Comments on PHY Abstraction March 2004 Comments on PHY Abstraction John S. Sadowsky ( john.sadowsky@intel.com ) Intel John S. Sadowsky, Intel

References on PER Prediction March 2004 References on PER Prediction Sadowsky & Li, 11-03-0863, Packet Error Probability Prediction for 802.11 MAC Simulation Sadowsky, 11-04-0304, PER Prediction for 802.11n MAC Simulation Key reference for claims made here Ketchum, et. al., 11-04-0174, PHY Abstraction for System Simulation Ketchum, et. al, 11-04-0269, PHY Abstraction based on PER Prediction John S. Sadowsky, Intel

The Methodologies Black Box PER Prediction March 2004 The Methodologies Black Box TGn channel models  channel state LUT( channel state)  Rate LUT from ensemble average PHY simulation Ensemble average over channel realizations Ensemble average over link adaptation PER Prediction TGn channel models Space-frequency post detection SNRs Point of abstraction = Viterbi decoder Parametric PER prediction John S. Sadowsky, Intel

Best Known Methods - GSM March 2004 Best Known Methods - GSM Frequency hopping, temporal fading and multi-burst interleaving  variable quality soft metrics to the Viterbi decoder ITU fading channels and LUT PER prediction Point of abstraction = FEC decoder References Olofsson, et. al., “Improved interface between link level and system level simulations applied to GSM, ICUPC ’97. Hamalain, et. al., “A novel interface between link and system level simulations,” Mogensen & Wigard, “A simple mapping from C/I to FER and BER for GSM type air-interface,” PIMRC ’96. Malkamaki, et. al., “A method for combining radio link simulations and system simulations for a slow frequency hopped cellular system,” ’94. Many 3GPP GERAN technical contributions John S. Sadowsky, Intel

Fighting Jensen Ensemble average LUT March 2004 Fighting Jensen Ensemble average LUT Eg.: g(x) = rate adaptation where x = channel states How do you characterize the “Jensen’s error” in the black box method? This slide is a mathematical abuse. However the key issue is accurate capture of rate adaptation algorithms. Ensemble averaging may inject bias. John S. Sadowsky, Intel

Interference PER prediction Black Box March 2004 Interference PER prediction Space-frequency-time post detection SNRs Accurate capture of CCI and ACI Black Box Complexity explosion ??? John S. Sadowsky, Intel

Impairments Black Box PER Prediction March 2004 Impairments Black Box Full TGn impairments included in PHY simulations  LUT PER Prediction Prediction model is validated against PHY simulations with full TGn impairments Key impairments is captured in post detection SNR calculation Channel estimation error John S. Sadowsky, Intel

Complexity Black Box PER Prediction LUT for each channel model March 2004 Complexity Black Box LUT for each channel model LUT extrapolation for multiple packet lengths Interference scenarios ??? PER Prediction Common prediction formula for all channel models Formula parameters depend on code rate and modulation See 11-04-0304 Packet length scales fluidly John S. Sadowsky, Intel

Summary Black Box PER Prediction Point of Abstraction March 2004 Summary Black Box PER Prediction Point of Abstraction LUT on channel state FEC decoder Relation to best-known-methods ? GSM literature TGn Channels yes Space-Frequency Post Detection SNRs no John S. Sadowsky, Intel

in MAC sim. (where it should be) March 2004 Summary Black Box PER Prediction Accurate capture of CCI & ACI no yes Rate Adaptation in PHY sim.  LUT in MAC sim. (where it should be) Complexity of method very high moderate Characterization of PHY abstraction error unknown Yes (11-04-0304) Impairments & receiver mismatch yes (full in LUT) yes (approx. in sim., full validation) John S. Sadowsky, Intel