The conventional TFSK scheme modulates binary bits to the shifted time-frequency position of the whistles [
13]. Ref. [
13] does not provide the requirements of the time- and frequency-shifting units which guarantee the orthogonality of whistles, and if zero-chirp rate whistles are transmitted, the detection performance degrades by the time ambiguity. Thus, few selected whistles may be utilized to attain the large BER.
Figure 1 shows the spectrograms of the real whistles. In
Figure 1, the zero-chirp rate whistles are frequently found. If these zero-chirp rate whistles are not transmitted to avoid the detection ambiguity of the TFSK, the DoM of the TFSK decreases for the long-term observation.
We theoretically derive the orthogonality requirements in time- and frequency-shifting units and propose a novel scheme that multiplies the sequence with the large autocorrelation to the whistles to increase the BER performance in time. Thus, all whistles are utilized for the transmission whistles, which increases the DoM.
2.1. TFSK Performance Analysis According to Whistle Pattern
For the derivation of the time- and frequency-shifting unit requirements, the whistles are mathematically modeled, and the orthogonality requirements are derived. Assume that the whistle pattern is modeled as a function (
), which is presented as [
12]:
Since the conventional TFSK modulation scheme shifts the whistle patterns by the time and the frequency according to transmit the binary bits, let the time- and frequency-shifting units be and , respectively, and the total number of time and frequency grids be and , respectively. If the whistle is modulated with input bits, a reference whistle is shifted from to in the time domain and from to in the frequency domain, respectively. Gray coding to the time-frequency mapping may be utilized to increase the BER. For and grids, the TFSK modulation conveys bits per one whistle.
If the whistle is modulated with
and
grid, which is an integer less than
and
, respectively, the TFSK modulated whistle (
) is expressed as:
where
denotes a convolutional operation.
Figure 2 shows the two different TFSK modulated whistle examples.
In
Figure 2,
and
are four each and a total of four bits are allocated. The gray-colored whistle denotes a reference whistle, and blue and red whistles denote the TFSK modulated ones. In
Figure 2a, a zero-chirp rate whistle is modulated. The red and blue whistles are modulated by 01 and 11 in time, respectively, and by 10 in frequency. In
Figure 2b, a whistle with a large-chirp rate is modulated. The red and blue ones are modulated by 01 and 11 in time, respectively, and by 10 in frequency. In
Figure 2a, the blue and the red whistles are overlapped in the time-frequency domain because of the zero-chirp rate whistle and the small
, and the receiver is unable to correctly determine the transmitted bits by the overlapped region. If
is larger than that of the whistle length, two whistles are separated in the time domain and the detection of the whistle has no ambiguity. Then, the receiver correctly decodes the transmitted bits. In
Figure 2b, however, even though the same `
is utilized, two modulated whistles are orthogonal in the time-frequency domain, and the receiver has a low BER. Thus, the BER performance of the TFSK method depends on the whistle pattern, and to increase the BER, finding the good
and
satisfying the orthogonal requirements of the TFSK needs for the given whistle pattern.
For
, the calculation of the orthogonality requirement for an arbitrary whistle pattern is difficult. However, if the receiver demodulates the received signal using the multiplication of the complex conjugate to the transmitted whistle, the demodulated signal has a zero-chirp rate pattern. Then, the orthogonality requirement of
becomes similar to that of the conventional FSK modulation. When the time length of the whistle is
, the
satisfying orthogonal requirements of the TFSK is given as:
For , more manipulation is needed. Firstly, assume that is less than . If is larger than , no overlap and no misdetection occur, but the data rate decreases. Thus, this case is not considered in this paper. Since the whistle patterns are varied and non-linear function, the derivation of the orthogonality requirement of for the whistles is difficult. If the non-linear whistle pattern is divided into short-intervals, a piece of the whistle can simply be modeled as a linear function (), e.g., 1 with a chirp rate (), which is defined as where and denote the time- and frequency-differences, respectively. Thus, if is derived from the smallest chirp-rate of the whistle, the derived satisfies the orthogonality of the whistle with the length ().
Assume that
is the decline of the whistle in the time-frequency domain and defined as
. To calculate the orthogonality requirement of the whistle with
, we rotate two whistles to
clockwise in the time-frequency domain in
Figure 3. The two modulated whistles are separated in the time-frequency domain keeping the orthogonality. In
Figure 3a, if one whistle is shifted by
in the time domain, the overlapped time is given as
and the frequency difference is given as
Hz. In
Figure 3b, after the rotation by
, the time duration of
is overlapped in the time domain and the frequency gap of
is obtained in the frequency domain.
If the frequency gap in
Figure 3b is larger than the inverse of the overlapped time duration, two whistles satisfy the orthogonality in the time-frequency domain. Therefore, the orthogonality requirement for
is calculated as:
Since
is equal to
in
Figure 3a, Equation (4) is rewritten as:
For the whistle with the length of
, the minimum
that distinguishes the whistle slops is calculated by
[
20]. Thus, if
is smaller than
, the whistle is considered as the zero-slop whistle and
is set as
. If
is larger than
,
is derived from Equation (5). In Equation (5),
is the variable of the 2nd order convex function, and the solution of
is obtained as
. Since the minimum
is of interest, the orthogonality requirement for the minimum
to
and
is obtained as:
If dissatisfies Equation (6), the different two whistles are not orthogonal in the time-frequency region. For the orthogonal requirement of the zero-chirp rate whistle, needs to be equal to Lw, which decreases the data rate of the biomimetic TFSK. Note that the whistle duration is several hundred msec to a few seconds.
If the conventional biomimetic TFSK uses few available whistles for the small , the same whistles are frequently re-transmitted, which results in the low DoM. Therefore, this paper proposes the TFSK-based biomimetic modulation using all whistle patterns to increase the DoM.
2.2. Proposed Bio-Mimetic TFSK Modulation Method
For the large DoM and the low BER, all types of whistles including the zero-slop whistles need to be utilized and the orthogonal requirements of the short-time unit are crucial.
The proposed method utilizes the sequence with a large autocorrelation to solve the orthogonality problem for the zero-slop-like whistles and the short-time unit problem for the high data rate. The sequence with the large autocorrelation performance is widely used in digital communications to detect the exact time-frequency location when multiple signals exist at the same time [
21,
22,
23,
24,
25]. If the different good and long sequences are multiplied to the multiple whistles, the time location of each whistle is precisely detected when the multiplied sequence at the receiver is the same as that used in the transmitted whistle. This is because the autocorrelation value of the sequence is large only at the time zero. Thus, if the sequence is utilized in the TFSK, the receiver can detect the exact time location even though
is smaller than that in the minimum in Equation (6) and the zero-slop whistles exist.
If the sequence is the vector whose size is
, the sequence (
) is represented as
, where
denotes the
-th element of the sequence with a value of 1 or −1, and
is the length of the sequence, i.e., cardinality. The ideal
satisfies the following property [
21,
22,
23,
24,
25]:
where
denotes the autocorrelation value of the sequence and
denotes a time-lag. Equation (7) is only satisfied when the sequence length (
) is infinite. However, if the sequence with a small
has a good autocorrelation characteristic, the autocorrelation at
has a very small value which can be considered as zero in Equation (7).
When the sequence
is multiplied to the whistle, the whistle is divided by the cardinality of
and each vector element is sequentially multiplied to the divided whistles. Since the cardinality of
is
, the length (
) of the whistle is divided by
, and a piece (
) of the whistle is obtained by
. The proposed transmission signal multiplied by the sequence is modeled as:
where
denotes the ceiling function of
. In Equation (8), since
is inversely proportional to the time resolution, if
increases, the time detection resolution of the whistle also increases, whereas the frequency bandwidth of the whistle is spread out, which distorts the originality of the whistle. Thus, the best
needs to be determined to maximize the time resolution and to minimize the whistle distortion. In this paper, the maximum
is determined by satisfying the undistorted whistle requirement that human does not recognize the distortion of the whistle. The human assessment method is described in
Section 3 and
Section 4.
Assuming the best length of the sequence is
, the spread frequency (
) of the whistle is calculated as:
To satisfy Equation (7),
needs to be larger than half of
, and to satisfy the orthogonality in the frequency domain,
needs to be greater than the twice of
. Thus, the time- and orthogonality-requirements of the proposed method are derived as:
Note that if the sequence length is large,
in Equation (10) is smaller than the minimum value in Equation (6). The block diagram of the proposed transmitter is shown in
Figure 4.
For the communication of the proposed method, transmission frames are utilized as in the conventional TFSK [
13]. Every frame has a preamble which is made by a whistle with a large chirp rate, and the consecutive whistle patterns and the original time- and frequency-locations of the whistles in the frame are known to the transmitter and the receiver. Thus, the reference information of
and
for every whistle is known, too. What the receiver needs to detect is to estimate the time- and frequency-differences from the reference information.
For the precise detection of the time- and frequency information from the received TFSK whistles, the maximum likelihood (ML) based receiver is proposed. Assume that the whistle is shifted by
in frequency and
in time for an arbitrary input. Then, the modulated whistle (
) is transmitted. If
passes through the underwater acoustic (UWA) channel (
), the received signal (
is modeled as:
where
denotes AWGN. For the ML detection, the receiver generates the conjugate of the transmitted whistle (
for all available time-frequency shifts, in which
and
vary from
to
and
to
, respectively. Then, all conjugate whistles are multiplied to the received whistle and each multiplication result is integrated. This procedure is the same as calculating the correlation value (
) at
and
.
is obtained as:
If
and
satisfy Equation (10),
has the largest value at
and
, otherwise,
has a very small value. Thus, the estimated
and
of the transmitted indices is calculated by:
The estimated time-frequency indices using Equation (13) are de-mapped to obtain the transmitted bits. The block diagram of the proposed ML receiver is shown in
Figure 5.
The proposed method utilizes the sequence multiplied whistles to transmit any whistle patterns including the zero-slop whistles and increases the DoM. However, the multiplication of the sequence to the whistle causes the frequency spread, and the amount of spread is determined by the sequence length . In practice, a small amount of frequency spread is unrecognizable to the human. Thus, if a sequence length that generates the unrecognizable frequency spread is chosen, the proposed method can increase the BER without sacrificing the DoM.
The next section describes how to find the unrecognizable sequence length for minimizing , and the DoM assessment for the proposed method.