Unified “Black Box” PHY Abstraction Methodology Month 2004 doc.: IEEE 802.11-04/0172r0 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

PHY Abstraction problem Month 2004 doc.: IEEE 802.11-04/0172r0 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

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

Two Basic Approaches Model PHY as black box using tables (more here) Month 2004 doc.: IEEE 802.11-04/0172r0 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

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

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

Using Capacity to Characterize Channels Month 2004 doc.: IEEE 802.11-04/0172r0 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 802.11-04/0064 (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

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

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

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

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

Extending Channel Capacity to MIMO Month 2004 doc.: IEEE 802.11-04/0172r0 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

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

Including Rate Adaptation w/ PHY Month 2004 doc.: IEEE 802.11-04/0172r0 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

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

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 11-04-0184-00-000 (STMicroelectronics). Atheros / Mitsubishi ITE / ST Micro

Black Box PHY Method Summary Month 2004 doc.: IEEE 802.11-04/0172r0 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

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

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

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: 25, 26, 27, 28, 28, 27, 26, 26, 25 Atheros / Mitsubishi ITE / ST Micro

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: 25, 26, 27, 28, 28, 27, 26, 26, 25 Atheros / Mitsubishi ITE / ST Micro

Many-Rate PHY Operation Month 2004 doc.: IEEE 802.11-04/0172r0 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

Month 2004 doc.: IEEE 802.11-04/0172r0 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

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

Simulation Requirements Atheros / Mitsubishi ITE / ST Micro

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

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

References 11-03/0863 Packet Error Probability Prediction 802.11 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 802.11n MAC Simulations Using Soft PER Estimates (Marvell) 11-04/0183 Record and Playback PHY Abstraction 802.11n MAC Simulations using Binary PER Estimates (Marvell) 11-04/0184 Proposal PHY Abstraction In MAC Simulators (STm) Atheros / Mitsubishi ITE / ST Micro

Appendix Atheros / Mitsubishi ITE / ST Micro

CC to PER mapping validation Month 2004 doc.: IEEE 802.11-04/0172r0 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

Month 2004 doc.: IEEE 802.11-04/0172r0 Atheros / Mitsubishi ITE / ST Micro Jeff Gilbert et. al., Atheros / Mitsubishi ITE

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

Accuracy on Average BEL Atheros / Mitsubishi ITE / ST Micro

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

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

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

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

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

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