# UAVs-Enabled Maritime Communications: Opportunities and Challenges

Muhammad Waseem Akhtar, Nasir Saeed

###### Abstract

Next-generation communication system is expected to integrated the terrestrial
and non-terrestrial networks. Maritime communication, in this sense, can plays
an important role in marine activities. Unmanned aerial vehicles (UAVs) are
proposed as the aerial base stations for the terrestrial networks. However,
applications of UAVs for maritime communication is still un-explored. In this
paper, we highlight different aspects of UAV-based maritime communication
which includes the use cases, channel characteristics, maritime network design
prospects, research challenges an future direction.

###### Index Terms:

Maritime communication, unmanned aerial vehicals (UAVs), blockchains , machine
learning, artificial intelligence, massive mimo.

##  I Introduction

As being known, around 70$\% $ of the surface of our planet is covered by
seawater and over 90$\%$ of the world’s products are moved by a commercial
fleet consisting around 46.000 ships [uav1]. Thousands of ships are out of
sight from shore or any other vessel all the time. Therefore, reliable
maritime communications are considered to play a significant role in maritime
operations. However, current maritime systems comprise mainly either too low
bandwidth legacy analog very high frequency (VHF) radios or too high-cost
satellite communication (SatCom) networks to support the international
Maritime Safety Organization (IMO) eNavigation concept, which needs wideband
low-cost communication systems to achieve better security, surveillance, and
environmental control, efficient working conditions for the crew on board, and
Internet services for passengers. On the other hand, the Wireless Broadband
Access (WBA) mainly consisting of wireless fidelity (Wi-Fi) and othe 4th
generation wireless technologies are regarded to have the potential of
fulfilling the IMO eNavigation concept because they support the required
features such as high data rate and bandwidth efficiency, mobility with the
low-latency handover, good security and quality of service (QoS) and d) low-
cost deployment. However, it is questionable how the WBA can work optimally in
maritime areas, and hence the research on radio propagation overseas is vital.

In the previous decades, the world has experienced an ever-growing booming
marine economy. Conventional sectors such as fisheries and transportation have
been continually developed, while new maritime activities including oil
exploitation, environment monitoring, and tourism have evolved. All these
require a greater data rate and more dependable wireless communications.
Existing maritime communication systems mainly rely on satellite and/or
customized communication systems operating in HF/very high frequency (VHF)
bands, such as the navigational telex (NAVTEX) system, the automatic
identification system (AIS), as well as the evolving VHF data transfer system
(VDES)[uav1]. Therefore, the typical marine networks are commonly seen as
integrated satellite-ground-sea networks with mesh topology among the users.
The satellite-based system, albeit undergoing quick development which
considerably boosts its potential to deliver high-speed data coverage for a
wide area, suffers from unavoidable big propagation delay and expensive
implementation cost. On the other hand, the HF/VHF-based systems, mainly being
utilized for vessel identification, tracking/monitoring, and security
alerting, also have inherent difficulties such as the demand of special
devices and insufficient bandwidth. To improve user experience, a near coast
marine user should be able to access effortlessly highspeed terrestrial mobile
networks (such as fourth generation (4G) or fifth generation (5G) networks).
As a result, near coast maritime communications have garnered substantial
interest, where the major purpose is to provide wide-area broadband coverage
for offshore users with the aid of terrestrial base stations (BSs) and/or
relays, and with the technologies utilized for WiFi and LTE [uav2].

Similarly, some other works have examined employing an unmanned aerial vehicle
(UAV) in marine communications. Furthermore, 5G technology such as massive
multiple-input multiple-output (MIMO), millimeter wave (mmWave), and user-
centric network, can potentially deliver a greater data rate coverage to
widely spread maritime users. The implementation of these physical layer
approaches and network designs is expected to be a promising direction in
future maritime communication systems.

###  I-A Background

With the rapid development of marine industries such as marine transportation,
marine fishing, marine exploration, marine tourism, marine rescue, and marine
military deployments in recent years, the number of ships has increased
continuously, resulting in rapid growth of marine business and data volume. It
is vital to boost the performance of marine communication networks in order to
keep pace with the increasing growth of marine activities. At the moment,
maritime satellites are largely utilized to interact with maritime terminals.
While a satellite network can give wider coverage, it suffers from long
propagation times and is unable to keep up with the increasing expansion of
maritime communications and computing requirements and prices.

The infancy and youth of radio technology were predominantly tied to nautical
applications. Following his development of the first operational radio
transceiver in 1895, Guglielmo Marconi performed transmission experiments
between two Italian warships off the port of Spezia in 1897, when he managed
to exchange radio signals over a distance of 22 km. Later he resumed his
experiments in England, where on Christmas Eve in 1898 he achieved
radiotelegraphy contact between the “East Goodwin” lightship and South
Foreland Lighthouse in South East England. On 3rd March 1899, the steamship
“RF Matthews” crashed with this lightship, which frightened the lighthouse
ashore to request aid. This was the first time a distress call was transmitted
via radio from a ship at sea.

However, despite the significant breakthroughs in radio technology since that
time, innovations in maritime networks are substantially trailing behind their
land equivalent, and fresh solutions are needed to fulfill the approaching
user requirements. Extension and economic reliance of an ocean area nearly 6
times the size of its landmass. The wide geographic distances and the economic
importance of activities at sea in remote places necessitate fresh and
inventive radio-based solutions. A ship at sea can’t connect directly to land-
based sites or other ships via cable. It is consequently postulated that
wireless communication is the key solution for effective communication. Before
the introduction of wireless communications, ships at sea could only
communicate within a visible distance and were constrained either to the use
of various light forms and/or flags. Even today signal flags are an important
way of ship-to-ship communication.

Ships began to be equipped with wireless communication gear at approximately
the same time as Marconi’s experiment on intercontinental wireless
communication. At that time, steamships carrying people launched an expanding
need for telegrams. However, there was no organized arrangement for distress
communications. As the “R.M.S. Titanic” was sinking in April 1912, a distress
signal was transmitted by radio. And yet, the multiple casualties that ensued
sparked the establishment of the treaty addressing the distress and safety
communications for ships.

###  I-B Vision and Literature Survey

Today’s land-based communications systems are subject to ongoing enhancements
and upgrades of current infrastructure due to the ever-increasing requirement
for widely available, quick, and stable exchange of enormous amounts of
information in real-time. On the other hand, despite efforts to improve,
maritime communications systems remain trail in this respect and are largely
characterized by low speeds, relatively high costs, and restricted
availability and capacity. Recent advances in marine transport include the
rising digitization of ships and related maritime services and introducing the
notion of autonomous ship operation.

Unlike the commonly utilized Automatic Identification System (AIS), where a
limited amount of preset data is provided to vessels and coast stations within
the range, the digital ship and autonomous ship ideas demand continuous real-
time transmission of vast volumes of digital data. Moreover, since these data
are to be communicated to the land-based stations and operators for further
analysis and processing, satellite communications are employed for this
purpose instead of terrestrial systems. In addition, the necessity for high-
speed and dependable Internet connectivity for both ship’s personnel and
passengers is continuously expanding. Thus, the previously noted constraints
of existing maritime communications systems may prevent adopting and
implementing these revolutionary concepts in the maritime business. Therefore,
modifications and modernisation of current systems and installation of new
systems will be necessary. However, modernization necessitates massive
interventions into the equipment and the commitment of significant financial
and material resources.

An additional way to reducing the stress on maritime communications systems
could be to minimize the size of the transmitted data by adopting compression
techniques based on specialized algorithms for shipboard data encoding. These
algorithms could be efficiently implemented in the ship communication system,
thus avoiding substantial interventions and investments.

Figure 1: A depiction of basic network architecture for UAV-aided maritime
communication

###  I-C Maritimes Networks

The maritime communications infrastructure will handle both the crew’s and
passengers’ networking requirements. Crew communication may be limited to
vessels-to-vessels (decision support systems, computer-supported collaborative
work for equipment inspection and maintenance, telemedicine systems, etc).
(decision support systems, computer-supported collaborative work for equipment
inspection and maintenance, telemedicine systems, etc.). The services that can
be supplied by this infrastructure can encompass the complete range of network
services, including conversational, messaging, retrieval, and presentation
services as described by the ITU I.211 taxonomy. Numerous categories above
incorporate real-time multimedia communications, which requires equal network
needs.

Wireless communication is the only realistic way for ships at sea to
communicate information with other ships and land-based stations. Before the
introduction of contemporary wireless communications systems, ship
communication was confined to communication within a visible distance
utilizing signals with various lights and flags. Ships began to be equipped
with wireless communication devices with the invention of radiotelegraphs.

Maritime communications have been utilized for three basic purposes: distress
and safety communications, communications in support of navigation, and
general communications. Distress and safety communications include distress
calls and communication during search and rescue (SAR) operations for vessels
in distress. In contrast, communications supporting navigation refer to
exchanging information with surrounding vessels and port managers during a
voyage. Finally, general-purpose communications encompass numerous public
communication services that serve similar functions as on land.

Maritime communications have recently seen an increased transition from analog
to digital communications, as well as a major increase in the requirement to
share larger volumes of data in general. With the implementation of the Global
Maritime Distress and Safety System (GMDSS) in 1999, it became possible to
send digital distress signals automatically via satellite communications,
instead of the already obsolete methods of sending warning or Mayday messages
via telegraph and telephone, respectively. Communications in support of
navigation have also developed with the introduction of digital radio
communications systems, such as AIS, which permits the interchange of
navigational information with neighboring vessels and coast stations via
terrestrial VHF communications. General public communications include using
various Internet services by ship’s crew and passengers, which is achieved
through marine communications satellites.

####  I-C1 Over the Sea Communication Network

In another sense, the next generation maritime communication will be
universal. It will be available to vessels of all sorts and sizes, with a
choice of services and applications customized to their needs. Likewise,
automatic identification systems are anticipated to follow the same direction.
One of the advantages of future marine communication system will be the
capability to accommodate all prospective users. Additionally, this will
result in an expansion in the worldwide market population from a few thousand
to several million vessels (including pleasure ships), resulting in enormous
economies of scale. Once this future maritime communications system is in
place, the 6G and beyond vision of user-centricity, ongoing invention of new
services, and flexible business models appears to be substantially more
possible, even for ships at sea in the most distant regions of the world.

With their activation in the second half of the twentieth century,
communications satellites became widely employed for long-distance maritime
communications, which until then had been limited to the use of radio waves in
the Medium Frequency/High Frequency (MF/HF) band. Today, mobile communications
via Inmarsat satellites, among others, are employed, giving diverse services,
such as Fleet Xpress, FleetBroadband, and Inmarsat C. In addition to the
initially employed voice communication, data transmission via satellite
communications is getting more and more developed. However, satellite
communications still employ a narrow band, which results to a decreased
transmission speed. In addition to the lesser bandwidth, the fees for using
satellite communication are still quite high, so that this method of
communication is not an effective alternative for transferring bigger volumes
of maritime data across longer distances.

AIS supports the 9.6 kbps digital communication over VHF communication
channels utilizing Gaussian minimum shift keying (GMSK) modulation. The AIS-
based communication is used to send digital voyage data acquired aboard the
ship. In addition to delivering pertinent data, the ship’s AIS transmitter may
concurrently collect input from neighboring ships, allowing them to identify
each other. The AIS transceivers use the 156 MHz radio band, with an output
power of up to 12.5 W. Therefore, vessels and stations can receive the AIS
signal within a range of 30–40 km.

####  I-C2 Underwater Communication Networks

A wireless LAN or pico-cellular wireless network aboard the station will offer
crew communication and access to all apps according to the crew member’s
allocated security profile. Additionally, crew communications devices will
contain position-fixing technologies (e.g., WLAN- or GPS/Galileo-based, indoor
GPS) to provide location-based services (LBS) such as man overboard (MOB) and
other distress alarms to occur regardless of the vessel’s location. Crews are
probably to be early users of wearable computing gadgets. Owing to the
sensitivity of his profession and his demand for access to a huge amount of
decision-support information, the seafarer is a likely early adopter of such
technology. Passengers will be able to access infotainment, internet services,
and voice contact with the shore via the ship’s wireless network, using their
own communication handsets. They may be offered LBS as they travel about the
ship, with their location being established either by the ship’s pico-cellular
wireless network or by their own handsets equipped with GPS or other position-
fixing technology. Underwater wireless sensing systems are envisioned for
stand-alone applications and control of autonomous underwater vehicles (AUVs)
and as an adjunct to cabled systems. For example, cabled ocean observatories
are being erected on undersea cables to deploy an enormous fiber-optic network
of sensors (cameras, wave sensors, and seismometers) covering miles of the
ocean floor. These cables can enable communication access points, very much
like cellular base stations are connected to the telephone network, allowing
users to roam and interact from regions where cables cannot reach. Another
example is cabled submersibles, commonly known as remotely controlled vehicles
(ROVs) (ROVs). These vehicles, which may weigh more than 10 metric tonnes, are
connected to the mother ship by a cable that can run over several kilometers
and give tremendous power to the remote end, along with high-speed
communication messages. Today, both vehicle technology and sensor technology
are sophisticated enough to motivate the idea of underwater sensor networks.
To convert this theory into reality, however, one must address the challenge
of communications. Underwater communication methods today largely use acoustic
technology. Complementary communication approaches, such as optical and radio-
frequency, or even electrostatic communication, have been proposed for short-
range networks (usually 1–10m), where their very high bandwidth (MHz or more)
can be leveraged. These signals attenuate very rapidly, within a few meters
(radio) or tens of meters (optical), requiring either high-power or big
antennas. Acoustic communications offer wider ranges but are hindered by three
factors: limited and distance-dependent bandwidth, time-varying multipath
propagation, and low speed of sound.

Figure 2: UAV as a use case of reliable maritime communication in dynamically
changing environmental conditions.

##  II Use Cases of UAV-Aided Maritime Communication

Following are the major use cases of UAV-aided maritime communication.

###  II-A Ubiquitous Transmission and Coverage

An AIS ensures the safety of navigation by delivering identification,
tracking, and other information about the ship to other ships and coastal
destinations automatically. It is regarded as a complimentary way to maritime
radars to share navigational data in the VHF frequency region for collision
avoidance. First and foremost, a maritime MTC system is necessary to enable
ubiquitous connectivity between vessels and shore on a worldwide scale,
notably over open oceans including the most distant places of the planet like
the Polar Regions, to assure uninterrupted and consistent presence of maritime
services. Currently, the availability of services in offshore settings is
constrained by a lack of information and communication infrastructures.
Moreover, the services have always been in a campus-style deployment; cross-
region continuity of maritime service remains patchy and even nonexistent.
Ultimately, a global cooperative maritime IoT network is required for the
undisrupted services across organizational, regional, and national boundaries
especially in times of crisis.

###  II-B UAV-based Relaying

Generally, one common assumption in legitimate eavesdropping is that the
legitimate monitor may be far away from the suspicious party for concealment
and hence possesses inadequate eavesdropping channels. To deal with such an
issue, various works proposed to utilize jamming launched by the monitor to
lower the suspicious rate, to assist the efficient eavesdropping by the
monitor itself. Specifically, [uav4] first presented one jamming-assisted
eavesdropping approach in a single-hop suspicious system. Subsequently, [uav5]
further considered the legitimate eavesdropping in the multi-input multi-
output (MIMO) network, the one-way and two-way relaying systems. UAV-based
communications are getting growing importance for a -de range of applications
particularly with the arrival of the high altitude long endurance platforms
such as Tier II+. UAVs can be rapidly deployed, enabling BLOS communications
in support of a range of military activities. The UAV airborne relay will not
only enable range extension for theater communications but will also allow new
services including wideband COTM. With the developments in miniaturized
technology and better transmitter efficiency, multi-function, multi-band
transponders may be carried in UAVs within the size, weight, and power
dissipation budgets.

On the other hand, with the flexible mobility and high possibilities of line-
of-sight (LoS) air-to-ground links, UAVs-enabled relays are displaying
increasingly important advantages in wireless communication [uav6]. Therefore,
instead of employing the fixed relay(s) as in [uav7], the wise suspicious
parties can also deploy UAV(s) as the relay(s) for more efficient suspicious
information transmission by exploiting UAV’s rate adaptation, which, however,
will cause enormous hardship for the legitimate party to implement efficient
eavesdropping. The main reasons are I with given jamming environment setting
by the monitor, the wise UAV obviously can adjust its location for maximizing
the suspicious communication rate adaptively, which thus causes a fundamental
obstacle for the monitor to figure out its exact jamming effect; ii) for the
monitor, the UAV’s adaptive deployment will also affect its received quality
of the suspicious signals via the air-ground link.

Figure 3: A depiction of UAV handover in maritime network.

###  II-C UAV-Aided Maritime IoT Data Harvesting

While research on underwater sensor networks has substantially advanced in
recent years, it is obvious that major obstacles remain to be solved. With the
flurry of new ways to communication, media access, networking, and
applications, effective analysis, integration, and testing of these concepts
are paramount—the field must generate fundamental insights, as well as
comprehend what stands up in practice. For these reasons, we believe that the
development of new theoretical models (both analytical and computational) is
very much needed and that greater use of testbeds and field experiments is
essential; such work will support more accurate performance analysis and
system characterization, which will feed into the next generation of
underwater communications and sensing. In addition, integration and testing of
current ideas will stress the seams that are typically concealed in more
specialized laboratory studies, such as total system cost, energy consumption,
and general robustness in diverse settings. Applications drive the development
of underwater sensing and networking. Inexpensive computing, sensing, and
communications have enabled terrestrial sensor networking in the past couple
of decades; we predict that cheap computing, together with lower-cost enhanced
acoustic technology, communication, and sensing, will enable underwater
sensing applications as well.

###  II-D Maritime Wireless Power Transfer

Wireless charging has been acknowledged as a viable technology to provide
energy supply for battery-limited nodes, such as Internet of Things (IoT)
devices and sensors. There have been quite a few works on the use of UAVs for
WPT. For instance, UAV-enabled WPT systems were presented in [uav8], where the
UAV was used to broadcast wireless energy to ground receivers. Due to the
line-of-sight (LoS) linkages between the UAV and ground sensors, the UAV-
enabled WPT system may improve the energy transfer efficiency substantially by
deploying the UAV as a mobile energy transmitter in these works. Specifically,
the reference evaluated the attainable energy area of a basic two-user UAV-
enabled WPT system by optimizing the UAV’s trajectory with limits on the UAV’s
maximum speed.

###  II-E Maritime Computation Offloading

Because of great sensitivity to time and energy consumption, many computation-
and data-intensive jobs are difficult to accomplish on mobile terminals and
cannot fulfill the needs of the rapid development of mobile networks. To
overcome this challenge, mobile edge computing (MEC) appears to be a promising
solution. Decision-making applications relying on real-time video streaming
and image processing tend to exceed the local data processing power of low-
cost UAVs or may significantly prolong the time required for completing their
activities. To address this issue, mobile-edge computing (MEC) may
beneficially work with UAVs, for easing computational offloading from the UAV
to the edge nodes. The cooperation between UAVs and MEC systems can be shown
through crowd surveillance. More explicitly, UAV-mounted high-resolution
cameras are capable of broadcasting real-time footage, which improves the
discovery of offenders using face recognition. However, both the weak
computational performance and limited power supply of UAVs inhibit the
aforementioned real-time recognition on board. To tackle these issues, the
assistance of MEC systems might be used for offloading numerous computational
activities for increasing the face recognition performance on time. To be
particular, the data collected are partitioned into two segments, one to be
computed at the UAV and the other to be offloaded to the edge node through a
gateway or access point (AP). Computation offloading is extensively utilized
to overcome the computing power restrictions of a resource-constrained mobile
monitor. Offloading conducts some elements of a work or a job of the mobile
monitor on servers on behalf of the mobile monitor. If the execution cost of
the compute operations in the portion of the task in the mobile monitor is
more than the execution cost considering the offloading mechanism, the part of
the task is done on remote servers. In some applications such as mobile video
surveillance systems, the offloading decision method considers real-time
features, as well as energy economy. Nimmagadda et al. [uav9] suggested a
real-time moving object tracking technique using mobile robots based on
compute offloading. Their offloading choice algorithm assesses the computation
and communication time of the offloaded task and decides to execute the task
on a robot or servers to satisfy time limitations and reduce the total
response time.

###  II-F Maritime Localization

The location and recognition of ship targets play a significant role in the
SAGIN environment. Maritime location leverages the numerous measurement
instruments of a ship to locate the position of other nautical targets.
Maritime location can give more accurate location services for ship
navigation, hydrographic surveys, maritime resource research, etc. Ocean
surveillance satellites can leverage the advantages of space and altitude to
cover extensive oceans, monitor the actions of ships and submarines in real-
time, and identify and monitor radar signals transmitted by ships. Then, the
positions of target ships may be tracked and located. However, the position
precision based on satellites may not satisfy unforeseen situations with high
precise location needs, such as ocean rescue and noncooperative (enemy) ship
location. UAVs are autonomous vehicles that can be controlled from a distance
without pilot control. In the 21st century, UAVs are increasingly utilized in
military surveillance, target tracking, topographic surveys, etc. [uav10]. The
self-positioning of UAV platforms and the determination of the location of
unknown marine targets by UAVs have become hot subjects in recent years. For
static targets, a popular passive locating strategy is to measure the time-
difference-of-arrival (TDOA) [uav11] of the broadcast signal from the target
ship to each observation UAV. In general, target ship location based on TDOA
observations requires the collaboration of many UAVs. If the TDOA can be
properly measured, the target ship location can be determined by computing the
intersection of the hyperbolas of the TDOAs[uav12]. However, in real
applications, TDOA measurements often contain random noise, which decreases
the target location accuracy. In addition, TDOA-based approaches are more
suitable for single target ship locations.

##  III UAV-Aided Maritime Communication Network Architecture

###  III-A Maritime Control Station

A marine control station (MCS) is a control center positioned on the water to
give the facilities for operators of unmanned vehicles in space or air. MCSs
play a highly crucial function in unmanned aerial systems, arguably as vital
as unmanned aircraft. MCSs may be either stationary or transportable
software/hardware devices which are used to command and monitor unmanned
aircraft. Small UAV MCSs are created with a computer and a small maritime
terminal. At this stage, it is vital to emphasize that the control segments do
not have to be positioned on the water. Other control segment types are
represented by underwater control stations or airborne control stations.
Computers used as MCSs can take many sorts of gadgets such as PDA, laptop
computer, wearable computer or several transport boxes full of equipment. The
systems can take different shapes, from simple with an antenna hooked to a
laptop computer to more complex/complicated like rat’s nest with computers,
electronics boxes, monitors, antennas, wires, and joysticks.

###  III-B Control Links

data links allow the operator to direct and control the unmanned vehicle
status. The uplink is used to command the unmanned vehicle, while the downlink
is utilized for ground receipt of status and condition information.

###  III-C Data Links

Communication technology is part of the UAS responsible for data delivery
between system elements and external units. The fundamental challenges of
communication systems are adaptability, flexibility, security as well as
cognitive controllability of frequency, bandwidth, and data/information flow.
UAV communication is mostly important in terms of safety when it comes to
integration between UAV and MCS. Many elements can form communication systems
and be incorporated into various combinations.

####  III-C1 UAV-Ship and Satellite-Ship Data Links

This link delivers information from the unmanned vehicle to a sea-based
reception device. The payload data link is usually important for missions but
not for flights.

####  III-C2 UAV-Satellite, UAV-UAV, and Satellite-Satellite Data Links

UAVs can cooperate with other airborne platforms, such as satellites and other
UAVs which demand air-to-air communication between the platforms.

##  IV Channel Characteristics

To create efficient maritime communication systems, the first and fundamental
necessity is to develop a framework to comprehend the wireless channels. In an
integrated air-ground-sea communications network, there are two major types of
channels to be investigated, namely the air-to-sea channel (e.g., for
communication links from the aircraft-based base stations or relays) and the
near-sea-surface channel (for land-to-ship/ship-to-land or ship-to-ship
communications). Due to the unique features of the maritime propagation
environment such as sparse scattering, sea wave movement, and the ducting
effect over the sea surface, the modeling of these maritime channel links
differs from conventional terrestrial wireless channels in many aspects and,
consequently, will result in a significant impact on the transceiver design.
To create an efficient maritime communication system, it is necessary to
understand the corresponding wireless channels thoroughly and develop
appropriate channel models. Whereas the marine satellite channel has been
explored extensively [uav13], the wireless channels in the integrated air-
ground-sea network are less well understood for the near coast situation. In
both academia and industry, researchers have lately started various
measurement initiatives and have developed several analytical methodologies to
describe maritime wireless channels. In a near coast integrated air-ground-sea
communication network, there are two major types of channel links to be
considered, namely, the air-to-sea channel link which is used for the
transmission from aircraft (balloons or UAVs)-based BSs or relays, and the
near-sea-surface channel link which is used to support land-to-ship/ship-to-
land and ship-to-ship communications. On the one hand, various air-to-ground
wireless channel models have recently been adapted to apply to the air-to-sea
propagation environment. The two most essential and distinguishing properties
of the maritime wireless channels are scarce and location entirely dependent:
1) The sparse feature is widely encountered in the marine environment, in
several aspects including both the scattering and the user distribution. 2)
The location dependent feature implies that for a marine user, fully different
model structures should be applied for the channels in different location
regions. Consequently to these traits, new difficulties and opportunities
develop in the design of future maritime communication systems.

###  IV-A Air-to-Sea Channel

The air-to-ground channels have been intensively studied in the literature
[uav14], the three features of the maritime environment, i.e., sparsity,
instability, and ducting effect, bring unique characteristics to the air-to-
sea channels and therefore result in notable differences in the channel
modeling. In most circumstances, the LOS path and the surface reflection path
are two main paths in an air-to-sea channel. Considering that the transmitter
is in general at a high altitude and the transmission distance is large, the
so-called curved-Earth two ray (CE2R) model is commonly employed to take into
account the earth curvature [uav15]. In some cases, other scattered weak paths
need to be considered besides the two main paths. The dispersion often happens
around the receiver due to the high transmitter altitude [uav16]. As
indicated, whereas the local scattering could be rich for inland receivers
(e.g., in the near-urban area), a maritime user is predicted to confront more
sparser scattering, and so the over-water configuration may simplify the
modeling as compared to the inland air-to-ground channels. As mentioned, the
air-to-sea channel can be represented by a standard two-ray or three-ray a
model. In this situation, due to the destructive summing of the two or three
independent beams with different phases, the channel will meet deep nulls at
particular receiver positions as confirmed by [37], [38]. 3 The deep nulls
emerge with a larger probability in the maritime environment due to their
sparse character, but for the inland air-to-ground channels, the path loss
curve would be smoother with rich scattering. Two elements that may alter the
path loss model need to be considered in the air-to-sea propagation
environment: 1) Earth curvature: In maritime communications, normally, a long
coverage distance is expected. Therefore, the use of the CE2R model will be
necessary, which subsequently leads to different path loss models from those
found under the flat-earth assumption. 2) Ducting effect: Although the height
of the transmitter is normally higher than the duct layer, part of the radio
energy could still be trapped in the duct layer when the grazing angle (i.e.,
the angle between the direct path and the sea surface plane) is less than a
particular threshold. In this situation, the ray-trapping action of the
evaporation duct (and evaluated duct) will considerably increase the energy of
the received signals, resulting in path loss reduction.

###  IV-B Near Sea-Surface Channel

In this section, we focus on the channel links in the land-to-ship/ship-to-
land and ship-to-ship communications, i.e., the near-sea-surface channels. For
the air-to-sea channels, a crucial aspect may be inferred as “angle-
dependant”. More particular, the air-to-sea channel properties would be
considerably different for different grazing angles. As an example, the duct
layer propagation only exists when the grazing angle is smaller than a
specific threshold, hence creating distinct path loss models and delay spread.
In contrast, the near-sea-surface wireless channel can be characterized as
“location-dependent”. different channel models should be used based on the
Transmitter-Receiver distance. When the Transmitter-Receiver distance is
modest, the channel could be represented using the standard two-ray model,
where the LOS and the surface reflection routes are the two most significant
components of the channel. As the distance grows, the first ray from the
evaporation duct layer appears (if existent) . In this example, the three-ray
model provides a more precise description of the channel. If the receiver goes
even farther away, both the LOS and the surface-reflection routes finally
vanish due to the earth’s curvature. However, the receiver can still receive a
signal in the presence of the duct layer, provided that the direction of the
transmit beam is properly configured. At conclusion, as the Transmitter-
Receiver distance increases, the propagation characteristic may change from
two-ray to three-ray and becomes duct-only in the end. Beyond-LOS (B-LOS)
transmission is possible in marine communications thanks to the ducting effect
across the sea surface. As a potential way to enable long-distance and high-
security transmission, B-LOS transmission using the ducting effect has gained
great attention in military communications. For X-band communications, the
communication range can be considerably expanded to up to 1000 km with the aid
of the duct layer.

##  V Network Design Prospects

###  V-A Energy Efficient UAV Placement and Trajectory Design

Despite their ample applications, UAV communication systems face many new
obstacles . In particular, the endurance and performance of UAV systems are
fundamentally constrained by the onboard energy, which is virtually finite due
to the aircraft’s size and weight limits. Thus, energy-efficient communication
for maximizing the data transfer per unit energy consumption of the UAV is of
crucial importance. Note that energy-efficient solutions for UAV communication
systems are notably different from those in the previous literature on
terrestrial communication systems . Firstly, although the motive for energy-
efficiency maximization in terrestrial communications is mostly for minimizing
energy consumption and cost, that for UAV systems is more crucial due to the
limited on-board energy. For example, given the maximum quantity of energy
that can be carried by aircraft, an improvement in energy efficiency
immediately increases the number of information bits that can be transmitted
with the UAV before it needs to be recalled for recharging/refueling.
Secondly, besides the conventional energy expenditure on communication-related
functions, such as communication circuits and signal transmission, the UAV
systems are subject to the additional propulsion power consumption for
maintaining the UAV aloft and supporting its mobility (if necessary), which is
usually much higher than the communication power consumption. Note that the
UAV’s propulsion energy consumption is determined by its flying condition
including velocity and acceleration, which thus needs to be taken into account
in energy-efficient design for UAV communications.

###  V-B IRS-Aided UAV Deployments

An intuitive notion is to develop a more regulated wireless environment to
improve the secure performance of UAV communication systems. Recently,
intelligent reflecting surface (IRS) has emerged as a novel approach due to
its capacity to restructure wireless channels , which offers more degrees of
freedom to build a smart and reconfigurable wireless environment in a
controllable manner. Technically, these tunable and low-cost reflecting
elements installed on IRS are capable of dynamically altering the phase shifts
and absorbing the signal energy, then the intended signals can be amplified
although the interference signals are lowered simultaneously . Therefore, IRS
can be properly built to improve the undesired propagation conditions to
promote UAV communications. However, integrating IRS into UAV-enabled secure
communication systems faces obstacles from network characterisation to
performance optimization. To address the technical issue of limited onboard
energy due to the battery limits of the UAV, a simple option is to use
lightweight, low-power relaying devices on board. The IRS which merely
reflects the incident signals and functions as a passive relaying satisfies
this criteria. Due to its lightweight and passive feature, it will lower the
UAV energy consumption significantly, and thus, the operational period of the
UAV relay may be dramatically prolonged. In addition, as the reflecting
surface element may be downsized, an incredibly small UAV air platform can be
deployed for one-to-one relaying service to improve the quality of service of
mobile relaying custom-made for each ground user. The performance boost is
accomplished by introducing the UAV-based IRS mainly owing to an additional
equivalent line-of-sight (ELoS) channel which greatly enhances the received
signal-to-noise ratio (SNR) at the ground user. On the other hand, as the
ground user is mobile especially at the edge of a cell, the UAV-based IRS
relaying can monitor the ground user to offer an ELoS link to strengthen the
poor remote-edge channel for better performance. In , the authors analyzed the
capacity of an IRS-based UAV communication system when the phase compensation
was imperfect, and the authors in maximized the system sum rate using non-
convex optimization for IRS-assisted UAV OFDMA communication, which can be
treated as the symbiotic UAV and IRS systems.

###  V-C UAV-aided Mobile Edge Computing

As an emerging distributed computing paradigm, mobile edge computing (MEC)
presents a viable solution for these difficulties. MEC distributes processing,
storage, and network control capabilities from the data center to the network
edge so that offering low-latency and distributed services. One of the major
issues in maritime communications is the structure of the services provider
chain, which is growing more and more complex. As market conditions are
changing frequently, important players in the value chain are continually
repositioning themselves in their market regions and/or change/acquire new
positions in the chain moving to or between various market areas (via
strategic acquisitions, new ventures, or partnerships).

###  V-D Massive MIMO-Aided UAV Deployments

Although UAVs normally operate at significantly higher altitudes than ground-
based user equipments (GUEs), thus requiring the GBSs to deliver three-
dimension (3D) signal coverage for them. However, the ground base station
(GBS) antennas in the existing wireless network are normally inclined downward
to offer ground coverage solely while limiting the inter-cell interference
among GUEs. This results in insufficient coverage for communications with UAVs
in the sky for resolving the aforementioned aerial-ground interference and 3D
coverage issues, massive multiple-input multiple-output (MIMO) has been
applied recently for supporting UAVs by leveraging the advantage of a large
number of GBS antennas . Compared to typical two-dimension (2D) beamforming
toward the ground alone, massive MIMO provides 3D beamforming with finer-
grained angle resolutions in both azimuth and elevation dimensions. Thus, it
delivers significantly more effective interference mitigation capability in
both the UAV uplink and downlink communications by utilizing the elevation
angle difference between UAVs and GUEs . Furthermore, 3D beamforming enhances
connection and coverage for UAVs in the sky due to more flexible elevation-
domain beamforming. Although massive MIMO is promising for interference
suppression and coverage extension in cellular-connected UAV communications,
it faces several practical challenges in serving UAVs, for example, pilot
contamination caused by UAVs with strong LoS channels and channel/beam
tracking for UAVs with 3D high mobility. Moreover, supporting the UAV swarm
and implementing realistic hybrid GBS beamforming architecture for huge MIMO
further complicate pilot contamination and channel/beam tracking challenges.
This prompts this paper to present an outline of the new concerns and
challenges in massive MIMO for supporting UAV communications.

###  V-E Machine Learning and Artificial Intelligence

A fundamental issue appearing not only in UAV networks but also in wireless
networks, in general, is interference. Recently, ML-based techniques have been
invoked for effective interference management in UAV networks. The paper in
Reference examined opportunistic channel access by UAVs. This topic was
formulated as a non-cooperative interference mitigation game, considering the
different properties of data flow and UAV clustering. After, these qualities
are included in the utility function, suitably allocating the weight
coefficients to each characteristic and linearly combining them are conducted.
Moreover, a distributed log-linear learning technique is applied to attain the
Nash Equilibrium (NE) of the interference mitigation game. The learning
algorithm is based on the fact that a UAV, suffering from intra-cluster
interference, is randomly chosen to update its joint channel-slot selection
according to its experienced interference level, slot interval, and cluster
rewards in each step and stochastically determines the channel selection. The
simulations focused on convergence behavior, selection behavior, and
performance evaluation, underlining the necessity of establishing optimal
weights for improved interference control by the log-linear method. In this
fashion, the proposed algorithm converges fast to attain the optimal network
utility and the minimal weighted interference level. A 3D and dynamic
architecture that is introduced in the UAV-cellular networks, resource
management, network planning, content-caching, and user association tasks are
very demanding, since many contradictory requirements should be considered,
such as low latency, increased throughput, low overhead, support of a massive
number of devices, and dynamic conditions. In that perspective, the ML
framework has been employed to facilitate resource management in a pretty
efficient manner. In Reference , the authors attempted to forecast the success
and failure rates in a UAV network by applying ML methods based on linear
regression (LR) and SVM. Since UAV connectivity is time-variant due to their
movement, the success probability of the transmission diminishes as the
wireless links’ distance grows.

###  V-F Blockchain-Aided UAV Placement

The UAVs have the potential to be widely used in crucial IoT applications due
to their benefits to detect data and expand network coverage. Securing the
communication of UAVs with the corresponding network is regarded one of the
most critical hard topics. Blockchain technology, to protect the data transfer
of wireless communication supported UAV sensing systems for marine IoT
applications. Authenticating the existence of registered UAVs in the maritime
environment for communication purposes, help to better respond to the security
problems. Utilizing the notion of Blockchain, false UAVS can be precisely
identified in a distributed manner, and therefore banning them for further
communication, in contrast to existing systems that aim to safeguard the IDs
of UAVs in centralized databases. The validation of the deployed network
element’s authenticity in the marine IoT is a significant issue to satisfy the
security of the overall network. Therefore, how to develop a really trusted
communication paradigm remains an issue. To tackle this difficulty, Blockchain
can be utilized to develop a trustworthy authentication method, to confirm the
authenticity of a wireless communications-assisted UAV sensing system in
maritime IoT. The Blockchain is an immutable ledger that can provide suitable
integrity and security solutions for IoT applications. Blockchain has recently
garnered major appeal because to its distributive character, where Blockchain
decouples the centralized hold from a single entity in the network and
distributes control to several participating nodes actively using the
Blockchain network.

###  V-G mmWave and TeraHz Communication for UAVs

Propagation measurements at roughly 500 MHz across sea path have been done
some years ago where the association between the field intensity, weather
circumstances, and varying refractivity were carefully explored. Subsequently,
statistical information about ducting/super-refraction and signal fading
effects was acquired, and signal level dependence on the tidal waves was
analyzed at 248 and 341 MHz. Fixed wireless links over sea routes were
measured by the use of a wireless LAN system at 2.4 GHz , indicating that the
operation rate could be enhanced from 90$\%$ to 100$\%$ by exploiting space
diversity, and the limit of the sea roughness for specular reflections was
given. Similarly, digital TV systems in UHF bands from 470 MHz to 710 MHz have
been tested worldwide. The ITU-R Recommendation P.1546-2 presented an
empirical model to predict point-to-area field strength for numerous services,
including maritime mobile in the frequency range 30 MHz to 3000 MHz within
1000 km, in which, however, small-scale channel characterizations were not
included. In relation to , measurement data on channel properties of a mobile
wideband channel overseas at 1.9 GHz were provided. However, the scenario for
open water conditions was not described, and reflection coefficient and
Doppler shift from sea surface were not a part of the work objectives.

##  VI Research Challenges and Directions

###  VI-A Dynamically Varying Channel Conditions

With the developments in acoustic modem technology, study has moved into the
domain of networks. The key problems were highlighted over the past decade,
pointing once again to the basic disparities between acoustic and radio
transmission. For example, acoustic signals propagate at 1500 mps, generating
propagation delays as long as a few seconds across a few kilometers. With bit
rates of the order of 1000 bps, propagation delays are not negligible with
regard to typical packet durations, a situation very different from that
observed in radio-based networks. Moreover, acoustic modems are often limited
to half-duplex operation. These limits suggest that acoustic-conscious
protocol design can give greater efficiency than straight use of protocols
created for terrestrial networks (e.g., 802.11 or transmission control
protocol (TCP)). In addition, for anchored sensor networks, energy efficiency
will be as crucial as in terrestrial networks, since battery re-charging
hundreds of meters below the sea surface are difficult and expensive. Finally,
underwater instruments (sensors, robots, modems, and batteries) are neither
cheap nor disposable. This fact may be the single most essential aspect that
(at least for now) distinguishes underwater sensor networks from their
terrestrial counterpart, and profoundly modifies many network design concepts
that are typically taken for granted.

###  VI-B UAV 3-Dimensions Trajectory Design

Exploiting the UAV’s high mobility is projected to unlock the full potential
of UAV-to-ground communications . Specifically, by considering a linear
topology scenario for ground receivers, the work in optimizes the one-
dimensional (1D) UAV trajectory and communication resource allocation to
disclose the basic rate limits of the UAV-enabled multiple access channel. For
a UAV-enabled uplink NOMA network, the joint 2D UAV trajectory and power
control problem is proposed in to optimize the sum rate, which is then turned
into a UAV deployment issue. The work in develops an efficient solution to
tackle the max-min average rate problem by optimizing the 2D UAV trajectory
and resource allocation for the time division multiple access (TDMA) and NOMA
schemes. However, all the aforementioned research efforts either focus on the
CR based or NOMA based UAV network, but the integration of CR with NOMA has
not been fully researched.

###  VI-C UAV-to-Sea and UAV-to-UAV Interference Management

UAV trajectory optimization is vital in such cases. An online path planning
that accounts for wireless measurements is crucial and would, in essence,
assist in tackling the aforementioned interference concerns together with new
advances in the design of the network, such as 3D frequency reuse. Such a path
planning approach allows the UAVs to change their movement based on the rate
requirements of both aerial UAV-UEs and ground UEs, thus increasing the
overall network performance. For UAV-RMS applications, UAVs will largely send
data in the uplink. Nevertheless, the capacity of cellular-connected UAVs to
establish LoS communication with several ground BSs might lead to severe
mutual interference among them as well as to the ground users. To overcome
this difficulty, additional advances in the architecture of future cellular
networks such as enhanced receivers, cell coordination, 3D frequency reuse,
and 3D beamforming, are needed. For instance, because to their capabilities of
detecting and categorizing images, CNNs can be implemented on each UAV in
order to recognize numerous elements of the environment such as the location
of UAVs, BSs, and ground UEs. Such a method will enable each UAV to change its
beamwidth tilt angle so as to minimize the interference on the ground UEs.
Moreover, in streaming applications, UAV trajectory optimization is also
critical. In particular, physical layer technologies such as 3D beamforming,
can be paired with an interference-aware path planning system to provide more
efficient communication links for both ground and aerial users. Such a path
planning strategy (e.g., such as the one we described in ) allows the UAVs to
change their movement based on the rate requirements of both aerial UAV-UEs
and ground UEs, thus increasing the overall network performance.

###  VI-D 3D Mobility Management(3D Handoffs)

To fully enjoy the benefits of UAV deployment, beyond visual line of sight
activities are of crucial relevance where UAVs acting as aerial users, can
retain contact with the ground base stations (GBSs) for command and control
(C&C) functions in the downlink (DL) . UAVs flying in the sky may be served by
the sidelobes of base station antennas which provide lower antenna gains .
This creates considerable issues for the mobility management (MM) for the
cellularconnected UAVs based on reference signal received power (RSRP) (RSRP).
The GBS delivering the maximum RSRP might be placed far distant from the UAV.
This type of patchy signal coverage of GBSs would result in poor mobility
performance such as handover failure (HOF), radio connection failure, as well
as unnecessary handovers (HOs), called ping-pong occurrences. Apart from
these, due to the loss of the C$\&$C signal, the UAV may collide with a
commercial aircraft or even crash into a populated area which can result in
dangerous events. Hence, excellent MM for enabling reliable connections
between UAVs and GBSs is of essential relevance. MM approaches for ground user
equipment (GUE) in both homogeneous and heterogeneous cellular networks have
been investigated extensively in the literature . However, the research in MM
for cellular-connected UAVs is still in its infancy.

###  VI-E Beam-forming for High Mobility Ships and UAVs

For reliable wireless communications between APs and UAVs, we have the
following critical design issues: I beamformer design; ii) power control
design. These design difficulties are challenging owing to frequent AP
switching when UAVs traveling at a fast speed. Moreover, since a UAV contains
two smart antenna systems, they should be collaboratively operated. This leads
in concurrent two-point beamforming and two-point power control. The conjunct
power control challenge is to minimize the average of total transmission power
over a complete section with restrictions of the received SINR and maximum
transmission powers by taking advantage of knowing the location of UAVs and
APs inside a section. Here, each subsection could be small enough so that the
SINR variation by transferring one subsection to another is insignificant.
However, when fixed beamforming weight vectors are utilized for a complete
subsection, there would be some SINR fluctuation due to the change of angle of
arrival (AoA) or angle of departure (AoD). The beamformer design difficulty is
to reduce the SINR fluctuation caused by AoA/AoD changing in each subsection.
At greater heights, vertical beamforming or up-titled BS antennas may be
needed to provide improved coverage. The novel situations will require further
research, simulations, and field measurements. The properties of air–ground
wireless channels are different from those of terrestrial wireless channels.
This is one of the root causes for the interference and mobility difficulties
highlighted in this article. More empirical measurements will be of
substantial value for constructing more accurate statistical air–ground
channel models. Take Doppler effects, for example. Characterizing Doppler
effects clearly in channel measuring campaigns will be of interest, especially
for drones flying at high speeds.

##  VII Conclusion

In this paper, we….

|  Muhammad Waseem Akhtar is currently a PhD (Electrical Engineering) student
at School of Electrical Engineering and Computer Science (SEECS), National
university of Science and Technology (NUST), Islamabad, Pakistan. He has
received the Master of Science in Electrical (Telecommunication) Engineering
degree from NUST, Islamabad, in 2014, and B.Sc. Telecommunication Engineering
degree from University of Engineering and Technology (UET), Peshawar,
Pakistan, in 2009. His current research interests include cooperative
communication, energy and bandwidth efficient network designing, massive MIMO
and D2D communication, artificial intelligence, machine learning and
blockchains technologies.
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|  NASIR SAEED (Senior Member, IEEE) received the bachelor’s degree in
telecommunication from the University of Engineering and Technology, Peshawar,
Pakistan, in 2009, the master’s degree in satellite navigation from the Polito
di Torino, Italy, in 2012, and the Ph.D. degree in electronics and
communication engineering from Hanyang University, Seoul, South Korea, in
2015. He was an Assistant Professor with the Department of Electrical
Engineering, Gandhara Institute of Science and IT, Peshawar, from August 2015
to September 2016. He has worked as an Assistant Professor with IQRA National
University, Peshawar, from October 2016 to July 2017. From July 2017 to
December 2020, he was a Postdoctoral Research Fellow with the Communication
Theory Laboratory, King Abdullah University of Science and Technology. He is
currently an Associate Professor with the Department of Electrical
Engineering, National University of Technology, Islamabad, Pakistan. His
current research interests include cognitive radio networks, underwater
wireless communications, aerial networks, dimensionality reduction, and
localization.
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