Numerous mega low-Earth orbit (LEO) satellite constellation plans have recently become a significant part of the future satellite communication era. Since the existing ground-based and geostationary Earth orbit (GEO)-based telemetry system is unsuitable for monitoring the working status of mega LEO constellations, the networked satellite telemetry system is used to achieve the full time, low delay telemetry in this article, which is a significant scenario of satellite Internet of things. In order to satisfy the data transmission requirements of extensive satellites, this article formulates the channel allocation problem, which aims at maximizing the total transmitted data value by allocating multiple medium-Earth orbit (MEO) beams in multiple time slots to serve multiple LEO satellites. Considering that the data generation states of LEO satellites are hybrid constant and stochastic, that the MEO satellites could allocate channels more timely than the ground mission center, and that the action space for channel allocation is too large, the multiagent deep-reinforcement-learning-based algorithm is adopted to solve the channel allocation problem. Furthermore, this article designs the connections of the output layer of the deep$Q$network so as to reduce the computation and storage overhead. Finally, the upper bound performance (UBP) of the channel allocation problem is analyzed and numerical simulation is performed to verify the effectiveness of our proposed channel allocation algorithm.

Numerous mega low earth orbit (LEO) satellite constellation plans have recently emerged as an indispensable part of the future satellite communication era. Since the traditional ground-based and geostationary earth orbit (GEO)-based telemetry systems are unsuitable for monitoring the operation status of mega constellations, a networked telemetry system is adopted to achieve full time, low delay telemetry in this paper. In order to satisfy the data transmission requirements of extensive satellites, this paper formulates the channel allocation problem, which aims at maximizing the overall transmitted data value by allocating different medium earth orbit (MEO) beams in different time slots to different LEO satellites. Since the data generation states of LEO satellites are hybrid constant and stochastic, the MEO satellites could allocate channels more timely than the ground mission center, and the action space for channel allocation is too large, the multi-agent deep reinforcement learning based algorithm is consequently adopted to solve the channel allocation problem. This paper verifies the effectiveness of our proposed channel allocation algorithm by numerical simulation.

Recently, numerous mega LEO satellite constellation plans have emerged as an indispensable part supporting the future 6G satellite communications. Since the traditional telemetry systems are inappropriate for monitoring the operation status of all satellites, a networked telemetry system is adopted to achieve full time, low delay telemetry for mega LEO satellite constellations, which is a significant scenario of satellite Internet of Things. Furthermore, this article formulates a channel allocation problem to maximize the overall transmitted data amount. Adopting the dual decomposition method, this problem can be decomposed and transformed into a dual problem and multiple path scheduling subproblems for each LEO satellite. This article develops an optimal dynamic programming algorithm to solve the path scheduling subproblems and an iterative algorithm to solve the channel allocation problem. Numerical simulations show that the proposed channel allocation algorithm improves the transmitted data amount and approximates the upper bound performance.

In recent years, various mega-constellation plans have been issued and the number of planned satellites has been increasing. However, the boundary of satellite number that existing tracking, telemetry and command (TT&C) resources can support has never been analyzed. This paper discusses TT&C capacity of mega-constellations for the first time. Under the requirement of instantaneity and reliability of TT&C system, the boundary of constellation satellites number that can be supported based on the inter-satellite link is explored. We first establish the satellite TT&C model based on N*D/D/1 queuing model. Then we analyze the relationship between satellites number, waiting delay and package loss probability under normal, compressed and abnormal telemetry transmission scenarios. Finally, the results of theoretical analysis, numerical and Monte Carlo simulation are given. Results show that the networked TT&C system with inter-satellite link will greatly increase the number of satellites that can be supported.

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and space-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems , which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system.

Network slicing is one the key features of software-defined networks (SDNs) and can be used in next-generation communication networks. Admission control of network slices is the basis of providing the heterogeneous quality-of-service performance guarantee and maximizing the optimization objectives of the network operator. Various admission control mechanisms have been proposed in the literature, including those based on traffic forecasting. However, recurrent neural network-based probabilistic forecasting models have not been given thorough consideration for slice admission control. In this study, the network slicing scheme design problem is formulated mathematically, with an equivalent formulation of the constrained bandwidth-sharing scheme. Then, a DeepAR-based slice admission control mechanism is proposed for sequential decision making for network slice requests in SDN, with the support of the SDN controller. An improved variant is further proposed with a closed-loop parameter update mechanism. The experiments based on real-world historical traffic data validate the effectiveness of the proposed mechanisms, with metrics, including revenue, resource reservation and utilization ratios, and service admission ratio.

In the upcoming 6G communication era, the satellite Internet based on mega constellations will become an indispensable extension of the terrestrial communication network. However, it is difficult for the traditional ground-based and geostationary earth orbit (GEO)-based telemetry systems to satisfy the requirements on monitoring the mega constellation. This paper designs the networked telemetry system, where data is transmitted through the inter-satellite-links (ISL) of the low earth orbit (LEO) and medium earth orbit (MEO) satellites. Furthermore, the resource allocation problem for the networked telemetry system is decomposed using the block coordinate descent method into the access scheduling and the subchannel-power coordinate allocation subproblems, which are iteratively solved to optimize the resource allocation scheme. Finally, the simulation results shows that the proposed resource allocation algorithm effectively increases the transmitted data amount of the system and approximates the upper bound performance.

As an important development trend of satellite communication networks in the future, Ka-band satellite communication has the characteristics of wide bandwidth, high speed, excellent anti-interference performance and small equipment. However, there is a disadvantage that Ka-band signals are easily affected by rain attenuation, so it is necessary to reasonably select ground stations to avoid this attenuation. This paper first introduces the general function and site selection principle of ground stations and analyzes the problems of these ground stations. Then, this paper presents the site selection method of Ka-band ground stations from the two aspects of the site selection process and average accessible rate calculation, considering rain attenuation. Finally, the actual average transmission rates of some possible ground stations in China are calculated under the constraints of some parameters given by a Ka-band MEO satellite constellation.

Abstract Multipath interference commonly exists in wireless communication, navigation and radar systems, which may cause severe signal fading and bit error rate (BER) performance degradation. Therefore the detection of multipath interference is urgently needed. This paper proposes a high order statistics (HOS) based multipath interference detection method, since the HOS of the received signal show distinct difference when multipath interference exists. Moreover, the generalized theoretical values of the moments and cumulants, which release the demand for prior knowledge of timing information and symbol period, are deduced in this paper. It makes the proposed HOS features based detection method robust in blind environment. Computer simulations are performed to verify the proposed method.