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This paper presents an analytical exploration of sixth-generation (6G) satellite–terrestrial integrated networks, focusing specifically on their applications within air mobility operations, such as those involving unmanned aerial vehicles (UAVs). As the integration of satellite and terrestrial networks promises to revolutionize mobile communication by extending coverage and enhancing connectivity, this study delves into two critical aspects: link budget analysis and handover and mobility analysis for UAVs. The link budget analysis assesses the communication requirements necessary to ensure robust and consistent connectivity between satellites and UAVs, accounting for factors such as path loss, antenna gains, and power transmission. Meanwhile, the handover and mobility analysis investigates the challenges and solutions associated with UAVs transitioning between different network nodes and layers in a dynamic aerial environment. This paper utilizes theoretical models and simulations to provide insights into the design and optimization of these networks, aiming to enhance the reliability and efficiency of UAV operations in the context of the emerging 6G landscape. The findings propose not only technological advancements in network architecture but also practical guidelines for the deployment of UAVs in complex environments, marking a significant step toward the realization of a fully integrated, satellite-terrestrial ecosystem. [ABSTRACT FROM AUTHOR]

Through offloading computing tasks to visible satellites for execution, the satellite edge computing architecture effectively issues the high-delay problem in remote grids (e.g., mountain and desert) when tasks are offloaded to the urban terrestrial cloud (TC). However, existing works are usually limited to offloading tasks in pure satellite networks and make offloading decisions based on the predefined models. Additionally, runtime consumption for offloading decisions is rather high. Furthermore, privacy information may be maliciously sniffed since computing tasks are transmitted via vulnerable satellite networks. In this paper, we study the task-offloading problem in satellite–terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. A privacy leakage scenario is described, and we consider preserving privacy by sending extra random dummy tasks to confuse adversaries. Then, the offloading cost with privacy protection consideration is modeled, and the offloading decision that minimizes the offloading cost is formulated as a mixed-integer programming (MIP) problem. To speed up solving the MIP problem, we propose a deep reinforcement learning-based task-offloading (DRTO) algorithm. In this case, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that the offloading overhead is reduced by 17.5% and 23.6% compared with pure TC computing and pure SatEC computing, while the runtime consumption of DRTO is reduced by at least 42.6%. The dummy tasks are exhibited to effectively mitigate privacy leakage during offloading. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC)-enabled satellite–terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications' tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite's MEC server are insufficient, but the computing resources of neighboring satellites' MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC)-enabled satellite–terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications’ tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite’s MEC server are insufficient, but the computing resources of neighboring satellites’ MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.

The explosive growth of various services boosts the innovation and development in terrestrial communication systems for the implementation of the next generation mobile communication networks. However, simply utilizing limited resources in terrestrial communication networks is difficult to support the massive quality of service (QoS) aware requirements and it is hard to guarantee seamless coverage in far remote regions. Leveraging the intrinsic merits of high altitude and the ability of multicasting or broadcasting, satellite communication systems provide an opportunity for novel mobile communication networks with its tight interaction and complementary characteristics to traditional terrestrial networks. It is believed that the convergence of satellite and terrestrial networks can solve the problems existing in current mobile communication systems and make a profound effect on global information dissemination. In this paper, we make a comprehensive survey on the convergence of satellite and terrestrial networks. First, motivations and requirements of satellite-terrestrial network convergence are identified. Then, we summarize related architectures of existing literature, classify the taxonomy of researches on satellite-terrestrial networks, and present the performance evaluation works in different satellite-terrestrial networks. After that, the state-of-the-art of standardization, projects and the key application areas of satellite-terrestrial networks are also reviewed. Finally, we conclude the survey by highlighting the open issues and future directions.

This paper investigates a novel architecture of coordinated multi-point (CoMP) transmission in the cognitive satellite-terrestrial network associated with an unmanned aerial vehicle (UAV). We consider the downlink communication where the UAV and base station (BS) cooperatively serve the terrestrial user by sharing the licensed satellite network spectrum. The goal of this paper is to maximize the achievable rate of the terrestrial user by jointly optimizing BS/UAV transmit power allocation and UAV trajectory, subjecting to the interference temperature threshold imposed on satellite network as well as UAV mobility constraint. The formulated problem is shown in a complicated non-convex form, which is hard to tackle. To this end, we decompose the original problem into two sub-problems, and the block coordinate descent method is employed to solve the two sub-problems alternately. Specifically, for the first sub-problem, the optimal BS/UAV transmit power is obtained with a given UAV trajectory by using the Lagrangian dual method. For the second sub-problem, the UAV trajectory is attained with a given BS/UAV power allocation by using the successive convex approximation technique. Simulation results show that our proposed joint CoMP transmission scheme significantly improves the terrestrial network throughput compared with other benchmark schemes.

With the development of satellite networks, there is an emerging trend to integrate satellite networks with terrestrial networks, called satellite-terrestrial networks (STNs). The improvements of STNs need innovative information and communication technologies, such as networking, caching, and computing. In this paper, we propose a software-defined STN to manage and orchestrate networking, caching, and computing resources jointly. We formulate the joint resources allocation problem as a joint optimization problem, and use a deep Q-learning approach to solve it. Simulation results show the effectiveness of our proposed scheme. [ABSTRACT FROM AUTHOR]

Satellite communication systems are a promising solution to extend and complement terrestrial networks in unserved or under-served areas, as reflected by recent commercial and standardization endeavors. In particular, 3GPP recently initiated a study item for new radio, i.e., 5G, non-terrestrial networks aimed at deploying satellite systems either as a stand-alone solution or as an integration to terrestrial networks in mobile broadband and machine-type communication scenarios. However, typical satellite channel impairments, as large path losses, delays, and Doppler shifts, pose severe challenges to the realization of a satellite-based NR network. In this paper, based on the architecture options currently being discussed in the standardization fora, we discuss and assess the impact of the satellite channel characteristics on the physical and medium access control layers, both in terms of transmitted waveforms and procedures for enhanced mobile broadband and narrowband-Internet of Things applications. The proposed analysis shows that the main technical challenges are related to the PHY/MAC procedures, in particular random access, timing advance, and hybrid automatic repeat request and depending on the considered service and architecture, different solutions are proposed. [ABSTRACT FROM AUTHOR]

Cognitive satellite–terrestrial networks (CSTNs) have been recognized as a promising network architecture for addressing spectrum scarcity problem in next-generation communication networks. In this paper, we investigate the secure transmission for CSTNs where the terrestrial base station serving as a green interference resource is introduced to enhance the security of the satellite link. Adopting a stochastic model for the channel state information uncertainty, we propose a secure and robust beamforming framework to minimize the transmit power, while satisfying a range of outage (probabilistic) constraints concerning the signal-to-interference-plus-noise ratio (SINR) recorded at the satellite user and the terrestrial user, the leakage-SINR recorded at the eavesdropper, as well as the interference power recorded at the satellite user. The resulting robust optimization problem is highly intractable and the key observation is that the highly intractable probability constraints can be equivalently reformulated as the deterministic versions with Gaussian statistics. In this regard, we develop two robust reformulation methods, namely \mathcalS-procedure and Bernstein-type inequality restriction techniques, to obtain a safe approximate solution. In the meantime, the computational complexities of the proposed schemes are analyzed. Finally, the effectiveness of the proposed schemes are demonstrated by numerical results with different system parameters. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the performance of a two-way hybrid satellite multi-terrestrial cooperative network with hardware impairments (HIs). Particularly, opportunistic relay selection scheme is employed in the considered network, which can substantially enhance the spectral efficiency and spatial diversity gain. By considering both the amplify-and-forward (AF) and decode-and-forward (DF) relay protocols, the closed-form expressions of the outage probability (OP) and throughput for the system are derived. Furthermore, in order to observe the effects of HIs level and the number of terrestrial relays on the system performance at high signal-to-noise ratios (SNRs), the asymptotic expressions of the system performance are also derived. Finally, computer results are presented to reveal the correctness of the analytical results. [ABSTRACT FROM AUTHOR]