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

2. 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.

3. 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 n MAC Simulations”[5] among others
PHY Abstraction for System Simulation [3]
PER Prediction for n 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. PHY Abstraction Methodology
Sep. 2009
Review* of
Unified “Black Box”
PHY Abstraction Methodology
*Slides are taken from [1]
Yung-Szu Tu, Ralink Tech.

5. 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.

6. 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.

7. 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.

8. 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.

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

10. 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 for validation
Yung-Szu Tu, Ralink Tech.

11. 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.

12. 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.

13. 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.

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

15. 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.

16. 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 ( )
Impairments & receiver mismatch
yes (full in LUT)
yes (approx. in sim.,
full validation)
*Table is taken from [6]
Yung-Szu Tu, Ralink Tech.

17. 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.

18. 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.

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. Sep. 2009
References
[1] 11-04/0218r3 Unified “Black Box” PHY Abstraction Methodology (Atheros, Mitsubishi, and STM)
[2] 11-04/0304r1 PER Prediction for n 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 n MAC Simulations (Marvell)
[6] 11-04/0316r1 Comments on PHY Abstraction (Intel)
[7] 11-04/0301r 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.

25. Record and Playback PHY Abstraction for 802.11n MAC Simulations
Sep. 2009
Back-Up Slides:
Review* of
Record and Playback PHY Abstraction for n MAC Simulations
*Slides are taken from [5]
Yung-Szu Tu, Ralink Tech.

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

27. 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.