# Artificial Intelligence
for Satellite Communication: A Review

Fares Fourati, Mohamed-Slim Alouini Fares Fourati and Mohamed Slim Alouini are
with King Abdullah University of Science and Technology (KAUST), CEMSE
Division, Thuwal, 23955-6900 KSA, (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Satellite communication offers the prospect of service continuity over
uncovered and under-covered areas, service ubiquity, and service scalability.
However, several challenges must first be addressed to realize these benefits,
as the resource management, network control, network security, spectrum
management, and energy usage of satellite networks are more challenging than
that of terrestrial networks. Meanwhile, artificial intelligence (AI),
including machine learning, deep learning, and reinforcement learning, has
been steadily growing as a research field and has shown successful results in
diverse applications, including wireless communication. In particular, the
application of AI to a wide variety of satellite communication aspects have
demonstrated excellent potential, including beam-hopping, anti-jamming,
network traffic forecasting, channel modeling, telemetry mining, ionospheric
scintillation detecting, interference managing, remote sensing, behavior
modeling, space-air-ground integrating, and energy managing. This work thus
provides a general overview of AI, its diverse sub-fields, and its state-of-
the-art algorithms. Several challenges facing diverse aspects of satellite
communication systems are then discussed, and their proposed and potential AI-
based solutions are presented. Finally, an outlook of field is drawn, and
future steps are suggested.

###### Index Terms:

Satellite Communication, Artificial Intelligence, Machine Learning, Deep
Learning, Reinforcement Learning

##  I Introduction

The remarkable advancement of wireless communication systems, quickly
increasing demand for new services in various fields, and rapid development of
intelligent devices have led to a growing demand for satellite communication
systems to complement conventional terrestrial networks to give access over
uncovered and under-covered urban, rural, and mountainous areas, as well as
the seas.

There are three major types of satellites, including the geostationary Earth
orbit, also referred to as a geosynchronous equatorial orbit (GEO), medium
Earth orbit (MEO), and low Earth orbit (LEO) satellites. This classification
depends on three main features, i.e., the altitude, beam footprint size, and
orbit. GEO, MEO, and LEO satellites have an orbit around the Earth at an
altitude of 35786 km, 7000–25000 km, and 300–1500 km, respectively. The beam
footprint of a GEO satellite ranges from 200 to 3500 km; that of an MEO or LEO
beam footprint satellite ranges from 100 to 1000 km. The orbital period of a
GEO satellite is equal to that of the Earth period, which makes it appear
fixed to the ground observers, whereas LEO and MEO satellites have a shorter
period, many LEO and MEO satellites are required to offer continuous global
coverage. For example, Iridium NEXT has 66 LEO satellites and 6 spares,
Starlink by SpaceX plans to have 4425 LEO satellites plus some spares, and O3b
has 20 MEO satellites including 3 on-orbit spares [1].

Satellite communication use cases can also be split into three categories: i)
service continuity, to provide network access over uncovered and under-covered
areas; ii) service ubiquity, to ameliorate the network availability in cases
of temporary outage or destruction of a ground network due to disasters; and
iii) service scalability, to offload traffic from the ground networks. In
addition, satellite communication systems could provide coverage to various
fields, such as the transportation, energy, agriculture, business, and public
safety fields [2].

Although satellite communication offers improved global coverage and increased
communication quality, it has several challenges. Satellites, especially LEO
satellites, have limited on-board resources and move quickly, bringing high
dynamics to the network access. The high mobility of the space segments, and
the inherent heterogeneity between the satellite layers (GEO, MEO, LEO), the
aerial layers (unmanned aerial vehicles (UAVs), balloons, airships), and the
ground layer make network control, network security, and spectrum management
challenging. In addition, achieving high energy efficiency for satellite
communication is more challenging than for terrestrial networks.

Several surveys have discussed different aspects of satellite communication
systems, such as handoff schemes [3], mobile satellite systems [4], MIMO over
satellite [5], satellites for the Internet of Remote Things [6], inter-
satellite communication systems [7], Quality of Service (QoS) provisioning
[8], space optical communication [9], space-air-ground integrated networks
[10], small satellite communication [11], physical space security [12],
CubeSat communications [13], and non-terrestrial networks [2]. Meanwhile,
interest in artificial intelligence (AI) increased in recent years. AI,
including machine learning (ML), deep learning (DL) and reinforcement learning
(RL), has shown successful results in diverse applications in science and
engineering fields, such as electrical engineering, software engineering,
bioengineering, financial engineering, and medicine etc. Several researchers
have thus turned to AI techniques to solve various challenges in their
respective fields and have designed diverse successful AI-based applications,
to overcome several challenges in the wireless communication field.

Many researchers have discussed AI and its applications to wireless
communication in general [14, 15, 16, 17]. Others have focused on the
application of AI to one aspect of wireless communication, such as wireless
communications in the Internet of Things (IoT) [18], network management [19],
wireless security [20], emerging robotics communication [21], antenna design
[22] and UAV networks [23, 24]. Vazquez et al. [25] briefly discussed some
promising use cases of AI for satellite communication, whereas Kato et al.
[26] discussed the use of AI for space-air-integrated networks. The use of DL
in space applications has also been addressed [27].

Figure 1: Applications of artificial intelligence (AI) for different satellite communication aspects AE |  Autoencoder
---|---
AI |  Artificial intelligence
AJ |  Anti-jamming
ARIMA |  Auto regressive integrated moving average
ARMA |  Auto regressive moving average
BH |  Beam hopping
CNN |  Convolutional neural network
DL |  Deep learning
DNN |  Deep neural network
DRL |  Deep reinforcement learning
ELM |  Extreme learning machine
EMD |  Empirical mode decomposition
FARIMA |  Fractional auto regressive integrated moving average
FCN |  Fully convolutional network
FDMA |  Frequency division multiple access
FH |  Frequency hopping
GA |  Genetic algorithms
GANs |  Generative adversarial networks
GNSS |  Global navigation satellite system
IoS |  Internet of satellites
kNN |  k-nearest neighbor
LRD |  Long-range-dependence
LSTM |  Long short-term memory
MDP |  Markov decision process
ML |  Machine learning
MO-DRL |  Multi-objective deep reinforcement learning
NNs |  Neural networks
PCA |  Principal component analysis
QoS |  Quality of service
RFs |  Random forests
RL |  Reinforcement learning
RNNs |  Recurrent neural networks
RS |  Remote sensing
RSRP |  Reference signal received power
SAGIN |  Space-air-ground integrated network
SRD |  Short range dependence
SVM |  Support vector machine
SVR |  Support vector regression
SatIot |  Satellite Internet of Things
UE |  User equipment
VAEs |  Variational autoencoders
TABLE I: Acronyms and Abbreviations

Overall, several researchers have discussed wireless and satellite
communication systems, and some of these have discussed the use of AI for one
or a few aspects of satellite communication; however, an extensive survey of
AI applications in diverse aspects of satellite communication has yet to be
performed.

This work therefore aims to provide an introduction to AI, a discussion of
various challenges being faced by satellite communication and an extensive
survey of potential AI-based applications to overcome these challenges. A
general overview of AI, its diverse sub-fields and its state-of-the-art
algorithms are presented in Section II. Several challenges being faced by
diverse aspects of satellite communication systems and potential AI-based
solutions are then discussed in Section III; these applications are summarized
in Fig.1. For ease of reference, the acronyms and abbreviations used in this
paper are presented in Table I.

##  II Artificial Intelligence (AI)

The demonstration of successful applications of AI in healthcare, finance,
business, industries, robotics, autonomous cars and wireless communication
including satellites has led it to become a subject of high interest in the
research community, industries, and media.

This section therefore aims to provide a brief overview of the world of AI,
ML, DL and RL. Sub-fields, commonly used algorithms, challenges, achievements,
and outlooks are also addressed.

###  II-A Artificial Intelligence

Although AI sounds like a novel approach, it can be traced to the 1950s and
encompasses several approaches and paradigms. ML, DL, RL and their
intersections are all parts of AI, as summarized in Fig.2 [28]. Thus, a major
part of AI follows the learning approach, although approaches without any
learning aspects are also included. Overall, research into AI aims to make the
machine smarter, either by following some rules or by facilitating guided
learning. The former refers to symbolic AI; the latter refers to ML. Here
smarter indicates the ability to accomplish complex intellectual tasks
normally necessitating a human such as classification, regression, clustering,
detection, recognition, segmentation, planning, scheduling, or decision
making. In the early days of AI, many believed that these tasks could be
achieved by transferring human knowledge to computers by providing an
extensive set of rules that encompasses the humans’ expertise. Much focus was
thus placed on feature engineering and implementing sophisticated handcrafted
commands to be explicitly used by the computers. Although this symbolic AI has
been suitable for many applications, it has shown various limitations in terms
of both precision and accuracy for more advanced problems that show more
complexity, less structure, and more hidden features such as computer-vision
and language-processing tasks. To address these limitations, researchers
turned to a learning approach known as ML.

Figure 2: Artificial Intelligence, Machine Learning, Deep Learning and
Reinforcement Learning

###  II-B Machine Learning (ML)

Figure 3: Machine Learning Approach

ML, which encompasses DL and RL, is a subset of AI. In contrast to symbolic
AI, where the machine is provided with all the rules to solve a certain
problem, ML requires a learning approach. Thus, rather than giving the rules
to solve a problem, the machine is provided with the context to learn the
rules by itself to solve the issue, as shown in Fig.3 and best summarized by
the AI pioneer Alan Turing [29]: ”An important feature of a learning machine
is that its teacher will often be very largely ignorant of quite what is going
on inside, although he may still be able to some extent to predict his pupil’s
behavior,” An ML system is trained rather than programmed with explicit rules.
The learning process requires data to extract patterns and hidden structures;
the focus is on finding optimal representations of the data to get closer to
the expected result by searching within a predefined space of possibilities
using guidance from a feedback signal, where representations of the data refer
to different ways to look at or encode the data. To achieve that, three things
are mandatory: input data, samples of the expected output, and a way to
measure the performance of the algorithm [28]. This simple idea of learning a
useful representation of data has been useful in multiple applications from
image classification to satellite communication.

ML algorithms are commonly classified as either deep or non-deep learning.
Although DL has gained higher popularity and attention, some classical non-
deep ML algorithms are more useful in certain applications, especially when
data is lacking. ML algorithms can also be classified as supervised, semi-
supervised, unsupervised, and RL classes, as shown in Fig.4. In this
subsection, only non-RL, non-deep ML approaches are addressed; RL and DL are
addressed in sections II.C and II.D, respectively.

####  II-B1 Supervised, Unsupervised and Semi-supervised Learning

Supervised, unsupervised and semi-supervised learning are all ML approaches
that can be employed to solve a broad variety of problems.

During supervised learning, all of the training data is labeled, i.e., tagged
with the correct answer. The algorithm is thus fully supervised, as it can
check its predictions are right or wrong at any point in the training process.
During image classification, for example, the algorithm is provided with
images of different classes and each image is tagged with the corresponding
class. The supervised model learns the patterns from the training data to then
be able to predict labels for non-labeled data during inferencing. Supervised
learning has been applied for classification and regression tasks.

As labeling can be impossible due to a lack of information or infeasible due
to high costs, unsupervised learning employs an unlabeled data set during
training. Using unlabeled data, the model can extract hidden patterns or
structures in the data that may be useful to understand a certain phenomenon
or its output could be used as an input for other models. Unsupervised
learning has been commonly used for clustering, anomaly detection, association
and autoencoders (AEs).

As a middle ground between supervised and unsupervised learning, semi-
supervised learning allows a mixture of non-labelled and labaled portions of
training data. Semi-supervised learning is thus an excellent option when only
a small part of the data is labeled and/or the labeling process is either
difficult or expensive. An example of this technique is pseudo-labeling, which
has been used to improve supervised models [33].

Figure 4: Machine Learning Sub-fields

####  II-B2 Probabilistic Modeling

Probabilistic modeling as mentioned by its name, involves models using
statistical techniques to analyze data and was one of the earliest forms of ML
[30]. A popular example is the Naive Bayes classifier, which uses Bayes’
theorem while assuming that all of the input features are independent; as they
generally are not, this is a naive assumption [28]. Another popular example is
logistic regression; as the algorithm for this classifier is simple, it is
commonly used in the data science community.

####  II-B3 Support Vector Machine (SVM)

Kernel methods are a popular class of algorithms [31, 28]; where the most
well-known one of them is the SVM, which aims to find a decision boundary to
classify data inputs. The algorithm maps the data into a high dimensional
representation where the decision boundary is expressed as a hyperplane. The
hyperplane is then searched by trying to maximize the distance between the
hyperplane and the nearest data points from each class in a process called
maximizing the margin. Although mapping the data into a high dimensional space
is theoritically straightforward, it requires high computational resources.
The ’kernel trick’, which is based on kernel functions [32], is thus used to
compute the distance between points without explicit computation of
coordinates, thereby avoiding the computation of the coordinated of a point in
a high-dimensional space. SVMs have been the state-of-the-art for
classification for a fairly long time and have shown many successful
applications in several scientific and engineering areas [34]. However SVMs
have shown limitations when applied on large datasets. Furthermore, when the
SVM is applied to perceptual problems, a feature engineering step is required
to enhance the performance because it is a shallow model; this requires human
expertise. Although it has been surpassed by DL algorithms, it is still useful
because of its simplicity and interpretability.

####  II-B4 Decision Trees

Figure 5: Decision Tree

A decision tree is a supervised learning algorithm that represents features of
the data as a tree by defining conditional control statements, as summarized
in Fig.5 [35, 36]. Given its intelligibility and simplicity, it is one of the
most popular algorithms in ML. Further, decision trees can be used for both
regression and classification, as decisions could be either continuous values
or categories. A more robust version of decision trees, random forests (RFs),
combines various decision trees to bring optimized results. This involves
building many different weak decision trees and then assembling their outputs
using bootstrap aggregating (bagging) [37, 38]. Another popular version of
decision trees, that is often more effective than RFs is a gradient boosting
machine; gradient boosting also combines various decision tree models but
differs from RFs by using gradient boosting [39], which is a way to improve ML
models by iteratively training new models that focus on the mistakes of the
previous models. The XGBoost [40, 41] library is an excellent implementation
of the gradient boosting algorithm that supports C++, Java, Python, R, Julia,
Perl, and Scala. RFs and gradient boosting machines are the most popular and
robust non-deep algorithms that have been widely used to win various data
science competitions on the Kaggle website [42].

####  II-B5 Neural Networks (NNs)

Figure 6: Neural Networks

NNs contain different layers of interconnected nodes, as shown in Fig.6, where
each node is a perceptron that feeds the signal produced by a multiple linear
regression to an activation function that may be nonlinear [43, 44]. A
nonlinear activation function is generally chosen to add more complexity to
the model by eliminating linearity. NNs can be used for regression by
predicting continuous values or for classification by predicting probabilities
for each class. In a NN, the features of one input (e.g., one image) are
assigned as the input layer. Then, according to a matrix of weights the next
hidden layers are computed using matrix multiplications (linear manipulations)
and then non linear activation functions. The training of NNs is all about
finding the best weights. To do so, a loss function is designed to compare the
output of the model and the ground truth for each output, to find the weights
that minimize that loss function. Backpropagation algorithms have been
designed to train chains of weights using optimization techniques such as
gradient-descent [45]. NNs have been successfully used for both regression and
classification, although they are most efficient when dealing a high number of
features (input parameters) and hidden layers, which has led to the
development of DL.

###  II-C Deep Learning (DL)

In contrast to shallow models, this sub-field of ML requires high
computational resources [46, 28]. Recent computational advancements and the
automation of feature engineering have paved the way for DL algorithms to
surpass classical ML algorithms for solving complex tasks, especially
perceptual ones such as computer vision and natural language processing. Due
to their relative simplicity, shallow ML algorithms, require human expertise
and intervention to extract valuable features or to transform the data to make
it easier for the model to learn. DL models minimize or eliminate these steps
as these transformations are implicitly done within the deep networks.

####  II-C1 Convolutional Neural Networks (CNN)

CNN [47, 48], are a common type of deep NNs (DNNs) that are composed of an
input layer, hidden convolution layers, and an output layer and have been
commonly used in computer vision applications such as image classification
[50], object detection [51], and object tracking [52]. They have also shown
success in other fields including speech and natural language processing [53].
As their name indicates, CNNs are based on convolutions. The hidden layers of
a CNN consist of a series of convolutional layers that convolve. An activation
function is chosen and followed by additional convolutions. CNN architectures
are defined by by choosing the sizes, numbers, and positions of filters
(kernels) and the activation functions. Learning then involves finding the
best set of filters that can be applied to the input to extract useful
information and predict the correct output.

####  II-C2 Recurrent Neural Networks (RNNs)

Figure 7: Simplified Architecture of a Recurrent Neural Networks

RNNs [54] are another family of neural networks in which nodes form a directed
graph along a temporal sequence where previous outputs are used as inputs.
RNNs are specialized for processing a sequence of values x(0), x(1), x(2), …,
x(T). RNNs use their internal memory to process variable-length sequences of
inputs. Different architectures are designed based on the problem and the
data. In general, RNNs are designed as in Fig. 7, where for each time stamp
$t$, $x(t)$ represents the input at that time, $a(t)$ is the activation, and
$y(t)$ is the output, $W_{a}$, $W_{x}$, $W_{y}$, $b_{x}$ and $b_{y}$ are
coefficients that are shared temporarily and $g_{1}$ and $g_{2}$ are
activation functions.

$a(t)=g_{1}(W_{a}.a(t-1)+W_{x}.x(t)+b_{a})$ (1)

$y(t)=g_{2}(W_{y}.a(t)+b_{y})$ (2)

RNN models are most commonly used in the fields of natural language
processing, speech recognition and music composition.

####  II-C3 Autoencoders (AEs)

Figure 8: Autoencoder

AEs are another type of NNs used to learn efficient data representation in an
unsupervised way [55]. AEs encode the data using the bottleneck technique,
which comprises dimensionality reduction to ignore the noise of the input data
and an initial data regeneration from the encoded data, as summarized in
Fig.8. The initial input and generated output are then compared to asses the
quality of coding. AEs have been widely applied for for dimensionality
reduction [56] and anomaly detection [57].

####  II-C4 Deep generative models

Deep generative models [58] are DL models that involve the automatic
discovering and learning of regularities in the input data in such a way that
new samples can be generated. These models have shown various applications,
especially in the field of computer vision. The most popular generative models
are variational AEs (VAEs) and generative adversarial networks (GANs).

Of these, VAEs learn complicated data distribution using unsupervised NNs
[59]. Although VAEs are a type of AEs, their encoding distribution is
regularized during the training to ensure that their latent space (i.e.,
representation of compressed data) has good properties for generating new
data.

Figure 9: Generative Adverserial Networks GANs

GANs are composed of two NNs in competition, where a generator network G
learns to capture the data distribution and generate new data and a
discriminator model D estimates the probability that a given sample came from
the generator rather than the initial training data, as summarized in Fig. 9
[60, 61]. The generator thus is used to produce misleading samples and to that
the discriminator can determine whether a given sample is fake or real. The
generator fools the discriminator by generating almost real samples and the
discriminator fools the generator by improving its discriminative capability.

###  II-D Reinforcement Learning (RL)

This subset of ML involves a different learning method than those using
supervised, semi-supervised, or unsupervised learning [64]. RL is about
learning what actions to take in the hope to maximize a reward signal. The
agent must find which actions bring the most recompense by trying each action,
as shown in 10. These actions can affect immediate rewards as well as
subsequent rewards. Some RL approaches require the introduction of DL; such
approaches are part of deep RL (DRL).

Figure 10: Reinforcement Learning

One of the challenges encountred during RL is balancing the trade-off between
exploration and exploitation. To get a maximum immediate reward, an RL agent
must perform exploitation, i.e., choose actions that it has explored
previously and found to be the best. To find such actions, it must explore the
solution space, i.e., try new actions.

All RL agents have explicit goals, are aware of some aspects of their
environment, can take actions that impact their environments, and act despite
significant uncertainty about their environment. Other than the agent and the
environment, an RL system has four sub-elements: a policy, a reward signal, a
value function, and, sometimes, a model of the environment.

Here, learning involves the agent determining the best method to map states of
the environment to actions to be taken when in those states. After each
action, the environment sends the RL agent a reward signal, which is the goal
of the RL problem. Unlike a reward that brings immediate evaluation of the
action, a value function estimates the total amount of recompense an agent can
anticipate to collect in the longer-term. Finally, a model of the environment
mimics the behavior of the environment. These models can be used for planning
by allowing the agent to consider possible future situations before they
occur. Methods for solving RL problems that utilize models are called model-
based methods, whereas those without models are referred to as model-free
methods.

###  II-E Discussion

####  II-E1 Model Selection

AI is a broad field that encompasses various approaches, each of which
encompasses several algorithms. AI could be based on predefined rules or on
ML. This learning can be supervised, semi-supervised, unsupervised, or
reinforcement learning; in each of these categories learning can be deep or
shallow. As each approach offers something different to the world of AI,
interest in each should depend on the given problem; a more-complex approach
or algorithm does not necessarily lead to better results. For example, a
common assumption is that DL is better than shallow learning. Although this
holds in several cases, especially for perceptual problems such as computer
vision problems, it is not always applicable, as DL algorithms require greater
computational resources and large datasets which are not always available.
Supervised learning is an effective approach when a fully labeled dataset is
available. However, this is not always the case, as data can be expensive,
difficult or even impossible. Under these circumstances, semi-supervised or
unsupervised learning or RL is more applicable. Whereas unsupervised learning
can find hidden patterns in non-labeled data, RL learns the best policy to
achieve a certain task. Thus, unsupervised learning is a good tool to extract
information from data, Whereas RL is better suited for decision-making tasks.
Therefore, the choice of an approach or an algorithm should not be based on
its perceived elegance, but by matching the method to characteristics of the
problem at hand, including the goal, the quality of the data, the
computational resources, the time constraints, and the prospective future
updates. Solving a problem may require a combination of more than one
approach.

After assessing the problem and choosing an approach, an algorithm must be
chosen. Although ML has mathematical foundations, it remains an empirical
research field. To choose the best algorithm, data science and ML researchers
and engineers empirically compare different algorithms for a given problem.
Algorithms are compared by splitting the data into a training set and a test
set. The training set is then used to train the model, whereas the test set is
to compare the output between models.

In competitive data science, such as in Kaggle [42] competitions, where each
incrementation matters, models are often combined to improve their overall
results, and various ensemble techniques such as bagging [38], boosting [39],
and adaptive boosting [62] are used.

####  II-E2 Model Regularization

Figure 11: Training and test errors over the training time. Early stopping is
common technique to reduce overfitting by stopping the training process at an
early stage, i.e. when the test error starts to remarkably increasing

After the approach and algorithm have been selected, hyperparameter tuning is
generally done to improve the output of the algorithm. In most cases, ML
algorithms depend on many hyperparameters; choosing the best hyperparameters
for a given problem thus allows for higher accuracy. This step can be done
manually by intuitively choosing better hyperparameters, or automatically
using various methods such as grid search and stochastic methods [63].

A common trap in ML is overfitting, during which the machine stops learning
(generalizing) and instead begins to memorize the data. When this occurs, the
model can achieve good results on seen data but fails when confronted with new
data, i.e., a decreased training error and an increasing test error, as shown
in Fig. Fig.11. Overfitting can be discovered by splitting the data into
training, validation and testing sets, where neither the validation nor the
testing sets are used to train the model. The training set is used to train
the model, the validation set is used to verify the model predictions on
unseen data and for hyperparameter tuning, and the testing set is used for the
final testing of the model.

A variety of methods can be employed to reduce overfitting. It be reduced by
augmenting the size of the dataset, which is commonly performed in the field
of computer vision. For example, image data could be augmented by applying
transformations to the images, such as rotating, flipping, adding noise, or
cutting parts of the images. Although useful, this technique is not always
applicable. Another method involves using cross-validation rather than
splitting the data into a training set and a validation set Early stopping, as
shown in Fig.11, consists of stopping the learning process before the
algorithm begins to memorize the data. Ensemble learning is also commonly
used.

####  II-E3 The hype and the hope

Rapid progress has been made in AI research, including its various subfields,
over the last ten years as a result of exponentially increasing investments.
However, few substantial developments have been made to address real-world
problems; as such, many are doubtful that AI will have much influence on the
state of technology and the world. Chollet [28] compared the progress of AI
with that of the internet in 1995, the majority of people could not foresee
the true potential, consequences, and pertinence of the internet, as it had
yet to come to pass. As the case with the overhyping and subsequent funding
crash throughout the early 2000s before the widespread implementation and
application of the internet, AI may also become an integral part of global
technologies. The authors thus believe that the inevitable progress of AI is
likely to have long-term impacts and that AI will likely be a major part of
diverse applications across all scientific fields, from mathematics to
satellite communication.

##  III Artificial Intelligence for Satellite Communication

###  III-A Beam hopping

Figure 12: The demand–capacity mismatch among beams demonstrates the
limitation of using fixed and uniformly distributed resources across all beams
in a multi-beam satellite system Figure 13: Simplified architecture of beam
hopping (BH)

####  III-A1 Definition & limitations

Satellite resources are expensive and thus require efficient systems involving
optimizing and time-sharing. In conventional satellite systems the resources
are fixed and uniformly distributed across beams [65]. As a result,
conventional large multi-beam satellite systems have shown a mismatch between
the offered and requested resources; some spot beams have a higher demand than
the offered capacity, leaving the demand pending (i.e., hot-spots), while
others present a demand lower than the installed capacity, leaving the offered
capacity unused (i.e., cold-spots). Thus, to improve multi-beam satellite
communication, the on-board flexible allocation of satellite resources over
the service coverage area is necessary to achieve more efficient satellite
communication.

Beam hopping (BH) has emerged as a promising technique to achieve greater
flexibility in managing non-uniform and variant traffic requests throughout
the day, year and lifetime of the satellite over the coverage area [65], [66].
BH, involves dynamically illuminating each cells with a small number of active
beams, as summarized in 13, thus using all available on-board satellite
resources to offer service to only a subset of beams. The selection of this
subset is time-variant and depends on the traffic demand, which is based on
the time-space dependent BH illumination pattern. The illuminated beams are
only active long enough to fill the request for each beam. Thus, the
challenging task in BH systems is to decide which beams should be activated
and for how long, i.e., the BH illumination pattern; this responsibility is
left to the resource manager who then forwards the selected pattern to the
satellite via telemetry, tracking and command [67].

Of the various methods that researchers have provided to realize BH, most have
been based on classical optimization algorithms. For example, Angeletti et al.
[68], demonstrated several advantages to the performance of a system when
using BH and proposed the use of genetic algorithm (GA) to design the BH
illumination pattern; Anzalchi et al. [69], also illustrated the merits of BH
and compared the performance between BH and non-hopped systems. Alberti et al.
[70], proposed a heuristic iterative algorithm to obtain a solution to the BH
illumination design. BH has also been used to decrease the number of
transponder amplifiers for Terabit/s satellites [71]. An iterative algorithm
has also been proposed to maximize the overall offered capacity under certain
beam demand and power constraints in a joint BH design and spectrum assignment
[72]. Alegre et al. [73], designed two heuristics to allocate capacity
resources basing on the traffic request per-beam, and then further discussed
the long and short-term traffic variations and suggested techniques to deal
with both variations [74]. Liu et al. [75], studied techniques for controlling
the rate of the arriving traffic in BH systems. The QoS delay fairness
equilibrium has also been addressed in BH satellites [76]. Joint BH schemes
were proposed by Shi et al. [77] and Ginesi et al. [78] to further ameliorate
the efficiency of on-board resource allocation. To find the optimal BH
illumination design, Cocco et al. [79] used a simulated annealing algorithm.

Although employing optimization algorithms has achieved satisfactory results
in terms of flexibility and delay reduction of BH systems, some difficulties
remain. As the search space dramatically grow with the number of beams, an
inherent difficulty in designing the BH illumination pattern is finding the
optimal design rather than one of many local optima [72]. For satellites with
hundreds or thousands of beams, classical optimization algorithms may require
long computation times which is impractical in many scenarios.

Additionally, classical optimization algorithms, including the GAs or other
heuristics, require revision when the scenario changes moderately; this leads
to a higher computational complexity, which is impractical for on-board
resource management.

####  III-A2 AI-based solutions

Seeking to overcome these limitations and enhance the performance of BH, some
researchers have proposed AI-based solutions. Some of these solutions have
been fully based on the learning approach, i.e., end-to-end learning, in which
the BH algorithm is a learning algorithm. Others have tried to improve
optimization algorithms by adding a learning layer, thus combining learning
and optimization.

To optimize the transmission delay and the system throughput in multibeam
satellite systems, Hu et al [80] formulated an optimization problem and
modeled it as a Markov decision process (MDP). DRL is then used to solve the
BH illumination design and optimize the long-term accumulated rewards of the
modeled MDP. As a result, the proposed DRL-based BH algorithm can reduce the
transmission delay by up to 52.2% and increased the system throughput by up to
11.4% when compared with previous algorithms.

To combine the advantages of end-to-end learning approaches and optimization
approaches, for a more efficient BH illumination pattern design, Lei et al.
[67] suggested a learning and optimization algorithm to deal with the beam
hopping pattern illumination selection, in which a learning approach, based on
fully connected NNs, was used to predict non-optimal BH patterns and thus
address the difficulties faced when applying an optimization algorithm to a
large search space. Thus, the learning-based prediction reduces the search
space, and the optimization can be reduced on a smaller set of promising BH
patterns.

Researchers have also employed multi-objective DRL (MO-DRL) for the DVB-S2X
satellite. Under real conditions, Zhang et al. [81] demonstrated that the low-
complexity MO-DRL algorithm could ensure the fairness of each cell, and
ameliorate the throughput better than previous techniques including DRL [79]
by 0.172%. In contrast, the complexity of GA producing a similar result is
about 110 times that of the MO-DRL model. Hu et al. [82] proposed a multi-
action selection technique based on double-loop learning and obtained a multi-
dimensional state using a DNN. Their results showed that the proposed
technique can achieve different objectives simultaneously, and can allocate
resources intelligently by adapting to user requirements and channel
conditions.

###  III-B Anti-jamming

####  III-B1 Definition & limitations

Satellite communication systems are required to cover a wide area, and provide
high-speed, communication and high-capacity transmission. However, in tactical
communication systems using satellites, reliability and security are the prime
concerns; therefore, an anti-jamming (AJ) capability is essential. Jamming
attacks could be launched toward main locations and crucial devices in a
satellite network to reduce or even paralyze the throughput. Several AJ
methods have thus been designed to reduce possible attacks and guarantee
secure satellite communication.

The frequency-hopping (FH) spread spectrum method has been preferred in many
prior tactical communication systems using satellites [83, 84]. Using the
dehop–rehop transponder method employing FH-frequency division multiple access
(FH-FDMA) scenarios, Bae et al. [85] developed an efficient synchronization
method with an AJ capability.

Most prior AJ techniques are not based on learning and thus cannot deal with
clever jamming techniques that are capable of continuously adjusting the
jamming methodology by interaction and learning. Developing AI algorithms
offer advanced tools to achieve diverse and intelligent jamming attacks based
on learning approaches and thus present a serious threat to satellite
communication reliability. In two such examples, a smart jamming formulation
automatically adjusted the jamming channel [86], whereas a smart jammer
maximized the jamming effect by adjusting both the jamming power and channel
[87]. In addition, attacks could be caused by multiple jammers simultaneously
implementing intelligent jamming attacks based on learning approaches.
Although this may be an unlikely scenario, it has not yet been seriously
considered. Further, most researchers have focused on defending against AJ
attacks in the frequency-based domain, rather than spacebased AJ techniques,
such as routing AJ.

####  III-B2 AI-based solutions

By using a long short-term memory (LSTM) network, which is a DL RNN, to learn
the temporal trend of a signal, Lee et al. [88] demonstrated a reduction of
overall synchronization time in the previously discussed FH-FDMA scenario
[85]. Han et al. [89] proposed the use of a learning approach for AJ to block
smart jamming in the Internet of Satellites (IoS) using a space-based AJ
method, AJ routing, summarized in Fig.14. By combining game theory modeling
with RL and modeling the interactions between smart jammers and satellite
users as a Stackelberg AJ routing game, they demonstrated how to use DL to
deal with the large decision space caused by the high dynamics of the IoS and
RL to deal with the interplay between the satellites and the smart jamming
environment. DRL thus made it possible to solve the routing selection issue
for the heterogeneous IoS while preserving an available routing subset to
simplify the decision space for the Stackelberg AJ routing game. Based on this
routing subset, a popular RL algorithm, Q-Learning, was then used to respond
rapidly to intelligent jamming and adapt AJ strategies.

Han et al. [90] later combined game theory modeling and RL to obtain AJ
policies according to the dynamic and unknown jamming environment in the
Satellite-Enabled Army IoT (SatIoT). Here, a distributed dynamic AJ coalition
formation game was examined to decrease the energy use in the jamming
environment, and a hierarchical AJ Stackelberg game was proposed to express
the confrontational interaction between jammers and SatIoT devices. Finally,
RL-based algorithms were utilized to get the sub-optimal AJ policies according
to the jamming environment.

Figure 14: Space-based anti-jamming (AJ) routing. The red line represents the
found jammed path, and the green one represents the suggested path [89]

###  III-C Network Traffic Forecasting

####  III-C1 Definition & limitations

Network traffic forecasting is a proactive approach that aims to guarantee
reliable and high-quality communication, as the predictability of traffic is
important in many satellite applications, such as congestion control, dynamic
routing, dynamic channel allocation, network planning, and network security.
Satellite network traffic is self-similar and demonstrates long-range-
dependence (LRD) [91]. To achieve accurate forecasting, it is therefore
necessary to consider its self-similarity. However,forecasting models for
terrestrial networks based on self-similarity have a high computational
complexity; as the on-board satellite computational resources are limited,
terrestrial models are not suitable for satellites. An efficient traffic
forecasting design for satellite networks is thus required.

Several researchers have performed traffic forecasting for both terrestrial
and satellite networks; these techniques have included the Markov [92],
autoregressive moving average (ARMA) [93], autoregressive integrated moving
average (ARIMA) [94] and fractional ARINA (FARIMA) [95] models. By using
empirical mode decomposition (EMD) to decompose the network traffic and then
applying the ARMA forecasting model, Gao et al. [96] demonstrated remarkable
improvement.

The two major difficulties facing satellite traffic forecasting are the LRD of
satellite networks and the limited on-board computational resources. Due to
the LRD property of satellite networks, short-range-dependence (SRD) models
have failed to achieve accurate forecasting. Although previous LRD models have
achieved better results than SRD models, they suffer from high complexity. To
address these issues, researchers have turned to AI techniques.

####  III-C2 AI-based solutions

Katris and Daskalaki [95] combined FARIMA with NNs for internet traffic
forecasting, whereas Pan et al. [97] combined a differential evolution with
NNs for network traffic prediction. Due to the high complexity of classical
NNs, a least-square SVM, which is an optimized version of a SVM, has also been
used for forecasting [98]. By applying principal component analysis (PCA), to
reduce the input dimensions and then a generalized regression NN, Ziluan and
Xin [99] achieved higher-accuracy forecasting with less training time. Zhenyu
et al. [100] used traffic forecasting as a part of their distributed routing
strategy for LEO satellite network. An extreme learning machine (ELM) has also
been employed for traffic load forecasting of satellite node before routing
[101]. Bie et al. [91] used EMD to decompose the traffic of the satellite with
LRD into a series with SRD and at one frequency to decrease the predicting
complexity and augment the speed. Their combined EMD, fruit-fly optimization,
and ELM methodology achieved more accurate forecasting at a higher speed than
prior approaches.

###  III-D Channel Modeling

####  III-D1 Definition & limitations

A channel model is a mathematical representation of the effect of a
communication channel through which wireless signals are propagated; it is
modeled as the impulse response of the channel in the frequency or time
domain.

A wireless channel presents a variety of challenges for reliable high-speed
communication, as it is vulnerable to noise, interference, and other channel
impediments, including path loss and shadowing. Of these, path loss is caused
by the waste of the power emitted by the transmitter and the propagation
channel effects, whereas shadowing is caused by the obstacles between the
receiver and transmitter that absorb power [102].

Precise channel models are required to asses the performance of mobile
communication system and therefore to enhance coverage for existing
deployments. Channel models may also be useful to forecast propagation in
designed deployment outlines, which could allow for assessment before
deployment, and for optimizing the coverage and capacity of actual systems.
For small number of transmitter possible positions, outdoor extensive
environment evaluation could be done to estimate the parameters of the channel
[103, 104]. As more advanced technologies have been used in wireless
communication, more advanced channel modelling was required. Therefore the use
of stochastic models that are computationally efficient while providing
satisfactory results [105].

Ray tracing is used for channel modeling, which requires 3D images that are
generally generated using computer vision methods including stereo-vision-
based depth estimation [106, 107], [108, 109].

A model is proposed for an urban environment requires features, including road
widths, street orientation angles, and height of buildings [110]. A simplified
model was then proposed, by Fernandes and Soares [111] that required only the
proportion of building occupation between the receiver and transmitter, which
could be computed from segmented images manually or automatically [112].

Despite the satisfactory performance of some of the listed techniques, they
still have many limitations. For example, the 3D images required by ray
tracing r are not generally available and their generation is not
computationally efficient. Even when the images are available, ray tracing is
computationally costly and data exhaustive and therefore is not appropriate
for real-time coverage area optimization. Further, the detailed data required
for the model presented by Cichon and Kurner [110] is often unavailable.

####  III-D2 AI-based solutions

Some early applications of AI for path loss forecasting have been based on
classical ML algorithms such as SVM [113, 114], NNs [115, 116, 117, 118, 119,
120] and decision trees [121]. Interested readers are referred to a survey of
ML-based path loss prediction approaches for further details [122].

Figure 15: Channel parameters prediction. 2D aerial/satellite images used as
input to the deep convolutional neural network (CNN)to to predict channel
parameters. The model is trained separately for each parameter.

However, although previous ML efforts have shown great results, many require
3D images. Researchers have recently thus shifted their attention to using DL
algorithms with 2D satellite/aerial images for path loss forecasting. For
example, Ates et al. [123], approximated channel parameters, including the
standard deviation of shadowing and the path loss exponent, from satellite
images using deep CNN without the use of any added input parameters, as shown
in Fig.15.

By using a DL model on satellite images and other input parameters to predict
the reference signal received power (RSRP) for specific receiver locations in
a specific scenario/area, Thrane et al. [124] demonstrated a gain improvement
of $\approx 1$ and $\approx 4.7$ at 811 MHz and 2630 MHz respectively, over
previous techniques, including ray tracing. Similarly Ahmadien et al. [125],
applied DL on satellite images for path loss prediction, although they focused
only on satellite images without any supplemental features and worked on more
generalized data. Despite the practicality of this method, as it only needs
satellite images to forecast the path loss distribution, 2D images will not
always be sufficient to characterize the 3D structure. In these cases, more
features (e.g., building heights) must be input into the model.

###  III-E Telemetry Mining

####  III-E1 Definition & limitations

Telemetry is the process of recording and transferring measurements for
control and monitoring. In satellite systems, on-board telemetry helps mission
control centers track platform’s status, detect abnormal events, and control
various situations.

Satellite failure can be caused by a variety of things; most commonly, failure
is due to the harsh environment of space, i.e., heat, vacuum, and radiation.
The radiation environment can affect critical components of a satellite,
including the communication system and power supply.

Telemetry processing enables tracking of the satellite’s behavior to detect
and minimize failure risks. Finding correlations, recognizing patterns,
detecting anomalies, classifying, forecasting, and clustering are applied to
the acquired data for fault diagnosis and reliable satellite monitoring.

One of the earliest and simplest techniques used in telemetry analysis is
limit checking. The method is based on setting a precise range for each
feature (e.g., temperature, voltage, and current), and then monitoring the
variance of each feature to detect out-of-range events. The main advantage of
this algorithm is its simplicity limits, as can be chosen and updated easily
to control spacecraft operation.

Complicated spacecraft with complex and advanced applications challenges
current space telemetry systems. Narrow wireless bandwidth and fixed-length
frame telemetry make transmitting the rapidly augmenting telemetry volumes
difficult. In addition, the discontinuous short-term contacts between
spacecraft and ground stations limit the data transmission capability.
Analyzing, monitoring and interpreting huge telemetry parameters could be
impossible due to the high complexity of data.

####  III-E2 AI-based solutions

In recent years, AI techniques have been largely considered in space missions
with telemetry. Satellite health monitoring has been performed using
probabilistic clustering [126], dimensionality reduction, and hidden Markov
[127], and regression trees [128], whereas others have developed anomaly
detection methods using the K-nearest neighbor (kNN), SVM, LSTM and testing on
the telemetry of Centre National d’Etudes Spatiales spacecraft [129, 130,
131].

Further, the space functioning assistant was further developed in diverse
space applications using data-driven [132] and model-based [133] monitoring
methods. In their study of the use of AI for fault diagnosis in general and
for space utilization, Sun et al. [134] argued that the most promising
direction is the use of DL; suggested its usage for fault diagnosis for space
utilization in China.

By comparing different ML algorithms using telemetry data from the Egyptsat-1
satellite, Ibrahim et al. [135] demonstrated the high prediction accuracy of
LSTM, ARIMA, and RNN models. They suggested simple linear regression for
forecasting critical satellite features for short-lifetime satellites (i.e.,
3–5 years) and NNs for long-lifetime satellites (15-20 years).

Unlike algorithms designed to operate on the ground in the mission control
center, Wan et al. [136] proposed a self-learning classification algorithm to
achieve on-board telemetry data classification with low computational
complexity and low time latency.

###  III-F Ionospheric Scintillation Detecting

####  III-F1 Definition & limitations

Figure 16: Representation of ionospheric scintillation, where distortion
occurs during signal propagation. The blue, green, and red lines show the
line-of-sight signal paths from the satellite to the earth antennas, the
signal fluctuation, and the signal delay, respectively.

Signals transmission by satellites toward the earth can be notably impacted
due to their propagation through the atmosphere, especially the ionosphere,
which is the ionized part of the atmosphere higher layer, and is distinguished
by an elevated density of free electrons (Fig.16). The potential
irregularities and gradients of ionization can distort the signal phase and
amplitude, in a process known as ionospheric scintillation.

In particular, propagation through the ionosphere can cause distortion of
global navigation satellite system (GNSS) signals, leading to significant
errors in the GNSS-based applications. GNSSs are radio-communication satellite
systems that allow a user to compute the local time, velocity, and position in
any place on the Earth by processing signals transferred from the satellites
and conducting trilateration [137]. GNSSs can also be used in a wide variety
of applications, such as scientific observations.

Because of the low-received power of GNSS waves, any errors significantly
threaten the accuracy and credibility of the positioning systems. GNSS signals
propagating through the ionosphere face the possibility of both a temporal
delay and scintillation. Although delay compensation methods are applied to
all GNSS receivers [137], scintillation is still a considerable issue, as its
quasi-random nature makes it difficult to model [138]. Ionospheric
scintillation thus remains a major limitation to high-accuracy applications of
GNSSs. The accurate detection of scintillation thus required to improve the
credibility and quality of GNSSs [139]. To observe the signals, which are a
source of knowledge for interpreting and modeling the atmosphere higher
layers, and to raise caution and take countermeasures for GNSS-based
applications, networks of GNSS receivers, have been installed, both at high
and low latitudes, where scintillation is expected to occur [140, 141]. Robust
receivers and proper algorithms for scintillation-detecting algorithms are
thus both required [142].

To evaluate the magnitude of scintillation impacting a signal, many
researchers have employed simple event triggers, based on the comparison of
the amplitude and phase of two signals over defined interval [143]. Other
proposed alternatives, have included using wavelet techniques [144],
decomposing the carrier-to-noise density power propostion via adaptive
frequency-time techniques [145], and assessing the histogram statistical
properties of collected samples [146].

Using simple predefined thresholds to evaluate the magnitude of scintillation
can be deceptive due its complexity. The loss of the transient phases of
events could cause a delay in raising possible caution flags, and weak events
with high variance could be missed. Further, it can be difficult to
distinguish between signal distortions caused by other phenomena, including
multi-path. However, other proposed alternatives depend on complex and
computationally costly operations or on customized receiver architectures.

####  III-F2 AI-based solutions

Recently, studies have proved that AI can be utilized for the detection of
scintillation. For example, Rezende et al. [147], proposed a survey of data
mining methods, that rely on observing and integrating GNSS receivers.

A technique based on the SVM algorithm has been suggested for amplitude
scintillation detection [148, 149], and then later expanded to phase
scintillation detection [150, 151].

By using decision trees and RF to systematically detect ionospheric
scintillation events impacting the amplitude of the GNSS signals, Linty et
al.’s [152] methodology outperformed state-of-the art methodologies in terms
of accuracy (99.7%) and F-score (99.4%), thus reaching the levels of a manual
human-driven annotation.

More recently, Imam and Dovis [153] proposed the use of decision trees, to
differentiate between ionospheric scintillation and multi-path in GNSS
scintillation data. Their model, which annotates the data as scintillated,
multi-path affected, or clean GNSS signal, demonstrated an accuracy of 96%

###  III-G Managing Interference

####  III-G1 Definition & limitations

Interference managing is mandatory for satellite communication operators, as
interference negatively affects the communication channel, resulting in a
reduced QoS, lower operational efficiency and loss of revenue [154]. Moreover,
interference is a common event that is increasing with the increasing
congestion of the satellite frequency band as more countries are launching
satellites and more applications are expected. With the growing number of
users sharing the same frequency band, the possibility of interfering
augments, as does the risk of intentional interference, as discussed in
section III.B.

Interference managing is a thus essential to preserve high-quality and
reliable communication systems; management includes detection, classification,
and suppression of interference, as well as the application of techniques to
minimize its occurrence.

Interference detection is a well-studied subject that has been addressed in
the past few decades [155, 156], especially for satellite communication [154,
157].

However, researchers have commonly relied on the decision theory of hypothesis
testing, in which specific knowledge of the signal characteristics and the
channel model is needed. Due, to the contemporary diverse wireless standards,
the design of specific detectors for each signal category is fruitless
approach.

####  III-G2 AI-based solutions

Figure 17: Satellite selection and antenna adjustment

To minimize interference, Liu et al. [158], suggested the use of AI for moving
terminals and stations in satellite-terrestrial networks by proposing a
framework combining different AI approaches including SVM, unsupervised
learning and DRL for satellite selection, antenna pointing and tracking, as
summarized in Fig.17.

Another AI-based approach executes automatic real-time interference detection
is based on the forecasting of the following signal spectrum to be received in
absence of anomaly, by using LSTM trained on historical anomaly-free spectra
[159]. Here the predicted spectra is then compared to the received signal
using a designed metric, to detect anomalies.

Henarejos et al. [160] proposed the use of two AI-based approaches, DNN AEs
and LSTM, for detecting and classifying interference, respectively. In the
former, the AE is trained with interference free signals and tested against
other signals without interference to obtain practical thresholds. The
difference in error in signals with and without interference is then exploited
to detect the presence of interference.

###  III-H Remote sensing (RS)

####  III-H1 Definition & limitations

RS is the process of extracting information about an area, object or
phenomenon by processing its reflected and emitted radiation at a distance,
generally from satellite or aircraft.

RS has a wide range of applications in multiple fields including land
surveying, geography, geology, ecology, meteorology, oceanography, military
and communication. As RS offers the possibility of monitoring areas that are
dangerous, difficult or impossible to access, including mountains, forests,
oceans and glaciers it is a popular and active research area.

####  III-H2 AI-based solutions

The revolution in computer vision capabilities caused by DL has led to the
increased development of RS by adopting state-of-the-art DL algorithms on
satellite images, image classification for RS has become most popular task in
computer vision. For example, Kussul et al. [161] used DL to classify land
coverage and crop types using RS images from Landsat-8 and Sentinel-1A over a
test site in Ukraine. Zhang et al [162] combined DNNs by using a gradient-
boosting random CNN for scene classification. More recently, Chirayath et al.
[163] proposed the combination of kNN and CNN to map coral reef marine
habitats worldwide with RS imaging. RS and AI have also been used in
communication theory applications, such as those discussed in section III.D
[123], [124] and [125].

Many object detection and recognition applications have been developed using
AI on RS images [164]. Recently, Zhou et al. [165] proposed the use of YOLOv3
[166, 167], a CNN-based object detection algorithm, for vehicle detection in
RS images. Others have proposed the use of DL for other object detection
tasks, such as, building [168], airplane [169], cloud [170], [171, 172], ship
[173, 174], and military target [175] detection. AI has also been applied to
segment and restore RS images, e.g., in cloud restorations, during which
ground regions shadowed by clouds are restored.

Recently, Zheng et al. [176] proposed a two-stage cloud removal method in
which U-Net [177] and GANs are used to perform cloud segmentation and image
restoration, respectively.

AI proposed for on-board scheduling of agile Earth-observing satellites, as
autonomy improves their performance and allows them to acquire more images, by
relying on on-board scheduling for quick decision-making. By comparing the use
of RF, NNs, and SVM to prior learning and non-learning-based approaches, Lu et
al. [178] demonstrated that RF improved both the solution quality and response
time.

###  III-I Behavior Modeling

####  III-I1 Definition & limitations

Owing to the increasing numbers of active and inactive (debris) satellites of
diverse orbits, shapes, sizes, orientations and functions, it is becoming
infeasible for analysts to simultaneously monitor all satellites. Therefore,
AI, especially ML, could play a major role by helping to automate this
process.

####  III-I2 AI-based solutions

Mital et al. [179] discussed the potential of ML algorithms to model satellite
behavior. Supervised models have been used to determine satellite stability
[180], whereas unsupervised models have been used to detect anomalous behavior
and a satellites’ location [181], and an RNN has been used to predict
satellite maneuvers over time[182].

Accurate satellite pose estimation, i.e., identifying a satellite’s relative
position and attitude, is critical in several space operations, such as debris
removal, inter-spacecraft communication, and docking. The recent proposal for
satellite pose estimation from a single image via combined ML and geometric
optimization by Chen et al. [183] won the first place in the recent Kelvins
pose estimation challenge organized by the European Space Agency [184].

The amount of space debris has augmented immensely over the last few years,
which can cause a crucial menace to space missions due to the high velocity of
the debris. It is thus essential to classify space objects and apply collision
avoidance techniques to protect active satellites. As such, Jahirabadkar et
al. [185] presented a survey of diverse AI methodologies, for classification
of space objects using the curves of light as a differentiating property.

Yadava et al. [186] employed NNs and RL for on-board attitude determination
and control; their method effectively provided the needed torque to stabilize
a nanosatellite along three axes.

To avoid catastrophic events because of battery failure, Ahmed et al. [187]
developed an on-board remaining battery life estimation system using ML and a
logical analysis of data approaches.

###  III-J Space-Air-Ground Integrating

####  III-J1 Definition & limitations

Recently, notable advances have been made in ground communication systems to
provide users higher-quality internet access. Nevertheless, due to the
restricted capacity and coverage area of networks, such services are not
possible everywhere at all times, especially for users in rural or disaster
areas.

Figure 18: Space-air-ground integrated networks (SAGINs) [26]

Although terrestrial networks have the most resources and highest throughput,
non-terrestrial communication systems have a much broader coverage area.
However, non-terrestrial networks have their own limitations; e.g., satellite
communication systems have a long propagation latency, and air networks have a
narrow capacity and unstable links.

To supply users with better and more-flexible end-to-end services by taking
advantage of the way the networks can complement each other, researchers have
suggested the use of space-air-ground integrated networks (SAGINs) [10], which
include the satellites in space, the balloons, airships, and UAVs in the air,
and the ground segment, as shown in Fig.18.

The multi-layered satellite communication system which consists of GEO, MEO,
and LEO satellites, can use multi-cast and broadcast methods to ameliorate the
network capacity, crucially easing the augmenting traffic burden [10, 26]. As
SAGINs allow packet transmission to destinations via multiple paths of diverse
qualities, they can offer different packet transmissions methods to encounter
diverse service demands [26].

However, the design and optimization of SAGINs is more challenging than that
of conventional ground communication systems owing to their inherent self-
organization, time-variability, and heterogeneity [10]. A variety of factors
that must be considered when designing optimization techniques have thus been
identified [10, 26]. For example, the diverse propagation mediums, the sharing
of frequency bands by different communication types, the high mobility of the
space and air segments, and the inherent heterogeneity between the three
segments, make the network control and spectrum management of SAGIN arduous.
The high mobility results in frequent handoffs, which makes safe routing more
difficult to realize, thus making SAGINs more exposed to jamming. Further, as
optimizing the energy efficiency is also more challenging than in standard
terrestrial networks, energy management algorithms are also required.

####  III-J2 AI-based solutions

In their discussion of challenges facing SAGINs, Kato et al. [26] proposed the
use of a CNN for the routing problem to optimize the SAGIN’s overall
performance using traffic patterns and the remaining buffer size of GEO and
MEO satellites.

Optimizing the satellite selection and the UAV location to optimize the end-
to-end data rate of the Source-Satellite-UAV-Destination communication is
challenging due to the vast orbiting satellites number and the following time-
varying network architecture. To address this problem, Lee et al. [188]
jointly optimized the source-satellite-UAV association and the location of the
UAV via DRL. Their suggested technique achieved up to a 5.74x higher average
data rate than a direct communication baseline in the absence of UAV and
satellite.

For offloading calculation-intensive applications, a SAGIN edge/cloud
computing design has been developed in such a way that satellites give access
to the cloud and UAVs allow near-user edge computing. [189]. Here, a joint
resource allocation and task scheduling approach is used to allocate the
computing resources to virtual machines and schedule the offloaded tasks for
UAV edge servers, whereas an RL-based computing offloading approach handles
the multidimensional SAGIN resources and learns the dynamic network
conditions. Here, a joint resource allocation and task scheduling approach is
used to assign the computing resources to virtual machines and plan the
offloaded functions for UAV edge servers, whereas an RL-based computing
offloading approach handles the multidimensional SAGIN resources and learns
the dynamic network characteristics. Simulation results confirmed the
efficiency and convergence of the suggested technique.

As the heterogeneous multi-layer network requires advanced capacity-management
techniques, Jiang and Zhu [190] suggested a low-complexity technique for
computing the capacity among satellites and suggested a long-term optimal
capacity assignment RL-based model to maximize the long-term utility of the
system.

By formulating the joint resources assignment problem as a joint optimization
problem and using a DRL approach, Qiu et al. [191] proposed a software-defined
satellite-terrestrial network to jointly manage caching, networking, and
computing resources.

###  III-K Energy Managing

####  III-K1 Definition & limitations

Recent advances in the connection between ground, aerial, and satellite
networks such as SAGIN have increased the demand imposed on satellite
communication networks. This growing attention towards satellites has led to
increased energy consumption requirements. Satellite energy management thus
represents a hot research topic for the further development of satellite
communication.

Compared with a GEO Satellite, an LEO satellite has restricted on-board
resources and moves quickly. Further, an LEO satellite has a limited energy
capacity owing to its small size [192]; as billions of devices need to be
served around the world [193], current satellite resource capability can no
longer satisfy demand. To address this shortage of satellite communication
resources, an efficient resource scheduling scheme to take full use of the
limited resources, must be designed. As current resource allocation schemes
have mostly been designed for GEO satellites, however, these schemes do not
consider many LEO specific concerns, such as the constrained energy, movement
attribute, or connection and transmission dynamics.

####  III-K2 AI-based solutions

Some researchers have thus turned to AI-based solutions for power saving. For
example, Kothari et al. [27] suggested the usage of DNN compression before
data transmission to improve latency and save power. In the absence of solar
light, satellites are battery energy dependent, which places a heavy load on
the satellite battery and can shorten their lifetimes leading to increased
costs for satellite communication networks. To optimize the power allocation
in satellite to ground communication using LEO satellites and thus extend
their battery life, Tsuchida et al. [194] employed RL to share the workload of
overworked satellites with near satellites with lower load. Similarly,
implementing DRL for energy-efficient channel allocation in Satlot allowed for
a 67.86% reduction in energy consumption when compared with previous models
[195]. Mobile edge computing enhanced SatIoT networks contain diverse
satellites and several satellite gateways that could be jointly optimized with
coupled user association, offloading decisions computing, and communication
resource allocation to minimize the latency and energy cost. In a recent
example, a joint user-association and offloading decision with optimal
resource allocation methodology based on DRL proposed by Cui et al. [196]
improved the long-term latency and energy costs.

###  III-L Other Applications

####  III-L1 Handoff Optimization

Link-layer handoff occurs when the change of one or more links is needed
between the communication endpoints due to the dynamic connectivity patterns
of LEO satellites. The management of handoff in LEO satellites varies
remarkably from that of terrestrial networks, since handoffs happen more
frequently due to the movement of satellites [3]. Many researchers have thus
focused on handoff management in LEO satellite networks.

In general, user equipment (UE) periodically measures the strength of
reference signals of different cells to ensure access to a strong cell, as the
handoff decision depends on the signal strength or some other parameters.
Moreover, the historical RSRP contains information to avoid unnecessary
handoff.

Thus, Zhang [197] converted the handoff decision to a classification problem.
Although the historical RSRP is a time series, a CNN was employed rather than
an RNN because the feature map of historical RSRP has a strong local spatial
correlation and the use of an RNN could lead to a series of wrong decisions,
as one decision largely impacts future decisions. In the proposed AI-based
method, the handoff was decreased by more than 25% for more than 70% of the
UE, whereas the commonly used “strongest beam” method only reduced the average
RSRP by 3%.

####  III-L2 Heat Source Layout Design

The effective design of the heat sources used can enhance the thermal
performance of the overall system, and has thus become a crucial aspect of
several engineering areas, including integrated circuit design and satellite
layout design. With the increasingly small size of components and higher power
intensity, designing the heat-source layout has become a critical problem
[198]. Conventionally, the optimal design is acquired by exploring the design
space by repeatedly running the thermal simulation to compare the performance
of each scheme [199, 200, 201]. To avoid the extremely large computational
burden of traditional techniques, Sun et al. [202] employed an inverse design
method in which the layout of heat sources is directly generated from a given
expected thermal performance based on a DL model called Show, Attend, and Read
[203]. Their developed model was capable of learning the underlying physics of
the design problem and thus could efficiently forecast the design of heat
sources under a given condition without any performing simulations. Other DL
algorithms have been used in diverse design areas, such as mechanics [204],
optics [205], fluids [206], and materials [207].

####  III-L3 Reflectarray analysis and design

ML algorithms have been employed in the analysis and design of antennas [22],
including the analysis [208, 209] and design [210, 211] of reflectarrays. For
example, NNs were used by Shan et al. [212] to forecast the phase-shift,
whereas kriging was suggested to forecast the electromagnetic response of
reflectarray components [213]. Support vector regression (SVR) has been used
to accelerate the examination [214] and to directly optimize narrowband
reflectarrays [215]. To hasten calculations without reducing their precision,
Prado et al. [216] proposed a wideband SVR-based reflectarray design method,
and demonstrated its ability to obtain wideband, dual-linear polarized,
shaped-beam reflectarrays for direct broadcast satellite applications.

####  III-L4 Carrier Signal Detection

As each signal must be separated before classification, modulation,
demodulation, decoding and other signal processing, localization, and
detection of carrier signals in the frequency domain is a crucial problem in
wireless communication.

The algorithms used for carrier signal detection have been commonly based on
threshold values and required human intervention [217, 218, 219, 220, 221,
222], although several improvements have been made including the use of a
double threshold [223, 224]. Kim et al. [225] proposed the use of a slope-
tracing-based algorithm to separate the interval of signal elements based on
signal properties such as amplitude, slope, deflection width, or distance
between neighboring deflections.

More recently, DL has been applied to carrier signal detection; for example,
Morozov and Ovchinnikov [226] applied a fully connected NN for their detection
in FSK signals, whereas Yuan et al. [227] used DL, to morse signals blind
detection in wideband spectrum data. Huang er al. [228] employed a fully
convolutional network (FCN) model to detect carrier signal in the broadband
power spectrum. A FCN is a DL method for semantic image segmentation in which
the broadband power spectrum is regarded as a 1D image and each subcarrier as
the target object to transform the carrier detection problem on the broadband
to a semantic 1D image segmentation problem [229, 230, 231]. Here, a 1D deep
CNN FCN-based on was designed to categorize each point on a broadband power
spectrum array into two categories (i.e., subcarrier or noise), and then
position the subcarrier signals’ location on the broadband power spectrum.
After being trained and validated using a simulated and real satellite
broadband power spectrum dataset, respectively, the proposed deep CNN
successfully detected the subcarrier signal in the broadband power spectrum
and achieved a higher accuracy than the slope tracing method.

## Conclusion

This review provided an overview of AI and its different sub-fields, including
ML, DL, and RL. Some limitations to satellite communication were then
presented and their proposed and potential AI-based solutions were discussed.
The application of AI has shown great results in a wide variety of satellite
communication aspects, including beam-hopping, AJ, network traffic
forecasting, channel modeling, telemetry mining, ionospheric scintillation
detecting, interference managing, remote sensing, behavior modeling, space-
air-ground integrating, and energy managing. Future work should aim to apply
AI, to achieve more efficient, secure, reliable, and high-quality
communication systems.

## References

  * [1] G. Maral, M. Bousquet, and Z. Sun, “Introduction,” in _Satellite Communications Systems: Systems, Techniques and Technology, 6_ th ed. Hoboken, NJ, USA: Wiley, 2020, ch. $1$, sec. $3$, pp. _3–11._
  * [2] F. Rinaldi, H. L. Maattanen, J. Torsner, S. Pizzi, S. Andreev, A. Iera, Y. Koucheryavy, and G. Araniti “Non-Terrestrial Networks in 5G & Beyond: A Survey,” in _IEEE Access,_ vol. 8, pp. 165178-165200, 2020, doi: 10.1109/ACCESS.2020.3022981.
  * [3] P. Chowdhury, M. Atiquzzaman, and W. Ivancic, “Handover schemes in satellite networks: State-of-the-art and future research directions,” _IEEE Commun. Surveys Tuts.,_ vol. 8, no. 4, pp. 2-14, Aug. 2006.
  * [4] P. Chini, G. Giambene, and S. Kota, “A survey on mobile satellite systems,” _Int. J. Satell. Commun. Netw.,_ vol. 28, no. 1, pp. 29-57, Aug. 2009.
  * [5] P.-D. Arapoglou, K. Liolis, M. Bertinelli, A. Panagopoulos, P. Cottis, and R. De Gaudenzi, “MIMO over satellite: A review,” _IEEE Commun. Surveys Tuts.,_ vol. 13, no. 1, pp. 27-51, 1st Quart. 2011.
  * [6] M. De Sanctis, E. Cianca, G. Araniti, I. Bisio, and R. Prasad, “Satellite communications supporting Internet of remote things,” _IEEE Internet Things J.,_ vol. 3, no. 1, pp. 113-123, Feb. 2016.
  * [7] R. Radhakrishnan, W. W. Edmonson, F. Afghah, R. M. Rodriguez-Osorio, F. Pinto, and S. C. Burleigh, “Survey of inter-satellite communication for small satellite systems: Physical layer to network layer view,” _IEEE Commun. Surveys Tuts.,_ vol. 18, no. 4, pp. 2442-2473, May 2016.
  * [8] C. Niephaus, M. Kretschmer, and G. Ghinea, “QoS provisioning in converged satellite and terrestrial networks: A survey of the state-of-the-art,” _IEEE Commun. Surveys Tuts.,_ vol. 18, no. 4, pp. 2415-2441, Apr. 2016.
  * [9] H. Kaushal and G. Kaddoum, “Optical communication in space: Challenges and mitigation techniques,” _IEEE Commun. Surveys Tuts.,_ vol. 19, no. 1, pp. 57-96, 1st Quart. 2017.
  * [10] J. Liu, Y. Shi, Z. M. Fadlullah, and N. Kato, “Space-Air-Ground Integrated Network: A Survey,” _IEEE Communications Surveys & Tutorials,_ vol. 20, no. 4, pp. 2714-2741, Fourthquarter 2018, doi: 10.1109/COMST.2018.2841996.
  * [11] S. C. Burleigh, T. De Cola, S. Morosi, S. Jayousi, E. Cianca, and C. Fuchs, “From connectivity to advanced Internet services: A comprehensive review of small satellites communications and networks,” _Wireless Commun. Mobile Comput.,_ vol. 2019, pp. 1-17, May 2019.
  * [12] B. Li, Z. Fei, C. Zhou, and Y. Zhang, “Physical-layer security in space information networks: A survey,” _IEEE Internet Things J.,_ vol. 7, no. 1, pp. 33-52, Jan. 2020.
  * [13] N. Saeed, A. Elzanaty, H. Almorad, H. Dahrouj, T. Y. Al-Naffouri, and M. -S. Alouini, “CubeSat Communications: Recent Advances and Future Challenges,” _IEEE Communications Surveys & Tutorials,_ vol. 22, no. 3, pp. 1839-1862, thirdquarter 2020, doi: 10.1109/COMST.2020.2990499.
  * [14] O. Simeone, “A Very Brief Introduction to Machine Learning With Applications to Communication Systems,” _IEEE Transactions on Cognitive Communications and Networking,_ vol. 4, no. 4, pp. 648-664, Dec. 2018, doi: 10.1109/TCCN.2018.2881442.
  * [15] M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” _IEEE Communications Surveys & Tutorials,_ vol. 21, no. 4, pp. 3039-3071, Fourthquarter 2019, doi: 10.1109/COMST.2019.2926625.
  * [16] Y. Qian, J. Wu, R. Wang, F. Zhu, and W. Zhang, “Survey on Reinforcement Learning Applications in Communication Networks,” _Journal of Communications and Information Networks,_ vol. 4, no. 2, pp. 30-39, June 2020, doi: 10.23919/JCIN.2019.8917870.
  * [17] EC. Strinati, S. Barbarossa, JL. Gonzalez, D. Ktenas, N. Cassiau, L. Maret, and C. Dehos, “6G: The Next Frontier: From Holographic Messaging to Artificial Intelligence Using Subterahertz and Visible Light Communication,” _IEEE Vehicular Technology Magazine,_ vol. 14, no. 3, pp. 42-50, Sept. 2019, doi: 10.1109/MVT.2019.2921162.
  * [18] J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, T. Melodia, “Machine learning for wireless communications in the Internet of Things: A comprehensive survey,” _Ad Hoc Networks,_ Volume 93, Jan. 2019, 101913, ISSN 1570-8705, [online] Available: https://doi.org/10.1016/j.adhoc.2019.101913.
  * [19] G. P. Kumar and P. Venkataram, “Artificial intelligence approaches to network management: recent advances and a survey,” _Computer Communications,_ Volume 20, Issue 15, Dec. 1997, Pages 1313-1322, ISSN 0140-3664, [online] Available: https://doi.org/10.1016/S0140-3664(97)00094-7.
  * [20] Y. Zou, J. Zhu, X. Wang, and L. Hanzo, “A Survey on Wireless Security: Technical Challenges, Recent Advances, and Future Trends,” _Proceedings of the IEEE,_ vol. 104, no. 9, pp. 1727-1765, Sept. 2016, doi: 10.1109/JPROC.2016.2558521.
  * [21] S. H. Alsamhi, O. Ma, and M. S. Ansari. “Survey on artificial intelligence based techniques for emerging robotic communication,” _Telecommunication Systems: Modelling, Analysis, Design and Management,_ vol. 72, issue 3, no. 12, pp. 483-503, Mars 2019, doi: 10.1007/s11235-019-00561-z
  * [22] H. M. E. Misilmani and T. Naous, “Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms,” _2019 International Conference on High Performance Computing & Simulation (HPCS),_ Dublin, Ireland, 2019, pp. 600-607, doi: 10.1109/HPCS48598.2019.9188224.
  * [23] P. S. Bithas, E. T. Michailidis, N. Nomikos, D. Vouyioukas, and A. Kanatas “A survey on machine-learning techniques for UAV-based communications,” _Sensors 2019_ 19.23, Nov. 2019, [online] Available: https://doi.org/10.3390/s19235170.
  * [24] M. A. Lahmeri, M. A. Kishk, and MS. Alouini. “Machine learning for UAV-Based networks.” _arXiv preprint,_ 2020, arXiv:2009.11522.
  * [25] M. Á. Vázquez, P. Henarejos, A. I. Pérez-Neira, E. Grechi, A. Voight, JC. Gil, I. Pappalardo, FD. Credico, and R. M. Lancellotti, “On the Use of AI for Satellite Communications.” _arXiv preprint,_ 2020, arXiv:2007.10110.
  * [26] N. Kato, ZM. Fadlullah, F. Tang, B. Mao, S. Tani, A. Okamura, and J. Liu, “Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence,” _IEEE Wireless Communications,_ vol. 26, no. 4, pp. 140-147, August 2019, doi: 10.1109/MWC.2018.1800365.
  * [27] V. Kothari, E. Liberis, and N. D. Lane. “The Final Frontier: Deep Learning in Space,” _Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications,_ pp. 45-49., 2020.
  * [28] F. Chollet, “What is Deep Learning ?” in _Deep Learning with Python, 1_ st ed. New York, NY, USA: Manning, 2017, ch. $1$, pp. _3–24._
  * [29] A. M. Turing, “Computing Machinery and Intelligence,” in _Mind, 59_ th ed., 1950, ch. $1$, pp. _433–460._
  * [30] C. M. Bishop, “Linear Models for Classification,” in _Pattern Recognition and Machine Learning, 1_ st ed. Berlin, Heidelberg, Germany: Springer-Verlag, 2006, ch. $4$, pp. _179–224._
  * [31] C. M. Bishop, “Kernel Methods,” in _Pattern Recognition and Machine Learning, 1_ st ed. Berlin, Heidelberg, Germany: Springer-Verlag, 2006, ch. $6$, pp. _291–325._
  * [32] B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” _In Proceedings of the fifth annual workshop on Computational learning theory (COLT ’92),_ New York, NY, USA, Association for Computing Machinery, pp. 144–152., 1992, [online] Available: https://doi.org/10.1145/130385.130401
  * [33] F. Fourati., W. Souidene, and R. Attia, “An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset,” _arXiv preprint,_ 2020, arXiv:2009.11977.
  * [34] J. Cervantesa, F. Garcia-Lamonta, L. Rodríguez-Mazahuab, and A. Lopezc, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” _Neurocomputing,_ 2020, 408, 189-215.
  * [35] J. R. Quinlan, “Induction of decision trees.” Machine learning 1.1, 1986, _81–106_.
  * [36] C. M. Bishop, “Graphical Models,” in _Pattern Recognition and Machine Learning, 1_ st ed. Berlin, Heidelberg, Germany: Springer-Verlag, 2006, ch. $8$, pp. _359–423_.
  * [37] L. Breiman, “Random forests,” Machine learning 45.1, 2001, pp. _5-32_.
  * [38] L. Breiman, “Bagging predictors,” Machine learning 24.2, 1996, pp. _123-140_.
  * [39] J. H. Friedman “Greedy function approximation: a gradient boosting machine,” _Annals of statistics,_ 2001, pp._1189-1232_.
  * [40] [online] Available: https://xgboost.readthedocs.io/en/latest/
  * [41] T. Chen and T. He, “Xgboost: extreme gradient boosting.” Package Version: 1.3.2.1, Jan., 2021, [online] Available: https://cran.r-project.org/web/packages/xgboost/vignettes/xgboost.pdf
  * [42] [online] Available: https://www.kaggle.com/
  * [43] P. Baldi and K. Hornik, “Neural networks and principal component analysis: Learning from examples without local minima.” Neural networks 2.1, 1989, pp. _53–58_.
  * [44] C. M. Bishop, “Neural Networks” in _Pattern Recognition and Machine Learning, 1_ st ed. Berlin, Heidelberg, Germany: Springer-Verlag, 2006, ch. $5$, pp. _225–290_.
  * [45] R. Hecht-Nielsen, “Theory of the backpropagation neural network.” Neural networks for perception. Academic Press, 1992, pp. _65–93_.
  * [46] I. Goodfellow, Y. Bengio, and A. Courville, “Introduction,” in _Deep learning,_ Cambridge: MIT press, 2016, ch. $1$, pp. _1–26._ [online] Available: https://www.deeplearningbook.org/
  * [47] I. Goodfellow, Y. Bengio, and A. Courville, “Convolutional Networks,” in _Deep learning,_ Cambridge: MIT press, 2016, ch. $9$, pp. _326–366._ [online] Available: https://www.deeplearningbook.org/
  * [48] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” International Conference on Engineering and Technology (ICET), Antalya, 2017, pp. _1–6_ , doi: 10.1109/ICEngTechnol.2017.8308186.
  * [49] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  * [50] T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, M. Li “Bag of tricks for image classification with convolutional neural networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
  * [51] Z. Zou, Z. Shi, Y. Guo, and J. Ye, “Object detection in 20 years: A survey,” arXiv preprint arXiv:1905.05055, 2019.
  * [52] Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, and N. Yu “Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism,” Proceedings of the IEEE International Conference on Computer Vision. 2017.
  * [53] K. R. Chowdhary, “Natural language processing,” Fundamentals of Artificial Intelligence. Springer, New Delhi, 2020. pp. _603–649_.
  * [54] I. Goodfellow, Y. Bengio, and A. Courville, “Sequence Modeling: Recurrent and Recursive Nets,” in _Deep learning,_ Cambridge: MIT press, 2016, ch. $10$, pp. _367–415._ [online] Available: https://www.deeplearningbook.org/
  * [55] I. Goodfellow, Y. Bengio, and A. Courville, “Autoencoders,” in _Deep learning,_ Cambridge: MIT press, 2016, ch. $14$, pp. _499–523._ [online] Available: https://www.deeplearningbook.org/
  * [56] Y. Wang, Y. Hongxun , and Z. Sicheng, “Auto-encoder based dimensionality reduction,” Neurocomputing 184, 2016, pp. _232–242_.
  * [57] C. Zhou and RC. Paffenroth, “Anomaly detection with robust deep autoencoders,” Proceedings of the 23rd ACM Special Interest Group on Knowledge Discovery and Data Mining International Conference on Knowledge Discovery and Data Mining. 2017.
  * [58] I. Goodfellow, Y. Bengio, and A. Courville, “Deep generative models,” in _Deep learning,_ Cambridge: MIT press, 2016, ch. $20$, pp. _651–716._ [online] Available: https://www.deeplearningbook.org/
  * [59] C. Doersch, “Tutorial on variational autoencoders,” arXiv preprint arXiv:1606.05908. 2016.
  * [60] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, Advances in neural information processing systems, 2014.
  * [61] A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” IEEE Signal Processing Magazine 35.1. 2018. pp. _53-65_ .
  * [62] DD. Margineantu, and TG. Dietterich,“Pruning adaptive boosting,” ICML. Vol. 97. 1997.
  * [63] J. Snoek, H. Larochelle, and RP. Adams, “Practical bayesian optimization of machine learning algorithms,” Advances in neural information processing systems 25. 2012. _2951–2959_.
  * [64] RS. Sutton and GB. Andrew, “Reinforcement Learning: An Introduction,” A Bradford Book, Cambridge, MA, USA. 2018.
  * [65] J. Anzalchi, A. Couchman, P. Gabellini, G. Gallinaro, L. D’Agristina, N. Alagha, and P. Angeletti, “Beam hopping in multi-beam broadband satellite systems: System simulation and performance comparison with non-hopped systems,” in Proc. 5th Adv. Satell. Multimedia Syst. Conf. 11th Signal Process. Space Commun. Workshop, Sep. 2010, pp. _248–-255_.
  * [66] A. Freedman, D. Rainish, and Y. Gat, “Beam hopping: How to make it possible,” in Proc. Broadband Commun. Conf., Oct. 2015, pp. _1–-6_.
  * [67] L. Lei, E. Lagunas, Y. Yuan, M. G. Kibria, S. Chatzinotas, and B. Ottersten, “Beam Illumination Pattern Design in Satellite Networks: Learning and Optimization for Efficient Beam Hopping,” in IEEE Access, vol. 8, pp. 136655-136667, 2020, doi: 10.1109/ACCESS.2020.3011746.
  * [68] P. Angeletti, D. Fernandez Prim, R. Rinaldo, “Beam hopping in multi-beam broadband satellite systems: system performance and payload architecture analysis,” The 24th AIAA Int. Communications Satellite Systems Conf., San Diego, June 2006
  * [69] J. Anzalchi, A. Couchman, P. Gabellini, G. Gallinaro, L. D’Agristina, N. Alagha, and P. Angeletti, “Beam hopping in multibeam broadband satellite systems: system simulation and performance comparison with non-hopped systems,” The 2010 5th Advanced Satellite Multimedia Systems Conf. and the 11th Signal Processing for Space Communications Workshop, Cagliari, Italy, September 2010, pp. _248–-255_.
  * [70] X. Alberti, J. M. Cebrian, A. Del Bianco, Z. Katona, J. Lei, M. A Vazquez-Castro, A. Zanus, L. Gilbert, and N. Alagha, “System capacity optimization in time and frequency for multibeam multi-media satellite systems,” in Proc. 11th Signal Process. Space Commun. Workshop, Sep. 2010, pp. _226–-233_.
  * [71] B. Evans and P. Thompson, “Key issues and technologies for a Terabit/s satellite,” The 28th AIAA Int. Communications Satellite Systems Conf. (ICSSC 2010), Anaheim, California, USA, June 2010, p. 8713
  * [72] J. Lei and M. Vazquez-Castro, “Multibeam satellite frequency/time duality study and capacity optimization,” in Proc. IEEE Int. Conf. Commun., Oct. 2011, vol. 13, no. 5, pp. _471-–480_.
  * [73] R. Alegre, N. Alagha, MA. Vázquez, “Heuristic algorithms for flexible resource allocation in beam hopping multi-beam satellite systems,” The 29th AIAA Int. Communications Satellite Systems Conf. (ICSSC 2011), Nara, Japan, July 2011, p. 8001
  * [74] R. Alegre, N. Alagha, MA. Vázquez, “Offered capacity optimization mechanisms for multi-beam satellite systems,” The 2012 IEEE Int. Conf. on Communications (ICC), Ottawa, ON, Canada, June 2012, pp. _3180–-3184_
  * [75] H. Liu, Z. Yang, Z. Cao, “Max-min rate control on traffic in broadband multibeam satellite communications systems,” IEEE Commun. Lett., 2013, 17, (7), pp. _1396–-1399_
  * [76] H. Han, X. Zheng, Q. Huang, and Y. Lin, “QoS-equilibrium slot allocation for beam hopping in broadband satellite communication systems,” Wirel. Netw., 2015, 21, (8), pp. _2617–-2630_
  * [77] S. Shi, G. Li, Z. Li, H. Zhu, and B. Gao, “Joint power and bandwidth allocation for beamhopping user downlinks in smart gateway multibeam satellite systems,” Int. J. Distrib. Sensor Netw., 2017, 13, (5), doi: 1550147717709461
  * [78] A. Ginesi, E. Re, and P.D. Arapoglou, “Joint beam hopping and precoding in HTS systems,” Int. Conf. on Wireless and Satellite Systems, OXFORD, GREAT BRITAIN, U.K, 2017, _43-–51_
  * [79] G. Cocco, T. de Cola, M. Angelone, Z. Katona, and S. Erl, “Radio resource management optimization of flexible satellite payloads for DVB-S2 systems,” IEEE Trans. Broadcast., vol. 64, no. 2, Jun. 2018, pp. _266–-280_
  * [80] X. Hu, S. Liu, X. Hu, Y. Wang, L. Xu, Y. Zhang, C. Wang, and W. Wang, “Deep reinforcement learning-based beam Hopping algorithm in multibeam satellite systems,” IET Communications. pp. _2485–91_ , Jan. 2019.
  * [81] Y. Zhang, X. Hu, R. Chen, Z. Zhang, L. Wang, and W. Wang, “Dynamic Beam Hopping for DVB-S2X Satellite: A Multi-Objective Deep Reinforcement Learning Approach,” 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), Shenyang, China, 2019, pp. _164–169_ , doi: 10.1109/IUCC/DSCI/SmartCNS.2019.00056.
  * [82] X. Hu, Y. Zhang, X. Liao, Z. Liu, W. Wang, and F. M. Ghannouchi, “Dynamic Beam Hopping Method Based on Multi-Objective Deep Reinforcement Learning for Next Generation Satellite Broadband Systems,” in IEEE Transactions on Broadcasting, vol. 66, no. 3, Sept. 2020, pp. _630–646_ , doi: 10.1109/TBC.2019.2960940.
  * [83] M. K. Simon, J. K. Omura, and R. A. Sholtz “Spread spectrum communications,” vols. 1-3. Computer Science Press, Inc., 1985.
  * [84] D. Torrieri, “Principles of spread-spectrum communication systems,” Vol. 1. Heidelberg: Springer, 2005.
  * [85] S. Bae, S. Kim, and J. Kim, “Efficient frequency-hopping synchronization for satellite communications using dehop-rehop transponders,” in IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 1, pp. _261–274_ , Feb. 2016, doi: 10.1109/TAES.2015.150062.
  * [86] F. Yao, L. Jia, Y. Sun, Y. Xu, S. Feng, and Y. Zhu, “A hierarchical learning approach to anti-jamming channel selection strategies,” Wirel. Netw., vol. 25, no. 1, Jan. 2019, pp. _201–213_.
  * [87] C. Han and Y. Niu, “Cross-Layer Anti-Jamming Scheme: A Hierarchical Learning Approach,” IEEE Access, vol. 6, pp. 34874-34883, Jun. 2018.
  * [88] S. Lee, S. Kim, M. Seo, and D. Har, “Synchronization of Frequency Hopping by LSTM Network for Satellite Communication System,” in IEEE Communications Letters, vol. 23, no. 11, Nov. 2019, pp. _2054–2058_ , , doi: 10.1109/LCOMM.2019.2936019.
  * [89] C. Han, L. Huo, X. Tong, H. Wang, and X. Liu, “Spatial Anti-Jamming Scheme for Internet of Satellites Based on the Deep Reinforcement Learning and Stackelberg Game,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 5, May 2020, pp. _5331–5342_ , doi: 10.1109/TVT.2020.2982672.
  * [90] C. Han, A. Liu, H. Wang, L. Huo, and X. Liang, “Dynamic Anti-Jamming Coalition for Satellite-Enabled Army IoT: A Distributed Game Approach,” in IEEE Internet of Things Journal, vol. 7, no. 11, Nov. 2020, pp._10932–10944_ , doi: 10.1109/JIOT.2020.2991585.
  * [91] Y. Bie, L. Wang, Y. Tian, and Z. Hu, “A Combined Forecasting Model for Satellite Network Self-Similar Traffic,” in IEEE Access, vol. 7, 2019, pp. _152004–152013_ , doi: 10.1109/ACCESS.2019.2944895.
  * [92] L. Rossi, J. Chakareski, P. Frossard, and S. Colonnese, “A Poisson Hidden Markov Model for Multiview Video Traffic,” in IEEE/ACM Transactions on Networking, vol. 23, no. 2, April 2015, pp. _547–558_ , doi: 10.1109/TNET.2014.2303162.
  * [93] D. Yan and L. Wang, “TPDR: Traffic prediction based dynamic routing for LEO&GEO satellite networks,” 2015 IEEE 5th International Conference on Electronics Information and Emergency Communication, Beijing, 2015, pp. _104–107_ , doi: 10.1109/ICEIEC.2015.7284498.
  * [94] F. Xu, Y. Lin, J. Huang, D. Wu, H. Shi, J. Song, and Y. Li, “Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach,” in IEEE Transactions on Services Computing, vol. 9, no. 5, pp. 796-805, 1 Sept.-Oct. 2016, doi: 10.1109/TSC.2016.2599878.
  * [95] C. Katris, S. Daskalaki, Comparing forecasting approaches for Internet traffic, Expert Systems with Applications, Volume 42, Issue 21, 2015, _8172–8183_ , ISSN 0957-4174, [online] Available: https://doi.org/10.1016/j.eswa.2015.06.029.
  * [96] B. Gao, Q. Zhang, Y.-S. Liang, N.-N. Liu, C.-B. Huang, and N.-T. Zhang, “Predicting self-similar networking traffic based on EMD and ARMA,” 32. 47-56. 2011.
  * [97] X. Pan, W. Zhou, Y. Lu, and N. Sun, “Prediction of Network Traffic of Smart Cities Based on DE-BP Neural Network,” in IEEE Access, vol. 7, pp. _55807–55816_ , 2019, doi: 10.1109/ACCESS.2019.2913017.
  * [98] JX. LIU and ZH. JIA. “Telecommunication Traffic Prediction Based on Improved LSSVM,” International Journal of Pattern Recognition and Artificial Intelligence. 32. 10.1142/S0218001418500076. 2017.
  * [99] L. Ziluan and L. Xin. “Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks,” Journal of China Universities of Posts and Telecommunications. 25. 15-28+36. 10.19682/j.cnki.1005-8885.2018.0002. 2018.
  * [100] Z. Na, Z. Pan, X. Liu, Z. Deng, Z. Gao, and Q. Guo, “Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network,” Wireless Communications and Mobile Computing, vol. 2018, Article ID 3026405, 10 pages, 2018. [online] Available: https://doi.org/10.1155/2018/3026405
  * [101] GB. Huang, QY. Zhu, C. Siew, (2004). “Extreme learning machine: A new learning scheme of feedforward neural networks,” IEEE International Conference on Neural Networks - Conference Proceedings. 2. _985–990_ vol.2. 10.1109/IJCNN.2004.1380068.
  * [102] A. Goldsmith, “Path Loss and Shadowing,” in _Wireless Communications_ , Cambridge University Press, 2005, ch. $2$, pp. _25–48_.
  * [103] T. S. Rappaport, G. R. MacCartney, M. K. Samimi, and S. Sun, “Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design,” in IEEE Transactions on Communications, vol. 63, no. 9, pp. _3029–3056_ , Sept. 2015, doi: 10.1109/TCOMM.2015.2434384.
  * [104] S. Sangodoyin, S. Niranjayan, and A. F. Molisch, “A Measurement-Based Model for Outdoor Near-Ground Ultrawideband Channels,” in IEEE Transactions on Antennas and Propagation, vol. 64, no. 2, pp. _740–751_ , Feb. 2016, doi: 10.1109/TAP.2015.2505004.
  * [105] C. Wang, J. Bian, J. Sun, W. Zhang, and M. Zhang, “A Survey of 5G Channel Measurements and Models,” in IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 3142-3168, Fourthquarter 2018, doi: 10.1109/COMST.2018.2862141.
  * [106] B. Ai, K. Guan, R. He, J. Li, G. Li, D. He, Z. Zhong, and K. M. S. Huq, “On Indoor Millimeter Wave Massive MIMO Channels: Measurement and Simulation,” in IEEE Journal on Selected Areas in Communications, vol. 35, no. 7, July 2017, pp. _1678–1690_ , doi: 10.1109/JSAC.2017.2698780.
  * [107] G. Liang and H. L. Bertoni, “A new approach to 3-D ray tracing for propagation prediction in cities,” IEEE Trans. Antennas Propag., vol. 46, no. 6, Jun. 1998, pp. _853–-863_.
  * [108] M. Zhu, A. Singh, and F. Tufvesson, “Measurement based ray launching for analysis of outdoor propagation,” 2012 6th European Conference on Antennas and Propagation (EUCAP), Prague, 2012, pp. _3332–3336_ , doi: 10.1109/EuCAP.2012.6206329.
  * [109] Z. Yun and M. F. Iskander, “Ray Tracing for Radio Propagation Modeling: Principles and Applications,” in IEEE Access, vol. 3, 2015, pp. _1089–1100_ , doi: 10.1109/ACCESS.2015.2453991.
  * [110] D. J. Cichon and T. Kürner, “Propagation prediction models,” Florence, Italy, Tech. Rep. COST-231 TD (95) 66, Apr. 1995, pp. _115–207_.
  * [111] L. C. Fernandes and A. J. M. Soares, “Simplified characterization of the urban propagation environment for path loss calculation,” IEEE Antennas Wireless Propag. Lett., vol. 9, 2010, pp. _24–-27_.
  * [112] L. C. Fernandes and A. J. M. Soares, “On the use of image segmentation for propagation path loss prediction,” in IEEE MTT-S Int. Microw. Symp. Dig., Oct. 2011, pp. _129–-133_.
  * [113] M. Piacentini and F. Rinaldi, “Path loss prediction in urban environment using learning machines and dimensionality reduction techniques,” Comput. Manage. Sci., vol. 8, no. 4, Nov. 2011, _371–-385_.
  * [114] M. Uccellari, F. Facchini, M. Sola, E. Sirignano, G. M. Vitetta, A. Barbieri, and S. Tondelli, “On the use of support vector machines for the prediction of propagation losses in smart metering systems,” in Proc. IE
  * [115] S. P. Sotiroudis, S. K. Goudos, K. A. Gotsis, K. Siakavara, and J. N. Sahalos, “Application of a composite differential evolution algorithm in optimal neural network design for propagation path-loss prediction in mobile communication systems,” IEEE Antennas Wireless Propag. Lett., vol. 12, 2013, pp. _364–-367_.
  * [116] S. P. Sotiroudis and K. Siakavara, “Mobile radio propagation path loss prediction using Artificial Neural Networks with optimal input information for urban environments,” AEU-Int. J. Electron. Commun., vol. 69, no. 10, Oct. 2015, pp. _1453–-1463_.
  * [117] I. Popescu, I. Nafornita, and P. Constantinou, “Comparison of neural network models for path loss prediction,” in Proc. IEEE Int. Conf. Wireless Mobile Comput., Netw. Commun., Aug. 2005, pp. _44–-49_.
  * [118] E. Ostlin, H.-J. Zepernick, and H. Suzuki, “Macrocell path-loss prediction using artificial neural networks,” IEEE Trans. Veh. Technol., vol. 59, no. 6, Jul. 2010, pp. _2735-–2747_.
  * [119] B. J. Cavalcanti, G. A. Cavalcante, L. M. D. Mendonça, G. M. Cantanhede, M. M. de Oliveira, and A. G. D’Assunção, “A hybrid path loss prediction model based on artificial neural networks using empirical models for LTE and LTE-A at 800 MHz and 2600 MHz,” J. Microw., Optoelectron. Electromagn. Appl., vol. 16, Sep. 2017, pp._708–-722_.
  * [120] Y. Zhang, J. Wen, G. Yang, Z. He, and X. Luo, “Air-to-air path loss prediction based on machine learning methods in urban environments,” Wireless Commun. Mobile Comput., vol. 6, May 2018, Art. no. 8489326.
  * [121] C. A. Oroza, Z. Zhang, T. Watteyne, and S. D. Glaser, “A machinelearning-based connectivity model for complex terrain large-scale lowpower wireless deployments,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 4, Dec. 2017 pp. _576–-584_.
  * [122] Y. Zhang, J. Wen, G. Yang, Z. He, and J. Wang, “Path loss prediction based on machine learning: Principle, method, and data expansion,” Appl. Sci., vol. 9, p. 1908, May 2019.
  * [123] H. F. Ates, S. M. Hashir, T. Baykas, and B. K. Gunturk, “Path Loss Exponent and Shadowing Factor Prediction From Satellite Images Using Deep Learning,” in IEEE Access, vol. 7, 2019, pp. _101366–101375_ , doi: 10.1109/ACCESS.2019.2931072.
  * [124] J. Thrane, D. Zibar, and H. L. Christiansen, “Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz,” in IEEE Access, vol. 8, 2020, pp. _7925–7936_ , doi: 10.1109/ACCESS.2020.2964103.
  * [125] O. Ahmadien, H. F. Ates, T. Baykas, and B. K. Gunturk, “Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning,” in IEEE Access, vol. 8, 2020, pp. _64982–64991_ , doi: 10.1109/ACCESS.2020.2985929.
  * [126] T. Yairi, N. Takeishi, T. Oda, Y. Nakajima, N. Nishimura, and N. Takata, “A Data-Driven Health Monitoring Method for Satellite Housekeeping Data Based on Probabilistic Clustering and Dimensionality Reduction,” in IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 3, June 2017, pp. _1384–1401_ , doi: 10.1109/TAES.2017.2671247.
  * [127] T. Yairi, T. Tagawa, and N. Takata, “Telemetry monitoring by dimensionality reduction and learning hidden markov model,” in Proceedings of International Symposium on Artificial Intelligence, Robotics and Automation in Space, 2012.
  * [128] T. Yairi, M. Nakatsugawa, K. Hori, S. Nakasuka, K. Machida and N. Ishihama, “Adaptive limit checking for spacecraft telemetry data using regression tree learning,” 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), The Hague, 2004, pp. _5130–5135_ vol.6, doi: 10.1109/ICSMC.2004.1401008.
  * [129] T. Shahroz, L. Sangyup, S. Youjin, L. Myeongshin, J. Okchul, C. Daewon, and S. W. Simon, “Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA,” 25th ACM Special Interest Group on Knowledge Discovery and Data Mining International Conference, Alaska, USA, 2019.
  * [130] K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding,” 24th ACM Special Interest Group on Knowledge Discovery and Data Mining International Conference. London, UK, 2018.
  * [131] S. Fuertes, G. Picart, JY. Tourneret, L. Chaari, A. Ferrari, and C. Richard, “Improving Spacecraft Health Monitoring with Automatic Anomaly Detection Techniques,” 14th International Conference on Space Operations. Daejeon, Korea, 2016.
  * [132] D. L. Iverson, R. Martin, M. Schwabacher, L. Spirkovska, W. Taylor, R. Mackey, J. P. Castle and V. Baskaran, “General Purpose DataDriven Monitoring for Space Operations,” Journal of Aerospace Computing Information & Communication, 9(2):26-44 2012.
  * [133] PI. Robinson, M. H. Shirley, D. Fletcher, R. Alena, D. Duncavage, and C. Lee “Applying model-based reasoning to the fdir of the command and data handling subsystem of the international space station,” in Proc. of International Symposium on Artificial Intelligence, Robotics and Automation in Space, 2003.
  * [134] Y. Sun, L. Guo, Y. Wang, Z. Ma, and Y. Niu, “Fault diagnosis for space utilisation,” in The Journal of Engineering, vol. 2019, no. 23, pp. 8770-8775, 12 2019, doi: 10.1049/joe.2018.9102.
  * [135] S. K. Ibrahim, A. Ahmed, M. A. E. Zeidan, and I. E. Ziedan, “Machine Learning Methods for Spacecraft Telemetry Mining,” in IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 4, pp. _1816–1827_ , Aug. 2019, doi: 10.1109/TAES.2018.2876586.
  * [136] P. Wan, Y. Zhan, and W. Jiang, “Study on the Satellite Telemetry Data Classification Based on Self-Learning,” in IEEE Access, vol. 8, pp. 2656-2669, 2020, doi: 10.1109/ACCESS.2019.2962235.
  * [137] P. W. Ward, J. W. Betz, and C. J. Hegarty, “Satellite signal acquisition, tracking, and data demodulation in Understanding GPS: Principles and Applications,” Norwood, MA, USA: Artech House, pp. 153–241, 2006.
  * [138] A. V. Dierendonck, J. Klobuchar, and Q. Hua, “Ionospheric scintillation monitoring using commercial single frequency C/A code receivers,” in Proc. 6th Int. Tech. Meet. Satellite Div. Inst. Navig., Salt Lake City, UT, USA, vol. 93, pp. _1333–-1342_ , Sep. 1993.
  * [139] J. Lee, Y. T. J. Morton, J. Lee, H.-S. Moon, and J. Seo, “Monitoring and mitigation of ionospheric anomalies for GNSSbased safety critical systems: A review of up-to-date signal processing techniques,” IEEE Signal Process. Mag., vol. 34, no. 5, pp. _96-–110_ , Sep. 2017
  * [140] C. Cesaroni, L. Alfonsi, R. Romero, N. Linty, F. Dovis, S. V. Veettil, J. Park, D. Barroca, M. C. Ortega, and R. O. Perez, “Monitoring Ionosphere Over South America: The MImOSA and MImOSA2 projects,” 2015 International Association of Institutes of Navigation World Congress (IAIN), Prague, pp. _1–7_ , 2015, doi: 10.1109/IAIN.2015.7352226.
  * [141] L. Nicola, R. Rodrigo, C. Calogero, D. Fabio, B. Michele, C. J. Thomas, F. G. Joaquim, W. Jonathan, L. Gert, R. Padraig, C. Pierre, C. Emilia, and A. Lucilla, “Ionospheric scintillation threats to GNSS in polar regions: the DemoGRAPE case study in Antarctica,” in Proc. Eur. Navig. Conf., pp. _1–-7_ , 2016.
  * [142] J. Vila-Valls, P. Closas, C. Fernandez-Prades, and J. T. Curran, “On the ionospheric scintillation mitigation in advanced GNSS receivers IEEE Trans.” Aerosp. Electron. Syst., to be published.
  * [143] S. Taylor, Y. Morton, Y. Jiao, J. Triplett, and W. Pelgrum, “An improved ionosphere scintillation event detection and automatic trigger for GNSS data collection systems,” in Proc Int. Tech. Meet. Inst. Navig., pp. _1563–-1569_ , 2012.
  * [144] W. Fu, S. Han, C. Rizos, M. Knight, and A. Finn, “Real-time ionospheric scintillation monitoring,” in Proc. 12th Int. Tech. Meet. Satellite Div. Inst. Navig., vol. 99, pp. _14–-17_ , 1999.
  * [145] S. Miriyala, P. R. Koppireddi, and S. R. “Chanamallu Robust detection of ionospheric scintillations using MF-DFA technique Earth,” Planets Sp., vol. 67, no. 98, pp. _1–-5_ , 2015.
  * [146] R. Romero, N. Linty, F. Dovis, and R. V. Field, “A novel approach to ionospheric scintillation detection based on an open loop architecture,” in Proc. 8th ESA Workshop Satellite Navig. Technol. Eur. Workshop GNSS Signals Signal Process., pp. _1–-9_ , Dec. 2016.
  * [147] L. F. C. Rezende, E. R. de Paula, S. Stephany, I. J. Kantor, M. T. A. H. Muella, P. M. de Siqueira and K. S. Correa, “Survey and prediction of the ionospheric scintillation using data mining techniques,” Sp. Weather, vol. 8, no. 6, pp. _1–-10_ , 2010.
  * [148] Y. Jiao, J. J. Hall, and Y. T. “Morton Performance evaluations of an equatorial GPS amplitude scintillation detector using a machine learning algorithm,” in Proc 29th Int. Tech. Meet. Satellite Div. Inst. Navig., pp. _195–-199_ , Sep. 2016.
  * [149] Y. Jiao, J. J. Hall, and Y. T. Morton, “Automatic equatorial GPS amplitude scintillation detection using a machine learning algorithm,” IEEE Trans. Aerosp. Electron. Syst., vol. 53, no. 1, pp. _405–-418_ , Feb. 2017.
  * [150] Y. Jiao, J. J. Hall, and Y. T. Morton, “Automatic GPS phase scintillation detector using a machine learning algorithm,” in Proc. Int. Tech. Meet. Inst. Navig., Monterey, CA, USA, pp. _1160–-1172_ , Jan. 2017.
  * [151] Y. Jiao, J. J. Hall, and Y. T. Morton, “Performance evaluation of an automatic GPS ionospheric phase scintillation detector using a machine-learning algorithm Navigation,” vol. 64, no. 3, pp. _391–-402_ , 2017.
  * [152] N. Linty, A. Farasin, A. Favenza, and F. Dovis, “Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree,” in IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 1, pp. _303–317_ , Feb. 2019, doi: 10.1109/TAES.2018.2850385.
  * [153] R. Imam and F. Dovis, “Distinguishing Ionospheric Scintillation from Multipath in GNSS Signals Using Bagged Decision Trees Algorithm,” 2020 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), Vicenza, Italy, 2020, pp. 83-88, doi: 10.1109/WiSEE44079.2020.9262699.
  * [154] C. Politis, S. MalekiSina, M. Christos, G. TsinosChristos, G. T. Show, “On-board the Satellite Interference Detection with Imperfect Signal Cancellation,”
  * [155] A. V. Dandawate and G. B. Giannakis, “Statistical tests for presence of cyclostationarity,” in IEEE Transactions on Signal Processing, vol. 42, no. 9, pp. _2355–2369_ , Sept. 1994, doi: 10.1109/78.317857.
  * [156] O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “Survey of automatic modulation classification techniques: classical approaches and new trends,” in IET Communications, vol. 1, no. 2, pp. _137–156_ , April 2007, doi: 10.1049/iet-com:20050176.
  * [157] J. Hu, D. Bian, Z. Xie, Y. Li, and L. Fan, “An approach for narrow band interference detection in satellite communication using morphological filter,” International Conference on Information Technology and Management Innovation, Shenzhen, China, Sept.,
  * [158] Q. Liu, J. Yang, C. Zhuang, A. Barnawi, and B. A Alzahrani, “Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network,” in IEEE Access, vol. 7, pp. _177497–177503_ , 2019, doi: 10.1109/ACCESS.2019.2956544.
  * [159] L. Pellaco, N. Singh, and J. Jaldén. “Spectrum Prediction and Interference Detection for Satellite Communications,” arXiv preprint arXiv:1912.04716, 2019.
  * [160] P. Henarejos, M. Á. Vázquez, and A. I. Pérez-Neira, “Deep Learning For Experimental Hybrid Terrestrial and Satellite Interference Management,” 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019, pp. _1–5_ , doi: 10.1109/SPAWC.2019.8815532.
  * [161] N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data,” in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp. _778–782_ , May 2017, doi: 10.1109/LGRS.2017.2681128.
  * [162] F. Zhang, B. Du, and L. Zhang, “Scene Classification via a Gradient Boosting Random Convolutional Network Framework,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 3, pp. _1793–1802_ , March 2016, doi: 10.1109/TGRS.2015.2488681.
  * [163] A. S. Li, V. Chirayath, M. Segal-Rozenhaimer, J. L. Torres-Pérez, and J. van den Bergh, “NASA NeMO-Net’s Convolutional Neural Network: Mapping Marine Habitats with Spectrally Heterogeneous Remote Sensing Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. _5115–5133_ , 2020, doi: 10.1109/JSTARS.2020.3018719.
  * [164] S. A. Fatima, A. Kumar, A. Pratap, and S. S. Raoof, “Object Recognition and Detection in Remote Sensing Images: A Comparative Study,” 2020 International Conference on Artificial Intelligence and Signal Processing (AISP), Amaravati, India, pp. _1–5_ , 2020, doi: 10.1109/AISP48273.2020.9073614.
  * [165] L. Zhou, J. Liu, and L. Chen, “Vehicle detection based on remote sensing image of Yolov3,” 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, pp. _468–472_ , 2020, doi: 10.1109/ITNEC48623.2020.9084975.
  * [166] J. Redmon, et al. “You only look once: Unified, real-time object detection,” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  * [167] J. Redmon and A. Farhadi. “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.
  * [168] A. Femin and K. S. Biju, “Accurate Detection of Buildings from Satellite Images using CNN,” 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, pp. _1–5_ , 2020, doi: 10.1109/ICECCE49384.2020.9179232.
  * [169] A. Hassan, W. M. Hussein, E. Said and M. E. Hanafy, “A Deep Learning Framework for Automatic Airplane Detection in Remote Sensing Satellite Images,” 2019 IEEE Aerospace Conference, Big Sky, MT, USA, pp. _1–10_ , 2019, doi: 10.1109/AERO.2019.8741938.
  * [170] G. Mateo-Garcia, V. Laparra, D. Lopez-Puigdollers, and L. Gomez-Chova, “Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2020.3031741.
  * [171] Z. Shao, Y. Pan, C. Diao, and J. Cai, “Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 6, pp. _4062–4076_ , June 2019, doi: 10.1109/TGRS.2018.2889677.
  * [172] M. Tian, H. Chen, and G. Liu, “Cloud Detection and Classification for S-NPP FSR CRIS Data Using Supervised Machine Learning,” IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. _9827–9830_ , 2019, doi: 10.1109/IGARSS.2019.8898876.
  * [173] F. Wang, F. Liao, and H. Zhu, “FPA-DNN: A Forward Propagation Acceleration based Deep Neural Network for Ship Detection,” 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, pp. _1–8_ , 2020, doi: 10.1109/IJCNN48605.2020.9207603.
  * [174] L. Zong-ling et al., “Remote Sensing Ship Target Detection and Recognition System Based on Machine Learning,” IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. _1272–1275_ , 2019, doi: 10.1109/IGARSS.2019.8898599.
  * [175] H. Bandarupally, H. R. Talusani, and T. Sridevi, “Detection of Military Targets from Satellite Images using Deep Convolutional Neural Networks,” 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. _531–535_ , 2020, doi: 10.1109/ICCCA49541.2020.9250864.
  * [176] J. Zheng, X. -Y. Liu, and X. Wang, “Single Image Cloud Removal Using U-Net and Generative Adversarial Networks,” in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3027819.
  * [177] O. Ronneberger, P. Fischer, and T. Brox. “U-net: Convolutional networks for biomedical image segmentation,” International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
  * [178] J. Lu, Y. Chen, and R. He, “A Learning-Based Approach for Agile Satellite Onboard Scheduling,” in IEEE Access, vol. 8, pp. 16941-16952, 2020, doi: 10.1109/ACCESS.2020.2968051.
  * [179] R. Mital, K. Cates, J. Coughlin and G. Ganji, “A Machine Learning Approach to Modeling Satellite Behavior,” 2019 IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT), Pasadena, CA, USA, pp._62–69_ , 2019, doi: 10.1109/SMC-IT.2019.00013.
  * [180] K. Weasenforth, J. Hollon, T. Payne, K. Kinateder, and A. Kruchten, “Machine Learning-based Stability Assessment and Change Detection for Geosynchronous Satellites,” Advanced Maui Optical and Space Surveillance Technologies Conference, 2018.
  * [181] B. Jia, K. D. Pham, E. Blasch, Z. Wang, D. Shen, and G. Chen, “Space object classification using deep neural networks,” in 2018 IEEE Aerospace Conference, Big Sky, MT, pp. _1–-8_ , 2018.
  * [182] K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding,” in Proceedings of the 24th ACM Special Interest Group on Knowledge Discovery and Data Mining International Conference on Knowledge Discovery & Data Mining - KDD ’18, London, United Kingdom, pp. 387-–395, 2018.
  * [183] B. Chen, J. Cao, A. Parra, and T. Chin, “Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement,” 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), pp. _2816–2824_ , 2019, doi: 10.1109/ICCVW.2019.00343.
  * [184] M. Kisantal, S. Sharma, T. H. Park, D. Izzo, M. Märtens, and S. D’Amico, “Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results,” in IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 5, pp. _4083–4098_ , Oct. 2020, doi: 10.1109/TAES.2020.2989063.
  * [185] S. Jahirabadkar, P. Narsay, S. Pharande, G. Deshpande, and A. Kitture, “Space Objects Classification Techniques: A Survey,” 2020 International Conference on Computational Performance Evaluation (ComPE), Shillong, India, pp. _786–791_ , 2020, doi: 10.1109/ComPE49325.2020.9199996.
  * [186] D. Yadava, R. Hosangadi, S. Krishna, P. Paliwal, and A. Jain, “Attitude control of a nanosatellite system using reinforcement learning and neural networks,” 2018 IEEE Aerospace Conference, Big Sky, MT, pp. _1–8_ , 2018, doi: 10.1109/AERO.2018.8396409.
  * [187] A. M. Ahmed, A. Salama, H. A. Ibrahim, M. A. E. Sayed, and S. Yacout, “Prediction of Battery Remaining Useful Life on Board Satellites Using Logical Analysis of Data,” 2019 IEEE Aerospace Conference, Big Sky, MT, USA, pp. _1–8_ , 2019, doi: 10.1109/AERO.2019.8741717.
  * [188] JH. Lee, J. Park, M. Bennis, YC. Ko, “Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks,” arXiv preprint arXiv:2005.12521, 2020.
  * [189] N. Cheng, F. Lyu, W. Quan, C. Zhou, H. He, W. Shi, and X. Shen, “Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach,” in IEEE Journal on Selected Areas in Communications, vol. 37, no. 5, pp. _1117–1129_ , May 2019, doi: 10.1109/JSAC.2019.2906789.
  * [190] C. Jiang and X. Zhu, “Reinforcement Learning Based Capacity Management in Multi-Layer Satellite Networks,” in IEEE Transactions on Wireless Communications, vol. 19, no. 7, pp. _4685–4699_ , July 2020, doi: 10.1109/TWC.2020.2986114.
  * [191] C. Qiu, H. Yao, F. R. Yu, F. Xu, and C. Zhao, “Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks,” in IEEE Transactions on Vehicular Technology, vol. 68, no. 6, pp. _5871–5883_ , June 2019, doi: 10.1109/TVT.2019.2907682.
  * [192] W. Liu, F. Tian, and Z. Jiang, “Beam-hopping based resource allocation algorithm in LEO satellite network,” in Proc. Int. Conf. Space Inf. Netw. Singapore: Springer, pp. _113–-123_ , 2018.
  * [193] Z. Qu, G. Zhang, H. Cao, and J. Xie, “LEO satellite constellation for Internet of Things,” IEEE Access, vol. 5, pp. _18391–-18401_ , 2017.
  * [194] H. Tsuchida, Y. Kawamoto, N. Kato, K. Kaneko, S. Tani, S. Uchida, and H. Aruga, “Efficient Power Control for Satellite-Borne Batteries Using Q-Learning in Low-Earth-Orbit Satellite Constellations,” in IEEE Wireless Communications Letters, vol. 9, no. 6, pp. 809-812, June 2020, doi: 10.1109/LWC.2020.2970711.
  * [195] B. Zhao, J. Liu, Z. Wei, and I. You, “A Deep Reinforcement Learning Based Approach for Energy-Efficient Channel Allocation in Satellite Internet of Things,” in IEEE Access, vol. 8, pp. 62197-62206, 2020, doi: 10.1109/ACCESS.2020.2983437.
  * [196] G. Cui, X. Li, L. Xu, and W. Wang, “Latency and Energy Optimization for MEC Enhanced SAT-IoT Networks,” in IEEE Access, vol. 8, pp. 55915-55926, 2020, doi: 10.1109/ACCESS.2020.2982356.
  * [197] C. Zhang, “An AI-based optimization of handover strategy in non-terrestrial networks,” presented at the _12 th ITU Academic Conference Kaleidoscope Industry-driven digital transformation,_ Online, Dec. 7-11, 2020.
  * [198] X. Chen, W. Yao, Y. Zhao, X. Chen, and X. Zheng, “A practical satellite layout optimization design approach based on enhanced finite-circle method”, Struct. Multidisciplinary Optim., vol. 58, no. 6, pp. 2635-2653, Dec. 2018.
  * [199] K. Chen, J. Xing, S. Wang, and M. Song, “Heat source layout optimization in two-dimensional heat conduction using simulated annealing method”, Int. J. Heat Mass Transf., vol. 108, pp. 210-219, May 2017.
  * [200] Y. Aslan, J. Puskely, and A. Yarovoy, “Heat source layout optimization for two-dimensional heat conduction using iterative reweighted L1-norm convex minimization,” Int. J. Heat Mass Transf., vol. 122, pp. 432-441, Jul. 2018.
  * [201] K. Chen, S. Wang, and M. Song, “Temperature-gradient-aware bionic optimization method for heat source distribution in heat conduction”, Int. J. Heat Mass Transf., vol. 100, pp. 737-746, Sep. 2016.
  * [202] J. Sun, J. Zhang, X. Zhang, and W. Zhou, “A Deep Learning-Based Method for Heat Source Layout Inverse Design,” in IEEE Access, vol. 8, pp. 140038-140053, 2020, doi: 10.1109/ACCESS.2020.3013394.
  * [203] H. Li, P. Wang, C. Shen, and G. Zhang, ”Show, attend and read: A simple and strong baseline for irregular text recognition,” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019.
  * [204] Y. Zhang and W. Ye, “Deep learning–based inverse method for layout design”, Struct. Multidisciplinary Optim., vol. 16, no. 3, pp. 774-788, 2019.
  * [205] J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks”, Sci. Adv., vol. 4, no. 6, Jun. 2018.
  * [206] J. Tompson, K. Schlachter, P. Sprechmann, and K. Perlin, “Accelerating eulerian fluid simulation with convolutional networks”, Proc. 5th Int. Conf. Learn. Represent. ICLR, pp. 3424-3433, Apr. 2017, [online] Available: http://OpenReview.net.
  * [207] A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi, “Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters”, Integrating Mater. Manuf. Innov., vol. 3, no. 1, pp. 90-108, Dec. 2014.
  * [208] P. Robustillo, J. Zapata, J. A. Encinar, and J. Rubio, “ANN characterization of multi-layer reflectarray elements for contoured-beam space antennas in the Ku-band”, IEEE Trans. Antennas Propag., vol. 60, no. 7, pp. 3205-3214, Jul. 2012.
  * [209] A. Freni, M. Mussetta and P. Pirinoli, “Neural network characterization of reflectarray antennas”, Int. J. Antennas Propag., vol. 2012, pp. 1-10, May 2012.
  * [210] F. Güneş, S. Nesil, and S. Demirel, “Design and analysis of Minkowski reflectarray antenna using 3-D CST Microwave Studio-based neural network model with particle swarm optimization”, Int. J. RF Microw. Comput. Eng., vol. 23, no. 2, pp. 272-284, Mar. 2013.
  * [211] P. Robustillo, J. Zapata, J. A. Encinar, and M. Arrebola, “Design of a contoured-beam reflectarray for a Eutelsat European coverage using a stacked-patch element characterized by an artificial neural network”, IEEE Antennas Wireless Propag. Lett., vol. 11, pp. 977-980, 2012
  * [212] T. Shan, M. Li, S. Xu and F. Yang, “Synthesis of refiectarray based on deep learning technique”, Proc. Cross Strait Quad-Regional Radio Sci. Wireless Technol. Conf., pp. 1-2, Jul. 2018.
  * [213] M. Salucci, L. Tenuti, G. Oliveri, and A. Massa, “Efficient prediction of the EM response of reflectarray antenna elements by an advanced statistical learning method”, IEEE Trans. Antennas Propag., vol. 66, no. 8, pp. 3995-4007, Aug. 2018.
  * [214] D. R. Prado, J. A. López-Fernández, G. Barquero, M. Arrebola, and F. Las-Heras, “Fast and accurate modeling of dual-polarized reflectarray unit cells using support vector machines”, IEEE Trans. Antennas Propag., vol. 66, no. 3, pp. 1258-1270, Mar. 2018.