1. Comments on PHY Abstraction
March 2004
Comments on PHY Abstraction
John S. Sadowsky
( )
Intel
John S. Sadowsky, Intel

2. References on PER Prediction
March 2004
References on PER Prediction
Sadowsky & Li, , Packet Error Probability Prediction for MAC Simulation
Sadowsky, , PER Prediction for n MAC Simulation
Key reference for claims made here
Ketchum, et. al., , PHY Abstraction for System Simulation
Ketchum, et. al, , PHY Abstraction based on PER Prediction
John S. Sadowsky, Intel

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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
Packet length scales fluidly
John S. Sadowsky, Intel

9. 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

10. 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 ( )
Impairments & receiver mismatch
yes (full in LUT)
yes (approx. in sim.,
full validation)
John S. Sadowsky, Intel