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Record and Playback PHY Abstraction for n MAC Simulations

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1 Record and Playback PHY Abstraction for 802.11n MAC Simulations
March Record and Playback PHY Abstraction for n MAC Simulations Hemanth Sampath Erik Lindskog Ravi Narasimhan Atul Salhotra H. Sampath,E. Lindskog, R. Narasimhan, and A. Salhotra, Marvell

2 Record & Playback PHY Abstraction Scheme
March Record & Playback PHY Abstraction Scheme PHY Record: Generate n channel sequence of N samples, with sampling time dT Pass channel sequence through PHY simulator including rate adaptation [Black Box Methodology IEEE /01 72r0] Generate a PHY record with sequence of chosen rates and corresponding PASS/FAIL decisions. MAC Playback: The MAC replays PHY record for each user H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

3 Simulation Diagram March 15 2004
H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

4 Record & Playback Features
March Record & Playback Features PHY simulations do not scale with number of users Include rate adaptation (& power control) as part of PHY [IEEE /01 72r0]. Good modeling of 11n channel characteristics & variations. Accurate modeling of PHY proposals with all impairments. Easy interface to merge different PHY and MAC proposals ! No mapping approximations between PER, BER, rate, capacity etc. H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

5 Differences with capacity based PHY abstraction
March Differences with capacity based PHY abstraction Issue#1: The capacity to PER/rate mapping not unique. Example #1: Flat-fading and frequency selective fading channels can have same average capacity but different rates and PERs! Example #2: Problem exacerbated for MIMO channels. A full rank channel with low SNR can have same capacity as low rank channel with high SNR, but different rates and PERs! Simulations: Initial SISO simulations show high variability in capacity  PER mapping. Same capacity can yield different PERs for different channel realizations. [see Appendix] Issue #2: Capacity mapping does not allow MAC based rate adaptation schemes. Assumes only PHY based rate adaptation. Record & Replay method circumvents above issues H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

6 Channel Sequence Simulations Results:
March Channel Sequence Simulations Results: 25 channel coherence times is sufficient to capture richness of channel. Possible values of N and dT for ~ 25 coherence times (assuming ~ 160msec coherence time from 11n channel models at 5 GHz) ~ 4msec sampling with 1000 channels is sufficient for correctly predicting PHY performance with (PHY based rate adaptation.) Smaller N desirable for smaller PHY records N dT (msec) 250 16 500 8 1000 4 4000 1 H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

7 Performance Validation with N=1000 channel realizations
March Performance Validation with N=1000 channel realizations PHY performance accurately modeled with N=1000 & dT = 4msec H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

8 Capacity Calculation with N=1000 channels
March Capacity Calculation with N=1000 channels Capacity distribution accurately modeled with N=1000 & dT = 4msec H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

9 Example PHY Record (1000 byte pkt)
March Example PHY Record (1000 byte pkt) Avg SNR = 3 dB Avg SNR = 27 dB Avg SNR = 30 dB time R1 P/F T 6 t+dT 12 t+2dT 1 t+(P1)dt R1 P/F 54 1 48 R1 P/F 72 1 54 ……. Avg SNR can vary with path-loss and shadowing. [Example: 0:3:30] dB Records are computed for different packet sizes in usage model. H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

10 MAC Simulations t = Inter-packet spacing
March MAC Simulations For each user, playback sequence of recommended rates and associated packet pass or fail events t = Inter-packet spacing For time < dT, MAC packets are transmitted with identical rate and pass/fail decision (as specified by the PHY record) H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

11 Potential Issue & Improvements
March Potential Issue & Improvements Interpolation: Multiple packets are sent using the same pass/fail and rate, leading to increased throughput variability Define: Inter-packet spacing = t (< dT) Number of packets (M) transmitted per dT time = dT / t Worse case scenario: For dT ~ 4 msec and worse case t ~ 200 micro sec, M = 20 packets may be sent with identical rate & pass-fail decisions. Issue addressed: Decrease (dT) sampling time for worse case scenarios. Modify PHY record to include more than one rate for each channel realization. H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

12 MAC Throughput Validation for dT = 4msec, t = 0.2msec
March MAC Throughput Validation for dT = 4msec, t = 0.2msec MAC throughput for (dT = 4msec, t = 0.2 msec) is similar to ideal MAC simulation with dT = t = 0.2 msec (if PHY rate adaptation is accurate). H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

13 Potential Issue & Improvements
March Potential Issue & Improvements Issue: What about MAC based rate adaptation ? Issue addressed: The PHY record will have pass/fail decisions for recommended rate and alternative rates, for each channel realization. Enables MAC based rate adaptation. Algorithm for simulating alternate rates: If recommended rate FAILS, simulate lower rates until packet passes for the current channel realization. If recommended rate PASSES, simulate higher rates until packet fails for the current channel realization. H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

14 Example PHY Record with Alternate Rates
March Example PHY Record with Alternate Rates Avg SNR = 30 dB R1 P/F R2 R3 … 54 1 48 36 72 .. …. Only a few rates need to be simulated around the recommended rate regardless of total number of rates. (Record size does not increase drastically!) MAC based rate adaptation algorithms and feedback delays can be modeled MAC rate adaptation will further reduce throughput variation (jitter). H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

15 Example MAC Simulation
March Example MAC Simulation Example MAC adaptation: Increase rate if 2 consecutive packets pass. Decrease rate if 2 consecutive packets fail. H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

16 Estimated Simulation Time for Generating Records
March Estimated Simulation Time for Generating Records In Marvell MATLAB PHY simulator: Simulating a 1000 byte packet transmission on a 2 GHz processor takes 2.5 seconds on average. Record with N=1000 entries and 10 Avg. SNR indices (0, 3, 6,..,30 dB) with 1 rate per time instant would take 1000 entries x 2.5 seconds/entry x 10 SNR ~= 7 hours. Comparison: This time is similar to a typical PHY simulation that generates one PER vs SNR plot (assuming 1000 channels per SNR and 10 SNR points). Only 1 PHY record generated per channel model & packet-size. Simulation time does not scale with the number of users H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

17 Conclusions Record-Playback methodology has several advantages:
March Conclusions Record-Playback methodology has several advantages: Includes full n channel models. Complete modeling of PHY with impairments. Includes rate adaptation in PHY and MAC. Easy to merge different PHY and MAC proposals! No mapping approximations between BER, PER, capacity and rate! H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

18 March References 11-03/0863 Packet Error Probability Prediction MAC Simulation (Intel) 11-04/0064 Time Correlated Packet Errors in MAC Simulations (STm) 11-04/0120 PHY Abstraction to be Used in MAC Simulation (Mitsubishi) 11-04/0172 Black Box PHY Abstraction Methodology (Atheros / Mitsubishi) 11-04/0182 Record and Playback PHY Abstraction n MAC Simulations Using Soft PER Estimates (Marvell) 11-04/0183r1 Record and Playback PHY Abstraction n MAC Simulations using Binary PER Estimates (Marvell) 11-04/0184 Proposal PHY Abstraction In MAC Simulators (STm) H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

19 March Appendix H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

20 Capacity Calculation with N=1000 channels
March Capacity Calculation with N=1000 channels Capacity distribution accurately modeled with N=1000 & dT = 4msec H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

21 Capacity Calculation with N=1000 channels
March Capacity Calculation with N=1000 channels Capacity distribution accurately modeled with N=1000 & dT = 4msec H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

22 Capacity Calculation with N=1000 channels
March Capacity Calculation with N=1000 channels Capacity distribution accurately modeled with N=1000 & dT = 4msec H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

23 Performance Validation with N=1000 channel realizations
March Performance Validation with N=1000 channel realizations PHY performance accurately modeled with N=1000 & dT = 4msec H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

24 Variability in capacity  PER mapping
March Variability in capacity  PER mapping Note: Plot generated by ST-Microelectronics H. Sampath, E. Lindskog, R.Narasimhan, A. Salhotra, Marvell

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