signal processing for underwater communicationsewh.ieee.org/conf/spawc/2009/plenary_1.pdf · signal...
TRANSCRIPT
Signal Processing for
Underwater Communications
Milica Stojanovic [email protected]
Acknowledgments to the Office of Naval Research for supporting much of this research, and for the travel grant N62909-09-1-1024.
“If you cause your ship to stop and place the head of a long tube in the water
and place the outer extremity to your ear, you will hear ships at a great distance from you.“
(1490)
‘77 ‘85
Underwater wireless communications
Why?
Major scientific discoveries: cabled submersibles Cables are heavy, expensive, restrict motion (but offer high bandwidth) Applications: -ocean monitoring (climate, pollution, oil, fisheries, earthquakes,…) -underwater exploration (marine archaeology, natural resources, …) -search and survey (shipwrecks, mines, area mapping,…)
How?
Radio: ~100 Hz, very high attenuation (~m @ 10kHz) Optical: short distances (<100m), pointing precision Acoustical: a solution
What has been done?
Underwater telephone (WW2, analog SSB 8-11 kHz) DSP technology: acoustic modems (few kbps over few km) 70’s,80’s: noncoherent mod/demod commercially available 90’s: bandwidth-efficient mod/demod prototypes
What is next? More signal processing, networks.
Overview
communication channel
• single-carrier, multi-carrier • single-user, multi-user (SIMO/MIMO)
implications for networking
• attenuation and noise • multipath propagation
• time-variability
signal processing
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0 10 20 30 40 50 60 70 80 90 1000
5
10
15
20
25
30
35
distance [km]
optim
al fr
eque
ncy
[kH
z]
-10 logA(x,f)N(f)
fundamental limitation (also transducer)
Underwater acoustic channel: attenuation and noise
site-specific: man-made biological ice, rain seismic
noise= turbulence+ shipping+ surface+ thermal+ other
100
101
102
103
104
105
106
20
30
40
50
60
70
80
90
100
110
f [Hz]
no
ise
p.s
.d.
[dB
re
mic
ro P
a]
........ wind at 10 m/s
____ wind at 0 m/s
shipping activity 0, 0.5 and 1
(bottom to top)
10 log N(f)
~ 50 -18 log f
~ f2/100
“UWA=UWB”
0 100 200 300 400 500 600 700 800 900 10000
50
100
150
200
250
300
350
frequency [kHz]
abso
rptio
n co
effic
ient
[dB
/km
]
10log a(f)
: spreading + absorption A(x,f)~xkax(f) (k:1-2)
10 log a(f) = 0.11 f2/(1+f2)+44 f2/(4100+f2)+0.000275 f2+0.003 dB/km, for f [kHz]
0 10 20 30 40 50 60 70 80 90 10010
0
101
102
103
104
distance [km]
B [kH
z] an
d C
[kbps
]
0 10 20 30 40 50 60 70 80 90 100110
120
130
140
150
160
170
distance [km]
P [dB
re m
icro Pa
]
capa
city
, ban
dwid
th, p
ower
: de
pend
ence
on
dist
ance
A “high-rate” acoustic system is inherently wideband (“UWA=UWB”)
System is band-limited, narrowband assumption does not hold.
Implications : • need bandwidth efficient modulation methods (coherent detection) • cannot use signal processing methods that rely on narrowband assumption (array processing, synchronization)
Fundamental difference between radio, acoustics:
f
~GHz
~MHz
f
~kHz
~kHz AF
RF
Absolute bandwidth may be “low,” but it is not negligible w.r.t. center frequency (e.g. 1 kHz @ 1 kHz, 5 kHz @ 10 kHZ, 20 kHz @ 30 kHz).
Underwater acoustic channel: multipath and time-variability
depth
c
surface layer (mixing) const. temperature (except under ice) main thermocline temperature decreases rapidly
deep ocean constant temperature (4 deg. C) pressure increases
Sound speed increases with temperature, pressure, salinity.
continental shelf (~100 m)
continental slice
continental rise
abyssal plain
land sea
surf shallow deep
tx
distance c
tx rx
Channel variation: large/small scale surface motion, internal waves, turbulence, fine changes in the sound speed profile.
multipath spread~ 10 ms, 100 ms coherence time~ 0.1 s, 1s?
(“anti-causal”)
0
5
10
15
-40
-20
0
20
40
0
0.2
0.4
0.6
0.8
time [s]delay [ms]
tx depth : 100 m, rx depth : 640 m
Channel # 6 : omnidirectional
Rate : 333 sps
Range : 110 nautical miles
0
5
10
15-50
0
50
0
0.2
0.4
0.6
0.8
time [s]delay [ms]
tx depth : 25 m, rx depth : 23 m
Channel # 8 : omnidirectional
Rate : 333 sps
Range : 48 nautical milestime [s]delay [ms]
tx depth : 8 m, rx depth : 3.5 m
Channel # 1 : directional
Rate : 500 sps
Range : 2 nautical miles
02
46
810
-10
-5
0
5
100
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Examples: measured channel responses
There are no widely accepted statistical channel models.
Channel modeling (for signal processing)
tx rx
Each path in an acoustic channel acts as a (low-pass) filter.
(2) time-variation
Each path:
• length, delay • propagation loss A • reflection coefficient Γ
path filters: same shape, different gain
time variation: inherent, motion-induced (random, deterministic/unknown)
(1) time-invariant channel
Example Narragansett Bay, March 2008
400m
9-14m
3m 2m
8-18 kHz
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(soft bottom)
0 5 10 15 20 25 30 35 400
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
time [s]
tap magnitude
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(no motion of tx/rx)
Equivalent band-limited baseband discrete-path models
non-uniform tap spacing
uniform tap spacing
0 0.005 0.01 0.015 0.02 0.0250
0.5
1
1.5
2
2.5
delay [s]
resp
on
se
co
eff
icie
nts
(m
ag
nitu
de
)
0 0.005 0.01 0.015 0.02 0.0250
0.5
1
1.5
2
2.5
delay [s]
resp
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co
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nts
(m
ag
nitu
de
)
30 ms @ 5 ksps = 150 symbols.
…respect the physics of the channel, and …
win-win
tx/rx: 1 km / 75 m fc=10 kHz, B=5 kHz
Motion-induced time-variation: the Doppler effect
t t+at f f+af
a=v/c
…respect the physics of thy channel …
• inherent Doppler spreading • motion-induced Doppler shifting (and spreading)
adaptive channel estimation (slow) synchronization (fast)
tacking θ(t) vs. e j θ(t)
time dilation/compression frequency offset
Implications: explicit synchronization is necessary.
comparable only to LEO satellite systems
v ~ m/s c=1500 m/s a~10-4
with or without intentional motion
Time variation:
0 2000 40000
2
4
mse
[symbol intervals]0 2000 4000
-1
-0.5
0
0.5
fd=-0.02106Hz
phase [rad]
[symbol intervals]
-2 0 2-2
-1
0
1
2out.scatter
Re
Im
receiver parameters : 500 sps
N=60 (T/2) M=60
Le=0.998 Lc=0.99Kf1=0.0005 Kf2=5e-05
K=10 P=3
Pe~0SNRout~18.58dB
inp. K
com- biner forward
forward + _ decision
feedback
adaptation algorithm
inp.1
inp.2 data out
sync.
filter coefficients
training data
data est.
Single-carrier systems
Ex. New England Continental Shelf, 50 n.mi, 1 kHz
Current achievements:
Point-to-point (2/4/8PSK;8/16/64QAM) • medium range (1 km-10 km ~ 10kbps) • long range (10 km – 100 km ~1kbps) • basin scale (3000 km ~ 10bps) • vertical (10 m~150 kbps, 3 km~15 kbps, 10 km~5 kbps)
Mobile communications AUV to AUV at 5 kbps
Multi-user communications five users, ~ kbps in 5 kHz band “the faster the better”
CDMA underwater?
Conventional assumptions do not hold: • ISI is not negligible • channel is not constant over one symbol Receiver design: • chip-rate filtering • chip-rate adaptation
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“chip hypothesis feedback” recovers processing gain.
Mobile experiments in 5 kHz band: direct sequence spread spectrum feasible at 15 knots.
Experiment: • range: 2.5 km • center frequency: 33 kHz • chip rate: 19,200 chips/sec • four users, up to 2.5 kbps • spreading factor (15-255)
L=15 L=255
Tc …
… …
…
T=LTc
Tc~1/B B fixed T grows with L “processing gain” ≠ L
0 50 100 150 200 250 3000
5
10
15
20
25
30
35
spreading factor L
outp
ut S
NR
[dB
]
CHF
SDF L
Multi-carrier systems
FFT
FFT
c o m b i n e r
c o m b i n e r
.
.
.
.
.
.
in 1
in M
1
K
1
K
dec
dec
out 1
out K
goal: low-complexity processing (post-FFT)
f …
B=KΔf
Δf
f
fkfk(1+a)
transfer function of the channel
Problem:
Motion-induced Doppler distortion in a wideband system: non-uniform frequency shifting across the signal bandwidth
a=v/c
OFDM: +equalization -frequency offset
Pre-processing (initial synchronization)
ap’(n)fk<<Δf
t T=1/Δf
Tg OFDM block OFDM block OFDM block Tg Tg
guard interval ~ multipath spread tx
rx
rx after resampling by (1+a’’)
t
t
t
n=0 n=1 n=2 n=3
Trade-offs
Implications: have to estimate/track large number of phases.
“optimal” number of carriers: greatest for which post-FFT processing is still possible.
want large K … but this means small Δ f / large T, i.e. more vulnerability to residual frequency offset / inherent time-variation
T=10 ms, B=10 kHz K=100 T=50 ms, B=20 kHz K=1000
some numbers: Tcoh~0.1 ms; Tmp~10 ms
time-variation: • phase offset • ICI
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Buzzards Bay, 2.5 km, 24 kHz
A simple approach to “Doppler” tracking
• estimate the Doppler factor a’(n) • use this (single) estimate to compute all K phases
K=1024
OFDM: channel estimation
K coefficients (system parameter)
FFTK
J (out of L) coefficients (channel parameter)
transfer function Hk(n)
impulse response hl(n)
• non-adaptive: each block detected independently: need L pilots per block (~TmpB) • adaptive: each block detected using knowledge from previous block
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data-aided: can use all K symbols per block.
channel sparsing: keep J strongest taps only. J/L
L/K J/K
Panama City Beach, 1 km, 12 kHz
• Adaptive synchronization enables decision-directed operation (no null subcarriers)
• Decision-directed operation enables full-size channel estimation (low pilot overhead)
• Sparsing eliminates unnecessary estimation noise.
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Pushing performance limits: MIMO OFDM (for spatial multiplexing gain)
Increasing K increases block duration (T=K/B); ICI arises.
Increasing MT increases cross-talk between channels, size of the estimator; MIMO channel estimation becomes more difficult.
Q: What is the performance limit? Does it “depend on the weather?”
Want: MT , K as large as possible.
R/B = MT/ (1+TgB/K) symbols/sec/Hz
(MT≤K/L)
Work in progress: MIMO channel estimation, ICI equalization, pulse-shaped MCM, … Experiment: 4-tx, Martha’s Vineyard, 1 km, 10 kHz (nudging 10 bps/Hz)
channel: statistical characterization (for simulation, performance bounds)
tx: adaptive modulation / power control (channel state prediction) spectrum shaping, coding,…
rx: adaptive receivers (single- and multi-carrier; single- and multi-input) model-based processing (e.g., path-specific Doppler)
networks: efficient and scalable protocols on all layers topologies, architectures (“typical applications?”) capacity?
Open problems
system integration and optimization: from data compression to navigation (and back)
Underwater networks
0 10 20 30 40 50 60 70 80 90 10010
0
101
102
103
104
distance [km]
B [kH
z] and
C [kbp
s]
0 10 20 30 40 50 60 70 80 90 100110
120
130
140
150
160
170
distance [km]
P [dB re
micro P
a]
The case for relaying… d/N
d
(d/N)/c
L/R
n-1
n
t
t
delay per hop
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126
128
130
132
134
136
138
number of hops
cost (e
nerg
y)
[dB
]
d=10,20,30,40,50 km
K’0=120 dB
SNR0=20 dB
power and bandwidth improve with # hops
min. w.r.t. N
delay penalty negligible if bit rate adjusted to distance.
capacity depends on distance … is the case for signal processing.