1. Packet Error Probability Prediction for 802.11 MAC Simulation
November 2003
Packet Error Probability Prediction for MAC Simulation
John S. Sadowsky & Qinghua Li
( )
Intel
John S. Sadowsky & Qinghua Li, Intel

2. Overview of MAC Simulator
November 2003
Overview of MAC Simulator
Multiple APs and STAs
Collisions and Interference
Channel Models
Gross propagation and shadowing
Space-Frequency Selective Fading
PHY Model
Predict Pe = packet error probability, given
Space-frequency slective fading
Interference Profile
Will NOT do full link level simulation
John S. Sadowsky & Qinghua Li, Intel

3. Freq. Selective Fading & Interference
November 2003
Freq. Selective Fading & Interference
John S. Sadowsky & Qinghua Li, Intel

4. Step 1: Pe Prediction from Ps
November 2003
Step 1: Pe Prediction from Ps
Ps = probability of a Viterbi decoder error within the duration of a single OFDM symbol
Ps is independent of packet length
Eliminates one dimension in the model fit.
Basic Assumption
OFDM symbol errors are roughly independent
Why Ps?
OFDM symbols define the basic periodicity of soft metric quality at the input to the Viterbi decoder
multiple interference segments within packet
John S. Sadowsky & Qinghua Li, Intel

5. November 2003
One OFDM Symbol
John S. Sadowsky & Qinghua Li, Intel

6. November 2003
Pe Prediction from Ps
Validation of Pe Prediction using 40 MHz SISO Simulations with Model D channel and 1000 byte packets.
MCS
Symbol
Length
(bits)
N = Num.
Avg.
Rel. Error
RMS
R = ½
BPSK 48
167
11.5%
16.4%
R = 1/2
16 QAM
192
41.8
4.3%
5.7%
R = ¾ 72
111
16.8%
28.8%
288
27.9
9.2%
15.2%
QPSK 96 84
5.3%
7.5%
R = 2/3
64 QAM
384
20.9
6.6%
8.6%
144 56
17.1%
27.4%
432
18.6
6.2%
12.7%
John S. Sadowsky & Qinghua Li, Intel

7. Conclusions on Pe from Ps
November 2003
Conclusions on Pe from Ps
Pe Estimate is biased high
See average relative error
Pessimistic estimates are good
Symbol Length in Viterbi trellis stages
Should be larger than ~100 stages
R = ¾ needs longer symbols
Low rates  use “multi-symbols” to get length
Primary source of error is Ps prediction
Not Ps  Pe
John S. Sadowsky & Qinghua Li, Intel

8. Step 2: Ps Prediction Available Inputs
November 2003
Step 2: Ps Prediction
Available Inputs
Space-frequency selective channel realization
Interference profile
SNR does NOT predict Ps (or Pe) very well
SC (subcarrier) average SNR per channel realization does NOT work either
Solution: Compressor Function
Convert SC SNRs to Pb = raw bit error probability
Used in GSM/GPRS & WCDMA community
SC average Pb is a better Ps predictor
John S. Sadowsky & Qinghua Li, Intel

9. Ps Prediction Next 2 slides – how well does it work? November 2003
John S. Sadowsky & Qinghua Li, Intel

10. November 2003
Each point is one channel realization. Simulations were run until 500 packet errors occurred, per point.
John S. Sadowsky & Qinghua Li, Intel

11. Simple Predictor Same Data as previous slide.
November 2003
Simple Predictor
Same Data as previous slide.
Each point is one channel realization. Simulations were run until 500 packet errors occurred, per point.
John S. Sadowsky & Qinghua Li, Intel

12. Average Pb (raw bit error probability)
November 2003
Average Pb (raw bit error probability)
Provide an adequate predictor of Ps
Clear linear trend on log-log scale
Accuracy is best at larger Ps values
This is important because we need accuracte prediction of Pe only for Pe > 1%. Pe below 1% is effectively zero in a MAC simulator. For example, we don’t care if the true value was 0.01% if we predicted 0.1% - both are effectively zero.
John S. Sadowsky & Qinghua Li, Intel