Low Earth Orbit Satellite Networks Research Articles

Distributed Anti-Cascading Routing Scheme Based on Fuzzy Logic in LEO Satellite Networks

Distributed Anti-Cascading Routing Scheme Based on Fuzzy Logic in LEO Satellite Networks

Rate-Splitting for Joint Unicast and Multicast Transmission in LEO Satellite Networks with Non-Uniform Traffic Demand

Rate-Splitting for Joint Unicast and Multicast Transmission in LEO Satellite Networks with Non-Uniform Traffic Demand

A Novel Analytical Model for LEO and MEO Satellite Networks based on Cox Point Processes

A Novel Analytical Model for LEO and MEO Satellite Networks based on Cox Point Processes

Adaptive Snapshot-Based Elastic Route Reconstruction for Distributed LEO Satellite Networks

Adaptive Snapshot-Based Elastic Route Reconstruction for Distributed LEO Satellite Networks

FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks

FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks

Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks.

Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented "end-edge-cloud" multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.

Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy.

With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols.

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks.

In light of the high bit error rate in satellite network links, the traditional transmission control protocol (TCP) fails to distinguish between congestion and wireless losses, and existing loss differentiation methods lack heterogeneous ensemble learning models, especially feature selection for loss differentiation, individual classifier selection methods, effective ensemble strategies, etc. A loss differentiation method based on heterogeneous ensemble learning (LDM-HEL) for low-Earth-orbit (LEO) satellite networks is proposed. This method utilizes the Relief and mutual information algorithms for selecting loss differentiation features and employs the least-squares support vector machine, decision tree, logistic regression, and K-nearest neighbor as individual learners. An ensemble strategy is designed using the stochastic gradient descent method to optimize the weights of individual learners. Simulation results demonstrate that the proposed LDM-HEL achieves higher accuracy rate, recall rate, and F1-score in the simulation scenario, and significantly improves throughput performance when applied to TCP. Compared with the integrated model LDM-satellite, the above indexes can be improved by 4.37%, 4.55%, 4.87%, and 9.28%, respectively.

Distributed Detour Routing Scheme for Link Failure with Minimized Overhead in LEO Satellite Networks

The mobility of low Earth orbit (LEO) satellites causes the LEO satellite network to experience topology changes. Topology change includes periodic topology change that occurs naturally and unpredictable topology change that occurs due to instability of the inter-satellite link between satellites. Periodic and unpredictable topology change causes frequent topology change, requiring massive communications throughout the network due to frequent route convergence. LEO satellites have limited onboard power because they operate on batteries. The waste of limited satellite onboard resources shortens the lifespan of the satellite, and achieving stable end-to-end transmission is challenging for the network. In this regard, minimizing communication overhead is a fundamental consideration when designing a routing scheme. In this paper, we propose a distributed detour routing scheme with minimal communication overhead. This routing scheme consists of a rapid detour, selective flooding, and link recovery procedures. When a link failure occurs in the network, a rapid detour can detect link failure using only a precalculated routing table. Subsequently, selective flooding searches for the optimal detour point within the minimum hop region and flood to detour point. After link recovery, a procedure is defined to traverse the pre-detour path and switch it back to the original path. The simulation results show that the proposed routing scheme achieves a reduction of communication overhead by 97.6% compared with the n-hop flooding approach.

Airborne Internet: An Ultradense LEO Networks Empowered Satellite-to-Aircraft Access and Resource Management Approach

With the rapid development of low earth orbit (LEO) satellite technologies and constellation projects (like Starlink, OneWeb), the ongoing ultra-dense LEO satellite networks can provide an efficient solution to ubiquitous connection of aircraft. It is urgent to design the ultra-dense LEO network enabled satellite-to-aircraft communication for in-cabin users with high-speed Internet connectivity requirements. In this paper, we investigate the resource scheduling of ultra-dense LEO network enabled satellite-to-aircraft communication and the in-cabin communication inside aircraft in civil aviation. First, we adopt the Lyapunov optimization framework to tackle the high dynamic and fast mobility of satellites and aircraft. For the satellite-to-aircraft access and subchannel assignment, we propose a dynamic access and subchannel allocation algorithm based on matching theory, which can converge to a stable matching within a finite iterations. To guarantee customized needs of in-cabin users, we propose a power control algorithm based on the successive convex approximation technology to optimize the power allocation of satellites on each subchannel and the transmit power of aircraft for each in-cabin user, which is proven to obtain a saddle point or a local minimum with low complexity. Extensive simulations have been carried out to validate the effectiveness of the proposed resource allocation method.

Coverage in Cooperative LEO Satellite Networks

Coverage in Cooperative LEO Satellite Networks

A Joint Minimum Hop and Earliest Arrival Routing Algorithm for LEO Satellite Networks

A Joint Minimum Hop and Earliest Arrival Routing Algorithm for LEO Satellite Networks

Data scheduling and resource allocation in LEO satellite networks for IoT task offloading

Data scheduling and resource allocation in LEO satellite networks for IoT task offloading

Load-Balanced Collaborative Offloading for LEO Satellite Networks

Low earth orbit (LEO) satellite networks have become one of the hot research areas as an essential part of satellite communication networks. The dynamic topology and unbalanced traffic demand may lead to inter-satellite link congestion, thus, improving network load balancing performance is one of the key issues to be addressed in LEO satellite networks. We propose a load-balanced collaborative offloading (LBCO) strategy to achieve a balanced traffic distribution in LEO satellite networks. LBCO strategy consists of two algorithms, namely, channel-aware gradient fair association (CAGFA) algorithm and inter-satellite links collaborative offloading (ISLCO) algorithm. The CAGFA algorithm aims to maximize the aggregate weighted utility, and the ISLCO algorithm aims to achieve the traffic offloading and download observation data from the LEO satellite network. Specifically, we first determine the actual downloading satellite set and neighboring satellite set by constructing an earth station (ES) time-share graph and a space-time topology graph. Then, the LBCO strategy uses the CAGFA algorithm to obtain the optimal satellite terminal association indicator and the load of downloading satellites. Finally, the ISLCO algorithm is proposed to achieve proportional offloading of traffic among the neighboring satellites and download massive observation data. Simulations show that the proposed CAGFA algorithm improves the weighted utility by 3.3% and the convergence by 47.6% compared with the benchmark stochastic gradient descent-based association (SGDA) algorithm. We also validate the performance of the LBCO strategy by data download throughput, which performs better than other benchmark algorithms under three different load scenarios.

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Time-Varying Graph and Binary Tree Search Based Routing Algorithm for LEO Satellite Networks

Though low earth orbit (LEO) satellite networks have superiorities in short delay, wide coverage and flexible networking, the end-to-end (E2E) transmission has been confronted with severe challenges due to frequent changes of network topology. In this paper, a time-varying graph and binary tree search based routing algorithm for LEO satellite networks is proposed to reduce the E2E transmission delay and improve the network quality of service. Firstly, a coordinate graph (CG) model is adopted to characterize the topology structure and resource distribution of LEO satellite networks. Then, an E2E minimum-hop area is determined based on CG model, so that a minimum-hop binary tree (MHBT) is constructed. Finally, the pruning traversal of MHBT is performed to obtain all minimum-hop paths, based on which the E2E path with the shortest delay is determined. Simulation results show that the proposed routing algorithm not only reduces the path delay, but also improves the file delivery ratio.

Robust integrated planning for LEO satellite network design and service operations

We try to understand Low Earth Orbit (LEO) satellites from an operations research perspective. We propose an integrated model to support the strategic designing of ground stations, as well as the operational planning of traffic routes. A Mixed-Integer Nonlinear Program is proposed to solve the integrated model. Through a case study using satellite simulation platforms, we find that the responsiveness of key satellites is more sensitive to system design parameters compared with that of non-key satellites.

Low delay fragment forwarding in LEO satellite networks based on named data networking

The LEO satellite network with cache and computing capabilities has become an important evolution of the next generation (6G) mobile communication network. The satellite networks employing Named Data Networking (NDN) can achieve swift content access through in-network caches and data multicast. The data packet is the basic unit of transmission in NDN. In order to adapt to the limited MTU of links, large data packets must be transmitted in fragments. However, due to the issues such as large transmission delay, redundant reassembly, and infeasible fragment reuse, the existing fragmentation methods in NDN cannot be applied to satellite communication. In this paper, we propose a low delay fragment forwarding method in NDN: Direct Forwarding and Reuse of Fragments (DFRF). Specifically, the content in the large data packet will be split and packaged into fragments that conform to NDN semantics. Once the fragment is received by the satellite router, it can be forwarded directly without reassembly, thus saving the forwarding delay. In addition, we design a new fragment format and the list-based cache, so that all fragments generated from the same data packet can be cached intact in a list, and each fragment can be reused as a complete packet in the content store (CS). The proposed approach has been further evaluated and the simulation results show that compared with the NDN Link Protocol (NDNLP), using the DFRF method, the end-to-end transmission delay of large content is reduced by up to 51%, and the content acquisition delay is reduced by up to 60% through fragment reuse.

A comparative study of IP-based and ICN-based link-state routing protocols in LEO satellite networks

A comparative study of IP-based and ICN-based link-state routing protocols in LEO satellite networks

NDN-Based Coded Caching Strategy for Satellite Networks

To solve the transmission correlation issue arising from traditional named data networking (NDN) caching during segmenting contents, where users must obtain data blocks related to the requested file blocks to recover the source data, in this paper, we propose an NDN-based coded caching strategy for low Earth orbit (LEO) satellite networks, using coding operations to remove the transmission correlation of data. To achieve efficient content distribution, the satellite node’s coded package is strategically placed with the objectives of minimizing the backhaul link load and content acquisition latency. The optimization problem is solved by using a multi-colony ant colony algorithm, enabling fast content retrieval. We designed a cluster cooperative distribution mechanism to simplify the satellite network’s management and reduce the load of satellite links for content distribution when covered by multiple satellite nodes. Finally, we compare the multi-objective coded caching (CCMO) introduced in this paper with the most popular (MP) strategy and the random-based coded caching (CCR) strategy; the average cache hit ratio, backhaul link load, and content acquisition delay are compared and analyzed. The results show that CCMO performs better.