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Doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 1 Modified “Black Box” PHY Abstraction Methodology.

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Presentation on theme: "Doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 1 Modified “Black Box” PHY Abstraction Methodology."— Presentation transcript:

1 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 1 Modified “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 Erik Lindskog, Hemanth Sampath, Ravi Narasimhan Marvell Semiconductors

2 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 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

3 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 3 Ideal PHY / MAC System Simulation 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 MAC / System Model Channel Model Channel Packet error and chan feedback info PHY Model Rate Selection Distance Model #

4 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 4 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

5 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 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

6 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 6 PHY performance PHY Model Channel Model Table Channel PHY Simulation Pre-generates table for MAC simulations Table MAC Simulation Uses PHY simulation data for MAC simulation MAC / System Model Black Box Data rates Concept: Abstract PHY performance vs. channel condition into table Difficulties: Reducing channel dimensionality and num data rates dependence Channel Model Channel PHY Performance Black Box PHY Overview Distance Model #

7 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 7 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(n NumFreqBins*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)

8 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 8 Validation of PER vs CC vs SNR Validation carried out by using: –SISO channel D; –802.11a Rate 54 Mbps; –Continuous packet transmission; –Comparison of results obtained with Pure Link-Level simulation (50,000 simulated packets) Markov Chain & PERvsCCvsSNR LUTs (500,000 simulated packets); considered cases (  C [b/s/Hz],  t [ms]) – (1, 1), (1, 0.5), (0.5, 0.5) Full channel model & PERvsCCvsSNR LUTs (coming soon) –Metrics for comparison: PER, ABEL,STDBEL, pdf of BEL

9 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 9 Accuracy on PER Further details in appendix

10 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 10 Rate vs capacity mapping

11 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 11 PHY performance PHY Model Channel Model Table Channel PHY Simulation Pre-generates table for MAC simulations Table MAC Simulation Uses PHY simulation data for MAC simulation MAC / System Model Black Box Data rates Concept: Reduce dimensionality of channel by using its MIMO capacity PHY model uses actual channel to compute performance Channel Model Channel PHY Performance Using Channel Capacity (CC) CC Distance Model # Capacity Calc.

12 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 12 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

13 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 13 PHY Simulation MAC Simulation Conventional LUT-based Methods Statistics of PERs per data rate and MPDU size Pass/Fail MAC / System Model w/ Rate Adaptation Data rate PHY Model Channel Model Table Channel Black Box Data rates CC Distance Model # Capacity Calc. Table Channel Model Channel CC Distance Model # Capacity Calc. Randomly choose pass / fail based on per-rate statistics Conventional table-based PHY simulations have difficulties simulating systems with many rates (ABL, MIMO etc) since PHY sims scale with the number of rates

14 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 14 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 NumRates NumTxStreams 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

15 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 15 PHY Model Rate Selection Feedback Black Box Rate Adaptation PHY Simulation MAC Simulation Rate Adaptive LUT-based Methods Rate/PE sequences or statistics of pairs of “data rates” / PERs Pass/Fail MAC / System Model Data rate Channel Model Table Channel CC Distance Model # Capacity Calc. Table Channel Model Channel CC Distance Model # Capacity Calc. Replay sequences or randomly choose data rate, PE based on stats Rate Adaptive table-based PHY simulations do not scale with the number of rates and the rich PHY / Rate Adaptation feedback is present Rate/PE sequences or statistics of pairs of “data rates” / PERs

16 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 16 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

17 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 17 The Table Data –For each location pair, the table contains statistics for many channel capacities (CCs) –For each CC, 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

18 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 18 PHY Model Rate Selection Feedback Black Box Rate Adaptation Generating the Table Data Data Rate Channel Model Channel CC Distance Model # Capacity Calc. Packet Error? Time CC (Mbps) Data Rate Packet Error? T0T0 3024PASS T0+tT0+t 3124PASS T 0 +2  t 3036FAIL T 0 +3  t 2924PASS T 0 +4  t 2724PASS T 0 +5  t 24 FAIL T 0 +6  t 2318PASS T 0 +7  t 2418FAIL T 0 +8  t 2724FAIL T 0 +9  t 2924PASS The run of the PHY model with rate adaptation over a channel sequence generates a sequence of (CC, DataRate, PacketError?) sets

19 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 19 Storing the Table Data Then store statistics in table by bin: CC Bin CC Range Statistics (Rate, %, PER) I20-24 (18, 66%, 0.5) (24, 33%, 1.0) II25-29(24, 100%, 0.0) III30-34 (24, 66%, 0.0) (36, 33%, 1.0) Bin input data Time CC (Mbps) Data Rate Packet Error? CC Bin T0T0 3024PASSIII T0+tT0+t 3124PASSIII T 0 +2  t 3036FAILIII T 0 +3  t 2924PASSII T 0 +4  t 2724PASSII T 0 +5  t 24 FAILI T 0 +6  t 2318PASSI T 0 +7  t 2418FAILI T 0 +8  t 2724FAILII T 0 +9  t 2924PASSII

20 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 20 Using Table Data (w/ interp) Statistics can be used with or without interpolation. With interpolation shown below: Input Per-rate statistics for stochastic method: CC Bin CC Range Statistics (Rate, %, PER) I20-24 (18, 66%, 0.5) (24, 33%, 1.0) II25-29(24, 100%, 0.0) III30-34 (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 TimeCC CC Bin Weights Net Statistics Random Draw: T1T1 25 I (40%) II (60%) (18, 27%, 0.5) (24, 73%, 0.2) 24F T1+tT1+t 26 I (20%) II (80%) (18, 13%, 0.5) (24, 87%, 0.1) 24P T 1 +2  t 27II (100%)(24, 100%, 0.0)24P T 1 +3  t 28 II (80%) III (20%) (24, 93%, 0.0) (36, 7%, 1.0) 24P T 1 +4  t 28 II (80%) III (20%) (24, 93%, 0.0) (36, 7%, 1.0) 36F T 1 +5  t 27II (100%)(24, 100%, 0.0)24P T 1 +6  t 26 I (20%) II (80%) (18, 13%, 0.5) (24, 87%, 0.1) 24F T 1 +7  t 26 I (20%) II (80%) (18, 13%, 0.5) (24, 87%, 0.1) 24P T 1 +8  t 25 I (40%) II (60%) (18, 27%, 0.5) (24, 73%, 0.2) 18P

21 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 21 Using Table Data (no interp) Statistics can be used with or without interpolation. W/o interpolation shown below: And a random CC sequence from the channel model and CC calculation: 25, 26, 27, 28, 28, 27, 26, 26, 25 TimeCCCC BinStatistics Random Draw: T1T1 25I (18, 66%, 0.5) (24, 33%, 1.0) 24F T1+tT1+t 26II(24, 100%, 0.0)24P T 1 +2  t 27III (24, 66%, 0.0) (36, 33%, 1.0) 24P T 1 +3  t 28IV(36, 100%, 0.2)36P T 1 +4  t 28IV(36, 100%, 0.2)36F T 1 +5  t 27III (24, 66%, 0.0) (36, 33%, 1.0) 24P T 1 +6  t 26II(24, 100%, 0.0)24P T 1 +7  t 26II(24, 100%, 0.0)24P T 1 +8  t 25I (18, 66%, 0.5) (24, 33%, 1.0) 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) I25.x (18, 66%, 0.5) (24, 33%, 1.0) II26.x(24, 100%, 0.0) III27.x (24, 66%, 0.0) (36, 33%, 1.0) IV28.x(36, 100%, 0.2)

22 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 22 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

23 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 23 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: –PER new = 1 - (1-PER old ) (NewLen/OldLen)

24 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 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 T coherence /100 –Rate adaptation/feedback Output of each set of PHY simulation: – (R i,  i ), 1  i  N, where R i is the data rate of packet i and  i is pass or fail. –Condense into: (R k, r k, P k ), where k is an arbitrary data rate index, r k is the probability of using rate k, and P k is the PER of rate k.

25 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 25 Accelerating PHY Simulations 1.Generate PHY record w/ 100 byte packets and extrapolate for longer lengths using PER new = 1 - (1-PER old ) (NewLen/OldLen) 2.Generate PHY record using soft PER estimates by using raw BER  PER mapping Time CC (Mbps) Data Rate Packet Error Estimate T0T0 30240.2 T0+tT0+t 31240.1 T 0 +2  t 30360.8 T 0 +3  t 29240.3 T 0 +4  t 27240.4 Advantage:  t spacing can be increased thereby reducing simulation time Binary PASS/FAIL replaced by Soft PER estimate

26 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 26 Example Raw BER to PER mapping ¾ FEC, Marvell Indoor 3, 1x1 SISO, 4QAM in red x ¾ FEC, Marvell Indoor 1, 3x4 MIMO, 64QAM in blue dots Two simulations: The two different cases have very similar Raw BER to PER mappings. PER=f(Raw BER,FEC,Pkt size)

27 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 27 Simulation Requirements

28 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 28 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

29 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 29 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

30 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 30 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)

31 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 31 Appendix

32 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 32 Accuracy on Average BEL

33 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 33 Accuracy on Standard Deviation of BEL

34 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 34 Accuracy of PDF of BEL (SNR = 16 dB)

35 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 35 Accuracy of PDF of BEL (SNR = 20 dB)

36 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 36 Accuracy of PDF of BEL (SNR = 24 dB)

37 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 37 Accuracy of PDF of BEL (SNR = 28 dB)

38 doc.: IEEE 802.11-04/0216r0 Submission March 2004 Atheros / Mitsubishi ITE / ST Micro / MarvellSlide 38 Accuracy of PDF of BEL (SNR = 32 dB)

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