1. PER Prediction for 802.11n MAC Simulation
March 2004
PER Prediction for n MAC Simulation
John S. Sadowsky
( )
Intel
John S. Sadowsky, Intel

2. Overview Review of methodology PHY Model Fit Example Summary
March 2004
Overview
Review of methodology
PHY Model Fit Example
Summary
References
11-03/0863 (Sadowsky & Li)
11-04/0174 (Ketchum, Bjerke, Nanda, Walton & Sadowsky)
John S. Sadowsky, Intel

3. Freq. Selective Fading & Interference
March 2004
Freq. Selective Fading & Interference
John S. Sadowsky, Intel

4. PER Prediction from PSymb
March 2004
PER Prediction from PSymb
Psymb = prob. of a Viterbi decoder error within the duration of a single OFDM symbol
Psymb is independent of packet length
Allows scaling to arbitrary packet lentghs
Basic Assumption: symbol errors are ~ independent
OFDM symbols > several constraint lengths  good approx.
See for validation
John S. Sadowsky, Intel

5. March 2004
One OFDM Symbol
John S. Sadowsky, Intel

6. March 2004
Psymb Calculation
John S. Sadowsky, Intel

7. Soft Bit SNRs as delivered to Viterbi decoder
March 2004
Soft Bit SNRs as delivered to Viterbi decoder
OFDM Symbol
OFDM Symbol
OFDM Symbol
The OFDM symbol window is the natural block size for PER prediction because the soft bit SNRs, as presented to Viterbi decoder, are periodic with this with period = to this block size.
John S. Sadowsky, Intel

8. Post Detection SNRs Channel Linear Equalizer March 2004
John S. Sadowsky, Intel

9. Post Detection SNRs  Example: Ideal Zero Forcing (unbiased)
March 2004
Post Detection SNRs
Example: Ideal Zero Forcing 
(unbiased)
Example: Zero Forcing with Channel Estimation Error
where
channel estimation error added as a random matrix of
variance determined by the estimators processing gain
John S. Sadowsky, Intel

10. Parametric Model Fit = mean capacity
March 2004
Parametric Model Fit
Capacity statistics calculated from subcarrier-spatial stream capacities
= mean capacity
CV = capacity coefficient of variation
(std. deviation / mean)
John S. Sadowsky, Intel

11. Example Model Fit MIMO Receiver = MMSE Two MIMO Spatial Streams
March 2004
Example Model Fit
MIMO Receiver = MMSE
random channel estimation errors (PG = 3 dB)
Two MIMO Spatial Streams
2x2 configuration  no diversity
2x3 configuration  Rx diversity
64 QAM, Rate ¾
576 coded bits, 432 data bits
Channel Models: B, D & F (NLOS)
John S. Sadowsky, Intel

12. PHY Simulations For k = 0, …, N generate a channel realization
March 2004
PHY Simulations
For k = 0, …, N
generate a channel realization
calculate and CV for current channel
simulate with fixed channel stop after 500 packet errors
store , CV and estimates
Packet size = 1000 bytes
 19 symbols per packet
John S. Sadowsky, Intel

13. March 2004
~400 data points
John S. Sadowsky, Intel

14. Same Data – organized by CV (instead of B-D-F and 2x2 v 2x3)
March 2004
Same Data – organized by CV
(instead of B-D-F and 2x2 v 2x3)
John S. Sadowsky, Intel

15. Parametric Model Summary
March 2004
Parametric Model Summary
One Fit works for ALL Channel Models
This is a worst case example! (weak coding and no diversity w/ 2x2)
Quality of Fit
RSS for =  +40% or -30% standard error on
Fit parameters
RSS = Residual Sum of Squares
John S. Sadowsky, Intel

16. March 2004
John S. Sadowsky, Intel

17. Summary Methodology Advantages TGn channels generated in MAC simulator
March 2004
Summary
Methodology
TGn channels generated in MAC simulator
PHY abstraction at FEC decoder
Receiver captured in MSE calculations
MSE calculation  subcarrier SNR  subcarrier capacity
Subcarrier-spatial stream capacity statistics
Receiver captured in Predict symbol error prob.  PER
Advantages
Simple and accurate PER prediction
NO lookup tables!
Common fit across all channel models!
All MAC functions implemented in MAC simulator
eg. rate adaptation is NOT fold into an ensemble average LUT
John S. Sadowsky, Intel