Available via license: CC BY 4.0
Content may be subject to copyright.
Academic Editors: Stefano Caputo
and Lorenzo Mucchi
Received: 4 December 2024
Revised: 24 December 2024
Accepted: 31 December 2024
Published: 2 January 2025
Citation: Kodama, T.; Tanaka, K.;
Kuwahara, K.; Kariya, A.; Hayashida,
S. Depth-Adaptive Air and
Underwater Invisible Light
Communication System with Aerial
Reflection Repeater Assistance.
Information 2025,16, 19. https://
doi.org/10.3390/info16010019
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
Depth-Adaptive Air and Underwater Invisible Light
Communication System with Aerial Reflection
Repeater Assistance †
Takahiro Kodama 1, * , Keita Tanaka 2, Kiichiro Kuwahara 1, Ayumu Kariya 1and Shogo Hayashida 2
1Faculty of Engineering and Design, Kagawa University, 2217-20 Hayashi-cho,
Takamatsu 761-0396, Kagawa, Japan; s21t416@kagawa-u.ac.jp (K.K.); s23g406@kagawa-u.ac.jp (A.K.)
2LED Backhaul Project, Sangikyo Corporation, 4509 Ikebe-machi, Tsuzuki-ku,
Yokohama-shi 224-0053, Kanagawa, Japan; tanakakei@sangikyo.co.jp (K.T.); hayashidas@sangikyo.co.jp (S.H.)
*Correspondence: kodama.takahiro@kagawa-u.ac.jp; Tel.: +81-87-864-2231
†This article is a revised and expanded version of a paper entitled [4K Real-Time Video Transmission in
Invisible Infrared-band Underwater-Airborne Bidirectional Optical Wireless System Using a Single Silver
Mirror as an Aerial Repeater], which was presented at [CLEO: Science and Innovations 2024, Charlotte, NC,
USA and 5–10 May 2024].
Abstract: This study proposes a novel optical wireless communication system for high-
speed, large-capacity data transmission, supporting underwater IoT devices in shallow
seas. The system employs a mirror-equipped aerial drone as a relay between underwater
drones and a terrestrial station, using 850 nm optical signals for low atmospheric loss and
enhanced confidentiality. Adaptive modulation optimizes transmission capacity based on
SNR, accounting for air and underwater channel characteristics. Experiments confirmed an
exponential SNR decrease with distance (0.6–1.8 m) and demonstrated successful 4K UHD
video streaming in shallow seawater (turbidity: 2.2 NTU) without quality loss. The design
ensures cost-effectiveness and stable optical alignment using advanced posture control.
Keywords: optical wireless communication; air and underwater transmission; orthogonal
frequency division multiplexing
1. Introduction
In recent years, underwater Internet of Things (IoT) devices in sea regions have pro-
liferated, driving a growing demand for high-speed and large-capacity communication
technologies [
1
–
6
]. Against this backdrop, underwater wireless optical communication
(UWOC) is a potential solution to the unique challenges of underwater environments,
where electromagnetic waves rapidly attenuate while efficiently transmitting data [
7
–
12
].
By leveraging its high bandwidth, UWOC enables large-capacity, low-latency data trans-
mission, outperforming traditional acoustic and radio communication technologies [
13
–
17
].
The applications of this technology have been widely explored, including ocean monitoring,
seabed resource exploration, environmental conservation, and communication between
underwater drones and autonomous underwater vehicles [
18
–
20
]. In addition, adaptive
modulation techniques tailored to channel fluctuations caused by water flow and turbidity
changes have been reported [21–23].
Real-time data transmission in shallow sea regions has applications in fisheries support,
marine ecosystem monitoring, and early warning systems during disasters. For example,
environmental data such as water temperature, salinity, and dissolved oxygen levels
measured by underwater IoT devices can be transmitted in real time via aerial drones to
Information 2025,16, 19 https://doi.org/10.3390/info16010019
Information 2025,16, 19 2 of 12
land-based analysis systems, enabling swift decision-making [
24
–
26
]. Furthermore, this
technology is expected to contribute to ship collision prevention and advanced management
of fishing grounds in response to environmental changes. Developing technologies that
efficiently transfer underwater communication data through air channels is essential for
realizing such applications.
Additionally, methods for efficiently transferring the data collected by underwater
drones to fixed terrestrial stations have been explored [
27
]. One such method uses a mirror
mounted on an aerial drone to establish an all-optical path connection [
28
]. This method
improves the data transfer efficiency by transmitting optical signals underwater through
air. In addition, visible light wavelengths are less attenuated in water and are considered
suitable for UWOC applications [29].
However, challenges are associated with the use of visible light wavelengths for optical
wireless communication in shallow sea regions. Visible light signals are easily observable
and pose a high risk of eavesdropping on communication channels [
30
]. Although tech-
niques such as key distribution [
31
] have been developed to address this issue, there is an
increasing demand for novel high-confidentiality methods that do not rely on conventional
encryption technologies. Moreover, because shallow sea regions are within human activity
zones, high-power visible light beams pose risks of eye damage. Thus, communication
technologies utilizing non-visible wavelengths (above 830 nm) that are both secure against
eavesdropping and safe for human exposure have gained attention [32].
Furthermore, although studies on underwater-to-underwater [
33
] and underwater-to-
air optical wireless communication using non-visible light have been reported [
34
], research
on direct transmission between two points without complex relay structures is limited.
Therefore, technologies that utilize the bidirectionality of optical signals while maintaining
the simplicity of relay devices are essential.
This paper proposes a bidirectional optical wireless communication system using
non-visible light (850 nm) via a mirror-equipped aerial relay between underwater and
terrestrial optical wireless devices. Based on previous research that conducted proof-of-
concept experiments at fixed water depths [
35
], this study examines a method to maximize
transmission capacity by applying adaptive modulation techniques based on received
signal-to-noise ratio (SNR) across underwater channel distances ranging from 0.6 m to
1.8 m. The proposed system reflects 850 nm optical signals, complies with eye-safety
standards, and uses a single mirror, eliminating the need for separate mirror configurations
for bidirectional communication. This study presents a novel communication method for
efficiently and securely transmitting underwater IoT data through air channels in shallow
sea regions, thereby contributing to technological advances in marine monitoring and
industrial applications.
The remainder of this paper is structured as follows: Section 2describes the sys-
tem architecture and key components, comparing optical/electrical/optical (O/E/O) and
mirror-based relays. Section 3describes the experimental setup and signal-processing
methods. Section 4presents the results of the SNR and transmission capacity evaluations.
Section 5presents a 4K ultra high definition (UHD) video transmission experiment that
validates the performance of the system in shallow seawater conditions.
2. System Configuration and Application
Figure 1illustrates the structure of the optical wireless communication system that
facilitates data transmission between an underwater drone and a terrestrial fixed station via
a relay installed on an aerial drone. Figure 1a,b show the configurations of the two types
of aerial relays: an O/E/O converter and a mirror-based relay. Table 1summarizes the
configuration characteristics. The O/E/O relay involves active signal processing, requiring
Information 2025,16, 19 3 of 12
two pairs of transmitters (Tx) and receivers (Rx) for the entire system as well as an interface
to connect the transceivers and a power supply, such as a battery. In contrast, mirror-based
relays use passive processing, requiring only one pair of transceivers for the entire system
and eliminating the need for batteries. Consequently, mirror-based relays are superior to
O/E/O relays in terms of cost, weight, power consumption, and latency. However, the
O/E/O relay is advantageous for extended vertical and horizontal transmission distances
because it enables signal regeneration and ensures the power budget for the entire system.
Specifically, the mirror-based relay is more suitable in shallow sea regions, where the
underwater drone is positioned near the surface, and sufficient transmission power can be
maintained through the air channel.
Information 2025, 16, x FOR PEER REVIEW 3 of 12
types of aerial relays: an O/E/O converter and a mirror-based relay. Table 1 summarizes
the configuration characteristics. The O/E/O relay involves active signal processing, re-
quiring two pairs of transmiers (Tx) and receivers (Rx) for the entire system as well as
an interface to connect the transceivers and a power supply, such as a baery. In contrast,
mirror-based relays use passive processing, requiring only one pair of transceivers for the
entire system and eliminating the need for baeries. Consequently, mirror-based relays
are superior to O/E/O relays in terms of cost, weight, power consumption, and latency.
However, the O/E/O relay is advantageous for extended vertical and horizontal transmis-
sion distances because it enables signal regeneration and ensures the power budget for
the entire system. Specifically, the mirror-based relay is more suitable in shallow sea re-
gions, where the underwater drone is positioned near the surface, and sufficient transmis-
sion power can be maintained through the air channel.
Figure 1. Structure of (a) O/E/O relay, (b) mirror-based relay.
Table 1. Comparison of the characteristics of the two relay types.
O/E/O Relay Mirror-Based Relay
Cost High Low
Weight Heavy Light
Power consumption High Low
Power budget Large Small
Latency High Low
Both underwater and aerial drones are equipped with aitude control mechanisms
that enable bidirectional communication by aligning their optical axes perpendicular to
the water surface. Under stable conditions with minimal wave activity, horizontal bidi-
rectional communication between the aerial drone and terrestrial fixed station is achieved.
Underwater drones employ dynamic control technology using thrusters to maintain a sta-
ble position and angle despite water currents [36]. In addition, high-precision pressure
sensors and gyroscopes are integrated to control the depth and orientation, ensuring ac-
curate optical axis alignment for underwater communication [37]. The aerial drone uses a
proportional–integral–derivative control algorithm that integrates an inertial measure-
ment unit and a global positioning system to minimize the effects of wind and vibrations,
thereby achieving stability in the air [38]. These advanced aitude-control technologies
enable high-precision and stable optical-axis alignment between underwater and aerial
drones.
In this system, the transmission distances were defined as
h
air
L
(horizontal air dis-
tance),
v
air
L
(vertical air distance), and L
underwater
(vertical underwater distance). The loss
per unit distance in the air channel is relatively small compared to that in the underwater
channel, which has a significantly higher loss that greatly affects the received SNR. To
Figure 1. Structure of (a) O/E/O relay, (b) mirror-based relay.
Table 1. Comparison of the characteristics of the two relay types.
O/E/O Relay Mirror-Based Relay
Cost High Low
Weight Heavy Light
Power consumption High Low
Power budget Large Small
Latency High Low
Both underwater and aerial drones are equipped with attitude control mechanisms
that enable bidirectional communication by aligning their optical axes perpendicular to the
water surface. Under stable conditions with minimal wave activity, horizontal bidirectional
communication between the aerial drone and terrestrial fixed station is achieved. Underwa-
ter drones employ dynamic control technology using thrusters to maintain a stable position
and angle despite water currents [
36
]. In addition, high-precision pressure sensors and
gyroscopes are integrated to control the depth and orientation, ensuring accurate optical
axis alignment for underwater communication [37]. The aerial drone uses a proportional–
integral–derivative control algorithm that integrates an inertial measurement unit and a
global positioning system to minimize the effects of wind and vibrations, thereby achieving
stability in the air [
38
]. These advanced attitude-control technologies enable high-precision
and stable optical-axis alignment between underwater and aerial drones.
In this system, the transmission distances were defined as
Lairh
(horizontal air distance),
Lairv
(vertical air distance), and L
underwater
(vertical underwater distance). The loss per unit
distance in the air channel is relatively small compared to that in the underwater channel,
which has a significantly higher loss that greatly affects the received SNR. To maximize the
transmission capacity, the system adapts the modulation scheme of orthogonal frequency-
division multiplexing (OFDM) signals based on the water depth, following the water-
filling theorem.
Information 2025,16, 19 4 of 12
Assuming negligible loss at the mirror in the mirror-based configuration, the SNR
characteristics for receiving signals with aligned optical transceivers can be expressed as
follows [21]:
SNRmirror =PtD2
4tan2θPn
·e−Kair(Lairh+Lairv)−Kunderwater Lunderwater
L2(1)
Here, P
t
is the transmission power; P
n
is the noise power at the receiver;
θ
is the
half-power beamwidth; K
air
and K
underwater
are the attenuation coefficients for air and
underwater, respectively; and Dis the receiver aperture diameter. In the O/E/O relay type,
the vertical SNR can be expressed as follows:
SNROEOv=PtD2
4tan2θPn
·e−Kair Lairv−Kunderwater Lunderwater
L2(2)
When fixing the air channel distance and varying the water depth, the received SNR expo-
nentially decreases with transmission distance on a linear scale and linearly decreases on a
logarithmic scale. When
KairLairh+Lairv≪Kunderwater Lunderwater
holds, and considering
that the underwater channel design is largely determined by the vertical water depth, the
effect of SNR improvement using O/E/O becomes minimal. Therefore, a mirror-based
method without O/E/O is considered sufficient.
Additionally, because the near-infrared light output of the optical wireless transceiver
operates in the non-visible spectrum, communication channels cannot be visually observed
by potential eavesdroppers. This characteristic enhances the confidentiality of the system.
The system defines uplink communication as data transmission from the underwater drone
to the terrestrial fixed station and downlink communication as transmission from the
terrestrial fixed station to the underwater drone and evaluates the performance of both.
This system leverages advanced attitude control technologies for underwater and
aerial drones and employs non-visible optical wireless communication to enable efficient
data transmission from underwater to terrestrial communication channels.
3. Principal Experimental Setup
In this experiment, underwater and air channels were simulated in a laboratory rather
than in a marine setting. The underwater channel was tested under static conditions, with-
out wave generation. Additionally, the underwater optical transceivers were positioned in
the air facing the water tank rather than being submerged.
Figure 2illustrates the experimental setup for the optical wireless communication sys-
tem, including the air and underwater transmission paths and the two optical transceivers
connected to the system. This system employs LED Backhaul
®
(Sangikyo, Kanagawa,
Japan) optical transceivers, which adhere to the IEEE 802.15.13 standards for PHY and
MAC layers [
39
]. The transmission rate characteristic relative to SNR, a fundamental
property of optical transceivers, is similar to that reported in reference [
33
]. The system
consists of a 1.2 m underwater channel, a 5 m vertical air channel above the water surface,
and a 5 m horizontal air channel parallel to the water surface. Additionally, the underwater
channel was evaluated for distances of 0.6 m, 1.2 m, and 1.8 m by varying the dimensions
of two water tanks. As confirmed by a turbidity sensor, shallow seawater with a turbidity
of 2.2 nephelometric turbidity unit (NTU) was used in the underwater channel [
40
]. The
turbidity was measured using a specific gravity turbidimeter (WGZ-1B) that applied the
principle of a scattering photometer. The turbidity unit, NTU, was defined based on light
scattering by a solution containing 1 mg of formazin standard per liter of purified water. A
standard silver-coated glass mirror was used for optical path reflection.
Information 2025,16, 19 5 of 12
Information 2025, 16, x FOR PEER REVIEW 5 of 12
applied the principle of a scaering photometer. The turbidity unit, NTU, was defined
based on light scaering by a solution containing 1 mg of formazin standard per liter of
purified water. A standard silver-coated glass mirror was used for optical path reflection.
Figure 2. Setup of the optical wireless communication system: air and underwater channels.
Figure 3 depicts the signal-processing blocks within the optical transceivers. On the
transmier side, the input data were processed by a forward error correction (FEC) en-
coder, which scrambles the data and encodes them using low-density parity-check
(LDPC) codes, generating a bit stream with redundant error correction codes. The FEC
code rate was adjusted based on the modulation scheme, and the block size was set to 540
bytes. Subsequently, a demultiplexer split the bit stream into subcarrier-specific bits and
converted the serial bit stream into parallel data. The supported modulation schemes in-
cluded binary phase-shift keying (BPSK) and M-ary quadrature amplitude modulation
(QAM) with M = 4, 16, or 64.
The OFDM signal modulator performed dynamic bit allocation based on the received
SNR by adjusting the number of bits allocated to each subcarrier. Figure 4 shows the adap-
tive modulation flowchart for the modulation format decision for each subcarrier.
Dummy data for channel estimation were assigned when the SNR was below the mini-
mum threshold. This process uses an adaptive modulation algorithm that selects the op-
timal modulation scheme based on SNR [41]. This algorithm combines open- and closed-
loop control mechanisms. Open-loop control adjusts the SNR threshold based on the ob-
served block error rates (BLER); the threshold increases if the BLER is high and decreases
if the BLER is low. Consequently, the QAM table changes dynamically. The SNR thresh-
old gradually evolves because the BLER aggregation is performed over extended periods.
The dynamic bit allocation algorithm implemented in the optical transceiver conformed
to the ITU-T G.9960 [42] and ITU-T G.9961 [43] standards, which govern the transceiver
and data link layers in optical wireless communication.
Furthermore, an inverse discrete Fourier transform was applied to generate multiple
subcarriers, and a cyclic prefix (CP) was added to improve the resistance to intersymbol
interference. A digital-to-analog converter converted the resulting OFDM signal into an
analog signal, which was then output by the transmier.
Figure 2. Setup of the optical wireless communication system: air and underwater channels.
Figure 3depicts the signal-processing blocks within the optical transceivers. On
the transmitter side, the input data were processed by a forward error correction (FEC)
encoder, which scrambles the data and encodes them using low-density parity-check
(LDPC) codes, generating a bit stream with redundant error correction codes. The FEC
code rate was adjusted based on the modulation scheme, and the block size was set to
540 bytes. Subsequently, a demultiplexer split the bit stream into subcarrier-specific bits
and converted the serial bit stream into parallel data. The supported modulation schemes
included binary phase-shift keying (BPSK) and M-ary quadrature amplitude modulation
(QAM) with M= 4, 16, or 64.
Information 2025, 16, x FOR PEER REVIEW 6 of 12
On the receiver side, the incoming signal was converted into a digital signal using an
analog-to-digital converter. The OFDM demodulator removed the CP and separated the
subcarriers using a discrete Fourier transform. Each subcarrier symbol was converted into
a symbol stream via a multiplexer. Finally, the FEC decoder processed the data using
LDPC decoding and descrambling to restore the original data. This evaluation maximized
the transmission capacity via error correction while maintaining the signal quality using
adaptive modulation.
Figure 3. Signal-processing blocks in optical transceivers.
Figure 4. Adaptive modulation flow chart.
4. Principal Experimental Results
Both the static and dynamic characteristics were evaluated. The static and dynamic
characteristic evaluations represent the average values and the results of the characteris-
tics sampled and recorded, respectively, at one-second intervals.
Figure 5a,b illustrate the changes in the SNR and transmission capacity of the receiver
for underwater channel distances of 0.6 m, 1.2 m, and 1.8 m. The results were used to
evaluate the transceiver performance characteristics. For uplink and downlink communi-
cation, the received SNR linearly decreased on a logarithmic scale at 8.4 dB/m and 8.9
dB/m, respectively, with increasing channel distance. Ideally, the uplink and downlink
exhibit identical SNR aenuation characteristics under identical underwater channel con-
ditions. However, a slight discrepancy was observed, possibly owing to minor variations
in turbidity across the underwater channel.
Received signal
Cyclic prefix removing
DFT
MUX from OFDM
symbols to bit stream
LDPC decoder
Descrambling
Data
Data
Scrambler
LDPC encoder
DeMUX from bit stream
to OFDM symbols
Adaptive bit loading
Cyclic prefix adding
Transmission signal
IDFT
FEC encoder OFDM modulator
DAC ADC
OFDM demodulator FEC decoder
SNR measurement
Transmitter Receiver
Modulation format
decision
SNR thresholding
Signal demodulation
BLER measurement
SNR threshold
control
BLER thresholding
Open loop
Underwater channel
Air channel
SNR > Threshold 2
BPSK
SNR > Threshold 3
4QAM
SNR > Threshold 4
16QAM 64QAM
SNR calculation for each subcarrier Threshold information
End
Yes
Yes
Yes
No
No
No
SNR > Threshold 1 Yes
No
No mapping
Dummy data
Figure 3. Signal-processing blocks in optical transceivers.
The OFDM signal modulator performed dynamic bit allocation based on the received
SNR by adjusting the number of bits allocated to each subcarrier. Figure 4shows the
adaptive modulation flowchart for the modulation format decision for each subcarrier.
Information 2025,16, 19 6 of 12
Dummy data for channel estimation were assigned when the SNR was below the minimum
threshold. This process uses an adaptive modulation algorithm that selects the optimal
modulation scheme based on SNR [
41
]. This algorithm combines open- and closed-loop
control mechanisms. Open-loop control adjusts the SNR threshold based on the observed
block error rates (BLER); the threshold increases if the BLER is high and decreases if the
BLER is low. Consequently, the QAM table changes dynamically. The SNR threshold
gradually evolves because the BLER aggregation is performed over extended periods. The
dynamic bit allocation algorithm implemented in the optical transceiver conformed to the
ITU-T G.9960 [
42
] and ITU-T G.9961 [
43
] standards, which govern the transceiver and data
link layers in optical wireless communication.
Information 2025, 16, x FOR PEER REVIEW 6 of 12
On the receiver side, the incoming signal was converted into a digital signal using an
analog-to-digital converter. The OFDM demodulator removed the CP and separated the
subcarriers using a discrete Fourier transform. Each subcarrier symbol was converted into
a symbol stream via a multiplexer. Finally, the FEC decoder processed the data using
LDPC decoding and descrambling to restore the original data. This evaluation maximized
the transmission capacity via error correction while maintaining the signal quality using
adaptive modulation.
Figure 3. Signal-processing blocks in optical transceivers.
Figure 4. Adaptive modulation flow chart.
4. Principal Experimental Results
Both the static and dynamic characteristics were evaluated. The static and dynamic
characteristic evaluations represent the average values and the results of the characteris-
tics sampled and recorded, respectively, at one-second intervals.
Figure 5a,b illustrate the changes in the SNR and transmission capacity of the receiver
for underwater channel distances of 0.6 m, 1.2 m, and 1.8 m. The results were used to
evaluate the transceiver performance characteristics. For uplink and downlink communi-
cation, the received SNR linearly decreased on a logarithmic scale at 8.4 dB/m and 8.9
dB/m, respectively, with increasing channel distance. Ideally, the uplink and downlink
exhibit identical SNR aenuation characteristics under identical underwater channel con-
ditions. However, a slight discrepancy was observed, possibly owing to minor variations
in turbidity across the underwater channel.
Received signal
Cyclic prefix removing
DFT
MUX from OFDM
symbols to bit stream
LDPC decoder
Descrambling
Data
Data
Scrambler
LDPC encoder
DeMUX from bit stream
to OFDM symbols
Adaptive bit loading
Cyclic prefix adding
Transmission signal
IDFT
FEC encoder OFDM modulator
DAC ADC
OFDM demodulator FEC decoder
SNR measurement
Transmitter Receiver
Modulation format
decision
SNR thresholding
Signal demodulation
BLER measurement
SNR threshold
control
BLER thresholding
Open loop
Underwater channel
Air channel
SNR > Threshold 2
BPSK
SNR > Threshold 3
4QAM
SNR > Threshold 4
16QAM 64QAM
SNR calculation for each subcarrier Threshold information
End
Yes
Yes
Yes
No
No
No
SNR > Threshold 1 Yes
No
No mapping
Dummy data
Figure 4. Adaptive modulation flow chart.
Furthermore, an inverse discrete Fourier transform was applied to generate multiple
subcarriers, and a cyclic prefix (CP) was added to improve the resistance to intersymbol
interference. A digital-to-analog converter converted the resulting OFDM signal into an
analog signal, which was then output by the transmitter.
On the receiver side, the incoming signal was converted into a digital signal using
an analog-to-digital converter. The OFDM demodulator removed the CP and separated
the subcarriers using a discrete Fourier transform. Each subcarrier symbol was converted
into a symbol stream via a multiplexer. Finally, the FEC decoder processed the data using
LDPC decoding and descrambling to restore the original data. This evaluation maximized
the transmission capacity via error correction while maintaining the signal quality using
adaptive modulation.
4. Principal Experimental Results
Both the static and dynamic characteristics were evaluated. The static and dynamic
characteristic evaluations represent the average values and the results of the characteristics
sampled and recorded, respectively, at one-second intervals.
Figure 5a,b illustrate the changes in the SNR and transmission capacity of the receiver
for underwater channel distances of 0.6 m, 1.2 m, and 1.8 m. The results were used to
evaluate the transceiver performance characteristics. For uplink and downlink commu-
nication, the received SNR linearly decreased on a logarithmic scale at 8.4 dB/m and 8.9
dB/m, respectively, with increasing channel distance. Ideally, the uplink and downlink
Information 2025,16, 19 7 of 12
exhibit identical SNR attenuation characteristics under identical underwater channel condi-
tions. However, a slight discrepancy was observed, possibly owing to minor variations in
turbidity across the underwater channel.
Information 2025, 16, x FOR PEER REVIEW 7 of 12
Figures 6 and 7 depict the dynamic characteristics of the SNR over time and trans-
mission rate over time, respectively, for underwater channel distances of 0.6 m, 1.2 m, and
1.8 m. These evaluations represent the characteristics of the uplink and downlink commu-
nications over 60 s.
The 5σ values for uplink SNR at underwater channel distances of 0.6 m, 1.2 m, and
1.8 m were 0.45 dB, 0.48 dB, and 0.45 dB, respectively. For downlink SNR, the 5σ values
for underwater channel distances of 0.6 m, 1.2 m, and 1.8 m were 0.47 dB, 0.41 dB, and
0.47 dB, respectively. Here, σ denotes the standard deviation. Assuming that temporal
variations follow a normal distribution, the range of 5σ corresponds to 0.999999426 of the
samples within both positive and negative deviations from the mean. The scaering ef-
fects caused by the movement of suspended particles, which serve as the scaering media,
varied temporally.
Figure 5. Impact of underwater channel distance on (a) received SNR and (b) transmission rate.
Figure 6. Dynamic characteristics of SNR over time for underwater channel distances of (a) 0.6 m,
(b) 1.2 m, and (c) 1.8 m.
Figure 7. Dynamic characteristics of transmission rate over time for underwater channel distances
of (a) 0.6 m, (b) 1.2 m, and (c) 1.8 m.
5. 4K Video Transmission Experiment
Figure 8 shows the experimental setup for transmiing a 4K UHD video stream
through air and underwater channels via a mirror-equipped aerial relay. The setup in-
volves filling a tank with shallow seawater of 2.2 NTU turbidity, measured in advance
using a turbidity sensor. The underwater channel distance L
underwater was set to 1.2 m,
whereas the horizontal and vertical air channel distances Lair_h and Lair_v were both set to 5
m. A standard silver-coated glass mirror was used in the aerial relay to reflect optical
(b)(a)
0
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Figure 5. Impact of underwater channel distance on (a) received SNR and (b) transmission rate.
Figures 6and 7depict the dynamic characteristics of the SNR over time and trans-
mission rate over time, respectively, for underwater channel distances of 0.6 m, 1.2 m,
and 1.8 m. These evaluations represent the characteristics of the uplink and downlink
communications over 60 s.
Information 2025, 16, x FOR PEER REVIEW 7 of 12
Figures 6 and 7 depict the dynamic characteristics of the SNR over time and trans-
mission rate over time, respectively, for underwater channel distances of 0.6 m, 1.2 m, and
1.8 m. These evaluations represent the characteristics of the uplink and downlink commu-
nications over 60 s.
The 5σ values for uplink SNR at underwater channel distances of 0.6 m, 1.2 m, and
1.8 m were 0.45 dB, 0.48 dB, and 0.45 dB, respectively. For downlink SNR, the 5σ values
for underwater channel distances of 0.6 m, 1.2 m, and 1.8 m were 0.47 dB, 0.41 dB, and
0.47 dB, respectively. Here, σ denotes the standard deviation. Assuming that temporal
variations follow a normal distribution, the range of 5σ corresponds to 0.999999426 of the
samples within both positive and negative deviations from the mean. The scaering ef-
fects caused by the movement of suspended particles, which serve as the scaering media,
varied temporally.
Figure 5. Impact of underwater channel distance on (a) received SNR and (b) transmission rate.
Figure 6. Dynamic characteristics of SNR over time for underwater channel distances of (a) 0.6 m,
(b) 1.2 m, and (c) 1.8 m.
Figure 7. Dynamic characteristics of transmission rate over time for underwater channel distances
of (a) 0.6 m, (b) 1.2 m, and (c) 1.8 m.
5. 4K Video Transmission Experiment
Figure 8 shows the experimental setup for transmiing a 4K UHD video stream
through air and underwater channels via a mirror-equipped aerial relay. The setup in-
volves filling a tank with shallow seawater of 2.2 NTU turbidity, measured in advance
using a turbidity sensor. The underwater channel distance L
underwater was set to 1.2 m,
whereas the horizontal and vertical air channel distances Lair_h and Lair_v were both set to 5
m. A standard silver-coated glass mirror was used in the aerial relay to reflect optical
(b)(a)
0
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Uplink
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Figure 6. Dynamic characteristics of SNR over time for underwater channel distances of (a) 0.6 m,
(b) 1.2 m, and (c) 1.8 m.
Information 2025, 16, x FOR PEER REVIEW 7 of 12
Figures 6 and 7 depict the dynamic characteristics of the SNR over time and trans-
mission rate over time, respectively, for underwater channel distances of 0.6 m, 1.2 m, and
1.8 m. These evaluations represent the characteristics of the uplink and downlink commu-
nications over 60 s.
The 5σ values for uplink SNR at underwater channel distances of 0.6 m, 1.2 m, and
1.8 m were 0.45 dB, 0.48 dB, and 0.45 dB, respectively. For downlink SNR, the 5σ values
for underwater channel distances of 0.6 m, 1.2 m, and 1.8 m were 0.47 dB, 0.41 dB, and
0.47 dB, respectively. Here, σ denotes the standard deviation. Assuming that temporal
variations follow a normal distribution, the range of 5σ corresponds to 0.999999426 of the
samples within both positive and negative deviations from the mean. The scaering ef-
fects caused by the movement of suspended particles, which serve as the scaering media,
varied temporally.
Figure 5. Impact of underwater channel distance on (a) received SNR and (b) transmission rate.
Figure 6. Dynamic characteristics of SNR over time for underwater channel distances of (a) 0.6 m,
(b) 1.2 m, and (c) 1.8 m.
Figure 7. Dynamic characteristics of transmission rate over time for underwater channel distances
of (a) 0.6 m, (b) 1.2 m, and (c) 1.8 m.
5. 4K Video Transmission Experiment
Figure 8 shows the experimental setup for transmiing a 4K UHD video stream
through air and underwater channels via a mirror-equipped aerial relay. The setup in-
volves filling a tank with shallow seawater of 2.2 NTU turbidity, measured in advance
using a turbidity sensor. The underwater channel distance L
underwater was set to 1.2 m,
whereas the horizontal and vertical air channel distances Lair_h and Lair_v were both set to 5
m. A standard silver-coated glass mirror was used in the aerial relay to reflect optical
(b)(a)
0
100
200
300
400
500
600
700
00.511.52
Bit rate [Mbps]
Underwater channel length [m]
Uplink
Downlink
0
5
10
15
20
25
30
00.511.52
SNR [dB]
Underwater channel length [m]
Uplink
Downlink
0
5
10
15
20
25
30
35
40
0 102030405060
SNR [dB]
Time [ s]
Uplink
Downlink
0
5
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0 102030405060
SNR [dB]
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Downlink
(a) (b) (c)
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25
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Downlink
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0
100
200
300
400
500
600
700
800
0 102030405060
Bit rate [Mbps]
Time [ s]
Uplink
Downlink
Figure 7. Dynamic characteristics of transmission rate over time for underwater channel distances of
(a) 0.6 m, (b) 1.2 m, and (c) 1.8 m.
The 5
σ
values for uplink SNR at underwater channel distances of 0.6 m, 1.2 m, and
1.8 m were 0.45 dB, 0.48 dB, and 0.45 dB, respectively. For downlink SNR, the 5
σ
values
for underwater channel distances of 0.6 m, 1.2 m, and 1.8 m were 0.47 dB, 0.41 dB, and
0.47 dB, respectively. Here,
σ
denotes the standard deviation. Assuming that temporal
variations follow a normal distribution, the range of 5
σ
corresponds to 0.999999426 of the
samples within both positive and negative deviations from the mean. The scattering effects
caused by the movement of suspended particles, which serve as the scattering media,
varied temporally.
Information 2025,16, 19 8 of 12
5. 4K Video Transmission Experiment
Figure 8shows the experimental setup for transmitting a 4K UHD video stream
through air and underwater channels via a mirror-equipped aerial relay. The setup involves
filling a tank with shallow seawater of 2.2 NTU turbidity, measured in advance using a
turbidity sensor. The underwater channel distance L
underwater
was set to 1.2 m, whereas
the horizontal and vertical air channel distances L
air_h
and L
air_v
were both set to 5 m. A
standard silver-coated glass mirror was used in the aerial relay to reflect optical signals.
The static and dynamic characteristics in this transmission experiment match the results
obtained under the conditions described in Section 4.
Information 2025, 16, x FOR PEER REVIEW 8 of 12
signals. The static and dynamic characteristics in this transmission experiment match the
results obtained under the conditions described in Section 4.
A 4K video stream was transmied for the uplink transmission from Transceiver #2
to Transceiver #1. The video captured by the 4K camera was input into a 4K encoder
(HLD-5000E, IBEX Technology, Kanagawa, Japan), and the encoded video signal was sent
to the transmier of Transceiver #1. The optical signal then passed through the underwa-
ter channel and was transmied via the air channel before being received by Transceiver
#2. The output signal from Transceiver #2 was input into a 4K decoder (HLD-5000D) and
displayed on a monitor as a high-resolution video. When the HLD-5000E and HLD-5000D
pairs were used, the codec delay was 20 ms, enabling near-real-time video playback.
Figure 9a,b show photographs of the experimental setup (including the transmier,
relay, and receiver sides) during 4K video transmission. Suspended particles in the sea-
water tank cause time-dependent variations in the scaering effects; however, the impact
on transmission was negligible. Figure 10a,b show images of the 4K UHD video before
and after transmission, respectively, confirming that the video information remained at
high resolution at the receiver. These results demonstrate that the air-to-underwater opti-
cal wireless communication system utilizing an aerial relay can transmit video even under
turbid shallow seawater channel conditions.
Figure 8. Experimental setup for transmiing a 4K UHD video stream.
Figure 8. Experimental setup for transmitting a 4K UHD video stream.
A 4K video stream was transmitted for the uplink transmission from Transceiver #2
to Transceiver #1. The video captured by the 4K camera was input into a 4K encoder
(HLD-5000E, IBEX Technology, Kanagawa, Japan), and the encoded video signal was sent
to the transmitter of Transceiver #1. The optical signal then passed through the underwater
channel and was transmitted via the air channel before being received by Transceiver #2.
The output signal from Transceiver #2 was input into a 4K decoder (HLD-5000D) and
displayed on a monitor as a high-resolution video. When the HLD-5000E and HLD-5000D
pairs were used, the codec delay was 20 ms, enabling near-real-time video playback.
Figure 9a,b show photographs of the experimental setup (including the transmitter,
relay, and receiver sides) during 4K video transmission. Suspended particles in the seawater
tank cause time-dependent variations in the scattering effects; however, the impact on
transmission was negligible. Figure 10a,b show images of the 4K UHD video before and
after transmission, respectively, confirming that the video information remained at high
resolution at the receiver. These results demonstrate that the air-to-underwater optical
wireless communication system utilizing an aerial relay can transmit video even under
turbid shallow seawater channel conditions.
Information 2025,16, 19 9 of 12
Information 2025, 16, x FOR PEER REVIEW 8 of 12
signals. The static and dynamic characteristics in this transmission experiment match the
results obtained under the conditions described in Section 4.
A 4K video stream was transmied for the uplink transmission from Transceiver #2
to Transceiver #1. The video captured by the 4K camera was input into a 4K encoder
(HLD-5000E, IBEX Technology, Kanagawa, Japan), and the encoded video signal was sent
to the transmier of Transceiver #1. The optical signal then passed through the underwa-
ter channel and was transmied via the air channel before being received by Transceiver
#2. The output signal from Transceiver #2 was input into a 4K decoder (HLD-5000D) and
displayed on a monitor as a high-resolution video. When the HLD-5000E and HLD-5000D
pairs were used, the codec delay was 20 ms, enabling near-real-time video playback.
Figure 9a,b show photographs of the experimental setup (including the transmier,
relay, and receiver sides) during 4K video transmission. Suspended particles in the sea-
water tank cause time-dependent variations in the scaering effects; however, the impact
on transmission was negligible. Figure 10a,b show images of the 4K UHD video before
and after transmission, respectively, confirming that the video information remained at
high resolution at the receiver. These results demonstrate that the air-to-underwater opti-
cal wireless communication system utilizing an aerial relay can transmit video even under
turbid shallow seawater channel conditions.
Figure 8. Experimental setup for transmiing a 4K UHD video stream.
Figure 9. Photographs of the experimental setup during 4K video transmission, including the
(a) transmitter and relay and (b) receiver sides.
Information 2025, 16, x FOR PEER REVIEW 9 of 12
Figure 9. Photographs of the experimental setup during 4K video transmission, including the (a)
transmier and relay and (b) receiver sides.
Figure 10. Images of the 4K UHD video (a) before and (b) after transmission.
6. Conclusions
In this work, we proposed a bidirectional optical wireless communication system be-
tween underwater drones and terrestrial fixed stations that utilized a mirror-based relay
as an efficient and secure communication method for shallow sea environments. This sys-
tem ensured low atmospheric loss and confidentiality by employing invisible optical sig-
nals. In addition, adaptive modulation was incorporated to maximize the transmission
capacity based on the characteristics of the air and underwater channels.
The results of the fundamental experiments clarified the SNR and transmission ca-
pacity characteristics for underwater channel distances ranging from 0.6 m to 1.8 m, con-
firming the linearity of SNR aenuation on a logarithmic scale with increasing distance.
Furthermore, the 4K UHD video streaming experiments demonstrated that high-resolu-
tion video transmission was feasible even under shallow seawater turbidity conditions.
These findings provide a new, efficient, and reliable communication technology for vari-
ous applications, including marine monitoring and IoT data communication.
Future research should further analyze the impact of various factors such as water
depth and turbidity on communication performance. Optimizing the system’s transmis-
sion capacity for broader environmental conditions, such as varying turbulence, turbidity
levels, and air-to-water channel distances, to enable its practical implementation in di-
verse scenarios remains a critical challenge.
Author Contributions: Conceptualization, T.K.; methodology, T.K.; software, K.T. and S.H.; valida-
tion, K.T. and K.K.; formal analysis, T.K.; investigation, T.K., K.T., and K.K.; resources, S.H.; data
curation, T.K.; writing—original draft preparation, T.K.; writing—review and editing, K.T., A.K.,
and S.H.; visualization, T.K.; supervision, T.K.; project administration, T.K.; funding acquisition,
T.K. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Research and Development of the ICT Priority Technol-
ogy Project (JPMI00316).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The original contributions presented in this study are included in this
article, and further inquiries can be directed to the corresponding author.
Acknowledgments: We thank A. Iwata and R. Komatsubara of IBEX Technology and T. Matsui and
S. Yamamoto of Sangikyo Corporation for their cooperation in conducting the experiments.
Figure 10. Images of the 4K UHD video (a) before and (b) after transmission.
6. Conclusions
In this work, we proposed a bidirectional optical wireless communication system
between underwater drones and terrestrial fixed stations that utilized a mirror-based relay
as an efficient and secure communication method for shallow sea environments. This
system ensured low atmospheric loss and confidentiality by employing invisible optical
signals. In addition, adaptive modulation was incorporated to maximize the transmission
capacity based on the characteristics of the air and underwater channels.
The results of the fundamental experiments clarified the SNR and transmission capac-
ity characteristics for underwater channel distances ranging from 0.6 m to 1.8 m, confirming
the linearity of SNR attenuation on a logarithmic scale with increasing distance. Further-
more, the 4K UHD video streaming experiments demonstrated that high-resolution video
transmission was feasible even under shallow seawater turbidity conditions. These findings
provide a new, efficient, and reliable communication technology for various applications,
including marine monitoring and IoT data communication.
Future research should further analyze the impact of various factors such as water
depth and turbidity on communication performance. Optimizing the system’s transmission
capacity for broader environmental conditions, such as varying turbulence, turbidity
levels, and air-to-water channel distances, to enable its practical implementation in diverse
scenarios remains a critical challenge.
Information 2025,16, 19 10 of 12
Author Contributions: Conceptualization, T.K.; methodology, T.K.; software, K.T. and S.H.; vali-
dation, K.T. and K.K.; formal analysis, T.K.; investigation, T.K., K.T. and K.K.; resources, S.H.; data
curation, T.K.; writing—original draft preparation, T.K.; writing—review and editing, K.T., A.K. and
S.H.; visualization, T.K.; supervision, T.K.; project administration, T.K.; funding acquisition, T.K. All
authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the Research and Development of the ICT Priority Technology
Project (JPMI00316).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The original contributions presented in this study are included in this
article, and further inquiries can be directed to the corresponding author.
Acknowledgments: We thank A. Iwata and R. Komatsubara of IBEX Technology and T. Matsui and S.
Yamamoto of Sangikyo Corporation for their cooperation in conducting the experiments.
Conflicts of Interest: Keita Tanaka and Shogo Hayashida are employed by the Sangikyo Corporation.
The remaining authors declare that the research was conducted in the absence of any commercial or
financial relationships that could be construed as a potential conflict of interest.
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