Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)

1. Project: IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs) Submission Title: Comparison of S-V Channel Model to Empirical Data Date Submitted: 08 September, 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: Channel realizations generated by the S-V model are compared to measured channels. We show that the S-V model is able to accurately reproduce population means for 3 channel parameters: RMS delay spread, mean excess delay, and number of significant paths. In addition, a hueristic channel classification scheme is proposed, and we show that the S-V model is able to produce exemplars of each channel class except for one. We recommend that SG3a adopt a channel model consisting of a defined set of channel realizations generated by the S-V model, augmented with additional channel realizations necessary to capture the wide variety of channels that occur in typical WPAN application scenarios. 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. Marcus Pendergrass Time Domain Corporation (TDC)

2. Summary • S-V Model can match measured channels in terms of the population means of Mean Excess Delay, RMS Delay Spread, and Number of Significant Paths for three scenarios: • NLOS, 0 to 4 meters, office • LOS, 0 to 4 meters, office • NLOS, 4 to 6 meters, office • Variation of measured channels is very wide, even within a single scenario. • A heuristic channel taxonomy is proposed. • Single set of S-V parameters for each scenario is probably not sufficient to capture the wide variation among channels from a given category. • Recommendations • channel model should consist of a defined set of channel impulse response realizations • make sure all channel types within a given scenario are represented • multiple S-V parameter sets per scenario Marcus Pendergrass Time Domain Corporation (TDC)

3. Measurements S-V Model Case: Case: 1 2 3 1 2 3 Channel Parameter Population means Population means* Mean excess delay 10.38 5.05 14.18 10.04 5.28 15.16 RMS delay spread 8.03 5.28 14.28 8.33 5.50 15.07 Number of Paths (85 % energy capture) 36.1 24.0 61.54 35.88 24.39 61.77 *data provided by Jeff Foerster Case 1: Case 2: Case 3: NLOS, 0-4 meters, office LOS, 0-4 meters, office NLOS, 4-10 meters, office S-V Model Can Match Population Means of Data Marcus Pendergrass Time Domain Corporation (TDC)

4. Case 1: Case 2: Case 3: NLOS, 0-4 meters, office LOS, 0-4 meters, office NLOS, 4-10 meters, office Heuristic Channel Taxonomy Measurements S-V model Case: Case: Channel Type 1 2 3 1 2 3 Freespace ü ? Freespace + diffuse multipath ü ü ü ü Closely spaced dominant paths ü ü ü ? ü ü Closely spaced dominant paths + strong late arriving cluster ü ü LOS path not dominant, little diffuse multipath ü ü LOS path not dominant + diffuse multipath ü ü ? ü Strong late arriving cluster, little diffuse multipath ü ü ü ü ü ü Strong late arriving cluster + diffuse multipath ü ü ü ? ? ü Diffuse multipath, no single dominant path ü ü ? ? Marcus Pendergrass Time Domain Corporation (TDC)

5. A Channel Menagerie • The next several slides give visual depictions of selected channels from each Channel Type described on the previous slide. • Where possible, we show both measured and simulated channels for each Channel Type. • Purpose: • illustrate the variety of channels in the measurement data • indicate the ability of the S-V model to provide a reasonable facsimile to these channels • Measured channels have RED titles • Simulated channels have GREEN titles Marcus Pendergrass Time Domain Corporation (TDC)

6. Freespace Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

7. Freespace + diffuse multipath Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

8. Freespace + diffuse multipath S-V CIR S-V reconstructed waveform 95% energy capture 85% energy capture Marcus Pendergrass Time Domain Corporation (TDC)

9. Closely spaced dominant paths Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

10. Closely spaced dominant paths S-V CIR S-V reconstructed waveform 95% energy capture 85% energy capture Marcus Pendergrass Time Domain Corporation (TDC)

11. Closely spaced dominant paths, strong late-arriving cluster Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

12. Closely spaced dominant paths, strong late-arriving cluster S-V CIR S-V reconstructed waveform 95% energy capture 85% energy capture Marcus Pendergrass Time Domain Corporation (TDC)

13. LOS path not dominant, little diffuse multipath Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

14. LOS path not dominant, little diffuse multipath S-V CIR S-V reconstructed waveform 95% energy capture 85% energy capture Marcus Pendergrass Time Domain Corporation (TDC)

15. LOS path not dominant + diffuse multipath Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

16. LOS path not dominant + diffuse multipath S-V CIR S-V reconstructed waveform 95% energy capture 85% energy capture Marcus Pendergrass Time Domain Corporation (TDC)

17. Strong late-arriving cluster, little diffuse multipath Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

18. Strong late-arriving cluster, little diffuse multipath S-V CIR S-V reconstructed waveform 95% energy capture 85% energy capture Marcus Pendergrass Time Domain Corporation (TDC)

19. Strong late-arriving cluster + diffuse multipath Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

20. Strong late-arriving cluster + diffuse multipath S-V CIR S-V reconstructed waveform 95% energy capture 85% energy capture Marcus Pendergrass Time Domain Corporation (TDC)

21. Diffuse multipath, no single dominant path Measured data spectrum DSO Scan CIR Reconstructed Scan Marcus Pendergrass Time Domain Corporation (TDC)

22. NLOS, 0 to 4 meters, office • LOS, 0 to 4 meters, office • NLOS, 4 to 10 meters, office • NLOS, 0 to 4 meters, residential • LOS, 0 to 4 meters, residential • NLOS, 4 to 10 meters, residential Recommendations • Channel model should consist of a defined set of at least 50 channel impulse response realizations for each of the following 6 scenarios. • Channel realizations should include all types of channels found in the measured data for each scenario (see channel taxonomy, slide 3). • Current parameter sets cover all channel categories except one (very diffuse multipath), for the “office” scenarios. • Generate additional channel realizations to cover remaining scenarios and channel categories: • Preferred method: find S-V parameters to fill in the holes in the current set of realizations. • Alternate method: supplement S-V channel realizations with realizations taken from measured data. Marcus Pendergrass Time Domain Corporation (TDC)