PHY Abstraction based on PER Prediction

1. PHY Abstraction based on PER Prediction John Ketchum, Bjorn Bjerke, Irina Medvedev, Sanjiv Nanda, Rod Walton Qualcomm Inc. johnk@qualcomm.com 781-276-0915 John Ketchum et al, Qualcomm

2. MAC System Sim Requirements • Must have TGn channel model running under MAC • Must provide method for accurately determining post-detection SNRs (after Rx spatial processing) associated with specific channel state at packet arrival instant • Must provide method for predicting PER and generating error process from these SNRs • Details of rate adaptation techniques on which proposal performance is based must be included in simulation • Full disclosure, sufficient for validation, of simulation methodology must be included with simulation results, including • Method of calculating post-detection SNR • Method of calculating/predicting PER from post-detection SNR John Ketchum et al, Qualcomm

3. Basic Approach • Run TGn channel models with PHY abstraction under MAC simulation • Space-frequency channel is explicitly captured in the simulator • Caveat: • With this approach, specifics of PHY abstraction are likely to be dependent on details of coding, modulation, and spatial processing. • At most, TGn should specify general approach, and provide guidelines, suggestions for specifics. John Ketchum et al, Qualcomm

4. PHY Abstraction Assumptions • One or more spatially segregated streams resulting from MIMO processing • Each spatial stream consists of sequence of OFDM symbols. • Each spatial stream operates at a specific identifiable coded rate • Per-bin rates also supported • Channel is stationary over individual packet • =>each ofdm bin in each stream can reasonably be treated as an AWGN subchannel • Estimate of SNR in each OFDM bin of each spatial stream is available at the output of Rx spatial processing John Ketchum et al, Qualcomm

5. PHY Modeling Approach • Detailed link level simulation including • TGn channel model with Doppler • Rate control • Spatial processing • RF impairments • Coding/decoding, interleaving/deinterleaving, etc • Used to verify/calibrate PHY abstraction John Ketchum et al, Qualcomm

6. PHY Modeling Approach • Going back to basic principles, average frame error probability in a slowly fading channel is given bywhere is the frame error probability in an AWGN channel at SNR , and is the pdf of • In other words, if the channel is fading sufficiently slowly, we can treat the channel as if it were an AWGN channel with a fixed SNR over the course of a frame, then average the conditional error probability over the fading statistics. John Ketchum et al, Qualcomm

7. PHY Modeling Approach • Since the TGN channel models are slow fading channels relative to frame durations, we can take this approach in the PHY abstraction: • Treat the channel as an AWGN matrix channel for duration of frame • Post-detection snr determined by spatial processing • Determined by Gaussian noise plus any self-interference • Doppler over duration of frame manifests as channel impairments due to estimation/tracking errors John Ketchum et al, Qualcomm

8. SNR at Output of Spatial Processing • Received signal ,where is a transformation on the transmitted sign vector, • Output of received spatial processing:where is transformation on the received signal vector, and is the total noise vector, consisting of both AWGN and residual interference due to incomplete isolation of spatial streams. John Ketchum et al, Qualcomm

9. SNR at Output of Spatial Processing • Then post-detection SNR for stream i is • The transformation is determined by the spatial processing performed at Tx and Rx. • For linear processing, these are simple and well-known. • For non-linear processing (e.g. successive cancellation) exact functions may not be known, and approximations must be used. John Ketchum et al, Qualcomm

10. Calculating Effective SNR • Effective SNR for stream i:where a is a constant used to fit the approximation to PHY simulation results. John Ketchum et al, Qualcomm

11. Calculating PER from SNReff(i) • Get AWGN coded bit error probability for effective SNR: , for rate used in stream i, from LUT • Calculate approximate error event probability from Pb(i) • Calculate approximate probability of error in stream iwhere is the number of coded bits in stream i, , K is the code constraint length, and is the code rate used in stream i. • Then the PER is John Ketchum et al, Qualcomm

12. Results for 4 Cases • Full link sim includes channel estimation procedures at both ends of the link • Frequency and timing offset are set to 0 • 1 data stream, 18 Mbps: • a = 0 • R=3/4, QPSK • 2 data streams, 78 Mbps: • a = 0.2 (needs further fine-tuning) • R=5/6, 64-QAM • R=3/4, QPSK John Ketchum et al, Qualcomm

13. Results (cont’d) • 3 data streams, 108 Mbps: • a = -0.2 (needs further fine-tuning) • R=5/6, 64-QAM • R=3/4, 16-QAM • R=1/2, QPSK • 3 data streams, 156 Mbps: • a = 0 • R=7/8, 256-QAM • R=5/6, 64-QAM • R=3/4, QPSK John Ketchum et al, Qualcomm

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18. Summary • Verified accuracy of error model • Easily integrated into TGn channel model and NS or other system simulator • Incorporates details of TGn channel models into system simulator. John Ketchum et al, Qualcomm

19. Channel Model Memory Requirements • For each unique link configuration (Ntx,Nrx,Channel model): • 1.33 Mbytes persistent • 58.3 kbytes local/transient • For each link • 65.6 kbytes persistent • 1.55 Mbytes local/transient John Ketchum et al, Qualcomm