modelingthe underwateracoustic (uwa
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03/10/2012
1
Modeling the UnderWater Acoustic
(UWA) channel for simulation studies
Michele Zorzi, zorzi@dei.unipd.it
Collaborators: Beatrice Tomasi, James Preisig and Paolo Casari
Motivation
Channel modeling is important for
Michele Zorzi - UCOMMS 2012 - Italy
� Evaluation and comparisonof communicationtechniques and networkingprotocols
� Design and optimization of cross-layer communications and networking techniques
03/10/2012
2
Motivation
Channel modeling is important for
Simulations are
Michele Zorzi - UCOMMS 2012 - Italy
� Evaluation and comparisonof communicationtechniques and networkingprotocols
� Design and optimization of cross-layer communications and networking techniques
� Cheaper than experiments
� Based on channel modelswhich represent statistics both in time and in space
� Reliable only if models are sufficiently accurate and representative
� Therefore, accuracy vs
computational complexity
Long-term scientific objectives
IDENTIFY
� Key features of channel that most significantly affect the performance of communication systems
�
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
3
Long-term scientific objectives
IDENTIFY
� Key features of channel that most significantly affect the performance of communication systems
� Key features of channel that most significantly affect the performance of networking protocols
�
Michele Zorzi - UCOMMS 2012 - Italy
Long-term scientific objectives
IDENTIFY
� Key features of channel that most significantly affect the performance of communication systems
� Key features of channel that most significantly affect the performance of networking protocols
� Paradigms for modeling the channel that are most suitablefor simulation studies and evaluations
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
4
Scientific and technical approaches
Michele Zorzi - UCOMMS 2012 - Italy
� Data analysis� Time-varying channel conditions: types of dynamics, stationarity,
predictability
� Relationship between environment and channel quality dynamics
Scientific and technical approaches
Michele Zorzi - UCOMMS 2012 - Italy
� Data analysis� Time-varying channel conditions: types of dynamics, stationarity,
predictability
� Relationship between environment and channel quality dynamics
� Analytical derivations and model validation through data� Hybrid sparse/diffuse channel model
� Markov models for channel and protocol evaluations
03/10/2012
5
Scientific and technical approaches
Michele Zorzi - UCOMMS 2012 - Italy
� Data analysis� Time-varying channel conditions: types of dynamics, stationarity,
predictability
� Relationship between environment and channel quality dynamics
� Analytical derivations and model validation through data� Hybrid sparse/diffuse channel model
� Markov models for channel and protocol evaluations
� Tools and resources� Ray-tracer included in Network Simulator 2 (NS2): WOSS
� DESERT: an NS-Miracle extension to DEsign, Simulate, Emulate and Realize Test-beds for Underwater network protocols
� Making data availability to the community through a website
Key features of the UWA channel
� Statistics representing both temporal and spatial variations
� First order statistics and their temporal variations �
� Second order statistics and their temporal variations �
� First order statistics and their spatial variations �
� Second order statistics and their spatial variations �
� Complete statistical characterization in both time and space �
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
6
Key features of the UWA channel
� Statistics representing both temporal and spatial variations
� First order statistics and their temporal variations �
� Second order statistics and their temporal variations �
� First order statistics and their spatial variations �
� Second order statistics and their spatial variations �
� Complete statistical characterization in both time and space �
� Propagation Delays (sound velocity) �
Michele Zorzi - UCOMMS 2012 - Italy
Data analysis
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
7
Experimental data sets
Michele Zorzi - UCOMMS 2012 - Italy
KAM 11, Kauai (HI) June-July 2011
SubNet09, Pianosa (IT) May-September 2009
SPACE08, Martha’s Vineyard (MA), October 2008
Time-varying channel conditions
Michele Zorzi - UCOMMS 2012 - Italy
0 0.5 1 1.5 2 2.5 32
3
4
5
6
7
8
9
10
SN
R[d
B]
time [min]
SPACE08KAM11
0 20 40 60 80 100 1205
10
15
20
25
30
SN
R[d
B]
time [min]
SubNet09
Different SNR dynamics depend on scenario and environmentalconditions dominating propagation (KAM11 and SubNet09 water depth ~100 m -> ssp, SPACE08 water depth~15 m -> surface)
03/10/2012
8
Wide-sense stationarity of the channel
quality
Michele Zorzi - UCOMMS 2012 - Italy
0 0.5 1 1.5292
293
294
295
296
297
298
299
300
301
frequency [Hz]
time
[day
s]
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10−8
0 0.5 1 1.5292
293
294
295
296
297
298
299
300
301
frequency [Hz]
time
[day
s]
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
x 10−8
Time series of Power Spectral Density (PSD) of received energy at link Tx-S3
Time series of Power Spectral Density (PSD) of received energy at link Tx-S4
Adaptation and predictability
Michele Zorzi - UCOMMS 2012 - Italy
� Time-correlateddynamics enablepredictability of the channel quality
� Long propagation delaysmake the Channel State Information (CSI) outdated
� Therefore adaptivetechniques shouldinclude a learning phaseand prediction
0 1 2 3 4 5 61.5
2
2.5
3
3.5
4
4.5
lag, τ [minutes]
Thr
ough
put,
Θ(τ
) [b
/s/H
z]
2 am00 am
Throughput as a function of feedback delay in a variable-rate communication scheme.
03/10/2012
9
Channel dynamics and environmental
conditions
Michele Zorzi - UCOMMS 2012 - Italy
0 0.5 1 1.5292
293
294
295
296
297
298
299
300
301
frequency [Hz]
time
[day
s]
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10−8
Time series of Power Spectral Density (PSD) of received energy
Channel dynamics and environmental
conditions
Michele Zorzi - UCOMMS 2012 - Italy
0 0.5 1 1.5292
293
294
295
296
297
298
299
300
301
frequency [Hz]
time
[day
s]
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10−8
Periodic behaviors were observed in correspondence of low wind driven surface energy during SPACE08
Time series of Power Spectral Density (PSD) of received energy
03/10/2012
10
Open issues
� Relate typical environmental conditions and channelquality dynamics
� Model the channel dynamics
� Evaluate predictive techniques with limited observability
� Identify channel dynamics that most affect coherent and non-coherent communications techniques
� Evaluate communications and networking techniques forthe modeled channel dynamics
Michele Zorzi - UCOMMS 2012 - Italy
Analytical derivations and model
validation
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
11
Hybrid sparse-diffuse channel model
Michele Zorzi - UCOMMS 2012 - Italy
0 5 10 150
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
channel delay [ms]
|h|
Sparse componentDiffuse componentsqrt of PDP of diffuse comp.
Channel model for UWA communications via an Ultra-Wideband channel. Sparse channels have been proposed so far, however, a hybrid channel (both sparse and diffuse) is more accurate.
Hybrid sparse-diffuse channel model
Michele Zorzi - UCOMMS 2012 - Italy
−5 0 5 10 15 20 25 30
100
101
102
SNR [dB]
MS
E
G−Thres dataG−Thres modelLSSparse
0 5 10 150
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
channel delay [ms]
|h|
Sparse componentDiffuse componentsqrt of PDP of diffuse comp.
Channel model for UWA communications via an Ultra-Wideband channel. Sparse channels have been proposed so far, however, a hybrid channel (both sparse and diffuse) is more accurate.
03/10/2012
12
Markov and time-correlated models for UWA
channels
Michele Zorzi - UCOMMS 2012 - Italy
� Validated Markov and hidden Markov model to accurately represent the packet error process at the physical layer
0 10 20 30 40 500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
m
Pr[P
kt. n
+m e
rrone
ous
| Pkt
. n e
rrone
ous]
, ξn,
m
measurediidMM1HMMMM2
Markov and time-correlated models for UWA
channels
Michele Zorzi - UCOMMS 2012 - Italy
� Validated Markov and hiddenMarkov model to accuratelyrepresent the packet errorprocess at the physical layer
� Validated Markov model tobe sufficiently accurate forrepresenting the performance of Hybrid Automatic Repeat reQuesttechniques
−10 −5 0 5 10 15 20 250
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Average SNR [dB]
Thro
ughp
ut [P
kt/s
lot]
T1−H1 Analysis, HARQ−IIT1−H1 Analysis, HARQ−IT1−H1 Simulation, HARQ−IIT1−H1 Simulation, HARQ−IT3−H1 Analysis, HARQ−IIT3−H1 Analysis, HARQ−IT3−H1 Simulation, HARQ−IIT3−H1 Simulation, HARQ−I
0 10 20 30 40 500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
m
Pr[P
kt. n
+m e
rrone
ous
| Pkt
. n e
rrone
ous]
, ξn,
m
measurediidMM1HMMMM2
03/10/2012
13
Open issues
� Parametric models can be used to fit the different channel dynamics and conditions measured throughout different experiments
� Model validation through experimental data may not be conclusive, but current evidence is favorable
� The gap on the relationship between environmental and channel conditions limits the generalization of such results
Michele Zorzi - UCOMMS 2012 - Italy
Tools and resources
From simulation toward experiments
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
14
World Ocean Simulator System (WOSS)
Michele Zorzi - UCOMMS 2012 - Italy
� Ray-tracer (Bellhop) has been included in NS2, in order to get more accurate evaluations of the performance of networking protocols.
http://telecom.dei.unipd.it/ns/woss/
DESERT: DEsign, Simulate, Emulate and
Realize Test-beds
Michele Zorzi - UCOMMS 2012 - Italy
http://nautilus.dei.unipd.it/desert-underwater
03/10/2012
15
Controlled data access via website
Michele Zorzi - UCOMMS 2012 - Italy
� Experimental time series of both channel qualities and communication performance belonging to the 3 data sets are going to be public and downloadable through a login and password procedure
https://telecom.dei.unipd.it/uns/php/data.php
Open issues
� Time-varying channel conditions are not included in WOSS and are cumbersome to be recreated through a ray-tracer
� Lack of modeling able to represent how statistics of environmental conditions reflect into statistics of channel conditions (both in time and space)
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
16
Conclusions
Michele Zorzi - UCOMMS 2012 - Italy
Key features of the UWA channel
� Statistics representing both temporal and spatial variations
� First order statistics and their temporal variations �
� Second order statistics and their temporal variations �
� First order statistics and their spatial variations �
� Second order statistics and their spatial variations �
� Complete statistical characterization in both time and space �
� Propagation Delays (sound velocity) �
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
17
Key features of the UWA channel
� Statistics representing both temporal and spatial variations
� First order statistics and their temporal variations �
� Second order statistics and their temporal variations �
� First order statistics and their spatial variations �
� Second order statistics and their spatial variations �
� Complete statistical characterization in both time and space �
� Propagation Delays (sound velocity) �
Michele Zorzi - UCOMMS 2012 - Italy
Key features of the UWA channel
� Statistics representing both temporal and spatial variations
� First order statistics and their temporal variations �
� Second order statistics and their temporal variations �
� First order statistics and their spatial variations �
� Second order statistics and their spatial variations �
� Complete statistical characterization in both time and space �
� Propagation Delays (sound velocity) �
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
18
Key features of the UWA channel
� Statistics representing both temporal and spatial variations
� First order statistics and their temporal variations �
� Second order statistics and their temporal variations �
� First order statistics and their spatial variations �
� Second order statistics and their spatial variations �
� Complete statistical characterization in both time and space �
� Propagation Delays (sound velocity) �
Michele Zorzi - UCOMMS 2012 - Italy
Contributions
Michele Zorzi - UCOMMS 2012 - Italy
� Identified the most important characteristics of the channel to be included in simulators: temporal and spatial dynamics
� Made progress towards understanding the limitations in generalizing the results derived from data analysis
� Highlighted the important missing measurements and characterizations for effective simulation studies
� Provided important and useful instruments (WOSS, DESERT, and a website) to the community
03/10/2012
19
� Time- and space-varying behaviors are observed in UW channels
� Dealing with them is important and challenging
� The UW channel is very difficult to characterize, but recent results show some patterns (at least from a networking perspective)
� Understanding these effects and their relationship with environmental parameters is an emerging topic
� How to use this information will involve a mixture of adaptation, prediction, and learning
� Availability of open-source tools a key enabler for manygroups
Conclusions
Michele Zorzi - UCOMMS 2012 - Italy
� The US Office of Naval Research and ONR-Global
Thanks to…
Michele Zorzi - UCOMMS 2012 - Italy
03/10/2012
20
� The US Office of Naval Research and ONR-Global
� And to…
� The Italian Institute of Technology
� The European Commission
� The NATO Undersea Research Centre (now CMRE)
� The US National Science Foundation
Thanks to…
Michele Zorzi - UCOMMS 2012 - Italy
Further reading on our web site
� http://telecom.dei.unipd.it/underwater
� zorzi@dei.unipd.it
Michele Zorzi - UCOMMS 2012 - Italy
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