PHY Abstraction for System Simulation

1. PHY Abstraction for System Simulation John Ketchum, Bjorn Bjerke, Sanjiv Nanda, Rod Walton Qualcomm John S. Sadowsky, Intel johnk@qualcomm.com John Ketchum, et al, Qualcomm

2. MAC System Sim Requirements • Must include specifics of TGn channel models, including Doppler process as it evolves from packet to packet, running as part of MAC/system simulation • Must include frequency, time, and spatial correlation of Doppler process as embodied in TGn channel models. • Must include correlation between forward and reverse links associated with node pair • 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 and interference profile are 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 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 • SISO AWGN link level simulation or AWGN/Fourier channel link-level simulation gives performance as a function of SNR for suite of rates. • Use results of above to formulate PHY abstraction for MAC/system simulation John Ketchum, et al, Qualcomm

5. 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

6. SNR at Output of Spatial Processing • For example, consider a zero-forcing receiver, where transmitted signal vector in bin k is so received signal in bin k is • Then the ZF estimate of the transmitted symbol vector is where is the receiver’s estimate of the channel. • The SNR for the i-th element in estimate of is known to be John Ketchum, et al, Qualcomm

7. SNR at Output of Spatial Processing • Channel matrix and pathloss are provided by TGn channel model. • SNR calculations are straightforward • Other RX processing methods have equally straightforward SNR calculations • Estimation errors are easily modeled • Resulting SNR per bin, per stream, is used to calculate effective SNRs used in PER calculations as described in the following John Ketchum, et al, Qualcomm

8. PHY Abstraction Error Modeling • Two ways to compute effective SNR in stream i • Method 1: • Output SNR of bin k in stream i: • Effective SNR for stream i: • Where a is a parameter chosen to fit to simulation results John Ketchum, et al, Qualcomm

9. Getting PER from SNR(i) • Get AWGN coded bit error probability for SNR(i), Pb(i), for rate used in stream i, from LUT • Calculate PER from Pb(i) • Method is code-specific • For constraint length k convolutional codes • first calculate approximate error event probability: Pe(i) = 2Pb(i)/dmin • then PER ~ 1-(1- Pe(i))N/D • Where D ~ length of typical error event • For turbo codes, PER = 1-(1- Pb(i))N (bit errors are ~random) John Ketchum, et al, Qualcomm

10. PHY Abstraction Error Modeling • Method 2: Convolutional Codes • For each stream, convert bin SNRs to bit SNRs according to QAM symbol type used in bin • Apply deinterleaver function to the resulting sequence of bit SNRs • Assumes interleaving over single OFDM symbol • Compute average of consecutive sub-sequences of D bit SNRs (D~ length of error event) SNRj. j=1,…,N-D • A: Find worst-case sub-sequence of D bit SNRs, (i.e. min SNRj). This gives us effective SNR for stream i: SNR_Eff(i). Look up PER(i) for SNR_Eff(i) for stream PER. • Or B: Look up PERj for each SNRj. For stream i, PER(i) = Average PERj. • D can be set to be length of one OFDM symbol (or multiple symbols in case of lowest rates) • See S. Nanda, K. M. Rege, “Frame Error Rates for Convolutional Codes on Fading Channels and the Concept of Effective Eb/No,” IEEE Trans. Veh. Tech., vol 47, no4, Nov 1998, for more details and related approaches. John Ketchum, et al, Qualcomm

11. Getting PER from SNR(i) • Get AWGN coded PER for SNR(i) from LUT for fixed packet length, Np: PERnp(i). • Then stream PER is (N(i) bits in stream i) PER(i) ~ (N(i)/Np)PERnp(i) • PER ~sumi(PER(i)) John Ketchum, et al, Qualcomm

12. Calibration/Verification • Run full link sims with TGn channel models, impairments, etc • Include chosen error model in link sim and use it to compute performance simultaneously • Compare • Adjust error model with SNR margin to improve accuracy • Repeat until good match • Show results of calibration with system sim results. John Ketchum, et al, Qualcomm

13. MAC Modeling Approach • Extend baseline NS (2.26) and publicly available patches to model necessary features of MAC for TGn • Add messaging necessary to support rate control and other features • Add preamble, reference overheads • Add PHY abstraction including TGn channel models John Ketchum, et al, Qualcomm

14. PHY Abstraction Approach John Ketchum, et al, Qualcomm

15. PHY Abstraction in NS • TGn channel model realized as new C++ classes in NS • Also includes PHY abstraction • For each node pair in a sim scenario, initialize a realization of TGn model object • At each packet arrival look up channel state for data and interference links, compute PER, and generate error process. John Ketchum, et al, Qualcomm