1. Ahmad ElMoslimany1, Meng Zhou1
Tolga Duman2
Antonia Papandreou-Suppola1
1Airzona State University, USA
2Bilkent University, Turkey (on leave from Arizona State University)

2. Outline
• Introduction and Motivation
– A Sketch for the Proposed Communications
Scheme.
• Biomimetic Signal Modeling
• A Novel Communications Paradigm
– Receiver design for Gaussian channels
– Receiver design for multipath channels
• Experimental Results
• Conclusions
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3. Motivation
• We are targeting applications that require low
probability of intercept (LPI) and/or low probability
of detection (LPD).
• In other words, we seek covertness.
• Most of the existing communication schemes that
are designed for covertness rely on the spread
spectrum techniques.
• Signals may not look natural!
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4. A Sketch for the Proposed
Communications Scheme
• We propose a communications paradigm that uses
biomimetic signals to transmit digital information.
• Signals matched to mammal sounds, which are
robust to environmental changes, could be used for
covert UWA communication at relatively high transmit
power levels.
• They can also co-exit with other acoustic
communication systems without adversely affecting
their performance, or without being affected by them.
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5. Block Diagram for the Proposed
Communications Scheme
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6. The Basic Premise of the Proposed
Communications Scheme
• The transmitted signal will non-invasively mimic a
mammal sound.
• The signals generated will not sound artificial
since we employ signals similar to the natural
signals used by the underwater mammals.
• There will be no artificial embedding of digital
data on the host signal.
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7. Biomimetic Signal Modeling
(1/2)
• We model the mammalian biological sounds and use the
resulting models to design transmit waveforms for
underwater communications.
• Dolphins and whales whistles sounds were analyzed
using quadratic time-frequency representations (QTFRs).
• The analysis and modeling is based on measurements for
different real mammalian sounds.
• Based on this analysis, the time-frequency structure of
whistle sounds was modeled to match the instantaneous
frequency of generalized frequency-modulated signals.
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8. Biomimetic Signal Modeling
(2/2)
• Generalized frequency-modulated signals are defined
as
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s(t;b) = Aa(t)e
j2p cz t/tr( )+ f0t( )
, 0 < t £ Td

9. An Example for a Real Mammalian
Sound
• The spectrogram QTFR of an actual long-finned pilot
whale whistle.
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10. A Reconstructed Mammalian Sound
• The reconstruction of the whistle using the hyperbolic
FM signal.
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11. Different Parameters for Different
Sound Signals
• The table shows different sound signals and the
corresponding phase function
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12. A Novel Communications Paradigm
• We propose to use a signaling scheme that uses
biomimetic signals as the transmission signals that carry
digital data.
• We map a sequence of information bits to a carefully
designed signal parameters.
• We generate a continuous time waveform using the
selected parameters.
• At the receiver side, we first estimate the signal
parameters and de-map it to bits.
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13. System Model for AWGN Channel
• The transmitted signal s[n]
• The signal parameters we use to carry the digital
information bits are the amplitude A, the chirp rate c,
and the signal duration M.
• The received signal x[n]
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s[n] = A u[n]cos 2pcz[n]( ), n = 0,1,..., M -1
x[n] =
s[n]+ w[n] n = 0,1,..., M -1
w[n] n = M,..., N -1
ì
í
ï
î
ï

14. Receiver Design for AWGN
Channels (1/2)
• We use the MLE to estimate the signal parameters
• The conditional PDF of the received signal
• Thus, the MLE will be the solution of
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ˆc
ˆM
ˆA
é
ë
ê
ê
ê
ù
û
ú
ú
ú
= argmin
c,M ,A
x[n]- s[n]( )2
n=0
M -1
å + x[n]( )2
n=M
N-1
å
ì
í
î
ü
ý
þ
p(x;q) =
1
2ps 2
( )
N
2
exp
- x[n]- s[n]( )2
- x2
[n]n=M
N-1
ån=0
M -1
å
2s 2
é
ë
ê
ê
ù
û
ú
ú
ˆq = argmax
q
p(x;q)

15. Receiver Design for AWGN
Channels (2/2)
• The problem can be separated and rewritten
as
• The problem can be decomposed into two
subproblems and each subproblem can be
solved separately.
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16. Problem Reformulation
• Defining and
• The MLE problem can be reduced to
• The two-dimensional problem is reduced to a one-
dimensional one. An estimate for the amplitude
parameter is
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g(A,c; ˆM) = (x[n]- Ar[n])2
n=0
ˆM -1
å
ˆqc,A = argmin
c,A
g(A,c; ˆM)
ˆA =
x[n]r[n]n=0
ˆM -1
å
r2
[n]n=0
ˆM -1
å
r[n] = u[n]cos 2pcz[n]( )

17. Asymptotic Analysis
• Under certain regularity conditions, the MLE
has asymptotically a Gaussian distribution.
• The mean of this distribution is the true mean
and the covariance matrix given by the
inverse of the Fisher information matrix.
• This gives an insight about an equivalent
channel for the system.
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18. System Model and Receiver Design
for the Multipath Channel
• The received signal in the case of the multipath channel
is,
• We solve the MLE problem which results on the
following estimate for the amplitude
• Where,
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x[n] =
hl [n]s[n - l]
l=0
L-1
å + w[n] n = 0,1,..., M -1
w[n] n = M,..., N -1
ì
í
ï
ï
î
ï
ï
ˆA =
x[n]u[n]n=0
ˆM -1
å
u2
[n]n=0
ˆM-1
å
u[n] = hl[n]r[n -l]l=0
L-1
å

19. Setup of the KAM11 Experiment
• We decode the data recorded during the KAM11
experiment.
• The experiment is performed in shallow water off the
western coast of Kauai, Hawaii.
• We consider the fixed source scenario.
• We have multiple receive elements at the receiver.
• We consider different combining techniques to enhance
the decoding performance.
• The combining techniques include the majority voting
(MV), the selection combining (SC) and the weighted sum
(WS).
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20. Transmitted Signal and its
Parameters
• We use linear chirps such that the transmitted
signal is
• The parameters that are used to carry the
digital information bits are
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x(t) = Acos(2p f0t + 2pct2
), 0 < t < T
Parameter A f0 c T
Range [0.5 1] [22kHz 26kHz ] [2kHz 10kHz] [100ms 200ms]

21. Frame Structure and Transmission
Rates
• The frame structure for each recording is
• Each group has a different transmission rate depending
on the quantization of the parameters
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subgroup 1 2 3 4 5 6 7
Rate (bps) 107 127 147 167 187 207 227

22. Decoding Results for the MV
Combining Technique
• The un-coded error probability of the chirp parameters at
rate equals to 107bps using MV combining technique.
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23. Decoding Results for the SC
Combining Technique
• The un-coded error probability of the chirp parameters at
rate equals to 107bps using SC combining technique.
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24. Decoding Results for the WS
Combining Technique
• The un-coded error probability of the chirp parameters at
rate equals to 107bps using WS combining technique.
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25. Decoding Results for Different
Transmission Rates
• Error probabilities for different transmission rates
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26. Conclusions
• We design a new signaling scheme for covert
communications.
• We mimic mammalian sound and use these signals to
carry the digital bits.
• We model the mammalian sound and parameterize it.
• We modulate these parameters with the transmitted
information bits.
• We design receivers for AWGN and multipath channels.
• We verify the validity of the proposed scheme via
experimental results through the analysis of KAM’11
recorded data.
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27. Questions?!
26/27

28. Thank You!
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