Nov. 2013
doc.: IEEE 802.11-13/1390r0
PHY Abstraction for HEW System Level
Simulation
Date: 2013-11-11
Authors:
Name
Affiliations
Address
Phone
email
Yakun Sun
Marvell
Semiconductor
5488 Marvell Ln, Santa Clara,
CA 95054
1-408-2223847
[email protected]
Yan Zhang
Marvell
Semiconductor
Hongyuan Zhang
Marvell
Semiconductor
Hui-Ling Lou
Marvell
Semiconductor
Mingguang Xu
Marvell
Semiconductor
Submission
Slide 1
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
Introduction
• System simulation has been adopted as a powerful tool in
investigating network performance.
– Critical to evaluate HEW, whose target includes improving system
and edge-of-network throughput .
– Simulate multiple BSSs simultaneously on the intra- and inter-BSS
interactions.
• Physical layer abstraction is used to simplify the
complicated simulation of a large number of APs and STAs.
– Relieve system simulation from transmitting and decoding real PHY
packets, and align simulator behaviors from different companies.
– Predict if a packet can be successively received from instantaneous
channel conditions.
Submission
Slide 2
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
How Does PHY Abstraction Work?
•
System simulator transmitter “sends” a virtual encoded packet over
frequency-selective channels.
–
–
•
System simulator receiver “receives” the virtual packet by calculating the
post-processing SINR values per subcarrier.
–
•
Namely, a function with a vector of SINR values as input and a PER as output.
This function depends on the coding scheme (BCC, or LDPC)  one table per coding scheme.
System simulator takes the predicted PER to decide if this virtual packet has
passed through.
–
•
Equalizer/MIMO impact on performance kicks in.
PHY abstraction predicts instantaneous PER based on the SINR values (given
the current channel realization).
–
–
•
No encoding or signal generation actually happens.
No packet travels through channels but channel realizations are generated.
Flip a coin based on PER.
This approach has been widely used in IEEE 802.16m [1] and 3GPP [2].
Link Level
Mapping Function
e.g. MIESM, EESM
subchannel
·
·
BLERAWGN
(PHY Abstraction
Mapping)
System Level
SINR of each
BLER
Generate frequency
selective channel H(f)
Determine the received
SINR of each sub-carrier
Link adaptation, Scheduling,
ARQ, etc.
Throughput, packet error rate, etc.
Submission
Slide 3
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
Challenge on PHY Abstraction
• PHY abstraction function maps a vector to a scalar
– f: RNR; where N is the number of SINRs over frequency/time.
• This is a very challenging task:
– It is impossible to pre-store the mapping table due to N-to-1 mapping, as well
as arbitrary types of fading channels.
– It is, however, fairly easy to store a set of SNR vs. PER tables for AWGN
channels (i.e., 1-to-1 mapping).
• The solution is to find an AWGN channel at an equivalent SNR
level having PER performance the same as the fading channel.
– In other words, map (compress) a vector of SINR values to a single SNR scalar
 effective SNR mapping (ESM).
• The key factors of ESM are
– (1) simple, (2) accurate, (3) channel independent (the ESM method, and the
parameters do not change across different channel types).
– For example, linear/dB average SINR is NOT a good ESM method.
Submission
Slide 4
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
ESM for PHY Abstraction
• Effective SINR Mapping has been adopted in system
level simulation for IEEE 802.16m[1] and 3GPP LTE
[2,3].
• Effective SINR is an average mapped equalizer-output
SINR over all subcarriers.
– Hedge factors alpha and beta can be used to calibrate and
compensate any residual errors.
N
SINRn  
1  1
SINReff        


N


 n 1
• OFDM transmission is modeled as an AWGN channel
with one effective SINR.
Submission
Slide 5
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
SINR Mapping Functions
• A list of well-known SINR mapping functions
PHY Abstract
SINR Mapping
EESM [1, 2, 3]
Exponential mapping
MIESM (RBIR)
[1, 2, 4]
Mutual information
per symbol
MMIB [1, 2, 5]
Mutual information
per bit
Submission
  x   exp   x 
  x   log 2 M 
1
M
1
  x 
M
Slide 6


M
2

E
log


U
2   exp  U 

m 1
 k 1

M
x  sk  sm   U
 I  x   a J c
M
i 1
K
bi
k 1
k
k
x
2

Yakun Sun, et. Al.
 
Nov. 2013
doc.: IEEE 802.11-13/1390r0
MIESM for BICM
• Suppose a SISO channel,
1 

y  s  u u ~ CN  0,
; s  S
 SINR 
r  hs  n 
• RBIR in the previous table is mutual information for such a
SISO channel, achieved by coded modulation.

p y | z



  x   I  S ; Y | SINR  x   log 2 M  ES ,Y log 2


 log 2 M 
1
M

zS

M
2

E
log


U 
2   exp  U

m 1
 k 1

M
p  y | s 

x  sk  sm   U
2

 
  

• BICM is widely used for advanced wireless systems
including WiFi.
– CM based mutual information (RBIR) is overestimated for BICM.
Submission
Slide 7
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
MIESM for BICM (2)
• Considering BICM, MIESM can be given as [6]
– Referred as “RBIR-BICM”
• Mutual information for each bit is given as
I  bi ; Y   1  Eb,Y


log 2


 p  y | z  
 p  y | z  
zS
zSbi

• Mutual information for this channel use is given by


log 2 M
1 log2 M 1

  x    I  bi ; Y   log 2 M 
E



U log 2
i
M
i 1
i 1 b  0 sSb


Submission
Slide 8

 exp  
M
 exp 
k 1
sk Sbi

x s  s U 
x   sk  s   U
2
2
k






Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
Difference of RBIR Mapping
• RBIR and RBIR-BICM are close but with some gap.
– At most 1dB apart for 64QAM.
6
RBIR
RBIR-BICM
Mutual Information (bps)
5
64QAM
4
16QAM
3
2
QPSK
1
0
-10
Submission
-5
0
5
10
SNR (dB)
Slide 9
15
20
25
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
Performance of PHY Abstraction
• 11ac, 1x1, 8000 bit per packet, MCS0-MCS7, BCC
– EESM is not considered here without well known parameters for
BCC.
– Channel D-NLOS, AWGN
• Effective SNR vs. PER curves for D-NLOS are referenced to
SNR vs. PER curves for AWGN channels.
– The closer, the better!
• All three methods (MMIB, RBIR, RBIR-BICM) provides
good PER results referenced to AWGN.
– RBIR-BICM and MMIB (both bit-level MI) are closer than RBIR
(symbol level MI) to AWGN performance except MCS0.
– All three methods perform the same for MCS0 (BPSK).
Submission
Slide 10
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
Performance of PHY Abstraction
0
10
-1
PER
10
-2
10
AWGN
DNLOS, RBIR-BICM
DNLOS, RBIR
DNLOS, MMIB
-3
10
•
0
5
10
Effective SNR (dB)
15
20
The gap between effective SNR to SNR is no more than 0.6dB across MCSs.
Submission
Slide 11
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
RBIR-BICM Fine Tune
0
10
-1
PER
10
-2
10
AWGN
DNLOS, RBIR-BICM
DNLOS, RBIR-BICM, retune
-3
10
•
0
5
10
Effective SNR (dB)
15
20
After applying some hedge factors per MCS (basically dB shift), RBIRBICM can provides almost exact PER results as AWGN.
Submission
Slide 12
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
Comments on RBIR-BICM
• RBIR-BICM matches AWGN performance better than
RBIR.
• RBIR is easier to extend to high modulation than
MMIB for the availability of theoretical expressions.
– Although still requires numerical evaluation (or via Monte Carlo),
it does not require any curve fitting/parameter (a_k, c_k)
optimization as for MMIB.
Submission
Slide 13
Yakun Sun, et. Al.
Nov. 2013
doc.: IEEE 802.11-13/1390r0
Summary
• Both MMIB and RBIR can effectively predict OFDM
performance.
• RBIR-BICM and MMIB perform better than RBIR
referenced to AWGN results.
• RBIR-BICM is easier to extend to high modulations
than MMIB.
• RBIR-BICM with some dB shift can almost exactly
match AWGN performance.
• Suggest to take RBIR-BICM as the PHY abstraction
technique for HEW system simulations.
Submission
Slide 14
Yakun Sun, et. Al.
Sept 2013
doc.: IEEE 802.11-13/1390r0
References
[1] IEEE 802.16m-08/004r5, Jan. 2009
[2] R1-050680, “Text Proposal: Simulation Assumptions and Evaluation for
EUTRA”, 3GPP TSG RAN WG1 #41bis, June, 2005
[3] R1-061626, “LTE Downlink System Performance Evaluation Results”, 3GPP
TSG RAN1 #45, May, 2006
[4] 11-13-1131-00-0hew-phyabstraction-for-hew-system-level-simulation
[5] 11-13-1059-00-0hew-phy-abstraction-for-hew-evaluation-methodology
[6] “Bit-Interleaved Coded Modulation”, Giuseppe Caire, Giorgio Taricco, and
Ezio Biglieri, IEEE Trans. Of Info. Theory, 1998.
Submission
Slide 15
Yakun Sun, et. Al.