1. Unified “Black Box” PHY Abstraction Methodology
Month 2004
doc.: IEEE /0172r0
Unified “Black Box” PHY Abstraction Methodology
Jeff Gilbert, Won-Joon Choi, Qinfang Sun, Ardavan Tehrani, Huanchun Ye Atheros Communications
B.Jechoux, H.Bonneville Mitsubishi ITE
Stefano Valle, Angelo Poloni STMicroelectronics
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

2. PHY Abstraction problem
Month 2004
doc.: IEEE /0172r0
PHY Abstraction problem
PHY / MAC Interface can drastically impact overall results:
Time varying channel creates time varying PER
Time varying channel could affect systems with feedback
This affects overall delay, jitter and throughput
Challenge
Properly model detailed PHY characteristics
Keep flexibility to adapt to various PHYs
Keep simulation effort reasonable
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

3. Ideal PHY / MAC System Simulation
Distance Model #
Channel Model
Channel
PHY Model
Packet error and chan feedback info
Rate Selection
MAC / System Model
Advantages: Full accuracy of link-level PHY and detailed MAC / System simulation
Disadvantages: Large computational requirements for large simulations with many nodes
Approximations is required to make the simulations feasible
Atheros / Mitsubishi ITE / ST Micro

4. Two Basic Approaches Model PHY as black box using tables (more here)
Month 2004
doc.: IEEE /0172r0
Two Basic Approaches
Model PHY as black box using tables (more here)
Allows use of full-accuracy PHY and Channel model
PHY model used “as-is” – no formulas or approximations required
Approximations made at PHY/MAC boundary
Incorporate simplified PHY into MAC sim (Intel)
Use derived, approximate model of PHY
Incorporate directly into MAC/System simulations – interface cleaner
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

5. Features of Black Box Method
PHY simulations do not scale with the number of data rates
Good modeling of 11n channel characteristics & variations.
Accurate modeling of PHY proposals with all impairments
Includes rate adaptation as part of PHY
Easy interface to merge different PHY and MAC proposals
Atheros / Mitsubishi ITE / ST Micro

6. Black Box PHY Overview PHY Simulation MAC 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
Channel
Data rates
Black Box
PHY Model
Table
PHY Performance
PHY performance
MAC / System Model
Table
Concept: Abstract PHY performance vs. channel condition into table
Difficulties: Reducing channel dimensionality and num data rates dependence
Atheros / Mitsubishi ITE / ST Micro

7. Using Capacity to Characterize Channels
Month 2004
doc.: IEEE /0172r0
Using Capacity to Characterize Channels
The dimensionality of channel state must be reduced to limit table size
Otherwise for a freq-selective MIMO channel, the dimensionality would be O(nNumFreqBins*NumStreams)
As per /0064 (ST) Channel Capacity can be used to reduce the dimensionality while retaining fidelity
Achievable data rate vs. capacity mapping has been verified in initial tests (Atheros)
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

8. PER vs capacity (vs SNR) mapping
Suppressing SNR dimension
SNR
Atheros / Mitsubishi ITE / ST Micro

9. 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
Concept: Reduce dimensionality of channel by using its MIMO capacity PHY model uses actual channel to compute performance
Atheros / Mitsubishi ITE / ST Micro

10. Validation of the Methodology for SISO
Validation carried out by using:
802.11a, Rate 54 Mbps with SISO channel D;
Continuous packet transmission;
Comparison of the results obtained with two approaches:
Pure Link-Level simulation (50,000 simulated packets)
Full channel model (500,000 simulated packets) plus PERvsCC LUTs; parameters:
Channel Capacity resolution in LUTs: 0.5 and 1 b/s/Hz;
Time resolution in Channel Capacity generation: 0.5 and 1 ms;
with and without suppressing SNR dimension;
Metrics for comparison:
PER, Average of Burst of Error Length (BEL), Standard Deviation of BEL, pdf of BEL
Atheros / Mitsubishi ITE / ST Micro

11. Accuracy on PER Further details in appendix
Atheros / Mitsubishi ITE / ST Micro

12. Extending Channel Capacity to MIMO
Month 2004
doc.: IEEE /0172r0
Extending Channel Capacity to MIMO
Different SNR and channel conditions may lead to the same capacity
Initial tests show that capacity is sufficient to represent performance (more validation is needed)
If capacity alone is not sufficient, additional measurements may be added. Options:
SNR
Condition number of the channel
Near/far: within or beyond the distance break point of 11n channel models
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

13. Conventional LUT-based Methods
PHY Simulation
MAC Simulation
Distance Model #
Channel Model
Channel
Distance Model #
Channel Model
Capacity Calc.
Table
Channel CC
Data rates
Statistics of PERs per data rate and MPDU size
Black Box
PHY Model
Capacity Calc.
Randomly choose pass / fail based on per-rate statistics
Statistics of PERs per data rate and MPDU size CC
Data rate
Pass/Fail
Table
MAC / System Model w/ Rate Adaptation
Conventional table-based PHY simulations have difficulties simulating systems with many rates (ABL, MIMO etc) since PHY sims scale with the number of rates
Atheros / Mitsubishi ITE / ST Micro

14. Including Rate Adaptation w/ PHY
Month 2004
doc.: IEEE /0172r0
Including Rate Adaptation w/ PHY
Typical table-based systems record PER statistics for each data rate
For MIMO with independent rates on each stream, the number of rate combinations is NumRatesNumTxStreams
For Adaptive Bit Loading, rate set is continuous
This is solved by including rate adaptation w/ PHY
Number of runs does not grow with number of data rates
Richness of PHY / rate adaptation interface is not limited by storing in table
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

15. Rate Adaptive LUT-based Methods
PHY Simulation
MAC Simulation
Distance Model #
Channel Model
Distance Model #
Channel Model
Channel
Channel
Capacity Calc.
Black Box
Table
Feedback CC
Rate Adaptation
PHY Model
Statistics of pairs of “data rates” / PERs
Rate Selection
Capacity Calc.
Randomly choose data rate, PE based on stats
Statistics of pairs of “data rates” / PERs CC
Data rate
Pass/Fail
Table
MAC / System Model
Rate Adaptive table-based PHY simulations do not scale with the number of rates and the rich PHY / Rate Adaptation feedback is present
Atheros / Mitsubishi ITE / ST Micro

16. Statistics of pairs of “data rates” / PERs
Ideal PHY emulation
PHY Simulation
MAC Simulation
Distance Model #
Channel Model
Distance Model #
Channel Model
Channel
Channel
Capacity Calc.
Black Box
Feedback CC
Rate Adaptation
PHY Model
Rate Selection
Capacity Calc.
Spectral efficiency
>
CC? CC
Statistics of pairs of “data rates” / PERs
Data rate
Pass/Fail
Table
MAC / System Model
Ideal PHY (i.e. the one achieving a PER equal to the Outage Capacity) can be included in the MAC simulator; Rate Adaptation can be included as well. No LUT required for this case; see also (STMicroelectronics).
Atheros / Mitsubishi ITE / ST Micro

17. Black Box PHY Method Summary
Month 2004
doc.: IEEE /0172r0
Black Box PHY Method Summary
Consider PHY Model as a “black box” from MAC perspective
Critical to allow accurate modeling of all proposals’ PHY in an accurate and automated manner
Use of look-up tables giving PHY performance vs. channel conditions via channel capacity
Channel model run in system simulation to determine lookup into look-up tables
Rate adaptation modeled in the black box as well to allow rich interaction between PHY and rate adaptation
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

18. 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
Atheros / Mitsubishi ITE / ST Micro

19. Statistics (Rate, %, PER)
Storing the Table Data
Bin input data
Time
CC (Mbps)
Data Rate
Packet Error?
CC Bin T0 30 24
PASS
III
T0+Dt 31
T0+2Dt 36
FAIL
T0+3Dt 29 II
T0+4Dt 27
T0+5Dt I
T0+6Dt 23 18
T0+7Dt
T0+8Dt
T0+9Dt
Then store statistics in table by bin:
CC Bin
CC Range
Statistics (Rate, %, PER) I
20-24
(18, 66%, 0.5) (24, 33%, 1.0) II
25-29
(24, 100%, 0.0)
III
30-34
(24, 66%, 0.0) (36, 33%, 1.0)
Atheros / Mitsubishi ITE / ST Micro

20. Statistics (Rate, %, PER)
Using Table Data (w/ interp) Statistics can be used with or without interpolation. With interpolation shown below:
Time CC
CC Bin Weights
Net Statistics
Random Draw: T1 25
I (40%) II (60%)
(18, 27%, 0.5) (24, 73%, 0.2)
24F
T1+dt 26
I (20%) II (80%)
(18, 13%, 0.5) (24, 87%, 0.1)
24P
T1+2dt 27
II (100%)
(24, 100%, 0.0)
T1+3dt 28
II (80%) III (20%)
(24, 93%, 0.0) (36, 7%, 1.0)
T1+4dt
36F
T1+5dt
T1+6dt
T1+7dt
T1+8dt
18P
Input Per-rate statistics for stochastic method:
CC Bin
CC Range
Statistics (Rate, %, PER) I
20-24
(18, 66%, 0.5) (24, 33%, 1.0) II
25-29
(24, 100%, 0.0)
III
30-34
(24, 66%, 0.0) (36, 33%, 1.0)
And a random CC sequence from the channel model and CC calculation: , 26, 27, 28, 28, 27, 26, 26, 25
Atheros / Mitsubishi ITE / ST Micro

21. Statistics (Rate, %, PER)
Using Table Data (no interp) Statistics can be used with or without interpolation. W/o interpolation shown below:
Time CC
CC Bin
Statistics
Random Draw: T1 25 I
(18, 66%, 0.5) (24, 33%, 1.0)
24F
T1+dt 26 II
(24, 100%, 0.0)
24P
T1+2dt 27
III
(24, 66%, 0.0) (36, 33%, 1.0)
T1+3dt 28 IV
(36, 100%, 0.2)
36P
T1+4dt
36F
T1+5dt
T1+6dt
T1+7dt
T1+8dt
18P
Input: If Interpolation is not used, finer CC table granularity is required
Per-rate statistics for stochastic method:
CC Bin
CC Range
Statistics (Rate, %, PER) I
25.x
(18, 66%, 0.5) (24, 33%, 1.0) II
26.x
(24, 100%, 0.0)
III
27.x
(24, 66%, 0.0) (36, 33%, 1.0) IV
28.x
(36, 100%, 0.2)
And a random CC sequence from the channel model and CC calculation: , 26, 27, 28, 28, 27, 26, 26, 25
Atheros / Mitsubishi ITE / ST Micro

22. Many-Rate PHY Operation
Month 2004
doc.: IEEE /0172r0
Many-Rate PHY Operation
PHY simulation time is independent of the number of data rates
This is why rate adaptation needed to be incorporated into PHY simulation
If many different rates are selected, the statistics on each rate may be coarsely sampled but when aggregated they will be accurate
I.e. the PER accuracy scales with the total number of packets simulated, and not the number of packets per rate as with conventional table methods
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

23. Month 2004
doc.: IEEE /0172r0
Packet Length Effects
If the total number of packet lengths used in the system simulations is small, all used packet lengths can be generated in the table.
If there are many different lengths, a few representative rates (100, 1000, 10000) can be simulated and performance at intermediate lengths can be calculated by extrapolating from the closest rate via:
PERnew = 1 - (1-PERold)(NewLen/OldLen)
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

24. PHY Simulation Details
Run detailed PHY simulations to generate performance over a range of channel capacities. Run one set per:
MPDU size
(Binned distance or SNR offset) / Model
Each set of PHY simulations shall include:
Time variation due to Doppler and fading
N=10000 packets with packet spacing of Tcoherence/100
Rate adaptation/feedback
Output of each set of PHY simulation:
(Ri, di), 1 i  N, where Ri is the data rate of packet i and di is pass or fail.
Condense into: (Rk, rk, Pk), where k is an arbitrary data rate index, rk is the probability of using rate k, and Pk is the PER of rate k.
Atheros / Mitsubishi ITE / ST Micro

25. Simulation Requirements
Atheros / Mitsubishi ITE / ST Micro

26. Issues Co-channel or adjacent channel interference
Reduction in capacity can be directly modelled
Per-packet impacts of packet mis-rating not included
However usage models do not include much
CSMA/CA still handled correctly in MAC simulation
Rate adaptation approximations
Collision effects incorporated in MAC correctly result in packet losses but do not affect rate adaptation
Number of simulations to generate the table
Atheros / Mitsubishi ITE / ST Micro

27. Conclusions
The “Black Box PHY” methodology allows arbitrary PHYs to be included in MAC/System simulations with little MAC sim computation
Incorporating rate adaptation into PHY simulations facilitates the use of systems with many rates (MIMO, Adaptive Bit Loading)
Channel variation is presented as in the channel model
Some approximations in PHY / MAC interface
Atheros / Mitsubishi ITE / ST Micro

28. 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)
11-04/0183 Record and Playback PHY Abstraction n MAC Simulations using Binary PER Estimates (Marvell)
11-04/0184 Proposal PHY Abstraction In MAC Simulators (STm)
Atheros / Mitsubishi ITE / ST Micro

29. Appendix
Atheros / Mitsubishi ITE / ST Micro

30. CC to PER mapping validation
Month 2004
doc.: IEEE /0172r0
CC to PER mapping validation
100 channel realization considered;
1000 packets sent over each channel realization and PER estimated;
2 SNRs considered
Relative error (see slide 3) is defined as
where PERLut is the PER computed through linear interpolation of PERvsCC LUT values at the CC of interest and PERActual is the PER as estimated for the considered channel realization having the same CC.
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

31. Month 2004
doc.: IEEE /0172r0
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

32. Month 2004 doc.: IEEE 802.11-04/0172r0 Relative error
-5.00
0.00
5.00
10.00
15.00
20.00
25.00
30.00
0.001
0.01
0.1 1
PER
20 Individual
24 Individual
Atheros / Mitsubishi ITE / ST Micro
Jeff Gilbert et. al., Atheros / Mitsubishi ITE

33. Accuracy on Average BEL
Atheros / Mitsubishi ITE / ST Micro

34. Accuracy on Standard Deviation of BEL
Atheros / Mitsubishi ITE / ST Micro

35. Accuracy of PDF of BEL (SNR = 16 dB)
Atheros / Mitsubishi ITE / ST Micro

36. Accuracy of PDF of BEL (SNR = 20 dB)
Atheros / Mitsubishi ITE / ST Micro

37. Accuracy of PDF of BEL (SNR = 24 dB)
Atheros / Mitsubishi ITE / ST Micro

38. Accuracy of PDF of BEL (SNR = 28 dB)
Atheros / Mitsubishi ITE / ST Micro

39. Accuracy of PDF of BEL (SNR = 32 dB)
Atheros / Mitsubishi ITE / ST Micro