The integrated satellite-terrestrial network architecture has emerged as a hotspot for the Internet of Everything, which is a promising approach to provide communication service anytime and anywhere. In this paper, we aim to facilitate the system delay reduction through the two-tier cooperative edge computing of terrestrial base stations (BSs) and the satellite. To avoid the two-way propagation delay brought by the centralized decision process, we propose a distributed offloading scheme for the BSs and the satellite. Firstly, we equivalently decompose the offloading problem into multiple subproblems, based on which the optimal User-BS time slot allocation strategy for each BS is derived. Then, by means of multi-agent deep deterministic policy gradient reinforcement learning, the resource allocation strategy of each BS is obtained distributedly, which effectively avoids the two-way propagation delay. Based on the BS resource allocation strategy obtained, the optimal satellite computation capacity allocation strategy is proposed to minimize the total edge computing delay at the satellite. Finally, exhaustive simulations are implemented to demonstrate the performance of the proposed distributed offloading strategy.

Parallel and distributed learning frameworks are now widely employed for the training of large language models. However, the extremely large model size and data size have made the communication a bottleneck, severely reducing the training efficiency. In this paper, we introduce two novel hierarchical allreduce algorithms designed for 2D-torus topology. By exploiting the cooperation of the intra-cluster communication link and the inter-cluster communication link, the congestion bottleneck caused by the imbalanced link bandwidth can be alleviated. Numerical results are presented to demonstrate the performance of the proposed algorithms in comparison with existing baseline algorithms.

Due to the characteristics of open links, immense coverage and dynamic network topology, access authentication is crucial and challenging in integrated satellite-terrestrial back-haul networks (ISTBNs). Based on the access behavior of users, this paper proposes an ISTBN access authentication approach to prevent the spoofing attack, which is the most prominent security threat in ISTBNs. Concretely, we model the access authentication as a machine learning classification task with spatial-temporal characteristics. In this way, the dependency on identification information can be avoided, which is easy to be copied by adversaries in spoofing attacks. Then, we design an ensemble learning classifier to judge the legality of the access to ISTBNs. By organically integrating tree-based models and deep learning through the soft voting method, the detection performance of unauthorized access can be improved. Experiment results validate the effectiveness of our approach for preventing unauthorized access to ISTBNs.

In this paper, we investigate delay-optimized edge caching in the integrated satellite-terrestrial network with diverse content popularity distribution and user access modes. Based on the cooperation among the base stations, the satellite and the gateway, we propose a three-layer caching architecture to provide content service for both base station access users and satellite access users. Considering diverse content preferences for users in different areas, we formulate the content placement problem with the objective to minimize the average content retrieving delay of the network. By introducing the concept of the delay reduction gain and the caching benefit, we first derive the optimal caching strategy for base stations in different areas separately. Then, we propose two algorithms to calculate the cooperative caching strategy of the network, in which reduced search space is applied based on theoretical analysis. While the dynamic programming algorithm can achieve the optimal solution of the content placement problem, the submodular optimization based algorithm can provide guaranteed performance with relatively low complexity. Simulation results show that the proposed caching strategies can effectively improve the network delay performance.

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multi-tier computing in the integrated satellite-terrestrial network, in which the computation tasks of users are processed by leveraging the cooperation of devices, edge nodes, and cloud servers. Based on the proposed three-tier computing framework, we formulate the cooperative edge-cloud offloading problem to minimize the total delay of the network. Considering the computation task is dividable, we propose the optimal task splitting strategy based on the partial offloading model, in which the closed-form solution is derived for each computation task. With the optimal task splitting strategy obtained, the original optimization problem is reformulated as the problem of the time slot allocation strategy and the computation capacity allocation strategy. Then, we further propose the cooperative edge-cloud computing strategy to optimize the delay performance of the network. Finally, numerical results are presented to demonstrate the performance of the proposed three-tier computing architecture and the offloading strategies.

With the increasing global communication demands and the development of Internet of Things (IoT), extending the connectivity to rural and remote areas has become imperative for future networks. The sixth-generation (6G) network is expected to provide heterogeneous services and seamless network coverage for everyone and everything. Combining the advantages of both satellite and terrestrial networks, the integrated satellite-terrestrial network architecture is promising to provide global broadband access for all types of users, which has drawn much attention from both the academia and industry. In this article, we present a comprehensive survey of the state-of-the-art of integrated satellite-terrestrial networks toward 6G. First, an executive classification and summary of the integration architecture is presented from network design to performance optimization. Then, typical applications of the integrated satellite-terrestrial network are discussed based on the architecture. By considering the unique characteristics of the two networks, main challenges are pointed out when performing integration, such as the long propagation delay, complex link conditions, and high dynamics of the network topology. Finally, some promising future techniques are explored from the perspective of the integrated architecture. A detailed survey of the potential integration architectures is of great importance to enable more flexible network design and construction in future 6G networks. This article will provide a valuable guideline on future research and development of integrated satellite-terrestrial networks.

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multilayer edge caching in the integrated satellite-terrestrial network to reduce the communication delay, in which the base station cache, the satellite cache, and the gateway cache cooperatively provide content service for ground users. We first propose the three-layer cooperative caching model of the network, based on which we analyze the content retrieving process and derive the cache hit probability for different caching locations. Considering limited cache sizes, we formulate the content placement problem to minimize the average content retrieving delay of users. Then, two caching strategies, the non-cooperative caching strategy and the cooperative caching strategy, are proposed with exhaustive theoretical analysis. By introducing the concept of delay reduction gains, the optimal caching strategies are obtained based on the proposed iterative algorithms. Finally, numerical results are presented to demonstrate the performance of the proposed cooperative caching architecture and the caching strategies.

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogenous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the capacity performance of the three-layer heterogenous satellite network. We first construct the network model and the capacity model of the network with detailed design of the network parameters. Then, taking time structure into account, we propose a time structure based augmenting path searching method, which can significantly reduce the computing complexity. Finally, based on the model and method proposed, we analyze the capacity performance of the three-layer heterogenous satellite network with numerical results.

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogeneous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the problem of capacity management in the three-layer heterogeneous satellite network. We first construct the model of the network and propose a low-complexity method for calculating the capacity between satellites. Based on the time structure of the time expanded graph, the searching space is greatly reduced compared to traditional augmenting path searching strategies, which can significantly reduce the computing complexity. Then, based on Q-learning, we proposed a long-term optimal capacity allocation algorithm to optimize the long-term utility of the system. In order to reduce the storage and computing complexity, a learning framework with low-complexity is constructed while taking the properties of satellite systems into account. Finally, we analyze the capacity performance of the three-layer heterogeneous satellite network and also evaluate the proposed algorithms with numerical results.

In the tracking and data relay satellite system, users compete for the limited inter-satellite link antenna beam (ILAB) resource by excessive resource requests. This can lead to serious user request conflicts and considerably degrade the QoS (quality of service) of the system. Since those selfish users have no incentive to cooperate in a one-shot resource request, in this paper we construct a cooperation mechanism to maximize users' payoffs and meanwhile to reduce the conflict relying on a repeated game framework. Moreover, an efficient punishment and forgiveness strategy is proposed to prevent any user from breaking the cooperation. Simulation results confirm that the proposed scheme can significantly improve the system's operation performance. Compared with the payoffs under the Nash equilibrium in the one-shot game which is the traditional scheme, our repeated ILAB resource allocation game can acquire 1.36 to 3.46 times gain in respect to 2 to 10 users, respectively.

In this paper, we propose an architecture of cloud based integrated terrestrial-satellite networks, in which satellite and terrestrial networks that belong to the same operator cooperatively provide seamless coverage for mobile users. Meanwhile, a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Then, based on the delay constraint of users, we formulate the resource allocation problem for the operator to minimize the energy consumption. By decomposing the optimization problem into two subproblems and utilizing the theory of multidimensional knapsack problem, we eventually obtain the optimal resource allocation strategies for the operator. Furthermore, numerical results are provided to evaluate the performance of the proposed strategies.

In this paper, we investigate the downlink cooperative multigroup multicast transmission in the integrated terrestrial-satellite network, in which base stations (BSs) and the satellite provide the multicast service for ground users in a cooperative manner while reusing the entire bandwidth. For both terrestrial BSs and the satellite, multiantennas are equipped and beamforming techniques are utilized for improving the system performance. Based on the architecture, we formulate a weighted max-min fair (MMF) beamforming design problem to jointly optimize the beamforming vectors of BSs and the satellite, which is solved based on the relation between the quality of service problem and the MMF problem. When it comes to the large scale case, where large numbers of BSs are distributed within the coverage of the satellite, we propose a time division cooperative multigroup multicast scheme consisting of two phases for the BSs and the satellite. Then, an iterative algorithm is proposed to solve the weighted MMF problem in the time division case. Finally, numerical results are provided to evaluate the cooperative multicast schemes as well as the proposed algorithms.

This paper investigates a multimedia multicast beamforming scheme in the integrated terrestrial-satellite networks, where base stations (BSs) and the satellite work cooperatively provide ubiquitous services for ground users. Due to the contents diversity of multimedia services, users that request the same contents can be served as a group using multicasting. By utilizing multiple transmission antennas, multicast beamforming is performed among groups while reusing the entire bandwidth, which, however, can inevitably cause the co-channel interference among users. Taking both system performance and user fairness into account, we optimize the total system capacity performance under the satellite capacity constraint and derive the optimal power allocation schemes. Numerical results are presented in the end to evaluate the effectiveness of the proposed scheme compared with the greedy and suboptimal searching strategies.

The increasing traffic demand in both ground and satellite communication systems will lead to increasing spectrum demand. Spectrum sharing would become a challenging issue in future between terrestrial and satellite systems with frequency reusing, as well as the interference management. Upon this, we propose the concept of the Cloud Based Integrated Terrestrial-Satellite Network (CTSN), where both base stations of the cellular networks and the satellite are connected to a cloud central unit and the signal processing procedures are executed centrally at the cloud. By utilizing the channel state information (CSI), the interference from the mixed signal can be mitigated. When it comes to the case of imperfect CSI, we propose a resource allocation scheme in respect to subchannel and power to maximize the total capacity of the terrestrial system while limiting the total interference to the satellite. The optimization problem is solved by means of the dual decomposition method. Simulation results are provided to evaluate the effectiveness of the algorithm.

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max-min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.

The integrated and/or hybrid satellite and terrestrial network has become more and more important because of its broad application prospect and has received considerable attention. At the same time, the integrated network also brings many challenges, especially the problem of interference. Due to the lack of frequency spectrum, frequency reuse is considered in the satellite network and the terrestrial network for enhancing spectral efficiency. However, this will cause considerable Co-Channel Interference (CCI) and thus interference coordination is imperative. In this paper, we propose an interference coordination scheme for the integrated satellite and terrestrial network. The satellite sends pilots for channel estimation at terrestrial base-stations, and transmits the received data to the terrestrial gateway. Then interference coordination is performed at the terrestrial gateway, where the interference channel is updated according to both the estimated information and the predicted change based on the positions. Furthermore, based on the scheme, we analyze the precision that needs to be reached and obtain a direct view on how the precision may influence the system performance.

Understanding and characterizing the properties of vehicular networks has become hugely important because of their wide applications and fast development. Due to the unique property of vehicular networks that nodes change their locations at high speed in anytime, their connectivity is an important and unique property. In this poster, based on realistic vehicular mobility traces, we reveal the opportunistic connectivity property of large-scale urban vehicular networks. Moreover, through analysis, we unveil the fundamental relationships and tradeoffs between the opportunistic connectivity and network parameters in terms of data size, delivery delay, transmission energy, etc.