Simulation Methodology Proposal

1. Simulation Methodology Proposal Date: 2009-09-23 Authors: Yung-Szu Tu, Ralink Tech.

2. Goal • Propose a new simulation methodology which combines two approaches previously considered for 11n • Review comparison of previous methodologies • Observations, and new proposed approach Yung-Szu Tu, Ralink Tech.

3. Introduction • Simulation Methodology SpecialCommittee in TGn was established to determine simulation methodology • “Simulation methodology special committee was formed based on the belief by the majority of the TGn body that the modeling the PHY in MAC / System simulations should be examined and a mandatory or recommended (TBD) methodology should be developed”, quoted from [7] • As described in the report[7], two competing methodologies considered • Unified “Black Box” PHYAbstraction Methodology [1] • Merged “Record and Playback PHY Abstraction for 802.11n MAC Simulations”[5] among others • PHY Abstraction for System Simulation [3] • PER Prediction for 802.11n MAC Simulation [2] • PHY Abstraction based on PER Prediction [4] • No consensus was reached and the committee was disbanded • We propose a unified approach combining essential features of the two previous methodologies Yung-Szu Tu, Ralink Tech.

4. Review* of Unified “Black Box” PHYAbstraction Methodology *Slides are taken from [1] Yung-Szu Tu, Ralink Tech.

5. Table Channel Model Channel Model Black Box Capacity Calc. Capacity Calc. MAC / System Model Table Using Channel Capacity (CC) PHY Simulation Pre-generates table for MAC simulations MAC Simulation Uses PHY simulation data for MAC simulation DistanceModel # DistanceModel # Channel Channel Data rates PHY Model CC PHY Performance PHY performance CC 1st Approach: Use PHY simulation with Channel Capacity results to generate throughput Table for later use in MAC simulation Yung-Szu Tu, Ralink Tech.

6. Rate Adaptive LUT-based Methods PHY Simulation MAC Simulation DistanceModel # Channel DistanceModel # Channel Table Channel Model Channel Model Black Box Feedback CC Rate Adaptation PHY Model Statistics of pairs of“data rates” / PERs RateSelection MAC / System Model Capacity Calc. Capacity Calc. Statistics of pairs of“data rates” / PERs CC Modified Black Box Approach: Improved to include rate adaptation to reduce complexity of Table size Table Yung-Szu Tu, Ralink Tech.

7. Channel Model Capacity Calc. Generating the Table Data DistanceModel # Channel Black Box Feedback Rate Adaptation PHY Model RateSelection CC PacketError? DataRate The run of the PHY model with rate adaptation over a channel sequence generates a sequence of (CC, DataRate, PacketError?) sets Yung-Szu Tu, Ralink Tech.

8. Review* of PHY Abstraction for System Simulation *Slides are taken from [2~4] Yung-Szu Tu, Ralink Tech.

9. PHY Abstraction Approach Yung-Szu Tu, Ralink Tech.

10. Post Detection SNRs and PER Prediction from PSymb Linear Equalizer Channel • Psymb = prob. of a Viterbi decoder error within the duration of a single OFDM symbol • Psymb is independent of packet length • Allows scaling to arbitrary packet lentghs • Basic Assumption: symbol errors are ~ independent • OFDM symbols > several constraint lengths  good approx. • See 11-03-0863 for validation Yung-Szu Tu, Ralink Tech.

11. = mean capacity Parametric Model Fit Capacity statistics calculated from subcarrier-spatial stream capacities CV = capacity coefficient of variation (std. deviation / mean) Yung-Szu Tu, Ralink Tech.

12. SNR at Output of Spatial Processing and Effective SNR • Then post-detection SNR for stream i is • The transformation is determined by the spatial processing performed at Tx and Rx. • For linear processing, these are simple and well-known. • For non-linear processing (e.g. successive cancellation) exact functions may not be known, and approximations must be used. • Effective SNR for stream i:where a is a constant used to fit the approximation to PHY simulation results. Yung-Szu Tu, Ralink Tech.

13. Calculating PER for Unequal Modulation • Get AWGN coded bit error probability for effective SNR: , for rate used in stream i, from LUT • Calculate approximate error event probability from Pb(i) • Calculate approximate probability of error in stream iwhere is the number of coded bits in stream i, , K is the code constraint length, and is the code rate used in stream i. • Then the PER is Yung-Szu Tu, Ralink Tech.

14. Comparison, observations, and proposal Yung-Szu Tu, Ralink Tech.

15. TGn Comparison *Table is taken from [6] Yung-Szu Tu, Ralink Tech.

16. TGn Comparison (cont’d) *Table is taken from [6] Yung-Szu Tu, Ralink Tech.

17. Down-conversion, ADC, FFT, Spatial processing, etc Symbol Packet FEC decoder Channel state Pass or Fail Box of PER prediction Black Box Observations • The previous methods can be generalized into a single block diagram • The processing details within black box not as important as long as input-output mapping matches the PHY behavior • The mapping could/should be a function of spatial processing • The choice between LUT and curve-fitting to determine PER equally valid • Not using post-detection SNR may simplify implementation Yung-Szu Tu, Ralink Tech.

18. Observations (cont’d) • ACI & CCI: indeed, black box approach should consider ACI & CCI. A work-around is to consider them as part of the channel state • Rate adaptation: indeed, it should not be tied with PHY abstraction • Characterization of PHY abstraction error: the error of every methodology should be characterized • Just a matter of error-computation Bottom line: a method is a good candidate as long as it predicts the PER quickly and precisely given the channel state, spatial processing, etc., and properly mimics the operations between PHY and MAC Yung-Szu Tu, Ralink Tech.

19. Proposal • An approved channel model is required so that comparison between full spec. proposals is fair • PHY abstraction must be justified • Several simulation tools, e.g. NS2 and Opnet, are available for system simulation Yung-Szu Tu, Ralink Tech.

20. Proposal (cont’d) • Justification of PHY abstraction • Most PHY models of system simulators incorporate PHY abstraction to reduce the time of simulation Yung-Szu Tu, Ralink Tech.

21. Proposal (cont’d) • Justification of PHY abstraction • For example, channels B and C are used for PHY abstraction tuning • Channel D is reserved for justification and Diff(D) must be close to Diff(B) and Diff(C) Yung-Szu Tu, Ralink Tech.

22. Proposal (cont’d) • Channel sequence is pre-run, recorded, and played back • Save time and guarantee consistency between different system simulators • The implementation of rate adaptation should not be constrained in PHY • Optionally, rate is sent from MAC to PHY Yung-Szu Tu, Ralink Tech.

23. Straw Poll • [How to validate/verify abstraction models and simulation methodologies done by other parties?] • Should individual proposals include a PHY abstraction justification? • Y: • N: • A: Yung-Szu Tu, Ralink Tech.

24. References [1] 11-04/0218r3 Unified “Black Box” PHYAbstraction Methodology (Atheros, Mitsubishi, and STM) [2] 11-04/0304r1 PER Prediction for 802.11nMAC Simulation (Intel) [3] 11-04/0174r1 PHY Abstraction for System Simulation (Qualcomm and Intel) [4] 11-04/0269r0 PHY Abstraction based on PER Prediction (Qualcomm) [5] 11-04/0183r2 Record and Playback PHY Abstraction for 802.11n MAC Simulations (Marvell) [6] 11-04/0316r1 Comments on PHY Abstraction (Intel) [7] 11-04/0301r1 802.11 TGn Simulation Methodology SpecialCommittee March 2004 Report [8] 11-09/0698r0 Revisions to “IEEE_802_11_Cases.m” for TGac Channel Model (Qualcomm) Yung-Szu Tu, Ralink Tech.

25. Back-Up Slides: Review* of Record and Playback PHY Abstraction for 802.11n MAC Simulations *Slides are taken from [5] Yung-Szu Tu, Ralink Tech.

26. Simulation Diagram Yung-Szu Tu, Ralink Tech.

27. Example PHY Record with Alternate Rates Avg SNR = 30 dB • 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 Yung-Szu Tu, Ralink Tech.