International Journal of Satellite Communications and Networking最新文献

Free space optical communication has emerged as a promising technology for high-speed and secure data transmission between ground stations on Earth and orbiting satellites. However, this communication technology suffers from signal attenuation due to atmospheric turbulence and beam alignment precision. Low Earth orbit satellites play a pivotal role in optical communication due to their low altitude over the Earth surface, which mitigates the atmospheric precipitation effects. This paper introduces a novel data path control law for satellite optical communication exploiting artificial intelligence-based predictive weather forecasting and a node selection mechanism based on reinforcement learning. Extensive simulations on three case studies demonstrate that the proposed control technique achieves remarkable gains in terms of link availability with respect to other state-of-the-art solutions.

Signal-to-noise ratio (SNR) estimation is crucial for spectrum management and data transmission. However, the existing classical methods in satellite-to-ground (SG) communication links, particularly for broadband transmission and under ultra-low SNR conditions, often encounter substantial estimation errors. In this paper, a novel SNR estimation method based on time-domain channel impulse response (CIR) reconstruction is proposed. Least square (LS) algorithm in frequency domain and inverse fast Fourier transform (IFFT) with a rectangular window are employed to reconstructed CIR. The noise energy is calculated by computing the average energy outside the window. The signal power is obtained by subtracting the noise energy from the total energy inside the window. In addition, a numerical simulation with a signal bandwidth of 400 MHz is performed to evaluate the effectiveness of the proposed algorithm in real SG communication scenarios. The simulation results show that compared with existing classical methods, even under ultra-low SNR conditions, the proposed algorithm exhibits more accurate estimation ability and stronger resistance to frequency offset interference in nonterrestrial network (NTN) channels.

This paper presents low Earth orbit (LEO) satellite constellation configuration based on the performance of Doppler effect in laser intersatellite links (LISLs). It studies the impact of the LEO constellation's parameters on the performance of the Doppler effect in LISLs. The paper aims to develop LEO satellite constellation configurations that evolve LISLs with minimal Doppler shift. It evaluates the impact of the variation of the relative distance, the inclination angle of the LEO constellation orbital planes, the orbital planes number in the LEO constellation, and the altitude on the performance of Doppler wavelength shift (DWS) in LISLs, for different operating laser wavelengths (OLWs) with respect to two possible intersatellite links (ISL) connection modes within the constellation, straight ISL (n-to-n) and inclined ISL (n-to-n − 1). n is the order of the satellite in the orbital plane. Simulations are conducted to evaluate the performance of these configurations in terms of altitude, OLW, inclination angle, and the number of orbital planes. In addition, both OneWeb and Starlink constellations are studied to evaluate DWS performance. The study demonstrates that DWS decreases either with the diminution of the relative distance between linked LEO satellites, the inclination of LEO constellation, and the OLW, or with the augmentation of the orbital planes number and altitude. Moreover, the overall DWS between two LEO satellites in the proposed constellation is at least 50% lower than the constellation configuration in other literature. The paper proposes the LEO constellation's configurations that perform LISLs with less possible Doppler effect by optimizing the LEO constellation parameters that impact the Doppler effect. The result of this study helps in the early stage of LEO satellite constellation designing in terms of payload simplicity and cost.

Low Earth Orbit (LEO) satellite-based Internet of Things (IoT) has become a hot topic in IoT networks due to the ability of global coverage, especially in remote areas. How to design a commercial LEO satellite-based constellation to meet the IoT traffic requirement remains an open problem. In this paper, we propose a throughput optimization algorithm for LEO satellite-based IoT networks meanwhile reducing the number of LEO satellite. Based on stochastic geometry theory, a closed-form expression is derived for the throughput of a dynamic LEO satellite-based IoT networks when LEO satellite equips with capture effect (CE) receiver and successive interference cancelation (SIC) receiver, respectively. Furthermore, a joint altitude and beamwidth optimization problem is formulated under the constraint of Walker constellation to optimize the throughput and the number of LEO satellites. To solve this multi-objective optimization problem, we design an iterative non-dominated sorting genetic algorithm II (NSGA-II) for the rapid development of IoT traffic. Simulation results show that our proposed algorithm can effectively improve the throughput performance of random access (RA) protocol in LEO satellite-based IoT networks compared to benchmark problems.

This study investigates the unsplittable multicommodity flow (UMCF) as a routing algorithm for LEO constellations. Usually, LEO routing schemes enable the Floyd–Warshall algorithm (shortest path) to minimize the end-to-end latency of the flows crossing the constellation. We propose to solve the UMCF problem associated with the system as a solution for routing over LEO. We use a heuristic algorithm based on randomized rounding known in the optimization literature to efficiently solve the UMCF problem. Furthermore, we explore the impact of choosing the first/last hop before entering/exiting the constellation. Using network simulation over Telesat constellation, we show that UMCF maximizes the end-to-end links usage, providing better routing while minimizing the delay and the congestion level, which is an issue today over new megaconstellations.

The mobile satellite communication (MSC) system is a vital communication method in which mobile users and network control centers connect via satellites. Since the satellite's communication is wireless, communication security is a critical factor to ensure accountable communication. Key exchange and authentication (KEA) mechanism is widely used to achieve data security for data transmitted in an insecure or open channel. Different authentication systems are suggested in the last many years to establish safe communication, whereas most existing security protocols are based on factorization or discrete logarithm. However, these systems are no longer reliable by Shor's algorithm as any discrete logarithm and factorization can be resolved in polynomial time on quantum computers. Thus, developing a quantum secure KEA protocol for MSC systems is necessary. In this direction, recently a ring learning with an error-based KEA technique is proposed to ensure a quantum-safe environment. This scheme is quantum-safe and satisfies desirable security attributes but has low efficiency in computation and communication. Moreover, establishing a secure session requires six communications among involved entities. As a result, replay attack detection at an early stage is not possible for the central authority (server), which could delay the server response, and the adversary gets the advantage of drawing a denial of service scenario for authorized entities. We propose a lattice-based KEA protocol for the MSC system to improve computation, communication efficiency, and early-stage replay attack detection. The security analysis of the proposed scheme is presented in the random oracle model. Calculation of performance is also presented to observe advantages in computation and communication overhead.

Recently, the Low Earth Orbit (LEO) satellite and Geostationary Earth Orbit (GEO) satellites share the same frequency to enhance spectrum efficiency in response to the scarcity of spectral resources. However, overlapping coverage areas emerge when LEO-GEO and LEO-LEO beams cover the same ground area in the GEO-LEO co-existing satellite system. Thus, the GEO-LEO inter-system interference and the LEO intra-system interference are generated, which leads to more severe and complex interference. To solve the co-channel interference problem without sacrificing the performance of the LEO system, a collaborative interference avoidance technology is proposed for the GEO-LEO co-existing satellite system. Firstly, the LEO satellite and user regrouping strategy (SURS) is employed to effectively avoid severe co-channel interference by changing service satellites. Then, based on regrouping satellite and user pairs, a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) method is adopted to realize further continuous beam pointing optimization (BPO) of the LEO user antenna and reduce the co-channel interference. Finally, simulation results verify that the proposed scheme can achieve the goal of interference mitigation and continuous service.

Recently, the low earth orbit (LEO) satellite has attracted much attention due to its advantages of low latency and construction costs. However, the scarcity of frequency spectrum resources has resulted in these satellites sharing the same spectrum to provide services for ground users. So, challenges such as overlapping beam coverage and inter-beam interference occur in the multi-beam LEO satellite coexistence scenario. Therefore, this paper focuses on the issue of interference coexistence in LEO and LEO (LEO–LEO) co-existing scenario, where the initially established system is the primary system, and the later established system is the secondary system. The interference coexistence problem is modeled as a multi-objective optimization problem. The joint beam power and pointing management (JBPPM) based on particle swarm optimization (PSO) is proposed. The power control is applied to the LEO satellites in the secondary system to reduce the interference to the primary system user. Meanwhile, beam pointing optimization is adopted by the LEO users in the secondary system to avoid interference from the primary system and ensure the minimum communication ability. The simulation results verify that the proposed technique can adapt to various coexistence conditions and outperform the existing method.