DSL Troubleshooting: Intermittent Faults CommTech 14 May 2009 Jim Wahl Aware, Inc.

1. DSL Troubleshooting: Intermittent FaultsCommTech14 May 2009Jim Wahl Aware, Inc.

2. Slide 2

3. Slide 3 Aware Inc. Background 1986: Biometrics and Image Compression Products Fingerprint compression, facial imaging OEM products 1993: DSL Intellectual Property Licensing Silicon customers: Infineon, Ikanos/ADI 2002: Test and Diagnostics Products Hardware and Dr. DSL diagnostics software Products Test Equipment OEM: UDMT (ADSL2/VDSL2 CO/RT) modem modules with Dr. DSL diagnostics Dr. DSL Line Diagnostics Platform Technician Dr. DSL Diagnostics Customers TELUS, Spirent, Tollgrade, 3M, JDSU, Fluke, Sunrise, Occam, Alcatel, and many others.

4. Slide 4 DSL and Data Rate DSL data rate is based on the "space" between the signal attenuation and the noise floor. This is the signal-to-noise ratio (SNR). The greater the area between the signal and noise floor, the higher the SNR, and the higher data rate.

5. Slide 5 Loop Length and SNR / Data Rate

6. Slide 6 Noise and SNR / Data Rate

7. Slide 7 Intermittent DSL Troubles Changes in crosstalk levels:

8. Slide 8 Count of Impulses per Hour

9. Slide 9 Count of Impulses per Hour

10. Slide 10 Count of Impulses per Hour

11. Slide 11 What�s a �bad� DSL line? Depends on service. � High-speed internet service vs anything else. IPTV dramatically increases error-free QoS requirement. VoIP increases low-latency QoS requirement. PHY-layer IPTV Benchmarks Example 1: < 1 resync / week; 1 CRC / hour. Example 2: < 1 resync / day; 1 CRC / 4hrs. BBF WT-126: < 1 CRC / 30 min for SD; < 1 CRC / 4hrs for HD. Why the Discrepancy? �QoE� dependent on many factors, including content and compression ratios. PHY-layer errors can be corrected or mitigated at higher layer. Some packets (I- and P-frames) more important than others (B-frames).

12. Slide 12 What�s a �bad� DSL line? One that a generates poor customer experience. If a customer hasn�t complained, yet. � Identify lines that are likely to generate complaints. To identify these lines, we need metrics. If a customer is already complaining. Improve service. To measure improvements, we need metrics.

13. Slide 13 Key Stability Metrics MTBE (Mean Time Between Error) Average time between two CRCs (or Code Violations). Expressed in seconds or bits. MTBR (Mean Time Between Resyncs) Average time between resyncs. Excludes user-generated events (diagnostic test, consumer powering off modem, �). BAT-V (Bit Allocation Table Variation) Average number of minor and major �bit-swap� events. A general metric for how much the underlying physical layer is changing�more coming. �

14. Slide 14 Where do I get them? Not Test Equipment Stability metrics are collected over time. Test equipment provides snapshots. DSLAM? Partly. DSLAMs have the raw data, but not the analysis. Analysis Tools Collect data from multiple DSLAMs. Normalize and calculate metrics. Compare to other lines in node, neighborhood, network. Categorize: 1) DLM, 2) Dispatch, 3) Downgrade, 4) Upgrade

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16. Slide 16 Sources of Intermittent Faults

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18. Slide 18 Impulse Noise Problems it causes: Video pixelization Audio hiccups Possible sources: Rotary tools Drills, power sanders Household appliances with motors Electric toothbrushes, older printers, garage door openers, older refrigerators, sewing machines. Outdoor heavy equipment Construction machinery

19. Slide 19 Possible Solutions Moving the modem away from suspected interference sources Problems: Not always possible to relocate modem Interference may permeate throughout location Error Correction (INP, PHY-R, Delay) Problems: Introduces latency Adds overhead data, reducing core line data rate Often trial-and-error in the DSLAM profile.

20. Slide 20 Impulse Monitoring Sample #1

21. Slide 21 Impulse Monitoring Impulse Sample #1

22. Slide 22 Impulse Monitoring Impulse Sample #2

23. Slide 23 Impulse Monitoring Impulse Sample #2

24. Slide 24 Impulse Monitoring Impulse Sample #3

25. Slide 25 Impulse Monitoring Impulse Sample #3

26. Slide 26 Impulse Monitoring Impulse Sample #4

27. Slide 27 Impulse Monitoring Impulse Sample #4

28. Slide 28 Impulse Monitoring Impulse Sample #5

29. Slide 29 Impulse Monitoring Impulse Sample #5

30. Slide 30 Troubleshooting Impulses Voice-based Methods Limited Magnitude, duration, threshold counters miss important events. Not usually clear how to set thresholds. Auto-thresholds, Frequency Domain Set thresholds at levels likely to impact DSL service. Time + frequency domain provide more insight into types of events. Auto-analysis, Identification Recommend whether DSLAM profile can correct for impulse events. Characterize impulses to help segment / troubleshoot.

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32. Slide 32 Characterizing Noise Frequency Span How broad is disturber (T1 vs AM) Rise Time How quickly does noise appear. Magnitude Coincident PSD How many different shapes are present at the same time.

33. Slide 33 Characterizing Noise

34. Slide 34 Characterizing Noise

35. Slide 35 Mitigating Noise Error Correction INP, PHY-R, Delay Optimized Margin and Bit-swapping Artificial Noise / Virtual Noise Transmit PSD Masks

36. Slide 36 Mitigation Tools

37. Slide 37 Takeaways DSL Lines Vary Time of day, day of year. Stability metrics key for tracking performance. Two General Types of Disturbers Impulsive � troubleshoot with handhelds. RFI, crosstalk � troubleshoot with centralized tools. Remedy Impulse-free lines impossible, but minimize below DSL impact. Advanced DSLAM profiles protect against changes in RFI, crosstalk noise sources.

38. Jim WahlAware, Inc.www.aware.comjwahl@aware.com