The image task scheduling demand exhibits a thriving trend since China leverages heterogeneous low earth orbit (LEO) satellites to empower the earth observation field for Belt and Road Initiative (BRI) countries and regions. However, the high-dynamic mobility and intermittent inter-satellite links (ISLs) exacerbate communication, caching, and computing (3C) resources scarcity, while inefficient scheduling significantly deteriorates the successful transmission ratio (STR). To tackle this challenge, this paper optimizes the task scheduling strategy. To be specific, we first propose a dynamic resource mapping model (DRMM) that captures the dynamic characteristics of 3C resources through discontinuous ISLs and quantifies resources over continuous time. Based on the DRMM, we formulate an STR maximization problem under heterogeneous resource capacity constraints. A computing resources priority allocation algorithm (CRPAA) is designed to preferentially allocate onboard computing resources for task processing, thereby freeing up transmission and caching resources for additional tasks. Furthermore, the CRPAA leverages an incrementally searching slots range to reduce computational complexity and optimize the scheduling strategy, planning each task individually and clearing it promptly to prevent redundant scheduling while enhancing execution efficiency. The extensive simulation results validate that the proposed algorithm outperforms benchmark approaches.

Low Earth orbit (LEO) mega-constellations equipped with laser inter-satellite links (LISLs) is an important part of future sixth generation (6G). However, how solar background noise affects LEO mega-constellations remains an open research topic. To this regard, this paper first derives the spatio-temporal distribution of affected LISLs in LEO mega-constellations at a specific moment, based on the characteristics of the impact, such as its location and duration. This distribution is then generalized to account for all moments during the Earth's rotation, considering the positional relationship between the LEO mega-constellations and the Sun. Additionally, we define two key metrics: the maximum number of affected LISLs (MNAL) and the affected duration ratio (ADR) to quantify the impact on the constellations. Several examples are presented to show that the MNAL decreases as the phase factor increases and increases with rising inclination. The ADR, on the other hand, increases with the phase factor, but initially increases and then decreases as the inclination rises. This work offers theoretical insights that can guide the design of future LEO mega-constellations.

Satellite networks hold great promise for future 6G communications because of their benefits such as wide coverage and large capacity. Since the end-to-end (e2e) queuing delay is regarded as one key factor affecting the quality of service (QoS) in satellite networks, accurate delay prediction is a critically important topic. However, the delay prediction is complicated due to the irregular and time-varying features of inter-satellite links (ISLs) and satellite-ground links (SGLs), such as discontinuity and alternation. In this paper, we propose to establish the multi-node satellite-to-ground communication procedure as a multi-hop tandemly queuing model and present a heterogeneous heterogeneous multi-hop martingale model for queuing delay analysis. Due to the unique time-varying characteristics, we propose to model the SGL and ISL services as the stationary Markov processes using the Markov chain Monte Carlo approach. To match the intermittency and burstiness of traffic, the data arrival and service processes are handled using the Markov process. We propose to use a scaling factor for reflecting the heterogeneity of data processing capability, and then present a novel approach to ensure the stability condition requirement of the multi-hop system. Using the multi-hop heterogeneous martingale approach, the tight upper bounds of the delay and backlog in heterogeneous links are derived, and then precise delay prediction can be obtained. Finally, numerous simulations are conducted to demonstrate the effectiveness and accuracy of the proposed martingale method in analyzing the system delay and backlog when compared to the existing stochastic network calculus method.

Satellite telemetry, tracking, and command (TT &C) operations are critical to ensuring the normal operation of mega satellite networks. However, the distribution and number of ground stations are limited, making that the existing TT &C mission scheduling methods are difficult to satisfy the TT &C requirements in mega satellite networks, resulting in low TT &C mission completion rates. In this paper, we first construct a space-ground integrated distributed TT &C mission scheduling frame-work utilizing the broad coverage characteristics of geostationary earth orbit (GEO) satellites. Then, we explore the similarity in the TT &C mission scheduling process among adjacent ground stations or GEO satellites, that the TT &C missions execute within the visible time window between satellites and TT &C antennas. Building on this, we share similar features of mission scheduling between the stations through federated learning (FL) and capture the temporal features of TT &C mission scheduling using deep reinforcement learning (DRL) at each station. Therefore, we propose a federated deep reinforcement learning (FDRL) assisting TT &C mission scheduling algorithm in mega satellite networks to enhance TT &C mission completion rates. Finally, the effectiveness of the FDRL algorithm is verified through simulation experiments. Compare to the traditional space-ground integrated algorithm, the FDRL algorithm improves the TT&C mission completion rate by about 26.5 %.

With the growing demand for communications in remote and dispersed areas, Internet-of-Remote Things (IoRT) networks with joint unmanned aerial vehicle (UAV) access and geostationary orbit (GEO) satellite backhaul hold great promise to provide sufficient access services to Internet-of-Things (IoT) users and devices. As the fundamental of the performance optimization of IoRT networks, the performance analysis sheds light on the relationship between the network performance (i.e., backlog, delay, and throughput) and access scale (i.e., the numbers of UAVs and UAV users). Aiming at the challenges brought by the complex network structure (i.e., two-level queuing network along with the converged traffic), we introduce the stochastic network calculus-based min-plus convolution and the leftover service to mathematically describe the complex structure. For the analytical challenges of the continuous-time arrival process and heterogeneous two-level link capacities, we innovatively prove their supermartingale features and further derive the closed-form expressions of the network backlog and delay bounds based on the martingale theory. To pursue higher throughput while guaranteeing delay performance, we formulate a mixed-integer optimization problem of the access scale that contains a nondifferentiable variable derived from a transcendental equation. For the tractability, we propose a three-directional iterative (TDI) algorithm to search the optimal solution of the optimization problem. Simulation results verify the tightness of our performance bounds in contrast to the standard bound and the effectiveness of the proposed algorithm.

The satellite data relay networks (SDRNs) hold great promise in 6G communications for the timely offloading of the global traffic. Since the delay performance is regarded as one of the most important metrics reflecting the offloading efficiency, studying its relationship with network parameters becomes really essential to the development and application of the SDRN. However, the complex data offloading process and heterogeneity of traffic arrivals and transmission links pose many challenges to the stochastic delay analysis. To accurately model the data offloading process in SDRNs, we build a series-parallel queuing model with through and cross traffic while considering the propagation delay. On this basis, we respectively propose a propagation delay embedded min-plus convolution method based on stochastic network calculus and a Markov chain method based on Monte Carlo to depict the leftover services of the heterogeneous links received by the per-flow traffic in an aggregate. To eliminate the impacts of the heterogeneity, we uniformly characterize the arrivals and leftover services by their moment generating functions (MGFs) which contain the full moment information, and shield the heterogeneity by deriving the envelopes of the arrivals and leftover services with the help of MGFs, Chernoff bound and union bound. Then, in the light of the geometric relationship between the envelopes of the arrivals and leftover services, we analyze the upper bounds of the stochastic delay, which provides the guidance to the network configuration. Eventually, simulation results verify the effectiveness of the theoretical analysis and further reveal maximum four times the delay difference between the heterogeneous links influenced by traffic type, burstiness, and access number.

Software defined network technique is a novel approach introduced to manage low earth orbit (LEO) small satellite networks. One important challenge is the allocation of the scarce virtualized satellite network resources in space environment. We devise a virtual network functions orchestration based model to implement the virtualized resources management for LEO satellite networks. This model is formulated as an integer linear programming (ILP) problem. Further, we propose a method combining Dantzig-Wolfe decomposition, column generation and branch-and-bound algorithm for the ILP problem to attain the optimal solution. Finally, simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

Satellite Data Relay Networks (SDRNs) play an important role in the data relay from User Satellites (USs) to ground stations by Tracking Data Relay Satellites (TDRSs). For better exploitation of SDRNs, the development of the systematic model and accurate system analysis is essential. To describe the end-to-end data transmission in SDRNs, we construct an MMOO/MMSP/1/K-G/G/1 tandem queuing model where the two parts depict the traffic arrival and transmission service of USs and TDRSs, respectively. Because the active and inactive periods of the data transmission are determined by the visibility between two satellites, classical buffer state based vacation policies become imprecise. Moreover, these two kinds of periods appear alternatively and their duration varies over time so that it is hard to model such intermittent transmission by existing service models. To overcome these difficulties, we propose a Markov Chain Monte Carlo based Markov Modulated Service Process (MMSP) which can tightly match the distributions of the active and inactive periods. In this process, we propose two algorithms to calculate the service state transition probability and the number of the sub-states in each service state, respectively, which guarantees the alternative transition between the active and inactive states as well as the sojourn time spent in each state. For the quality of service analysis, we find the different features of the queue variation under different arrival and service rate conditions. By separately calculating the related mean queue lengths and emergence probabilities, we first derive the expressions of the system loss probability, mean queue length, and mean delay. Finally, we conduct numerous simulations to verify the accuracy of our system model and performance evaluation, which provides the guidance to the buffer design and transmission resource allocation.

Tracking and Data Relay Satellite System (TDRSS) is playing an important role in data relay for user satellites. Subject to the finite number of antenna, the non-negligible antenna slewing time, and the time-varying connectivity of inter-satellite links (ISLs), it is significantly challenging to improve the selection of antenna scheduling sequence to improve performances (e.g., higher throughput, shorter mean queue length, smaller mean scheduling number, etc). To overcome above challenges, we utilize the antenna slewing model, the track model, and the multi-queue single-server queuing model to calculate the satellite-specific attributes such as the practical antenna slewing time, the link availability period and the buffer state. Furthermore, we propose a Heuristic Algorithm based on Optimal Weight (HAOW) considering the obtained satellite-specific attributes to optimize the antenna scheduling sequence. With the optimized scheduling sequence, the network performances are analyzed by the proposed queuing model. Finally, we conduct numerous simulations for the performance comparisons of the proposed HAOW with classical scheduling algorithms.

Remote sensing satellite (RSS) plays an increasingly important role in satellite networks. Current studies have paid wide attention to system modeling and performance analysis of RSS traffic acquisition, storing and transmission processes. However, in traffic acquisition process, the transitions between the "on" state and the "off" state of the on-board sensor generally exhibit a Markovian feature. Besides, the continuous stream traffic arrives in the "on" states and no traffic arrives in the "off" states. These features have not been sufficiently investigated. Meanwhile, the idiomatic Poisson traffic models are no more accurate, which inevitably brings great challenges to precise performance analysis. Aiming at above features, we present a Markov Modulated Deterministic Process (MMDP) model to simulate the traffic acquisition process. Afterwards, according to the global coverage of relay satellites, we describe the integrated traffic acquisition, storing and transmission processes as an MMDP/D/1/K queueing model. Further, we derive the closed-form expressions of some important quality of service indices (i.e., the loss probability, the average queue length and the average delay). Finally, we conduct numerous simulations to verify the effectiveness of theoretical results.

The following topics are dealt with: cellular radio; MIMO communication; wireless channels; optimisation; radiofrequency interference; probability; Long Term Evolution; mobile radio; telecommunication traffic; radio networks.