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PER Prediction for 802.11n MAC Simulation

PER Prediction for 802.11n MAC Simulation. John S. Sadowsky ( john.sadowsky@intel.com ) Intel. Overview. Review of methodology PHY Model Fit Example Summary References 11-03/0863 (Sadowsky & Li) 11-04/0174 ( Ketchum, Bjerke, Nanda, Walton & Sadowsky).

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PER Prediction for 802.11n MAC Simulation

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  1. PER Prediction for 802.11nMAC Simulation John S. Sadowsky ( john.sadowsky@intel.com ) Intel John S. Sadowsky, Intel

  2. 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 John S. Sadowsky, Intel

  4. PePrediction from Ps • 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 11-03-0863 for validation John S. Sadowsky, Intel

  5. One OFDM Symbol John S. Sadowsky, Intel

  6. Psymb Calculation John S. Sadowsky, Intel

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

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

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

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

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

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

  15. 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 John S. Sadowsky, Intel

  16. 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

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