Simulation Methodology Proposal Sep. 2009 doc.: IEEE 802.11-09/xxxxr0 Sep. 2009 Simulation Methodology Proposal Date: 2009-09-23 Authors: Yung-Szu Tu, Ralink Tech. Yung-Szu Tu, Ralink Tech.

Sep. 2009 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.

Sep. 2009 Introduction Simulation Methodology Special Committee 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” PHY Abstraction 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.

PHY Abstraction Methodology Sep. 2009 Review* of Unified “Black Box” PHY Abstraction Methodology *Slides are taken from [1] Yung-Szu Tu, Ralink Tech.

Using Channel Capacity (CC) Sep. 2009 Using Channel Capacity (CC) PHY Simulation Pre-generates table for MAC simulations MAC Simulation Uses PHY simulation data for MAC simulation Distance Model # Channel Model Distance Model # Channel Model Channel Capacity Calc. Channel Data rates Table Black Box PHY Model Capacity Calc. CC PHY Performance PHY performance CC MAC / System Model Table 1st Approach: Use PHY simulation with Channel Capacity results to generate throughput Table for later use in MAC simulation Yung-Szu Tu, Ralink Tech.

Rate Adaptive LUT-based Methods Sep. 2009 PHY Model Rate Selection Feedback Black Box Rate Adaptation PHY Simulation MAC Simulation Rate Adaptive LUT-based Methods Statistics of pairs of “data rates” / PERs MAC / System Model Channel Model Table Channel CC Distance Model # Capacity Calc. Modified Black Box Approach: Improved to include rate adaptation to reduce complexity of Table size Yung-Szu Tu, Ralink Tech.

Generating the Table Data Sep. 2009 Generating the Table Data Time CC (Mbps) Data Rate Packet Error? T0 30 24 PASS T0+Dt 31 T0+2Dt 36 FAIL T0+3Dt 29 T0+4Dt 27 T0+5Dt T0+6Dt 23 18 T0+7Dt T0+8Dt T0+9Dt Distance Model # Channel Model Channel Black Box Feedback Rate Adaptation PHY Model Rate Selection Capacity Calc. Packet Error? CC Data Rate 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.

PHY Abstraction for System Simulation Sep. 2009 Review* of PHY Abstraction for System Simulation *Slides are taken from [2~4] Yung-Szu Tu, Ralink Tech.

PHY Abstraction Approach Sep. 2009 PHY Abstraction Approach Yung-Szu Tu, Ralink Tech.

Post Detection SNRs and PER Prediction from PSymb Sep. 2009 Post Detection SNRs and PER Prediction from PSymb Channel Linear Equalizer 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.

Parametric Model Fit = mean capacity Sep. 2009 Parametric Model Fit Capacity statistics calculated from subcarrier-spatial stream capacities = mean capacity CV = capacity coefficient of variation (std. deviation / mean) Yung-Szu Tu, Ralink Tech.

SNR at Output of Spatial Processing and Effective SNR Sep. 2009 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.

Calculating PER for Unequal Modulation Sep. 2009 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 i where 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.

Comparison, observations, and proposal Sep. 2009 Comparison, observations, and proposal Yung-Szu Tu, Ralink Tech.

TGn Comparison Sep. 2009 Black Box PER Prediction Point of Abstraction LUT on channel state FEC decoder Relation to best-known-methods ? GSM literature TGn Channels yes Space-Frequency Post Detection SNRs no *Table is taken from [6] Yung-Szu Tu, Ralink Tech.

TGn Comparison (cont’d) Sep. 2009 TGn Comparison (cont’d) Black Box PER Prediction Accurate capture of CCI & ACI no yes Rate Adaptation in PHY sim.  LUT in MAC sim. (where it should be) Complexity of method very high moderate Characterization of PHY abstraction error unknown Yes (11-04-0304) Impairments & receiver mismatch yes (full in LUT) yes (approx. in sim., full validation) *Table is taken from [6] Yung-Szu Tu, Ralink Tech.

Sep. 2009 Observations Channel state Pass or Fail FEC decoder Down-conversion, ADC, FFT, Spatial processing, etc Symbol Packet Box of PER prediction Black Box 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.

Observations (cont’d) Sep. 2009 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.

Sep. 2009 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.

Proposal (cont’d) Justification of PHY abstraction Sep. 2009 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.

Proposal (cont’d) Justification of PHY abstraction Sep. 2009 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.

Sep. 2009 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.

Sep. 2009 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.

Sep. 2009 References [1] 11-04/0218r3 Unified “Black Box” PHY Abstraction Methodology (Atheros, Mitsubishi, and STM) [2] 11-04/0304r1 PER Prediction for 802.11n MAC 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 Special Committee March 2004 Report [8] 11-09/0698r0 Revisions to “IEEE_802_11_Cases.m” for TGac Channel Model (Qualcomm) Yung-Szu Tu, Ralink Tech.

Record and Playback PHY Abstraction for 802.11n MAC Simulations Sep. 2009 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.

Sep. 2009 Simulation Diagram Yung-Szu Tu, Ralink Tech.

Example PHY Record with Alternate Rates Sep. 2009 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 Yung-Szu Tu, Ralink Tech.