1. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel PER Prediction for 802.11n MAC Simulation John S. Sadowsky ( john.sadowsky@intel.com )john.sadowsky@intel.com Intel

2. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel Overview Review of methodology PHY Model Fit Example Summary References –11-03/0863 (Sadowsky & Li) –11-04/0174 ( Ketchum, Bjerke, Nanda, Walton & Sadowsky)

3. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel Freq. Selective Fading & Interference

4. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel P e Prediction from P s P symb = prob. of a Viterbi decoder error within the duration of a single OFDM symbol –P symb 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 11-03-0863 for validation

5. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel One OFDM Symbol

6. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel P symb Calculation

7. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel OFDM Symbol Soft Bit SNRs as delivered to Viterbi decoder 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.

8. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel Post Detection SNRs ChannelLinear Equalizer

9. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel 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

10. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel Parametric Model Fit = mean capacity CV = capacity coefficient of variation (std. deviation / mean) Capacity statistics calculated from subcarrier-spatial stream capacities

11. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel 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)

12. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel PHY Simulations generate a channel realization calculate and CV simulate with fixed channel stop after 500 packet errors store, CV and estimates Packet size = 1000 bytes  19 symbols per packet For k = 0, …, N

13. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel ~400 data points

14. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel Same Data – organized by CV (instead of B-D-F and 2x2 v 2x3)

15. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel 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 = 0.0236  +40% or -30% standard error on Fit parameters RSS = Residual Sum of Squares

16. doc.: IEEE 11-04/0304r0 Submission March 2004 John S. Sadowsky, Intel 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