Long-Range Acoustic Communication Using Differential Chirp ...

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Long-Range Acoustic Communication UsingDifferential Chirp Spread Spectrum

Joohyoung Lee, Jeongha An, Hyung-in Ra and Kiman Kim *

Department of the Radio Communication Engineering, Korea Maritime and Ocean University,Busan 49112, Korea; [email protected] (J.L.); [email protected] (J.A.); [email protected] (H.-i.R.)* Correspondence: [email protected]

Received: 2 November 2020; Accepted: 9 December 2020; Published: 10 December 2020�����������������

Abstract: Here, we propose a new modulation method using chirp spread spectrum (CSS) modulationto indicate the result of long-range acoustic communication (LRAC). CSS modulation had outstandingmatched filter characteristics even though the channel was complex. The performance of the matchedfilter depends on the time–bandwidth product. We studied the method of using the same modulationmethod while increasing the amount of the time–bandwidth product. When differential encoding isapplied, the de-modulation is made using the difference between the current symbol and the previoussymbol. If the matched filter is applied using both the current and the previous symbol, such as theuse of two symbols, the amount of the time–bandwidth product can be doubled, and this methodcan make the output of the matched filter larger. The proposed method was verified in lake and seaexperiments, in which the experimental environment was analyzed and compared with the resultusing the channel impulse response (CIR) of the lake and sea. The lake experiment was conductedover a distance of about 100–300 m between the transmitter and receiver and at a depth of ~40 m.As a result of the communication, the conventional method’s bit error rate (BER) was 1.22 × 10−1,but the proposed method’s BER was 1.98× 10−2. The sea experiment was conducted over a distance of~90 km and at a depth of ~1 km, and the conventional method BER in this experiment was 1.83× 10−4,while the proposed method’s BER was 0.

Keywords: chirp spread spectrum; differential coding; long-range acoustic communication; time–bandwidth product; lake experiment; sea experiment

1. Introduction

Recently, research on long-range acoustic communication (LRAC) has been actively underway [1–7],largely in the military arena, such as for autonomous underwater vehicles (AUVs) [4,6]. Especially inthe military arena, LRAC should not only have reliable communication, but also confidentiality.The spread spectrum (SS) technique is a good method in terms of confidentiality of communication,and research on the SS method has recently been underway [6,8–11]. When the SS technique is applied,the energy of the signal spreads widely in the frequency domain, making it difficult to detect signals inthe interceptor. In addition, it is difficult to determine whether a signal is the signal itself or the soundgenerated by acoustic animals, because there are many living things in range [11]. These features makethe use of SS in LRAC an outstanding method. In addition, underwater research is underway for notonly SS but also Orthogonal Frequency Division Multiplexing (OFDM). OFDM has good performancein delay spreading and is good in channel variation because it deals with the channel block-to-block.However, over long distances, this is quite challenging, and we chose the SS method consideringcommunication over 90 km or longer distances [12–16].

First, SS communication can be divided into the direct sequence spread spectrum (DSSS) andfrequency hopping (FH) technique [8,9]. Recently, research on these two methods has been active

Appl. Sci. 2020, 10, 8835; doi:10.3390/app10248835 www.mdpi.com/journal/applsci

Appl. Sci. 2020, 10, 8835 2 of 14

in underwater communication [12,17–21]. The disadvantage of these techniques stands out incertain channels. The loss of LRAC can occur in two main ways: one is the problem of having a lowsignal-to-noise ratio (SNR) by distance and another is Doppler effects of the channel by platform motionsand ocean current. DSSS has good performance for problems caused by a low SNR [6,8,9,12,17–19];however, the channel has high Doppler effects, so the performance of DSSS will be reduced, in whichcase the FH technique will have better performance [8,9,20,21]. In the case of the chirp spread spectrum(CSS), because of the high matched-filter process gain, which seems to be good in the general channel,it has the advantage of always having a constant performance rather than being advantageous ordisadvantageous on a particular channel [22–24].

Underwater communication has five categories according to the distance [3], expressed in Table 1.We conducted the experiment at a long range of communication at a distance of 10–100 km, which hasa higher SNR compared to very long-range communication and has fewer multipaths and low variancecompared to medium-range communication. As a result of these characteristics, the CSS was adopted.

Table 1. Underwater acoustics communication classification.

Name Distance

Very short ~0.1 kmShort 0.1 km~1 kmMedium 1 km~10 kmLong 10 km~100 kmVery long 100 km~

The performance of CSS communication was determined by the process gain of the matched filter.The proposed method is to increase the process gain of the matched filter when the data rate is constant,and differential coding is used as a way to increase the time–bandwidth product cost. We increased theamount of time–bandwidth product by using two symbols to demodulate using the current symboland previous symbol. Details of these methods are explained in Section 3.

We analyzed performance according to SNR and Doppler through the use of a simple simulation [25–27].First, we identified the performance according to the SNR of the proposed method through simulation,and then we identified the difference in performance between the conventional method and the proposedmethod through the lake trial. The descriptions related to the simulation and lake experiments areexplained in Sections 4 and 5.1. Finally, the sea trial demonstrated that the proposed method is capableof communication at a distance of ~90 km. A detailed description of each is given in Section 5.2.

2. Chirp Spread Spectrum

The method of transmitting data using chirp signals in underwater communication is studied [22–24].The conventional CSS method transmits bit information in up or down chirps. The chirp signal isexpressed as

sChirp(t) = exp[−2 jπ f0t + πµt2

], (1)

where f0 is the initial frequency; µ is the chirp rate; t is a time; and j is an imaginary number. Then,µ > 0 becomes the up chirp, and µ < 0 becomes the down chirp. We can express Equation (1) usingmaximum frequency and minimum frequency, which are expressed as

scu(t) = exp[−2 jπt

(fmax − fmin

Tt + fmin

)], (2)

scd(t) = exp[−2 jπt

(fmin − fmax

Tt + fmax

)], (3)

Appl. Sci. 2020, 10, 8835 3 of 14

where scu is the up chirp signal; scd is the down chirp signal; fmax is the maximum frequency; and fmin

is the minimum frequency. Then, we modulated it according to bit information and the data packet isexpressed as

scss(t) ={

scu(t), i f bn = 0scd(t), i f bn = 1

, nT ≤ t ≤ (n + 1)T (4)

where bn is a bit sequence; n is the n-th bit; and T is a symbol length.There are three main methods to demodulate the CSS signal [14]: dump detector, matched filter

and Hough transform. We used the matched filter method because the dump detector is very vulnerableto Doppler and because Hough transform is complex. The matched filter method seeks the correlationbetween the received signal and transmitted signal.

The packet structure is represented in Figure 1, where linear frequency modulation (LFM) is usedto determine whether a signal was present or not, even if it was in the worst channel, and preamble isused to accurately measure the synchronization of the signal. The signal used either scu(t) or scd(t) toconstruct symbols based on the bit information.

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where 𝑆𝑐𝑢 is the up chirp signal; 𝑆𝑐𝑑 is the down chirp signal; fmax is the maximum frequency;

and fmin is the minimum frequency. Then, we modulated it according to bit information and the

data packet is expressed as

𝑠𝑐𝑠𝑠(t) = {𝑠𝑐𝑢(𝑡), 𝑖𝑓 𝑏𝑛 = 0

𝑠𝑐𝑑(𝑡), 𝑖𝑓 𝑏𝑛 = 1 , nT ≤ t ≤ (n + 1)T (4)

where 𝑏𝑛 is a bit sequence; n is the n-th bit; and T is a symbol length.

There are three main methods to demodulate the CSS signal [14]: dump detector, matched filter

and Hough transform. We used the matched filter method because the dump detector is very

vulnerable to Doppler and because Hough transform is complex. The matched filter method seeks

the correlation between the received signal and transmitted signal.

The packet structure is represented in Figure 1, where linear frequency modulation (LFM) is

used to determine whether a signal was present or not, even if it was in the worst channel, and

preamble is used to accurately measure the synchronization of the signal. The signal used either

𝑆𝑐𝑢(𝑡) or 𝑆𝑐𝑑(𝑡) to construct symbols based on the bit information.

Figure 1. The packet structure.

The matched filter used for CSS demodulation compares the two chirp signals. If the received

signal is expressed as 𝑅𝑥(t) and is divided into one symbol length, it can be described as 𝑠𝑛 =

𝑅𝑥(t), (n − 1)T ≤ t < nT. The cross-correlation between 𝑠𝑛 and the two chirps is checked, and the bit

to the larger chirp is determined. Equation (5) expresses the matched filter algorithm:

d(n) =𝑚𝑎𝑥 {𝑑𝑢 = 𝑚𝑎𝑥 (𝐹(𝑠𝑛) ∗ 𝐹(𝑠𝑐𝑢))

𝑑𝑑 = 𝑚𝑎𝑥 (𝐹(𝑠𝑛) ∗ 𝐹(𝑠𝑐𝑑)), (5)

where 𝐹(∙) is the value converted to the fast Fourier transform (FFT) of the input signal; 𝑑1 is the

maximum value of cross-correlation between 𝑠𝑐𝑢 and 𝑠𝑛 ; 𝑑2 is the maximum value of cross-

correlation between 𝑠𝑐𝑑 and 𝑠𝑛 ; and d(n) is the demodulated bits. If 𝑑𝑢 > 𝑑𝑑 , the bits are

demodulated by 0, else the bits are demodulated by 1.

A CSS modulation block diagram is shown in Figure 2. The signal that passes through a channel

leaves only the band energy using a band pass filter (BPF) and uses preamble for fine synchronization

of the data packet. Finally, two matched filters are used to demodulate the bit information.

Figure 2. Block diagram of a conventional chirp spread spectrum (CSS) method.

3. Differential Chirp Spread Spectrum

The differential chirp spread spectrum (DCSS) method, combining differential coding and CSS

methods, is proposed here. Differential coding is encoded using the previous bit and current bit’s

Figure 1. The packet structure.

The matched filter used for CSS demodulation compares the two chirp signals. If the receivedsignal is expressed as Rx(t) and is divided into one symbol length, it can be described as sn =

Rx(t), (n− 1)T ≤ t < nT. The cross-correlation between sn and the two chirps is checked, and the bitto the larger chirp is determined. Equation (5) expresses the matched filter algorithm:

d(n) = max{

du = max(F(sn) ∗ F(scu))

dd = max(F(sn) ∗ F(scd)), (5)

where F(·) is the value converted to the fast Fourier transform (FFT) of the input signal; d1 is themaximum value of cross-correlation between scu and sn; d2 is the maximum value of cross-correlationbetween scd and sn; and d(n) is the demodulated bits. If du > dd, the bits are demodulated by 0, else thebits are demodulated by 1.

A CSS modulation block diagram is shown in Figure 2. The signal that passes through a channelleaves only the band energy using a band pass filter (BPF) and uses preamble for fine synchronizationof the data packet. Finally, two matched filters are used to demodulate the bit information.

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where 𝑆𝑐𝑢 is the up chirp signal; 𝑆𝑐𝑑 is the down chirp signal; fmax is the maximum frequency;

and fmin is the minimum frequency. Then, we modulated it according to bit information and the

data packet is expressed as

𝑠𝑐𝑠𝑠(t) = {𝑠𝑐𝑢(𝑡), 𝑖𝑓 𝑏𝑛 = 0

𝑠𝑐𝑑(𝑡), 𝑖𝑓 𝑏𝑛 = 1 , nT ≤ t ≤ (n + 1)T (4)

where 𝑏𝑛 is a bit sequence; n is the n-th bit; and T is a symbol length.

There are three main methods to demodulate the CSS signal [14]: dump detector, matched filter

and Hough transform. We used the matched filter method because the dump detector is very

vulnerable to Doppler and because Hough transform is complex. The matched filter method seeks

the correlation between the received signal and transmitted signal.

The packet structure is represented in Figure 1, where linear frequency modulation (LFM) is

used to determine whether a signal was present or not, even if it was in the worst channel, and

preamble is used to accurately measure the synchronization of the signal. The signal used either

𝑆𝑐𝑢(𝑡) or 𝑆𝑐𝑑(𝑡) to construct symbols based on the bit information.

Figure 1. The packet structure.

The matched filter used for CSS demodulation compares the two chirp signals. If the received

signal is expressed as 𝑅𝑥(t) and is divided into one symbol length, it can be described as 𝑠𝑛 =

𝑅𝑥(t), (n − 1)T ≤ t < nT. The cross-correlation between 𝑠𝑛 and the two chirps is checked, and the bit

to the larger chirp is determined. Equation (5) expresses the matched filter algorithm:

d(n) =𝑚𝑎𝑥 {𝑑𝑢 = 𝑚𝑎𝑥 (𝐹(𝑠𝑛) ∗ 𝐹(𝑠𝑐𝑢))

𝑑𝑑 = 𝑚𝑎𝑥 (𝐹(𝑠𝑛) ∗ 𝐹(𝑠𝑐𝑑)), (5)

where 𝐹(∙) is the value converted to the fast Fourier transform (FFT) of the input signal; 𝑑1 is the

maximum value of cross-correlation between 𝑠𝑐𝑢 and 𝑠𝑛 ; 𝑑2 is the maximum value of cross-

correlation between 𝑠𝑐𝑑 and 𝑠𝑛 ; and d(n) is the demodulated bits. If 𝑑𝑢 > 𝑑𝑑 , the bits are

demodulated by 0, else the bits are demodulated by 1.

A CSS modulation block diagram is shown in Figure 2. The signal that passes through a channel

leaves only the band energy using a band pass filter (BPF) and uses preamble for fine synchronization

of the data packet. Finally, two matched filters are used to demodulate the bit information.

Figure 2. Block diagram of a conventional chirp spread spectrum (CSS) method.

3. Differential Chirp Spread Spectrum

The differential chirp spread spectrum (DCSS) method, combining differential coding and CSS

methods, is proposed here. Differential coding is encoded using the previous bit and current bit’s

Figure 2. Block diagram of a conventional chirp spread spectrum (CSS) method.

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3. Differential Chirp Spread Spectrum

The differential chirp spread spectrum (DCSS) method, combining differential coding and CSSmethods, is proposed here. Differential coding is encoded using the previous bit and current bit’sexclusive or (XOR) operation [28]. When the original bit is bi, the differential coding bit is expressed as

xbi = bi−1 ⊕ bi , 1 ≤ i ≤ N, (6)

where xbi is differential encoding bit and N is the number of bits. b0 is called the initial bit, and it isset to either 0 or 1. The differential encoding bits are decoded through XOR operation, so they canbe decoded into original bits. The signal only uses differential encoding bits and generates the sameequation as in Equation (4). This is expressed as

sdcss(t) ={

scu(t), i f xbn = 0scd(t), i f xbn = 1

, nT ≤ t ≤ (n + 1)T (7)

First, the received signal, cut into two symbol lengths, is expressed as sdn. There are four types ofmatched filters in DCSS. This is expressed as

suu(t) = scu(t1) + scu(t2)

sdd(t) = scd(t1) + scd(t2)

sud(t) = scd(t1) + scu(t2)

sdu(t) = scu(t1) + scd(t2)

, 0 ≤ t1 < T, T ≤ t2 < 2T, (8)

where suu(t) is an up chirp that comes after an up chirp; sdd(t) is a down chirp that comes after a downchirp; sud(t) is an up chirp comes after a down chirp; and sdu(t) is a down chirp come after an up chirp.If the received signal is expressed as Rx(t) and is divided into two symbol lengths, it can be describedas snn = Rx(t), (n− 1)T ≤ t < (n + 1)T. The cross-correlation between snn and the four symbols arechecked, and the bit to the largest symbol is determined. We used these filters for demodulation.That is, in the conventional method, a matched filter is constructed using only one symbol of up ordown chirp, but the size of this time–bandwidth product changes because the proposed method usestwo symbols. This is expressed as

d(n) = max

duu = max(F(snn) ∗ F(suu))

ddd = max(F(snn) ∗ F(sdd))

ddu = max(F(snn) ∗ F(sdu))

dud = max(F(snn) ∗ F(sud))

, (9)

where duu is the maximum value of cross-correlation between suu and snn; ddd is the maximum valueof cross-correlation between sdd and snn; ddu is the maximum value of cross-correlation between sduand snn ; and dud is the maximum value of cross-correlation between sud and snn. When duu or ddd havea maximum value, the bit is demodulated by 0, and when ddu or dud have the maximum value, the bitis demodulated by 1.

The DCSS method is shown in Figure 3. Although it is similar to the overall common CSSmethod, it uses differential encoding in the modulation process and two symbols at the receiver.The time–bandwidth product doubles because the chirp of the two symbols is determined whetherthere is a change in the chirp or not, and the matched filter is applied using two symbols. As thetime–bandwidth product increases, the filter output increases, and it is possible to have betterperformance even if the SNR becomes worse.

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Figure 3. Block diagram of the differential chirp spread spectrum (DCSS) method.

4. Simulation

The simulation performed two types of verification: performance according to SNR and

performance in Doppler effects. In other words, simulations showed how the time–bandwidth

product changes with the DCSS rather than CSS, and the performance of the two methods was also

compared in the Doppler shift.

The parameters used in the simulations are represented in Table 2. Through repeated

simulations with these parameters, the simulation was conducted with a total of 108 bits. The results

are shown in Figure 4a,b. Figure 4a shows the difference in performance of each method according

to the SNR. First, the black square is the conventional CSS method with a transmission rate of 50 bps,

and the blue circle is the conventional CSS method with a transmission rate of 100 bps. Assuming

that the TBP of 100 bps is 1, the TBP is 2 because the TBP is doubled at 50 bps, where the transmission

rate is half. Finally, the red scissors are a DCSS method with a transmission rate of 100 bps, and since

the matching filter is formed using two symbols, the TBP becomes 2. Figure 4b shows the error

accumulation rate according to the speed. This was used to analyze the performance according to the

error accumulation degree in the Doppler environment. The blue dotted line is 0 knots, the red solid

line is 2 knots and the black dashed line is 4 knots; the circled line is the conventional method and

the proposed method is indicated by the scissors mark. It was confirmed that the performance of the

proposed method in the figure is more durable due to Doppler than the conventional method.

(a) (b)

Figure 4. Results of the simulation: (a) performance according to the signal-to-noise ratio (SNR) and

(b) performance according to Doppler.

Figure 3. Block diagram of the differential chirp spread spectrum (DCSS) method.

4. Simulation

The simulation performed two types of verification: performance according to SNR andperformance in Doppler effects. In other words, simulations showed how the time–bandwidthproduct changes with the DCSS rather than CSS, and the performance of the two methods was alsocompared in the Doppler shift.

The parameters used in the simulations are represented in Table 2. Through repeated simulationswith these parameters, the simulation was conducted with a total of 108 bits. The results are shownin Figure 4a,b. Figure 4a shows the difference in performance of each method according to the SNR.First, the black square is the conventional CSS method with a transmission rate of 50 bps, and the bluecircle is the conventional CSS method with a transmission rate of 100 bps. Assuming that the TBP of100 bps is 1, the TBP is 2 because the TBP is doubled at 50 bps, where the transmission rate is half.Finally, the red scissors are a DCSS method with a transmission rate of 100 bps, and since the matchingfilter is formed using two symbols, the TBP becomes 2. Figure 4b shows the error accumulation rateaccording to the speed. This was used to analyze the performance according to the error accumulationdegree in the Doppler environment. The blue dotted line is 0 knots, the red solid line is 2 knots and theblack dashed line is 4 knots; the circled line is the conventional method and the proposed method isindicated by the scissors mark. It was confirmed that the performance of the proposed method in thefigure is more durable due to Doppler than the conventional method.

Table 2. Simulation parameters.

Parameters Value Parameters Value

Sampling frequency 192,000 Hz LFM interval 1 sCarrier frequency 16,000 Hz Guard interval 1 s

Data rate 100 bps Number of bits 500 bitsBandwidth 2000 Hz Preamble 128 bits

Figure 4a shows the performance of each method according to the SNR for simulations conductedat intervals of 1 dB from an SNR of −15 dB to 5 dB. The traditional CSS method began to generateerrors below −5 dB, and the proposed method was found to be near −8 dB. The CSS method proposedhad a BER of 10−2 near −10 dB, and the proposed method had a BER of about 10−2 near −13 dB,which improves the SNR by about 3 dB compared to the CSS method.

Figure 4b shows the analysis of the performance of each approach in the environment withDoppler effects. First, we assumed a situation in which the transceiver was moved away at a speedof 4 knots and in which the SNR is 0 dB. In this situation, the x-axis is expressed in the order of bitslisted in the packet, and the cumulative error by bits is expressed on the y-axis. This determines theperformance by increasing the error rate due to the effects of Doppler as the slope of the graph increased.

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The cause of the increase in error rate was confirmed by Doppler. The error was accumulated by themotive and the error bit was increased by the bit stream. The CSS method clearly showed that theerrors increased after the 200th bit stream. Next, the DCSS method was found to have a significantlymore stable performance than the CSS method, although some errors were accumulated after the 200thbit stream. This confirmed that the proposed method with increased time–bandwidth product costwas stronger for noise and Doppler than the conventional method.

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Figure 3. Block diagram of the differential chirp spread spectrum (DCSS) method.

4. Simulation

The simulation performed two types of verification: performance according to SNR and

performance in Doppler effects. In other words, simulations showed how the time–bandwidth

product changes with the DCSS rather than CSS, and the performance of the two methods was also

compared in the Doppler shift.

The parameters used in the simulations are represented in Table 2. Through repeated

simulations with these parameters, the simulation was conducted with a total of 108 bits. The results

are shown in Figure 4a,b. Figure 4a shows the difference in performance of each method according

to the SNR. First, the black square is the conventional CSS method with a transmission rate of 50 bps,

and the blue circle is the conventional CSS method with a transmission rate of 100 bps. Assuming

that the TBP of 100 bps is 1, the TBP is 2 because the TBP is doubled at 50 bps, where the transmission

rate is half. Finally, the red scissors are a DCSS method with a transmission rate of 100 bps, and since

the matching filter is formed using two symbols, the TBP becomes 2. Figure 4b shows the error

accumulation rate according to the speed. This was used to analyze the performance according to the

error accumulation degree in the Doppler environment. The blue dotted line is 0 knots, the red solid

line is 2 knots and the black dashed line is 4 knots; the circled line is the conventional method and

the proposed method is indicated by the scissors mark. It was confirmed that the performance of the

proposed method in the figure is more durable due to Doppler than the conventional method.

(a) (b)

Figure 4. Results of the simulation: (a) performance according to the signal-to-noise ratio (SNR) and

(b) performance according to Doppler.

Figure 4. Results of the simulation: (a) performance according to the signal-to-noise ratio (SNR) and(b) performance according to Doppler.

5. Experiment

5.1. Lake Trial

We conducted a lake trial using a proven signal through simulation. The experiment wasconducted in May 2020, in South Korea. The experiment was conducted slightly differently ondifferent dates.

The experimental configurations are shown in Table 3. The transmitter used Neptune D/17/BB,and the receiver used Reson TC-4032 [29,30]. The communication method was single-inputsingle-output (SISO) communication. There were two bases in the center of the lake that fixedthe transmitter and receiver, and one unfixed barge. The distance between the bases was fixed at about180 m. There was continuous travel by wind or by engine power. In the experimental configuration,the distance between the transceiver was fixed at about 180 m in Experiments 1 and 3, and the bargemoved unfixed between 100 m and 300 m in the 2nd experiment. The depth of the lake was about50 m, and the receiver was always fixed at a depth of about 25 m. The depth of the transmitter wasvaried: 20 m on the 1st, 10 m on the 2nd and 5 m on the 3rd experiment.

Table 3. Parameters for the lake experiment from May, 2020.

Parameters Value Parameters Value

Sampling frequency 192,000 Hz LFM interval 1 sCarrier frequency 16,000 Hz Guard interval 1 s

Data rate 100 bps Number of bits 336 bitsBandwidth 2000 Hz Preamble 511 bits

To analyze the signals, an understanding of the channel environment is essential. We analyzed thechannel characteristics of each channel at which the signal was transmitted in order to determine whetherthere was a change in performance according to the channel impulse response (CIR). The channel

Appl. Sci. 2020, 10, 8835 7 of 14

measured the LFM pings with a length of 0.128 ms by repeatedly transmitting 200 times. A measuredCIR is expressed as

sRx(t) = h(t) ⊗ sTx(t), (10)

where sRx is the received signal; sTx is the transmitted signal; and h is the CIR. In Equation (10), h is cutto the length of the ping and is expressed again in matrix H, which is expressed as

H = h(q, n), (11)

where q is the time delay and n is a time instant. We can calculate the delay in Doppler spread functionby H. The FFT of H gives a spreading function, as expressed by Equation (12),

S(q, k) = F(H) (12)

and summation of the delay axis provides an estimate of the Doppler spectrum Pv:

Pv =N−1∑q=0

∣∣∣S(q, k)∣∣∣2. (13)

The characteristics of each experiment are expressed in Figures 5–7. Figures 5a, 6a and 7a representthe CIR of each experiment; Figures 5b, 6b and 7b represents the delay-Doppler spread spectrum ofeach experiment; Figures 5c, 6c and 7c is the power delay profile; and Figures 5d, 6d and 7d is theDoppler power spectrum.

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channel measured the LFM pings with a length of 0.128 ms by repeatedly transmitting 200 times. A

measured CIR is expressed as

𝑆𝑅𝑥(𝑡) = ℎ(𝑡)⨂𝑆𝑇𝑥(𝑡), (10)

where 𝑆𝑅𝑥 is the received signal; 𝑆𝑇𝑥 is the transmitted signal; and ℎ is the CIR. In Equation (10),

ℎ is cut to the length of the ping and is expressed again in matrix 𝐇, which is expressed as

𝐇 = h(q, n), (11)

where q is the time delay and n is a time instant. We can calculate the delay in Doppler spread

function by 𝐇. The FFT of 𝐇 gives a spreading function, as expressed by Equation (12),

𝐒(q, k) = 𝐹(𝐇) (12)

and summation of the delay axis provides an estimate of the Doppler spectrum 𝑃𝑣:

𝑃𝑣 = ∑ |𝑺(𝑞, 𝑘)|2𝑁−1

𝑞=0

. (13)

The characteristics of each experiment are expressed in Figures 5–7. Figures 5a–7a represent the

CIR of each experiment; Figures 5b–7b represents the delay-Doppler spread spectrum of each

experiment; Figures 5c–7c is the power delay profile; and Figures 5d–7d is the Doppler power

spectrum.

The 1st experiment had a number of multipaths, and a slight tilt was seen in the CIR. The channel

was not constant, and a slight tilt was seen, so there was some time variance. The delay-Doppler

spread spectrum showed that it moved to −0.49 knot with a Doppler frequency of about −2.7 Hz. In

the 2nd experiment, the receiver was fixed in the same position, but the transmitter was placed in a

barge. So, the 2nd had 2 multipaths and a big tilt was seen in the CIR. The delay-Doppler spread

spectrum showed that it moved to 2.18 knots with a Doppler frequency of about 12 Hz. In the 3rd

experiment, the location was the same as in the 1st experiment, but there was a difference in

multipath, depending on the depth of the water. In the 3rd experiment, the CIR had only 1 multipath

and a SNR similar to the 1st experiment. The delay-Doppler spread spectrum showed that it moved

to 3.644 × 10−3 knots with a Doppler frequency of about 0.02 Hz.

(a) (b)

Appl. Sci. 2020, 10, x FOR PEER REVIEW 8 of 15

(c) (d)

Figure 5. The lake-measured channel in the 1st experiment: (a) CIR; (b) delay-Doppler spread

spectrum; (c) power delay profile; (d) Doppler power spectrum.

(a) (b)

(c) (d)

Figure 6. The lake-measured channel in the 2nd experiment: (a) CIR; (b) delay-Doppler spread

spectrum; (c) power delay profile; (d) Doppler power spectrum.

Figure 5. The lake-measured channel in the 1st experiment: (a) CIR; (b) delay-Doppler spread spectrum;(c) power delay profile; (d) Doppler power spectrum.

Appl. Sci. 2020, 10, 8835 8 of 14

Appl. Sci. 2020, 10, x FOR PEER REVIEW 8 of 15

(c) (d)

Figure 5. The lake-measured channel in the 1st experiment: (a) CIR; (b) delay-Doppler spread

spectrum; (c) power delay profile; (d) Doppler power spectrum.

(a) (b)

(c) (d)

Figure 6. The lake-measured channel in the 2nd experiment: (a) CIR; (b) delay-Doppler spread

spectrum; (c) power delay profile; (d) Doppler power spectrum. Figure 6. The lake-measured channel in the 2nd experiment: (a) CIR; (b) delay-Doppler spreadspectrum; (c) power delay profile; (d) Doppler power spectrum.

The 1st experiment had a number of multipaths, and a slight tilt was seen in the CIR. The channelwas not constant, and a slight tilt was seen, so there was some time variance. The delay-Dopplerspread spectrum showed that it moved to −0.49 knot with a Doppler frequency of about −2.7 Hz.In the 2nd experiment, the receiver was fixed in the same position, but the transmitter was placed ina barge. So, the 2nd had 2 multipaths and a big tilt was seen in the CIR. The delay-Doppler spreadspectrum showed that it moved to 2.18 knots with a Doppler frequency of about 12 Hz. In the 3rdexperiment, the location was the same as in the 1st experiment, but there was a difference in multipath,depending on the depth of the water. In the 3rd experiment, the CIR had only 1 multipath and a SNRsimilar to the 1st experiment. The delay-Doppler spread spectrum showed that it moved to 3.644× 10−3

knots with a Doppler frequency of about 0.02 Hz.Through the channels analyzed for each experiment, we compared the performance of the CSS

and DCSS techniques and also analyzed in which environment the chirp-based communication wasstronger. First, the communication performance is represented in Table 4. The results shown in Table 4indicate that the performance in the 3rd experiment was the best and the 1st experiment was the worst.The spectrograms of the received signals on each experiment are shown in Figure 8.

According to the channel measured on the 1st experiment, the symbolic length of the signal witha data rate of 100 bps was 10 ms, which caused inter-symbol interference (ISI) to be generated andreduced the output of the matched filter; the SNR was 6.361 dB. In this situation, increasing the outputof the matched filter by increasing the time–bandwidth product should improve the performance.The existing CSS method had a BER of 0.1696, but the proposed DCSS method showed a significantimprovement in performance with 0.03570. In the 2nd experiment, Doppler existed because the

Appl. Sci. 2020, 10, 8835 9 of 14

transmitter was not fixed, and the SNR was also lower than in the other experiments. The multipathwas very small in size, so only the direct path was clearly identified; the SNR was 1.879 dB. The existingmethod had a BER of 0.1071, and the proposed method had a BER of 0.0238 on the following channels.This shows that the proposed method also leads to improved performance in terms of Doppler effects;however, it was possible to infer that performance was improved even by a low SNR. Finally, the 3rdexperiment did not have many multipaths, and the SNR was the highest among the three experimentsof the study. Doppler was limited because both the transmitter and receiver were fixed; the SNRwas 8.067 dB. Both the channel’s time volatility and SNR were better than on the other experiments,so the existing method also had a fairly good performance at 0.00895. The proposed method furtherimproved this and had a 0 BER.

Appl. Sci. 2020, 10, x FOR PEER REVIEW 9 of 15

(a) (b)

(c) (d)

Figure 7. The lake-measured channel in the 3rd experiment: (a) CIR; (b) delay-Doppler spread

spectrum; (c) power delay profile; (d) Doppler power spectrum.

Through the channels analyzed for each experiment, we compared the performance of the CSS

and DCSS techniques and also analyzed in which environment the chirp-based communication was

stronger. First, the communication performance is represented in Table 4. The results shown in Table

4 indicate that the performance in the 3rd experiment was the best and the 1st experiment was the

worst. The spectrograms of the received signals on each experiment are shown in Figure 8.

(a) (b)

Figure 7. The lake-measured channel in the 3rd experiment: (a) CIR; (b) delay-Doppler spread spectrum;(c) power delay profile; (d) Doppler power spectrum.

Table 4. Lake experiment results.

No. SNRBER

CSS DCSS

1 6.361 dB 1.696× 10−1 3.570× 10−2

2 1.879 dB 1.071× 10−1 2.380× 10−2

3 8.067 dB 8.950× 10−2 0

Appl. Sci. 2020, 10, 8835 10 of 14

Appl. Sci. 2020, 10, x FOR PEER REVIEW 9 of 15

(a) (b)

(c) (d)

Figure 7. The lake-measured channel in the 3rd experiment: (a) CIR; (b) delay-Doppler spread

spectrum; (c) power delay profile; (d) Doppler power spectrum.

Through the channels analyzed for each experiment, we compared the performance of the CSS

and DCSS techniques and also analyzed in which environment the chirp-based communication was

stronger. First, the communication performance is represented in Table 4. The results shown in Table

4 indicate that the performance in the 3rd experiment was the best and the 1st experiment was the

worst. The spectrograms of the received signals on each experiment are shown in Figure 8.

(a) (b) Appl. Sci. 2020, 10, x FOR PEER REVIEW 10 of 15

(c) (d)

Figure 8. The spectrogram (a) of the transmitted signal; (b) received signal on the 1st experiment; (c)

received signal on the 2nd experiment; (d) received signal on the 3rd experiment.

Table 4. Lake experiment results.

No. SNR BER

CSS DCSS

1 6.361 dB 1.696 × 10−1 3.570 × 10−2

2 1.879 dB 1.071 × 10−1 2.380 × 10−2

3 8.067 dB 8.950 × 10−2 0

According to the channel measured on the 1st experiment, the symbolic length of the signal with

a data rate of 100 bps was 10 ms, which caused inter-symbol interference (ISI) to be generated and

reduced the output of the matched filter; the SNR was 6.361 dB. In this situation, increasing the output

of the matched filter by increasing the time–bandwidth product should improve the performance.

The existing CSS method had a BER of 0.1696, but the proposed DCSS method showed a significant

improvement in performance with 0.03570. In the 2nd experiment, Doppler existed because the

transmitter was not fixed, and the SNR was also lower than in the other experiments. The multipath

was very small in size, so only the direct path was clearly identified; the SNR was 1.879 dB. The

existing method had a BER of 0.1071, and the proposed method had a BER of 0.0238 on the following

channels. This shows that the proposed method also leads to improved performance in terms of

Doppler effects; however, it was possible to infer that performance was improved even by a low SNR.

Finally, the 3rd experiment did not have many multipaths, and the SNR was the highest among the

three experiments of the study. Doppler was limited because both the transmitter and receiver were

fixed; the SNR was 8.067 dB. Both the channel’s time volatility and SNR were better than on the other

experiments, so the existing method also had a fairly good performance at 0.00895. The proposed

method further improved this and had a 0 BER.

5.2. Sea Trial

A sea experiment was conducted in October 2018 in the East Sea, South Korea. The experimental

configuration was constructed as shown in Figure 9, and the experiment was conducted from a

vertical line array (VLA) of about 90 km. The water depth was ~1500 m. The experimental

configurations are shown in Table 5. The source, a Neptune T-161 projector, was located ~200 m

below the sea surface and transmitted signals over the 1.8 kHz centered frequency [31]. The 16-

element receiving hydrophones were located 179–221 m away under a sea surface buoy. This is a

vertical line array (VLA) with an inter-element spacing of 2 m. The collected acoustic data, as well as

some of the collected oceanographic data, were self-recorded, providing the VLA coordinates by

Figure 8. The spectrogram (a) of the transmitted signal; (b) received signal on the 1st experiment;(c) received signal on the 2nd experiment; (d) received signal on the 3rd experiment.

5.2. Sea Trial

A sea experiment was conducted in October 2018 in the East Sea, South Korea. The experimentalconfiguration was constructed as shown in Figure 9, and the experiment was conducted from a verticalline array (VLA) of about 90 km. The water depth was ~1500 m. The experimental configurations areshown in Table 5. The source, a Neptune T-161 projector, was located ~200 m below the sea surface andtransmitted signals over the 1.8 kHz centered frequency [31]. The 16-element receiving hydrophoneswere located 179–221 m away under a sea surface buoy. This is a vertical line array (VLA) with aninter-element spacing of 2 m. The collected acoustic data, as well as some of the collected oceanographicdata, were self-recorded, providing the VLA coordinates by continuous real-time monitoring via atelemetry buoy; this is represented in Figure 9a. The map in Figure 9b shows the geography of the EastSea. In Figure 9b, the circle is the receiver, the star shape represents the position of the transmitterand the line drawing between the star and circle is the chip route measured by a global positioningsystem (GPS).

Appl. Sci. 2020, 10, 8835 11 of 14

Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 15

continuous real-time monitoring via a telemetry buoy; this is represented in Figure 9a. The map in

Figure 9b shows the geography of the East Sea. In Figure 9b, the circle is the receiver, the star shape

represents the position of the transmitter and the line drawing between the star and circle is the chip

route measured by a global positioning system (GPS).

(a)

(b)

Figure 9. Sea experiment configuration: (a) configuration view from the side; (b) configuration view

from above.

Table 5. Parameters for the sea experiment.

Parameters Value Parameters Value

Sampling frequency 16,384 Hz LFM interval 5 s

Carrier frequency 1900 Hz Guard interval -

Data rate 4 bps Number of bits 114 bits

Bandwidth 200 Hz Preamble 255 bits

Figure 9. Sea experiment configuration: (a) configuration view from the side; (b) configuration viewfrom above.

Table 5. Parameters for the sea experiment.

Parameters Value Parameters Value

Sampling frequency 16,384 Hz LFM interval 5 sCarrier frequency 1900 Hz Guard interval -

Data rate 4 bps Number of bits 114 bitsBandwidth 200 Hz Preamble 255 bits

Before checking the results of the sea experiment, the channel was measured. The CIR is shown inFigure 10. The channel has one big multipath, which was reached before the main path. The delay wasabout 50 ms and there was about −3.24 Hz Doppler.

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Appl. Sci. 2020, 10, x FOR PEER REVIEW 12 of 15

Before checking the results of the sea experiment, the channel was measured. The CIR is shown

in Figure 10. The channel has one big multipath, which was reached before the main path. The delay

was about 50 ms and there was about −3.24 Hz Doppler.

(a) (b)

(a) (b)

Figure 10. The sea-measured channel at 90 km: (a) CIR; (b) delay-Doppler spread spectrum; (c) power

delay profile; (d) Doppler power spectrum.

The spectrogram of the signal received from the sea experiment is shown in Figure 11. In the sea

experiment, the SNR was measured as very low, about −6.51 dB. The reason that the SNR in the

channel impulse response looks better than that of the lake experiment is the effect of the increase in

the matched filter output value according to the length of the LFM. In the experiment, the BER for

the modified conventional method was 1.83 × 10−4, and the BER for the proposed method was 0

BER. It was thereby confirmed that the proposed method for long-distance communication shows

good improvement on performance.

Figure 10. The sea-measured channel at 90 km: (a) CIR; (b) delay-Doppler spread spectrum; (c) powerdelay profile; (d) Doppler power spectrum.

The spectrogram of the signal received from the sea experiment is shown in Figure 11. In thesea experiment, the SNR was measured as very low, about −6.51 dB. The reason that the SNR in thechannel impulse response looks better than that of the lake experiment is the effect of the increase inthe matched filter output value according to the length of the LFM. In the experiment, the BER forthe modified conventional method was 1.83× 10−4, and the BER for the proposed method was 0 BER.It was thereby confirmed that the proposed method for long-distance communication shows goodimprovement on performance.Appl. Sci. 2020, 10, x FOR PEER REVIEW 13 of 15

(a) (b)

Figure 11. The spectrogram of (a) the transmitted signal and (b) the received signal.

6. Conclusions

CSS communication is a good modulation to use wideband in a variety of environments. We

explained how to improve performance when using the duplication method using the matched filter

in the CSS method. If the size of the matched filter is doubled by applying a differential coding

method, performance is improved by taking advantage of the fact that the time–bandwidth product

cost increases. The simulation of the SNR proved that performance improved by about 3 dB and

confirmed that performance was also improved in the environment where the Doppler shift occurred.

In lake and sea experiments, we confirmed that the proposed method improved performance. The

lake experiment confirmed that the performance improves significantly when there are multipath

and Doppler effects, and the sea experiment confirmed the performance improvement in long-range

communication of 90 km. Next, a study is needed on the possibility of improving both the bit rate

and performance by utilizing differential coding in multiband CSS communication.

Author Contributions: All authors contributed significantly to the work presented in this manuscript. J.L. and

K.K. proposed the method described here; J.L., J.A. and K.K. conceived and designed the experiments; J.L., H.-

i.R. and J.A. performed the experiments and analyzed the data. All authors have read and agreed to the

published version of the manuscript.

Funding: This work was supported by the Agency for Defense Development, South Korea under Grant

UD200010DD.

Conflicts of Interest: The authors declare no conflict of interest.

References

1. Stojanovic, M. Recent advances in high-speed underwater acoustic communication. IEEE J. Ocean. Eng.

1996, 21, 125–136.

2. Stojanovic, M. Acoustic (underwater) communication. In Wiley Encyclopedia of Telecommunication; John

Wiely & Son: New York, NY, USA, 2003.

3. Mosca, F.; Matte, G.; Mingnard, V.; Rioblanc, M. Low frequency source for very long range underwater

communication. In Proceedings of the 2013 OCEANS—San Diego, San Diego, CA, USA, 23–27 September

2013; pp. 1–5.

4. Shimura, T.; Ochi, H.; Wantabe, Y.; Hattori, T. Time reversal communication in deep ocean—Result of

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Figure 11. The spectrogram of (a) the transmitted signal and (b) the received signal.

Appl. Sci. 2020, 10, 8835 13 of 14

6. Conclusions

CSS communication is a good modulation to use wideband in a variety of environments.We explained how to improve performance when using the duplication method using the matchedfilter in the CSS method. If the size of the matched filter is doubled by applying a differential codingmethod, performance is improved by taking advantage of the fact that the time–bandwidth product costincreases. The simulation of the SNR proved that performance improved by about 3 dB and confirmedthat performance was also improved in the environment where the Doppler shift occurred. In lake andsea experiments, we confirmed that the proposed method improved performance. The lake experimentconfirmed that the performance improves significantly when there are multipath and Doppler effects,and the sea experiment confirmed the performance improvement in long-range communication of90 km. Next, a study is needed on the possibility of improving both the bit rate and performance byutilizing differential coding in multiband CSS communication.

Author Contributions: All authors contributed significantly to the work presented in this manuscript. J.L. andK.K. proposed the method described here; J.L., J.A. and K.K. conceived and designed the experiments; J.L., H.-i.R.and J.A. performed the experiments and analyzed the data. All authors have read and agreed to the publishedversion of the manuscript.

Funding: This research received no external funding.

Conflicts of Interest: The authors declare no conflict of interest.

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