linear fit: p(t) = t Graph of the probability that an excess delay bin contains a reflection. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean + stdv. Mean Log Relative Magnitude This plot shows the relationship between the mean of the natural logarithm of relative magnitude and excess delay. Multiplying the y-coordinates of this plot by 2 would result in the average power decay profile (APDP), also called the multipath intensity profile. Linear fit: y(t) = t if t > 5 y(t) = t if t > 5 Mean - stdv. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Mean RMS Delay vs. Distance Mean + stdv. Mean RMS Delay Distance discretization unit: 1 meter. Linear fit: E[tRMS(d)] = d Note the intercept value of almost 7 ns: a reminder that we are dealing with NLOS channels. Mean - stdv. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

II. LOS, 0 to 4 meters, metal stud
<month year> doc.: IEEE <doc#> July 2002 II. LOS, 0 to 4 meters, metal stud Histogram of Number of Measurements per Meter There is a need for more LOS measurements. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 II. LOS, 0 to 4 meters, metal stud Histogram of Number of Multipath Components Per Channel Note the difference between the multipath component distribution here in the LOS case, as compared to the previous NLOS case. As expected, in the LOS case, there are typically fewer multipath components per channel than in the NLOS case. Average number of multipath components per channel: 24.0 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 II. LOS, 0 to 4 meters, metal stud Multipath Arrival Time Distribution Probability of Occupancy In the LOS case, there is a pronounced upturn in the graph in the region near t = 0 (t is excess delay). In the NLOS case, this feature is suppressed due to the obstruction of the line of sight path. Piecewise linear fit: p(t) = t if t > 4 p(t) = t if 0 < t < 4 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 II. LOS, 0 to 4 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean + stdv. Mean Log Relative Magnitude This plot shows the relationship between the mean of the natural logarithm of relative magnitude and excess delay. Multiplying the y-coordinates of this plot by 2 would result in the average power decay profile (APDP), also called the multipath intensity profile. Linear fit: y(t) = t if t > 8 y(t) = t if t > 8 Mean - stdv. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 II. LOS, 0 to 4 meters, metal stud Mean RMS Delay vs. Distance Mean + stdv. Mean RMS Delay As expected, lower RMS delay in the LOS case. Linear fit: E[tRMS(d)] = d Something of a puzzle why the rate of increase of RMS delay with distance should be higher in this case than in the NLOS case... Mean - stdv. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

III. NLOS, 4 to 10 meters, metal stud
<month year> doc.: IEEE <doc#> July 2002 III. NLOS, 4 to 10 meters, metal stud Histogram of Number of Measurements per Meter Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 III. NLOS, 4 to 10 meters, metal stud Histogram of Number of Multipath Components Per Channel Not surprisingly, these channels have the highest average number of components per channels of any in our data set. Average number of multipath components per channel: 61.6 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 III. NLOS, 4 to 10 meters, metal stud Multipath Arrival Time Distribution Probability of Occupancy Time discretization unit: 0.5 ns This plot shows how the probability of a multipath arrival in a time bin of size 0.5 ns varies with excess delay. Linear fit: p(t) = t Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 III. NLOS, 4 to 10 meters, metal stud Mean of Log Relative Magnitude vs. Excess Delay Mean + stdv. This plot shows the relationship between the mean of the natural logarithm of relative magnitude and excess delay. Multiplying the y-coordinates of this plot by 2 would result in the average power decay profile (APDP), also called the multipath intensity profile. Linear fit: y(t) = t Mean Log Relative Magnitude Mean - stdv. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 III. NLOS, 4 to 10 meters, metal stud Mean RMS Delay vs. Distance Mean + stdv. Mean RMS Delay Mean - stdv. Linear fit: E[tRMS(d)] = d Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Introduction Measurement Campaign Data Analysis Modeling the Channel Conclusions/Recommendations Will add the following items: IEEE channel model Using the empirical probabilities directly. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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 Need to include some explanatory slides on the D-K model. For now, just reference the literature. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Linear Fit For Arrival Time Conditional Distributions Negative conditional probability of occupancy: probability that the current excess delay bin is occupied, given that the previous bin is not occupied. Positive conditional probability of occupancy: probability that the current excess delay bin is occupied, given that the previous bin is occupied. Modeling assumption for the D-K model is that the ratio of these two probabilities is constant. Blue: negative conditionals Red: positive conditionals Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Ratio of Conditional Probabilities Provides K Estimate The arrival times for this data set fit the D-K assumptions well. There are other alternatives for calculating K. As a check, K was re-calculated for this data set using a minimum square error criterion. The resulting value was again found to be K = 0.85. K = 0.85 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud Linear Fit For Log Relative Magnitude Data Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud D-K Simulation Results: Multipath Arrival Time Distribution Blue: simulated channels Red: measured channels Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

<month year> doc.: IEEE <doc#> July 2002 I. NLOS, 0 to 4 meters, metal stud D-K Simulation Results: Log Relative Magnitude Distrubution The D-K model approximates the arrival times and amplitudes of the data well, due to the fact that these are the parameters that are fed into the model. Current results indicate that the D-K model overestimates the RMS delay of the channels in this data set by several nanoseconds (e.g. an average of 11 ns average delay instead of 8). This remains true when the linear or piecewise linear approximations used in the D-K model are replaced with the actual empirical occupancy probabilities and decay profiles. This indicates that these statistics do not fully characterize the data. Will flesh this out more in slides to be added before the presentation. Blue: simulated channels Red: measured channels Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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. Evidence to suppor t this recommendation will be added to this presentation shortly. Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 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 Handbook A Designer’s Companion”, 1999 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

Definitions/Terminology
July 2002 Definitions/Terminology Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

doc.: IEEE 802.15-<doc#>
<month year> doc.: IEEE <doc#> July 2002 Terminology 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 Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC) <author>, <company>

July 2002 Terminology CLEAN1 Variant of a serial correlation algorithm Channel Modeled as LTI filter, with impulse response h(t) of the form: Where: ak are the impulse amplitudes tk are the impulse delays Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

RMS Delay Spread can be expressed as:
July 2002 Terminology RMS Delay Spread can be expressed as: Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

Mean Excess Delay can be expressed as:
July 2002 Terminology Mean Excess Delay can be expressed as: Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

Relative Magnitude can be expressed as:
July 2002 Terminology Relative Magnitude can be expressed as: Where: Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

July 2002 Terminology Average Multipath Intensity Profile (MIP) (or Average Power Decay Profile (APDP) can be expressed as: Marcus Pendergrass and William C. Beeler, Time Domain Corporation (TDC)

doc.: IEEE <doc#>

Similar presentations

Presentation on theme: "doc.: IEEE <doc#>"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

doc.: IEEE <doc#>

Similar presentations

Presentation on theme: "doc.: IEEE <doc#>"— Presentation transcript:

Similar presentations

About project

Feedback