November 2009
doc.: IEEE 802.11-09/1173r1
Coherence Time Measurement
for TGac Channel Model
Date: 2009-11-17
Authors:
Name
Greg Breit
Submission
Affiliations
Address
Phone
email
Qualcomm
Incorporated
5775 Morehouse
Drive. San Diego,CA
92121
858-651-3809
[email protected]
Slide 1
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Abstract
A channel aging metric of 0.5 correlation was used to assess channel
coherence time in recent measurement campaigns. For channels with
stationary users, this metric is highly insensitive and in many cases
may be unachievable in measured data. Consequently, the coherence
time measurements to date in support of the TGac channel model
appear biased downward, suggesting an unnecessarily high level of
channel Doppler. This contribution discusses alternative analytical
methods which may produce more accurate and unbiased estimates of
channel coherence time from existing measurement data.
Submission
Slide 2
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Sample Coherence Time Analysis
•
TGn coherence time defined as delay at which autocorrelation drops to 0.5
–
–
•
Left hand plot superimposes autocorrelations (pos. lags only) for all TX, RX, and
tones
–
•
Standard definition from Rappaport, others
Same method used by Intel and NTT for TGac Channel Model
Autocorrelation is not scaled for overlap size, so coherence time>10s not measurable
Right hand plot is CDF of all coherence times (all corr=0.5 crossings)
Submission
Slide 3
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Asymptotes in Measured Coherence Time Distributions
Intel: 1.6s Meas. Duration
NTT: 6.4s Meas. Duration
1
0.9
probability
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
LOS
NLOS
C.D.F. (%)
0.8
99.99
99.9
99
95
90
80
70
50
30
20
10
5
1
.1
.01
0.01
Qualcomm: 20s Meas. Duration
0
0.1
•
0.2
0.3
0.4
0.5
coherence time (sec)
0.6
0.7
0.8
0.1
1
Coherence time (s)
3
All measurements exhibit asymptotic coherence time at high values
– Asymptote occurs at ~½ of the measurement duration
– Median value falls on asymptote in all cases
•
•
Does this impact the reported coherence times?
Qualcomm measurements taken in large auditorium with pedestrian motion
Submission
Slide 4
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Coherence Time CDF
Channel Autocorrelation
Impact
of Data Duration Data
onDuration
Observable
Coherence
Time
Data Duration = 20s
= 10s
Data Duration = 1.6s
•
Median: 9.3s
Median: ~800ms
10%ile: 5.0s
10%ile: 2.0s
10%ile: ~500ms
Coherence time was calculated from segments of complete 20s Qualcomm data record
–
•
Median: 4.8s
Data were reanalyzed using 10s and 1.6s segments (latter is similar to Intel meas. duration)
Distributions of coherence time are biased by the data duration
–
Asymptote is an analysis artifact – both 50% and 10% (left tail) values are impacted
•
Submission
Limiting Qualcomm data to 1.6s duration (right hand plots) produces results very similar to Intel values
Slide 5
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Computation of Correlation
• “Biased” form decreases
with increasing delay T
L 1T
 r (k )r (k  T )
*
Cbiased (T ) 
– Fewer overlapping samples in
summation in numerator
– Will always approach zero as
TL
k 0
L 1
*
r
(
k
)
r
(k )

k 0
L 1T
Cunbiased (T ) 
L
L T
 r (k )r
*
(k  T )
k 0
L 1
 r (k ) r
*
• Can alternatively use
“Unbiased” form
(k )
– Adjusts for number of
overlapping samples in
numerator summation
k 0
Submission
Slide 6
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Results using “Unbiased” Autocorrelation
CDF of Coherence Time -- Egress Period
1
0.9
0.9
0.8
0.8
0.7
0.7
Cumulative Probablility
Autocorrelation
All Autocorrelations -- Egress Period
1
0.6
0.5
0.4
0.3
0.5
0.4
0.3
0.2
0.2
0.1
0.1
0
•
0.6
0
2
4
6
8
10
12
Delay, sec
14
16
18
0
20
0
2
4
6
8
10
12
Coherence Time, s
14
16
18
20
Each correlation point is adjusted for the size of the data overlap
– Allows observation of coherence time out to total measurement duration
• Extreme lags are unreliable due to small number of overlapping samples
– No asymptote, but now have cases where 0.5 correlation is never reached
• ~90% of cases in this example
•
0.5 correlation is not a good metric to evaluate channels with stationary users
–
Fine for mobile channels, but too insensitive for the non-mobile case
Submission
Slide 7
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Relevance of Coherence Time Analysis
• We care about channel coherence time because it impacts the required
rate of CSI update for TxBF and DL MU-MIMO
• Direct observation of channel coherence time in static conditions is
difficult due to dependence on measurement duration
– Calculation is flawed when data duration is limited
– 0.5 correlation is a very insensitive metric of channel aging
• We need a more sensitive metric of channel aging
– Time delay to correlation value ρ (ρ>0.5)
– Time delay to XdBc MS channel error (e.g. -20dBc, -30dBc)
– Time delay to X% beamformed capacity degradation
• TxBF or MU-MIMO
• Both NTT (09/0303r1) and Intel (09/0538r4) considered this originally
• TGac Doppler model should use a value that reproduces the rate of
channel aging observed in measurements
Submission
Slide 8
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Revisiting the Intel Results (09/0538)
•
Original Intel analysis focused on TxBF capacity degradation as a function of
time delay
% degradation of TxBF gain relative to SDM
Motion Type
•
20ms delay
50ms delay
100ms delay
200ms delay
DM (arms waving at both links)
18.6
38.0
49.0
58.0
SM (arms waving at one link)
9.2
20.3
28.9
34.2
PM (pedestrian motion)
20.2
27.1
33.3
38.7
LM (light motion)
8.3
12.6
17.2
22.0
Measurements show 49% capacity degradation at 100ms delay
– Most extreme Doppler case (“DM”)
– 50% capacity loss never reached for more moderate cases (SM, PM, LM)
•
Intel applied same analysis to 11n Model D (~60ms coherence time)
– ~50% capacity degradation at 10ms delay
•
Suggests a measured coherence time at least 10x of 11n model
– 10x60ms = 600ms coherence time in the very worst case (DM)
– Longer than 600ms for all other test cases
Submission
Slide 9
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Revisiting the NTT Results (09/0303r1)
• NTT observed approximately 11% capacity degradation after 100ms
– Channel measurements performed with pedestrian motion
• These results may be compared to existing 11n model or current 11ac
model to estimate a coherence time value for the 11ac model
– Data suggest a more stable channel than observed by Intel
Submission
Slide 10
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Alternative Aging Metric – MS Error of Delayed CSI
•
CDFs of MS error between current and delayed channel (11ac Model D-NLOS)
–
–
•
“D/T” in legend refers to ratio of CSI delay to channel coherence time
–
–
–
–
•
Expressed in dBc (relative to channel power)
Statistics pooled over time samples and subcarriers
Sims were performed assuming 400ms coh time, but everything scales…
-30dBc error occurs when channel delay is 1.8% of model coherence time
-25dBc error occurs when channel delay is 2.5% of model coherence time
-20dBc error occurs when channel delay is 5% of model coherence time
This figure provides a basis by which to estimate coherence time from measurements in a
D-NLOS-like environment
–
Requires stable phase in channel estimates over time
Submission
Slide 11
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Alternative Aging Metric 2 – MS Error of Delayed CSI from Correlation
•
Straightforward to show that SNR ≡ |ρ|2/(1-|ρ|2), where ρ is the correlation coefficient
between S and S+N
–
–
•
MSE of CSI can be estimated from (1- |ρ|2)/|ρ|2 (e.g., ρ=0.995 ↔ -20dBc MSE)
Correlation is more immune to phase drift than direct MSE calculation
Figure shows CDFs of MS error estimated from complex correlation coefficient
–
Medians are indistinguishable from direct calculation of MSE (previous slide)
•
–
Slightly wider distribution
Preferred method for analysis of measured data due to phase drift immunity
Submission
Slide 12
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Sample Measurements
• Channel sounding system (5 GHz)
– PHY based on 11n: 20 MHz, 64 subcarriers (48 used for sounding)
– 4x4 MIMO channel measured every 40ms
• Large lab
– Open but highly cluttered environment
• Benches, racks, lab equipment, metal cabinets, ventilation shafts
– Good representation of Model D
• “Typical office, sea of cubes, large conference room”
– STA placed NLOS to AP (range 10m)
– Four test cases
• 1: Baseline – no deliberate motion in channel
– 5-10 people working seated in the lab, so some ambient motion
• 2-4: Deliberate pedestrian motion down middle of lab
– Performed three times for each STA location
• Different ped paths each time
• Ped path never passes between AP and STA
Submission
Slide 13
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Measurement Results (analysis of 90%ile)
Baseline
Ped Motion 1
-30dBc @80ms delay (90%ile)
 80ms/1.8% = 4.4s coh time
-20dBc @80ms delay (90%ile)
 80ms/5% = 1.6s coh time
Ped Motion 2
Ped Motion 3
-20dBc @80ms delay (90%ile)
 80ms/5% = 1.6s coh time
Submission
-18dBc @40ms delay (90%ile)
 40ms/5% = ~800ms coh time
Slide 14
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
Summary
•
Coherence time results to date (Intel, NTT, Qualcomm) appear flawed
–
Measurements are solid, but analysis is problematic
•
•
–
•
Flawed values were the basis for current 11ac Doppler model
Should evaluate data using a more sensitive metric of channel aging
–
Degradation of TxBF or MU-MIMO capacity vs. delay
•
–
–
Original approach by both Intel and NTT
Growth of CSI MS error vs. delay
•
•
•
Easy to calculate
Expresses channel aging in similar terms as other CSI impairments
Requires stable measurement phase over time
Correlation vs. delay (higher value than 0.5)
•
•
•
Insufficient measurement duration to observe ρ=0.5 accurately
Channel with stationary users may never reach ρ=0.5
Directly analogous to MSE analysis
More tolerant of phase drift than MSE
Channel model Doppler parameter should match measured data in terms of rate of
channel aging evaluated by a reliable metric
–
–
Current 400ms coherence time assumption appears conservative
TGac channel model should adopt a value >600ms (800ms or 1.6s suggested)
Submission
Slide 15
Greg Breit, Qualcomm Incorporated
November 2009
doc.: IEEE 802.11-09/1173r1
References
• Honma, N., et al., “Effect of SDMA in 802.11ac,” Doc. IEEE
802.11-09/303r1
• Perahia, E., “Investigation into the 802.11n Doppler Model,” Doc.
IEEE 802.11-09/0538r0
• Perahia, E., “Channel Coherence Time.” Doc. IEEE 802.1109/0784r0
• Yamada, W. et al., “Coherence Time Measurement in NTT Lab.”
Doc. IEEE 802.11-09/0828r0
Submission
Slide 16
Greg Breit, Qualcomm Incorporated