LEO Satellite Networks @ AEL - University of Manitoba (UM)
Advancing LEO Satellite Networks and Systems
At UM’s AEL lab, our work encompasses the fundamentals of LEO satellite communication and networking toward 6G, AI/ML-based management for SAGINs/NTNs, and its applications in engineering and societal challenges, such as digital divide and rural & remote connectivity.
An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground Terminals
Weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations of satellite Internet's essential ground terminal components, such as satellite antennas, significantly disrupting the space-ground link conditions between LEO satellites and ground stations. This challenge calls for not only region-based weather forecasts but also fine-grained detection capability on ground terminal components of fine-grained weather conditions. Such a capability can assist in fault diagnostics and mitigation for reliable satellite Internet, but its solutions are lacking, not to mention the effectiveness and generalization that are essential in real-world deployments. This paper discusses an efficient transfer learning (TL) method that can enable a ground component to locally detect representative weather-related conditions. The proposed method can detect snow, wet, and other conditions resulting from adverse and typical weather events and shows superior performance compared to the typical deep learning methods...
Published on 13 November 2024 in IEEE Transactions on Aerospace and Electronic Systems (Early Access)
UAV-assisted space-air-ground integrated networks: A technical review of recent learning algorithms
Recent technological advancements in space, air, and ground components have made possible a new network paradigm called "space-air-ground integrated network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed...
Published on 26 July 2024 in IEEE Open Journal of Vehicular Technology
On-Demand Routing in LEO Mega-Constellations with Dynamic Laser Inter-Satellite Links
Low Earth orbit (LEO) satellite mega-constellations are beginning to include laser inter-satellite links (LISLs) to extend the Internet to the most remote locations on Earth. Since the process of establishing these links incurs a setup delay on the order of seconds, a static network topology is generally established well in advance, which is then used for the routing calculations. However, this involves keeping links active even when they are not being used to forward traffic, leading to a poor energy efficiency. Motivated by technological advances that are gradually decreasing the LISL setup delays, we foresee scenarios in which it will be possible to compute routes and establish dynamic LISLs on demand. This will require considering setup delays as penalties that will affect the end-to-end latency. In this paper, we present a non-linear optimization model that considers these penalties in the cost function and propose ...
Published on 18 June 2024 in IEEE Transactions on Aerospace and Electronic Systems
Free-space optical (FSO) satellite networks performance analysis: Transmission power, latency, and outage probability
In free-space optical satellite networks (FSOSNs), satellites can have different laser inter-satellite link (LISL) ranges for connectivity. As the LISL range increases, the number of satellites from among all the satellites in the constellation that will be needed on the shortest path between a source and a destination ground station decrease, and thereby the number of the LISLs on the shortest path decreases. Greater LISL ranges can reduce network latency of the path but can also result in an increase in transmission power for satellites on the path. Consequently, this tradeoff between satellite transmission power and network latency should be investigated, and in this work we examine it in FSOSNs drawing on the Starlink Phase 1 Version 3 (i.e., the latest version of Starlink's Phase 1) and Kuiper Shell 2 (i.e., Kuiper's biggest shell) constellations for different LISL ranges and different inter-continental connections...
Published on 12 December 2023 in IEEE Open Journal of Vehicular Technology
Latency Versus Transmission Power Trade-Off in Free-Space Optical (FSO) Satellite Networks With Multiple Inter-Continental Connections
In free-space optical satellite networks (FSOSNs), satellites connected via laser inter-satellite links (LISLs), latency is a critical factor, especially for long-distance inter-continental connections. Since satellites depend on solar panels for power supply, power consumption is also a vital factor. We investigate the minimization of total network latency (i.e., the sum of the network latencies of all inter-continental connections in a time slot) in a realistic model of a FSOSN, the latest version of the Starlink Phase 1 Version 3 constellation. We develop mathematical formulations of the total network latency over different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections. We use practical system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program...
Published on 17 October 2023 in IEEE Open Journal of the Communications Society
HAPS-UAV-Enabled Heterogeneous Networks: A Deep Reinforcement Learning Approach
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs). However, the complexity, mobility, and heterogeneity of NTN entities and resources present various challenges from system design to deployment. This paper proposes a novel approach to designing a heterogeneous network consisting of HAPSs and unmanned aerial vehicles (UAVs) being LAPS entities. Our approach involves jointly optimizing the three-dimensional trajectory and channel allocation for aerial base stations, with a focus on ensuring fairness and the provision of quality of service (QoS) to ground users. Furthermore, we consider the load on base stations and incorporate this information into the optimization problem. The proposed approach utilizes a combination of ...
Published on 18 July 2023 in IEEE Open Journal of the Communications Society
Multivariate Variance-Based Genetic Ensemble Learning for Satellite Anomaly Detection
Proactive diagnosis of spacecraft issues and response to conceivable hazards has attracted considerable interest. Hidden anomalies in satellites can cause overall system degradation. In multivariate time series anomaly detection (AD), several sensors report to a fusion center at each timestamp. Each of these sensors measures a specific feature of the satellites. In this article, we first leverage the eigenvalues of covariance metrics of a multivariate time series to determine the anomaly score at each timestamp. Then, the anomaly scores are fed to various machine learning models. After that, we employ a Multivariate Variance-based Genetic Ensemble (MVVGE) learning method to ensemble the results of several models based on their corresponding performance. More specifically, we ensemble different Neural Network (NN), Random Forest (RF), and Linear Regression (LR) models based on their associated...
Published on 19 June 2023 in IEEE Transactions on Vehicular Technology
Routing Heterogeneous Traffic in Delay-Tolerant Satellite Networks
Delay-tolerant networking (DTN) offers a novel architecture that can be used to enhance store-carry-forward routing in satellite networks. Since these networks can take advantage of scheduled contact plans, distributed algorithms like the Contact Graph Routing (CGR) can be utilized to optimize data delivery performance. However, despite the numerous improvements made to CGR, there is a lack of proposals to prioritize traffic with distinct quality of service (QoS) requirements. This study presents adaptations to CGR to improve QoS-compliant delivery ratio when transmitting traffic with different latency constraints, along with an integer linear programming optimization model that serves as a performance upper bound. The extensive results obtained by simulating different scenarios show that the proposed algorithms can effectively improve the delivery ratio and energy efficiency while meeting latency constraints.
Published on 25 April 2023 in IEEE Journal of Radio Frequency Identification
Broadband Connectivity for Handheld Devices via LEO Satellites: Is Distributed Massive MIMO the Answer?
Significant efforts are being made to integrate satellite and terrestrial networks into a unified wireless network. One major aspect of such an integration is the use of unified user terminals (UTs), which work for both networks and can switch seamlessly between them. However, supporting broadband connectivity for handheld UTs directly from low Earth orbit (LEO) satellite networks is very challenging due to link budget reasons. This paper proposes using distributed massive multiple-input multiple-output (DM-MIMO) techniques to improve the data rates of handheld devices with a view to supporting their broadband connectivity by exploiting the ultra-dense deployment of LEO satellites and high-speed inter-satellite links. In this regard, we discuss DM-MIMO-based satellite networks from different perspectives, including the channel model, network management, and architecture...
Published on 7 March 2023 in IEEE Open Journal of the Communications Society
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout
Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this article, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several long short-term memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers...
Published on 22 September 2022 in IEEE Transactions on Aerospace and Electronic Systems
Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance
Community networks (CNs) have become an important paradigm for providing essential Internet connectivity in unserved and underserved areas across the world. However, an indispensable part of CNs is network management, where responsive and autonomous maintenance is much needed. With the technological advancement in telecommunications networks, a classical satellite-dependent CN is envisioned to be transformed into a satellite-integrated CN (SICN), which will embrace significant autonomy, intelligence, and scalability in network management. This article discusses the machine learning (ML)-based hierarchical approach to enabling autonomous self-maintenance for SICNs. The approach is split into the anomaly identification and anomaly mitigation phases, where the related ML methods, data collection means, deployment options, and mitigation schemes are presented...
Published on 13 January 2022 in IEEE Communications Magazine
Reinforcement learning for energy-efficient trajectory design of UAVs
Integrating unmanned aerial vehicles (UAVs) as aerial base stations (BSs) into terrestrial cellular networks has emerged as an effective solution to provide coverage and complement communication services in a fast and cost-effective manner. The three-dimensional (3-D) trajectories of UAVs have a remarkable impact on the performance of such networks. On the other hand, UAVs are battery limited, and thus optimizing their energy consumption is of high importance. In this regard, we propose a novel trajectory design mechanism for rotary-wing UAV-BSs in 3-D space to improve the energy efficiency of the network. In this approach, UAVs aim at maximizing an objective function that captures the tradeoff between energy consumption and throughput, while satisfying their ground users’ quality-of-service requirements. Using reinforcement learning, we model our problem as a multiarmed bandit...
Published on 6 October 2021 in IEEE Internet of Things Journal
Fairness-aware link optimization for space-terrestrial integrated networks: A reinforcement learning framework
The integration of space and air components considering satellites and unmanned aerial vehicles (UAVs) into terrestrial networks in a space-terrestrial integrated network (STIN) has been envisioned as a promising solution to enhancing the terrestrial networks in terms of fairness, performance, and network resilience. However, employing UAVs introduces some key challenges, among which backhaul connectivity, resource management, and efficient three-dimensional (3D) trajectory designs of UAVs are very crucial. In this paper, low-Earth orbit (LEO) satellites are employed to alleviate the backhaul connectivity issues with UAV networks, where we address the problem of jointly determining backhaul-aware 3D trajectories of UAVs, resource management, and associations between users, satellites and base stations (BSs) in an STIN while satisfying ground users' quality-of-experience requirements...
Published on 24 May 2021 in IEEE Access
Sensing for Space Safety and Sustainability: A Deep Learning Approach with Vision Transformers
The rapid increase of space assets represented by small satellites in low Earth orbit can enable ubiquitous digital services for everyone. However, due to the dynamic space environment, numerous space objects, complex atmospheric conditions, and unexpected events can easily introduce adverse conditions affecting space safety, operations, and sustainability of the outer space environment. This challenge calls for responsive, effective satellite object detection (SOD) solutions that allow a small satellite to assess and respond to collision risks, with the consideration of constrained resources on a small satellite platform. This paper discusses the SOD tasks and onboard deep learning (DL) approach to the tasks. Two new DL models are proposed, called GELAN-ViT and GELAN-RepViT, which incorporate vision transformer (ViT) into the Generalized Efficient Layer Aggregation Network (GELAN)...
The 12th Annual IEEE International Conference on Wireless for Space and Extreme Environments (WISEE 2024) in December 2024
Toward Multi-Layer Networking for Satellite Network Operations
Recent advancements in low-Earth-orbit (LEO) satellites aim to bring resilience, ubiquitous, and high-quality service to future Internet infrastructure. However, the soaring number of space assets, increasing dynamics of LEO satellites and expanding dimensions of network threats call for an enhanced approach to efficient satellite operations. To address these pressing challenges, we propose an approach for satellite network operations based on multi-layer satellite networking (MLSN), called ``SatNetOps''. Two SatNetOps schemes are proposed, referred to as LEO-LEO MLSN (LLM) and GEO-LEO MLSN (GLM)...
The 12th Annual IEEE International Conference on Wireless for Space and Extreme Environments (WISEE 2024) in December 2024
Signalling Load-aware Conditional Handover in 5G Non-Terrestrial Networks
Low Earth orbit (LEO) satellites based non-terrestrial networks (NTN) are envisioned to complement the fifth-generation (5G) terrestrial networks (TN), enabling global cellular services. However, the high mobility and large coverage of these satellites result in frequent and numerous inter-satellite handovers, leading to signalling storms that degrade the satellite gNodeB services. To address this, we mathematically formulate the handover problem and propose a novel signalling load-aware handover protocol based on conditional handover. We evaluate the effectiveness of the protocol using a customized discrete-event simulator, and compare against a set of baseline conditional handover schemes. Our findings show that the proposed protocol significantly reduces signalling peaks and balances the load more effectively, enhancing the robustness and efficiency of handover in 5G NTN...
To be presented on 29 October 2024 at the 20th International Conference on Network and Service Management (CNSM)
Secure and Efficient Group Handover Protocol in 5G Non-Terrestrial Networks
The growing low-Earth orbit (LEO) satellite con-stellations have become an essential part of the fifth-generation (5G) non-terrestrial network (NTN) market. These satellites can enable direct-to-cell connectivity for mobile devices and support various applications with ubiquitous coverage for 5G and beyond networks. However, satellite-based NTNs bring several challenges to the 5G handover protocol design. The high mobility of satellites can lead to signaling storms and security compromises during handovers. This paper addresses these challenges by proposing a secure and efficient group hand over protocol. The protocol's effectiveness is evaluated on a custom discrete-event simulator and compared against the baseline 5G hand over scheme. The simulator is made publicly available...
Presented on 12 June 2024 at the IEEE International Conference on Communications (ICC)
Closing the Performance and Management Gaps with Satellite Internet: Challenges, Approaches, and Future Directions
Recent advancements in low-Earth orbit (LEO) satellites represented by large constellations and advanced payloads provide great promises for enabling beyond 5G and 6G telecommunications and high-quality and ubiquitous Internet connectivity to everyone anywhere on Earth. LEO satellite networks are envisioned to bridge the urban-rural connectivity gap for the digital divide. However, the digital divide can hardly be closed by only providing connectivity to rural and remote areas. Various unprecedented challenges brought by the emerging satellite Internet still need to be resolved, such as inconsistent end-to-end performance guarantees and a lack of efficient management and operations in these areas, which are referred to as "performance gap" and "management gap", respectively. This position paper will briefly discuss these gaps, approaches to addressing the gaps, and some research directions based on our recent works....
Presented on 15 January 2024 at the IAB Workshop on Barriers to Internet Access of Services (BIAS) 2024
Closing the Digital Divide in Canada with Non-Terrestrial Networks
This talk covers the history and status quo of the digital divide in Canada and their solutions with non-terrestrial networks...
Invited Keynote Presentation on 6 September 2023, IEEE PIMRC 2023 Special Session on "The Role of Non-terrestrial Networks on 6G Communications: State-of-the-Art and Challenges"
Satellite Anomaly Detection using Variance Based Genetic Ensemble of Neural Networks
In this paper, we use a variance-based genetic ensemble (VGE) of Neural Networks (NNs) to detect anomalies in the satellite's historical data. We use an efficient ensemble of the predictions from multiple Recurrent Neural Networks (RNNs) by leveraging each model's uncertainty level (variance). For prediction, each RNN is guided by a Genetic Algorithm (GA) which constructs the optimal structure for each RNN model. However, finding the model uncertainty level is challenging in many cases. Although the Bayesian NNs (BNNs)-based methods are popular for providing the confidence bound of the models, they cannot be employed in complex NN structures as they are computationally intractable. This paper uses the Monte Carlo (MC) dropout as an approximation version of BNNs. Then these uncertainty levels and each predictive model suggested by GA are used to generate a new model...
Presented on 30 May 2023 at the IEEE International Conference on Communications (ICC)
SatAIOps: Revamping the Full Life-Cycle Satellite Network Operations
Recently advanced non-geostationary (NGSO) satellite networks represented by large constellations and advanced payloads provide great promises for enabling high-quality Internet connectivity to any place on Earth. However, the traditional approach to satellite operations cannot address the new challenges in the NGSO satellite networks imposed by the significant increase in complexity, security, resilience, and environmental concerns. Therefore, a reliable, sustainable, and efficient approach is required for the entire life-cycle of satellite network operations. This paper provides a timely response to the new challenges and proposes a novel approach called “SatAIOps” as an overall solution. Through our discussion on the current challenges of the advanced satellite networks, SatAIOps and its functional modules in the entire life-cycle of satellites are proposed, with some example technologies given...
Presented on 10 May 2023 at the IEEE/IFIP Network Operations and Management Symposium (NOMS)
A Cross-Layer Descent Approach for Resilient Network Operations of Proliferated LEO Satellites
With the proliferated low-Earth-orbit (LEO) satellites in mega-constellations, the future Internet will be able to reach any place on Earth, providing high-quality services to everyone. However, high-quality operations in terms of timeliness and resilience are lacking in the current solutions. This paper proposes a multi-layer networking approach called "Cross-Layer Descent (CLD)". Based on the proposed system model, principles, and measures, CLD can support foundational services such as telecommand (TC) transmissions for various network operation missions for LEO satellites compliant with the Consultative Committee for Space Data Systems (CCSDS) standards. The CLD approach enhances timing and resilience requirements using advanced communication payloads. From the simulation-based analysis, the proposed scheme outperforms other classical ones in resilience and latency for typical TC missions...
Presented on 29 March 2023 at the IEEE Wireless Communications and Networking Conference (WCNC)
Laser Inter-Satellite Link Setup Delay: Quantification, Impact, and Tolerable Value
Dynamic laser inter-satellite links (LISLs) provide the flexibility of connecting a pair of satellites as required (dynamically) while static LISLs need to be active continuously between the energy-constrained satellites. However, due to the LISL establishment time (termed herein as LISL setup delay) being in the order of seconds, realizing dynamic LISLs is currently unfeasible. Towards the realization of dynamic LISLs, we first study the quantification of LISL setup delay; then we calculate the end-to-end latency of a free-space optical satellite network (FSOSN) with the LISL setup delay; subsequently, we analyze the impact of LISL setup delay on the end-to-end latency of the FSOSN. We also provide design guidelines for the laser communication terminal manufacturers in the form of maximum tolerable value of LISL setup delay for which the FSOSN based on Starlink's Phase I satellite constellation will be meaningful...
Presented on 29 March 2023 at the IEEE Wireless Communications and Networking Conference (WCNC)
Enabling Resilient and Real-Time Network Operations in Space: A Novel Multi-Layer Satellite Networking Scheme
Recently advanced low-Earth-orbit (LEO) satellite networks represented by large constellations and advanced pay-loads provide great promises for enabling high-quality Internet connectivity to any place on Earth. However, the traditional access-based approach to satellite operations cannot meet the pressing requirements of real-time, reliable, and resilient operations for LEO satellites. A new scheme is proposed based on multi-layer satellite networking considering the advanced Ka-band and optical communications payloads on a satellite platform. The proposed scheme can enable efficient and resilient message transmissions for critical telecommand and telemetry missions through different layers of satellite networks, which consist of LEO, medium-Earth-orbit (MEO), and geostationary (GEO) satellites. The proposed scheme is evaluated in a 24-hr satellite mission and shows superior performance improvements...
Presented on 2 December 2022 at the IEEE Latin-American Conference on Communications (LATINCOM)
Routing Heterogeneous Traffic in Delay Tolerant Satellite Networks
Delay Tolerant Networking (DTN) has been proposed as a new architecture to provide efficient store-carryand-forward data transport in satellite networks. Since these networks relay on scheduled contact plans, the Contact Graph Routing (CGR) algorithm can be used to optimize routing and data delivery performance. However, in spite of the various improvements that have been made to CGR, there have been no significant proposals to prioritize traffic with different quality of service requirements. In this work we propose adaptations to CGR that allow performance improvements when sending traffic with different latency constraints, and develop a linear programming optimization model that works as a performance upper bound....
Presented on 12 October 2022 at the 10th Annual IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)
Satellite-Integrated Community Networks: Bridging the management gap with autonomous maintainability
This talk discusses the concept of satellite-integrated community networks (SICNs) and how to use autonomous maintenance capability to facilitate its management challenges...
Presented on 27 July 2022 at IETF 114
Re-envisioning space-air-ground integrated networks: Reinforcement learning for link optimization
To provide ubiquitous connectivity and achieve high reliability in the under-served and under-connected areas, the integration of aerial and space communication infrastructures into terrestrial networks is envisioned as a promising solution. In this regard, unmanned aerial vehicles (UAVs), in the role of aerial base stations (BSs), have been recommended to solve the coverage problem. In order to effectively leverage the advantages of UAVs deployment, UAV trajectory and resource management are required to effectively adapt to the network conditions. However, providing backhaul connectivity for terrestrial and aerial BSs deployed in these areas is another tremendous challenge. In this paper, we aim at jointly optimizing backhaul link and access link in a space-air-ground integrated network. We consider low Earth orbit (LEO) satellites as an effective backhaul solution. For access links...
Presented on 17 June 2021 at the IEEE International Conference on Communications (ICC)
IEEE LEO Satellite and Systems Space Environment Workshop
Low Earth orbit (LEO) satellites have many advantages over other satellites and ground communication systems: communication that can be both higher data rate and lower latency, better coverage of remote area and lower costs than other solutions. With compelling benefits there has been significant growth in the number of LEO satellites, with even greater future growth forecast. This raises questions of how the regions of space used by LEO satellites will be affected by this growth. Space debris is already a concern, and the addition of tens of thousands - or more - LEO satellites raises a number of questions. This IEEE Low Earth Orbit Satellites and Systems Space Environment Workshop covered the impact LEO Satellites are likely to have, and how we can gain the benefits these satellites while minimizing potential negative consequences....
Held on 2 May 2024
4th Workshop on Satellite Mega-Constellations in the 6G Era (6GSatComNet'24) - IEEE ICC 2024
There is rejuvenated interest in satellite communications & networking. Both the satellite and 3GPP industries aim at developing a seamlessly integrated one network. One main difference between the legacy satellite systems and the mega-constellations of the 6G era satellite system is the networking aspect with very high-speed inter-satellite links. For efficient operation, the network will have to be autonomous, intelligent, resilient, self organizing & self-controlling to reduce the cost and risk of human intervention. Distributed decision making, fault recovery, resilience, and scalability are among the important features. These networks will rely on AI techniques at all levels: Ground operations, onboard operations, inter-satellite and satellite-to-ground links. The satellite megaconstellations in the 6G era will create unprecedented opportunities once the unprecedented challenges are addressed by the research community...
Held on 13 June 2024