The “Black Box” PHY Abstraction Methodology

1. The “Black Box” PHY Abstraction Methodology Jeff Gilbert, Won-Joon Choi, Qinfang Sun, Ardavan Tehrani, Huanchun YeAtheros Communications B.Jechoux, H.BonnevilleMitsubishi ITE Jeff Gilbert et. al., Atheros / Mitsubishi ITE

2. 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 Jeff Gilbert et. al., Atheros / Mitsubishi ITE

3. Two Basic Approaches • Model PHY as black box using tables (more here) • Allows use of full-accuracy PHY 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 Jeff Gilbert et. al., Atheros / Mitsubishi ITE

4. The Black Box PHY Method • Consider PHY and Channel Model combo 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. range / environment • Bursty aspects of the PER modeled by PER distribution plus channel coherence time • Rate adaptation modeled in the black box as well to allow rich interaction between PHY and rate adaptation Jeff Gilbert et. al., Atheros / Mitsubishi ITE

5. Distance &Model Num Channel Model Black Box Randomly choose pass / fail based onper-rate statistics Table Conventional Table-BasedPHY Simulation Pre-generates table for MAC simulations Conventional Table-BasedMAC Simulation Uses PHY simulation data for MAC simulation Table Statistics of PERs per data rate andMPDU size Channel Data rates PHY Model Statistics of PERs per data rate andMPDU size Pass/Fail Data rate MAC / System Modelw/ 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 Jeff Gilbert et. al., Atheros / Mitsubishi ITE

6. 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 Jeff Gilbert et. al., Atheros / Mitsubishi ITE

7. Incorporating Channel Variation Two types of channel variation are incorporated: • Micro-variation • Channel variation seen over the time of few packets • Required to exercise and evaluate rate adaptation • Captured by evolving channel between pkts in PHY sims • Macro-variation • Channel variation seen over long time scales • Accounts for run to run variations and outage statistics • Captured by starting w/ several “representative” chans in PHY sims • Results from mix of representative chans used in MAC simulation Jeff Gilbert et. al., Atheros / Mitsubishi ITE

8. Channel Model Random selection ofwhich representativechannel(s) to use Interpolate between representative channels Randomly choose data rate, pass / fail based onstatistics Proposed PHY / Rate Adaptation Sim Pre-generates table for MAC simulations Proposed MAC Simulation Uses PHY simulation data for MAC simulation Distance &Model Num Table Statistics of pairs of“data rates” / PERsper representativechannel “Representative”channels Black Box Feedback Rate Adaptation PHY Model Data rates and PERs RateSelection Statistics of pairs of“data rates” / PERsper representativechannel Data rate, Pass/Fail MAC / SystemModel Table Jeff Gilbert et. al., Atheros / Mitsubishi ITE

9. The Table Data • For each location pair, the table contains statistics for several “representative channels” • For each representative channel, store histogram of rates selected and their PERs • The table is used to generate packets in MAC / System simulation by selecting a rate per packet based on statistics and using its PER to determine if the packet will succeed Jeff Gilbert et. al., Atheros / Mitsubishi ITE

10. Choosing Representative Channels • Generate a large number of channel realizations for given 802.11n channel model, • Compute MIMO capacity for each channel, • Sort channels in ascending order of capacity, ordered channels: • Choose channels Jeff Gilbert et. al., Atheros / Mitsubishi ITE

11. Channel Quality Q=0.00 Q=0.25 Q=0.50 Q=0.75 Q=1.00 PHY / MAC Table Data Structure Sequences Statistics Per-quality summary statistics include prob. of occurrence and PER for the data rates used Jeff Gilbert et. al., Atheros / Mitsubishi ITE

12. 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 Jeff Gilbert et. al., Atheros / Mitsubishi ITE

13. PHY Simulation Details • Run detailed PHY simulations using the 5 representative channels as initial conditions, one set of simulation per initial condition and MPDU size. • Each set of PHY simulation shall include: • Time variation due to small scale fading • N=100 packets with specified packet spacing • 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, pk is the probability of using rate k, and pk is the PER of rate k. Jeff Gilbert et. al., Atheros / Mitsubishi ITE

14. DT Distance &Model Num PHY / Rate Adaptation Simulation Channel Model Channel characteristics Choose R=5channels representing different quality points “Representative” channels Add Micro-variation Channel sequence Feedback Rate Adaptation PHY Model Run N=100 pkts Rate Data rate, PER, Prob of occurrence sets per “Representative” channel MPDU size(s) Table Jeff Gilbert et. al., Atheros / Mitsubishi ITE

15. Choosing DT • The PHY / rate adaptation needs to know an inter-packet interval to incorporate the correct amount of inter-packet variation • This must be determined prior to the PHY simulations heuristically from the simulation scenarios • The DT value only affects channel variation scaling • The DT could be a fixed value or distribution Jeff Gilbert et. al., Atheros / Mitsubishi ITE

16. Randomly select fail succeed based on PER Table MAC Simulation Pick random channel “quality” index between 0.0 and 1.0. Data rate, PER, Prob of occurrence sets per “Representative” channel New Quality = f(DT, tc ,old Quality) Quality Interpolate between two closest “quality” channels Data rate, PER, Prob. occur set Randomly choose data rate / PER pair based probability of occurrence Data rate, PER Data rate, Pass/Fail MAC Model Jeff Gilbert et. al., Atheros / Mitsubishi ITE

17. Channel Evolution Function • f(DT, tc ,old Quality) is used to capture macro scale variations • Some options include: • Generate random channel qualities and low-pass filter based on channel coherence time (tc ) • Markov models to model the channel variation(ST Microelectronics – 11-04/0064) • Evolve full channels based on channel model, determine capacity, map to quality and use this quality index Jeff Gilbert et. al., Atheros / Mitsubishi ITE

18. Channel Quality Q=0.00 Q=0.25 Q=0.50 Q=0.75 Q=1.00 Quality Interpolation /Packet Sequence Generation 40% weight 60% weight To generate Q=0.4: 9 Mbps: PASS 12 Mbps: PASS 12 Mbps: FAIL 12 Mbps: PASS 24 Mbps: FAIL 18 Mbps:PASS 12 Mbps: FAIL …. Sequence of packet for MAC simulation Jeff Gilbert et. al., Atheros / Mitsubishi ITE

19. Issues • Co-channel or adjacent channel interference would have to be modelled independent of MAC • 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 • The DT used in PHY / rate adaptation simulations determined a priori • Channel variation is present but not exact Jeff Gilbert et. al., Atheros / Mitsubishi ITE

20. 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 incorporated in an approximate manner • Some approximations in PHY / MAC interface Jeff Gilbert et. al., Atheros / Mitsubishi ITE