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Artificial Intelligence for Satellite Communication: A Review

Fares Fourati, Mohamed-Slim Alouini

arXiv:2101.10899v1eess.SPcs.AIeess.SY

TL;DR

Satellite communication promises broader and more resilient connectivity, but its limited resources and dynamic, heterogeneous networks create difficult management and control problems. This review surveys AI methods and their potential applications across satellite-communication challenges, reporting strong results in several application areas while identifying data and computational limitations.

  • Problem

    Satellite networks face challenging resource management, control, security, spectrum, and energy problems, while an extensive survey of AI applications across diverse satellite-communication aspects was lacking.

  • Method

    The paper reviews AI, its subfields and algorithms, and surveys proposed and potential AI-based solutions for diverse satellite-communication challenges.

  • Results

    AI applications show strong reported results across beam-hopping, traffic forecasting, telemetry mining, scintillation detection, interference management, remote sensing, integration, and energy management.

  • Takeaways & Limitations

    The review positions AI as a potential route toward more efficient, secure, reliable, and high-quality satellite communication systems.

  • Takeaways & Limitations

    Some surveyed approaches require unavailable or computationally costly data, including 3D ray-tracing images, while satellite onboard resources remain limited.

Abstract

from arXiv · show

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.

I. INTRODUCTION

Satellite communication can extend connectivity beyond terrestrial networks, but mobility, heterogeneity, limited resources, and other constraints complicate network operation. This review introduces AI and surveys its potential applications across diverse satellite-communication challenges.

  • Motivation: Satellite communication supports service continuity, ubiquity, and scalability across uncovered areas, temporary terrestrial outages, and traffic offloading.Potential coverage extends to transportation, energy, agriculture, business, and public safety.
  • Challenges: Limited onboard resources, rapid satellite mobility, and heterogeneous space, aerial, and ground layers complicate network control, security, spectrum management, and energy efficiency.
  • Research gap: Existing work covers selected satellite or wireless-communication topics, but an extensive survey of AI applications across diverse satellite-communication aspects was missing.
  • Scope: The review introduces AI, its subfields, and state-of-the-art algorithms, then discusses satellite challenges and potential AI-based solutions.
  • AI foundations: AI includes rule-based and machine-learning approaches, with learning organized into supervised, semi-supervised, unsupervised, and reinforcement learning.Learning approaches may be deep or shallow, and more complex methods do not necessarily produce better results.

B. Machine Learning (ML)

Machine learning trains models from data rather than explicit rules and includes deep, non-deep, supervised, semi-supervised, unsupervised, and reinforcement-learning approaches. The section presents representative algorithm families and explains why simpler methods can remain useful when data or computational resources are limited.

  • Definition: Machine learning learns patterns and representations from input data, expected outputs, and performance feedback instead of receiving explicit problem-solving rules.
  • Sub-fields: Machine-learning algorithms may be deep or non-deep and may use supervised, semi-supervised, unsupervised, or reinforcement learning.
  • Model selection: Classical non-deep methods can remain useful when data is scarce, while deep learning generally requires greater computational resources and larger datasets.
  • Learning paradigms: Supervised learning uses labeled data, unsupervised learning extracts structures from unlabeled data, and semi-supervised learning combines labeled and unlabeled portions.Semi-supervised learning is useful when labeling is difficult or expensive.
  • Algorithm families: Probabilistic models use statistical techniques, while support vector machines classify data by finding a maximum-margin decision boundary in a high-dimensional representation.The kernel trick avoids explicitly computing high-dimensional coordinates.

4) Decision Trees:

Decision trees represent data features through conditional branches and support both regression and classification. The section also introduces neural-network families, including convolutional, recurrent, autoencoder, and generative architectures.

  • Decision Trees: Decision trees represent data features as conditional control statements and support both regression and classification.Their intelligibility and simplicity contribute to their popularity in machine learning.
  • Decision Trees: Random forests combine many weak decision trees through bootstrap aggregating, while gradient boosting iteratively focuses new models on previous errors.Both are presented as robust non-deep algorithms.
  • Neural Networks: Neural networks use interconnected perceptron nodes and nonlinear activation functions for regression or classification.Weights connect input features through successive layers.
  • Neural Networks: Convolutional neural networks use convolutional hidden layers and are widely applied to image classification, object detection, and object tracking.They have also been used in speech and natural language processing.
  • Neural Networks: Recurrent neural networks process variable-length sequences by using previous outputs and internal memory.For each time step, inputs, activations, and outputs are connected through weighted transformations.
  • Deep Models: Autoencoders learn unsupervised data representations through bottleneck-based dimensionality reduction and reconstruction, supporting dimensionality reduction and anomaly detection.Generative models extend deep learning by learning input regularities so new samples can be generated.

4) Deep generative models:

Deep generative models learn regularities in input data to generate new samples. The section covers variational autoencoders, generative adversarial networks, and reinforcement learning as distinct learning paradigms.

  • Deep generative models: Deep generative models automatically discover regularities in input data so that new samples can be generated.Variational autoencoders and generative adversarial networks are identified as the most popular generative models.
  • Deep generative models: Variational autoencoders regularize their encoding distribution so the latent space has useful properties for generating new data.They are a type of autoencoder trained with unsupervised neural networks.
  • Deep generative models: Generative adversarial networks contain competing generator and discriminator networks.The generator produces samples intended to resemble the training data, while the discriminator estimates whether samples are generated or original.
  • Reinforcement Learning: Reinforcement learning selects actions by trying alternatives to maximize reward signals over immediate and subsequent rewards.Deep reinforcement learning incorporates deep learning into some reinforcement-learning approaches.
  • Reinforcement Learning: Reinforcement learning must balance exploration of new actions against exploitation of previously successful actions.Exploration searches the solution space, whereas exploitation selects actions already found to be effective.
  • Reinforcement Learning: A reinforcement-learning system includes an agent, environment, policy, reward signal, value function, and sometimes an environment model.The policy maps states to actions, while the value function estimates longer-term recompense.

1) Model Selection:

Model selection requires matching the learning approach and algorithm to the problem, data, and available resources. In satellite communications, this motivation extends to flexible beam-resource allocation, where beam hopping addresses demand variation but classical optimization faces scalability and adaptation limits.

  • Model Selection: AI approach selection should match the problem, data availability, and task rather than assume that more complex algorithms are better.The passage contrasts supervised, unsupervised, and reinforcement learning according to labeling and decision-making needs.
  • Model Selection: Algorithms are empirically compared by training on one data split and evaluating outputs on a separate test split.The training set fits the model, while the test set compares models.
  • Model Selection: Hyperparameter tuning can improve algorithm output by selecting suitable settings manually or through grid-search and stochastic methods.The passage links hyperparameter choice to higher accuracy.
  • Model Selection: Overfitting occurs when a model memorizes seen data, producing decreased training error and increasing test error on new data.Training, validation, and testing splits can help discover this behavior.
  • Satellite Resource Allocation: Fixed, uniform satellite-resource allocation creates hotspots with pending demand and cold-spots with unused capacity.The mismatch motivates flexible onboard allocation across the service coverage area.
  • Satellite Resource Allocation: Beam hopping dynamically illuminates a time-varying subset of cells to manage non-uniform and changing traffic requests.It uses available onboard resources for only a subset of beams at a time.
  • Satellite Resource Allocation: Classical beam-hopping optimization can struggle to find global designs as beam counts increase, making computation impractical for hundreds or thousands of beams.Changing scenarios can also require revising genetic algorithms and heuristics, increasing onboard computational complexity.

2) AI-based solutions:

AI-based beam-hopping methods combine learning and optimization to address difficult search and resource-allocation problems, while reinforcement learning improves delay, throughput, fairness, and complexity. Satellite anti-jamming research applies learning to adaptive routing and intelligent jamming environments.

  • Beam hopping: AI-based beam-hopping solutions use either end-to-end learning or a learning layer combined with optimization.The latter approach improves optimization by narrowing the search space to promising beam-hopping patterns.
  • Beam hopping: 52.2% lower transmission delay and 11.4% higher system throughput were achieved by a DRL-based beam-hopping algorithm versus previous algorithms.The method models beam-hopping illumination design as an MDP and optimizes long-term accumulated rewards.
  • Beam hopping: A learning-based predictor reduces the beam-hopping search space before optimization selects among promising patterns.Fully connected neural networks predict non-optimal patterns, allowing optimization to operate on a smaller candidate set.
  • Beam hopping: MO-DRL improved DVB-S2X throughput by 0.172% over previous techniques, while genetic algorithms required about 110 times its complexity for a similar result.The approach also aimed to ensure cell fairness under real conditions.
  • Anti-jamming: Satellite anti-jamming research addresses reliability and security because jamming can reduce or paralyze throughput, while adaptive learning-based jamming challenges non-learning defenses.Prior work also emphasizes frequency-domain defenses over space-based routing anti-jamming.

2) AI-based solutions:

Learning methods are applied to adaptive anti-jamming routing, traffic forecasting, and channel modeling in satellite networks. These applications respond to dynamic environments, long-range-dependent traffic, limited onboard computation, and the cost or data requirements of conventional channel-modeling methods.

  • Anti-jamming routing: DL and RL support space-based anti-jamming routing by handling IoS decision-space size, satellite-jammer interaction, and rapid strategy adaptation.A Stackelberg routing game uses DL to simplify routing selection and Q-Learning to respond to intelligent jamming.
  • Network traffic forecasting: Satellite traffic forecasting must account for long-range dependence while remaining computationally efficient for limited onboard resources.Short-range-dependence models are inaccurate, whereas prior long-range-dependence models can be too complex.
  • Network traffic forecasting: Traffic predictability supports congestion control, dynamic routing, channel allocation, network planning, and network security.The cited forecasting approaches include Markov, ARMA, ARIMA, FARIMA, EMD-ARMA, and neural-network combinations.
  • Channel modeling: A channel model represents wireless-channel effects as an impulse response in the frequency or time domain.Accurate models support deployment assessment, coverage and capacity optimization, and propagation forecasting.
  • Channel modeling: Ray tracing and detailed urban channel models are limited by unavailable or costly 3D data and computational demands that hinder real-time coverage optimization.Ray tracing requires 3D images, while some urban models require detailed structural features.

2) AI-based solutions:

Deep learning uses satellite or aerial imagery for channel and path-loss prediction, while telemetry processing supports satellite monitoring and fault diagnosis. These approaches remain bounded by incomplete 3D representation, input-feature requirements, and telemetry transmission and interpretation constraints.

  • Path-loss forecasting: Classical machine-learning path-loss methods include SVMs, neural networks, and decision trees, but many earlier approaches require 3D images.Recent work shifts toward deep learning with 2D satellite or aerial images.
  • Path-loss forecasting: ≈1 gain improvement at 811 MHz and ≈4.7 at 2630 MHz were reported for DL-based RSRP prediction over previous techniques including ray tracing.The model used satellite images and other input parameters for specific receiver locations and scenarios.
  • Path-loss forecasting: Two-dimensional satellite images may be insufficient to characterize three-dimensional structure, requiring additional features such as building heights.This limitation applies when the path-loss distribution depends on 3D environmental structure.
  • Telemetry: Telemetry records and transfers measurements that support satellite status tracking, abnormal-event detection, and control.Telemetry processing applies correlation finding, pattern recognition, anomaly detection, classification, forecasting, and clustering for monitoring and fault diagnosis.
  • Telemetry: Telemetry systems face narrow bandwidth, fixed-length frames, intermittent spacecraft-ground contacts, and highly complex data volumes.These constraints make transmitting, analyzing, monitoring, and interpreting telemetry increasingly difficult.

2) AI-based solutions:

AI-based methods support satellite telemetry analysis and ionospheric scintillation detection, addressing anomaly monitoring, signal distortion, and classification challenges.

  • Telemetry analysis: Telemetry monitoring uses clustering, dimensionality reduction, hidden Markov models, regression trees, kNN, SVM, and LSTM for health assessment and anomaly detection.These approaches have been tested on spacecraft telemetry, including Centre National d’Etudes Spatiales data.
  • Telemetry analysis: LSTM, ARIMA, and RNN models achieved high prediction accuracy on Egyptsat-1 telemetry, while model choice was linked to satellite lifetime.Simple linear regression was suggested for short-lifetime satellites, whereas neural networks were suggested for long-lifetime satellites.
  • Scintillation detection: Ionospheric scintillation distorts GNSS signal phase and amplitude, creating temporal delays and accuracy limitations for positioning applications.Its quasi-random nature makes scintillation difficult to model and detect reliably.
  • Scintillation detection: Threshold-based scintillation detection can miss weak or transient events and confuse multipath effects, while alternatives may require costly operations or customized receivers.The limitation motivates more robust detection algorithms and receiver designs.
  • Scintillation detection: Decision trees and random forests detected amplitude scintillation with 99.7% accuracy and 99.4% F-score, matching manual annotation levels.Decision trees also classified GNSS data as scintillated, multipath-affected, or clean with 96% accuracy.

G. Managing Interference

Interference management is essential because congestion and interference reduce satellite communication quality, efficiency, and revenue. AI-based methods address detection, classification, suppression, and adaptive satellite-terminal coordination.

  • Interference management: Interference management covers detection, classification, suppression, and techniques that minimize interference occurrence.The need increases as more users and applications share congested satellite frequency bands.
  • Limitations: Traditional hypothesis-testing detectors require specific signal and channel knowledge, making separate detectors for diverse wireless standards impractical.This dependence limits generality across contemporary heterogeneous signal environments.
  • AI-based solutions: An AI framework combines SVM, unsupervised learning, and deep reinforcement learning for satellite selection, antenna pointing, and tracking in satellite-terrestrial networks.The framework targets moving terminals and stations while supporting interference minimization.
  • AI-based solutions: LSTM-based forecasting detects anomalies by comparing received spectra with spectra predicted from historical anomaly-free data.Autoencoders trained on interference-free signals can also detect interference through reconstruction-error differences.

2) AI-based solutions:

AI supports remote sensing, autonomous satellite operations, behavior modeling, pose estimation, attitude control, and battery-life estimation across space applications.

  • Remote sensing: Deep learning has expanded remote-sensing applications including image classification, object detection, segmentation, and image restoration.Applications include land-cover classification, vehicle detection, and cloud removal using U-Net and GANs.
  • Satellite autonomy: Random forests improved both solution quality and response time for onboard scheduling of agile Earth-observing satellites.Onboard scheduling supports faster decisions and can allow satellites to acquire more images.
  • Satellite behavior: Machine learning models satellite stability, detects anomalous behavior and location, and predicts satellite maneuvers over time.These applications help automate monitoring as the number and diversity of satellites increase.
  • Space operations: A combined machine-learning and geometric-optimization method won first place in an ESA satellite pose-estimation challenge.Pose estimation supports debris removal, inter-spacecraft communication, and docking.
  • Spacecraft control and energy: Neural networks and reinforcement learning provided torque for nanosatellite stabilization along three axes, while machine learning estimated remaining battery life.Battery-life estimation was developed to help avoid catastrophic battery-failure events.

2) AI-based solutions:

AI-based methods address resource allocation, computing offloading, capacity management, and energy constraints in heterogeneous space-air-ground networks.

  • Computing and resource management: An RL-based computing-offloading approach handles multidimensional SAGIN resources alongside joint resource allocation and task scheduling.The design uses satellites for cloud access and UAVs for near-user edge computing.
  • Computing and resource management: A low-complexity RL model assigns satellite capacity over the long term to maximize system utility.The approach targets advanced capacity management in heterogeneous multilayer networks.
  • Computing and resource management: Deep reinforcement learning jointly manages caching, networking, and computing resources in a software-defined satellite-terrestrial network.The resource-assignment problem is formulated as a joint optimization problem.
  • Energy management: Satellite energy management is increasingly important as SAGIN connectivity raises energy demands and LEO satellites have limited resources and energy capacity.Efficient scheduling is needed because current satellite resources cannot satisfy growing device demand.
  • Energy management: AI-based power-saving approaches include DNN compression before transmission to reduce latency and save power.Battery dependence without solar illumination can shorten satellite lifetimes and increase network costs.

L. Other Applications

AI-based methods address diverse satellite-communication applications beyond the main communication-management challenges, including handoff optimization, thermal and antenna design, and carrier-signal detection. The review reports improvements and potential solutions across these applications.

  • Handoff Optimization: Handoffs in LEO networks occur frequently because satellite movement creates dynamic connectivity patterns.This distinguishes LEO handoff management from terrestrial-network handoffs.
  • Handoff Optimization: A CNN-based classification method decreased handoff by more than 25% for over 70% of UEs, while the strongest-beam method reduced average RSRP by 3%.The CNN was selected because historical RSRP has strong local spatial correlation, whereas sequential decisions can affect future handoffs.
  • Other Applications: The review also covers AI applications in thermal-layout design, antenna analysis and optimization, and other satellite-communication functions.Reported applications include neural networks, kriging, and support vector regression for reflectarray analysis and design.
  • Carrier Signal Detection: Carrier-signal detection is crucial because signals must be separated before classification, modulation, demodulation, and other processing tasks.Traditional threshold-based algorithms commonly required human intervention, motivating learned alternatives.
  • Carrier Signal Detection: Deep-learning approaches have been applied to carrier detection in FSK, Morse, and broadband-spectrum signals using neural-network and fully convolutional models.The fully convolutional network treats the broadband power spectrum as an input for carrier-signal detection.
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