Integrated Satellite-Terrestrial Networks Research Articles

Delay-Optimized Edge Caching in Integrated Satellite–Terrestrial Networks With Diverse Content Popularity Distribution and User Access Modes

Delay-Optimized Edge Caching in Integrated Satellite–Terrestrial Networks With Diverse Content Popularity Distribution and User Access Modes

Spatial Information Entropy-Assisted Integrated Sensing and Communication for Integrated Satellite-Terrestrial Networks

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators.

Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks.

The integrated satellite-terrestrial network (ISTN) provides a promising solution to achieve high-data-rate and ubiquitous connectivity in next-generation communication networks. Considering the scarce spectrum resources and unevenly distributed traffic demands, we investigate the resource allocation algorithms for ISTNs, where the beam-hopping (BH)-based satellite system and terrestrial systems share the same frequency band. Taking advantage of the scheduling flexibility of BH technology, the dynamical protection zones are constructed to avoid co-channel interference and improve the spectrum efficiency. Since both spectrum efficiency and user fairness are the key optimization indexes in practical systems, two resource allocation problems are formulated to maximize the weighted sum of capacity (MWSC) and maximize the minimum capacity-to-demand ratio (MMCDR) of ISTNs, respectively. By reformulating the problems as mixed-integer linear programming problems, optimal solutions are obtained. To reduce the computational complexity, two greedy suboptimal algorithms are proposed for the MWSC and MMCDR, respectively. The simulation results show that the proposed algorithms achieve higher spectrum efficiency and guarantee fairness between the satellite and terrestrial systems. It is also shown that both the greedy algorithms perform similarly to the optimal algorithms while having much lower complexity.

Joint Broadcast and Unicast Transmission Based on RSMA and Spectrum Sharing for Integrated Satellite–Terrestrial Network

Joint Broadcast and Unicast Transmission Based on RSMA and Spectrum Sharing for Integrated Satellite–Terrestrial Network

Access-Backhaul Strategy via gNB Cooperation for Integrated Terrestrial-Satellite Networks

Access-Backhaul Strategy via gNB Cooperation for Integrated Terrestrial-Satellite Networks

ISAC towards 6G Satellite–Terrestrial Communications: Principles, Status, and Prospects

With the evolution of fifth-generation (5G) to sixth-generation (6G) communication systems, the utilization of spectrum resources faces incremental challenges. Integrated sensing and communication (ISAC) technology, as a crucial element in 6G technology, is expected to enhance energy efficiency and spectrum utilization efficiency by integrating radar and communication signals, achieving environmental awareness, and enabling scene interconnection. Simultaneously, to realize the vision of seamless coverage in 6G, research on integrated satellite-terrestrial communication has been prioritized. To integrate the advantages, ISAC for integrated satellite–terrestrial networks (ISTNs) in 6G has emerged as a potential research direction. This paper offers an extensive overview of the present state of key technologies for ISAC and the development of ISTNs. Meanwhile, with a focus on the ISTN-oriented 6G ISAC system, several hotspot topics, including future application scenarios and key technological developments, are outlined and demonstrated.

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided for users by satellite networks, there will be high capital expenditures in terms of communication delay and bandwidth usage. To this end, in this paper, a cooperative-caching and resource-allocation problem is investigated in integrated satellite–terrestrial networks. Popular contents, which are cached on satellites and ground data centers, can be accessed via inter-satellite and satellite–terrestrial networks in a cooperative way. The optimization problem is formulated to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. A cooperative caching and resource allocation (CCRA) algorithm based on a neighborhood search is proposed to address the problem. The simulation results demonstrate that the proposed CCRA algorithm outperforms Greedy and BFS in reducing the deployment costs.

Resource allocation for integrated satellite‐terrestrial networks based on RSMA

AbstractTo achieve high data rate transmission of virtual reality (VR) applications globally, a resource allocation algorithm based on Rate‐Splitting Multiple Access (RSMA) in an integrated satellite‐terrestrial network (ISTN) is proposed. In this network, a user selection strategy is proposed. The maximum transmission power budgets of the base station and satellite, as well as the service quality constraints for all users, are considered. Then, a joint resource allocation problem is formulated. To tackle the non‐convex problem, auxiliary variables are introduced and the first‐order Taylor expansion method is used to convert it into a convex one. An iterative algorithm is proposed based on the successive convex approximation (SCA) to allocate rate and power resources. Simulation results demonstrate that the proposed algorithm significantly enhances the transmission rate and effectively satisfies the VR transmission rate requirements, when compared to benchmark algorithms.

Improving Broadcast System of Integrated Satellite-Terrestrial Network-based on Enhanced Ant colony Optimization

With the use of seamless high-speed worldwide network connectivity in future, it is anticipated that the integrated satellite-terrestrial network (ISTN) will be a possible option. Due to the inadequacy of topological data and the close coupling of data and control planes, deploying optimized rules on routers is difficult. The important factors in ISTN networks are routing rules and policies, link failure, and high-bandwidth communication. Software-defined networking (SDN) is an open innovation approach that enables programmability from a central location. The controller handles the complexity of the network, whereas the infrastructure layer devices relay the packets. Thus, we investigated the broadcast by dynamically adjusting routes for fault tolerance and energy efficiency of the ISTN using distance between nodes. In addition, using dynamic source routing, this study examines the competence functions of all managing nodes in a network. The routing design is distributed among all nodes to create numerous collective paths. For LEO satellite networks, the ant colony optimization-based routing algorithm is an improved version that considers the probability of faults and low-energy consumption. The proposed simulation ensures a seamless transition in the event of failures and avoids the requirement for an additional coordination service.

Optimization-Driven DRL-Based Joint Beamformer Design for IRS-Aided ITSN Against Smart Jamming Attacks

This paper investigates an intelligent reflecting surfaces (IRS) aided anti-jamming communication strategy in the integrated terrestrial-satellite network (ITSN), where the IRS is exploited to mitigate jamming interference and enhance the integrated system communication performance. In such a network, the terrestrial network and satellite network are co-existing with a spectrum-sharing scheme in the presence of a multi-antenna jammer. We aim at maximizing the weighted sum rate (WSR) of all users by jointly optimizing the terrestrial beamformers and IRS phase shifts while considering the signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users. Different from the non-convex optimization techniques utilized in the IRS-related problem, a novel optimization-driven deep reinforcement learning (DRL) algorithm is proposed, which leverages both the robustness of model-free learning approaches and the efficiency of model-based optimization methods. In the optimization module of the proposed algorithm, we analyze the smart jammer under the unknown jamming model and derive a lower bound of the anti-jamming uncertainty, such that the IRS-aided anti-jamming problem can be solved by alteration method with second-order cone programming (SOCP) algorithm and semidefinite relaxation (SDR) technique. Simulation results demonstrate that the IRS can enhance the anti-jamming performance efficiently, and the proposed optimization-driven DRL algorithm can improve both the learning rate and the system performance compared with existing solutions.

Dynamic resource management in integrated NOMA terrestrial–satellite networks using multi-agent reinforcement learning

The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. Moreover, in today’s interconnected world, connectivity’s reliable and widespread availability is increasingly important across various domains. This is especially more crucial for applications like the Internet of Things (IoT), remote sensing, disaster management, and bridging the digital divide. However, allocating the limited network resources efficiently and ensuring seamless handover between satellite and terrestrial networks present significant challenges. Therefore, this study introduces a resource allocation framework for integrated satellite–terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial–satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods. This research highlights the importance and addresses the need for efficient resource allocation and cache design in integrated terrestrial–satellite networks, paving the way for enhanced connectivity and improved network performance in various applications.

Joint Beamformer Design and User Scheduling for Integrated Terrestrial-Satellite Networks

The integrated terrestrial satellite networks (ITSNs) have been deemed as a promising solution to ubiquitous Internet access anytime and anywhere. In this paper, we investigate the spectrum sharing problem in ITSN, in particular focusing on the downlink transmission of the satellite network, which adopts the framing structure for the satellite users (SUs). We model the interference from both terrestrial downlink and uplink transmissions to SUs according to the beamforming techniques. For the terrestrial downlink transmission, we assume that terrestrial users (TUs) are served cooperatively by multiple small base stations (SBSs) via joint beamforming, while the virtual multiple access channel (VMAC) scheme is adopted for the terrestrial uplink transmission. We propose the optimization framework by jointly considering the terrestrial beamformer design and satellite user scheduling to maximize the sum rate of all users. The optimization problems are decomposed into three sub-problems: satellite user scheduling, terrestrial beamformer design, and time slot allocation, which are solved by deep clustering, second-order cone programming (SOCP) (or fractional programming (FP)), and linear programming, respectively. Then, an alternating iterative algorithm is designed to obtain the optimal solution. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm in multiple cases.

Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network.

To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on-off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on-off design.

Two-Timescale Learning-Based Task Offloading for Remote IoT in Integrated Satellite–Terrestrial Networks

In this paper, we propose an integrated satellite-terrestrial network (ISTN) architecture to support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite networks serve as a complement to terrestrial networks by providing additional communication resources, backhaul capacities, and seamless coverage. Under this architecture, we investigate how to jointly make offloading link selection and bandwidth allocation decisions for BSs and IoT users. Considering the differentiated decision-making time granularities, we formulate a two-timescale stochastic optimization problem to minimize the overall task offloading delay. To accommodate the two-timescale network dynamics and characterize state-action relations, we establish a hierarchical Markov decision process (H-MDP) framework with two separate agents tackling two-timescale network management decisions, and two evolved MDP-based subproblems are formulated accordingly. To efficiently solve the subproblems, we further develop a hybrid proximal policy optimization (H-PPO)-based algorithm. Specifically, a hybrid actor-critic architecture is designed to deal with the mixed discrete and continuous actions. In addition, an action mask layer and an action shaping function are designed to sample feasible task offloading decisions from the time-variant action set. Extensive simulation results have validated the superiority of the proposed ISTN architecture and the H-PPO-based algorithm, especially in scenarios with scarce spectrum resources and heavy traffic loads.

IRS-Aided Uplink Transmission Scheme in Integrated Satellite-Terrestrial Networks

This paper proposes an intelligent reflecting surface (IRS)-aided uplink transmission scheme to expand the wireless coverage and improve spectral efficiency for an integrated satellite-terrestrial network (ISTN). Here, multiple earth stations communicate with the satellite via non-orthogonal multiple access (NOMA) technology, while multiple direct users and blockage users access the cellular network through space division multiple access and IRS-aided NOMA technology, respectively. In particular, we first aim at maximizing the ergodic sum rate of the ISTN subject to the quality-of-service requirements and transmit power budgets of all users, yielding a constrained optimization problem. Then, based on the assumption that only the statistical channel state information (CSI) is available, we propose a two-layer alternating algorithm to solve the non-convex original problem. The algorithm combines successive convex approximation with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> -procedure approach to calculate the phase shifts of IRS and transmit powers of all users in the first layer, and uses Charnes-Cooper method with semi-definite programming to calculate beamforming vectors at the BS in the second layer. Furthermore, with the help of the zero-forcing beamforming principle, we propose a low-complexity algorithm to solve the problem that can reduce the computational load effectively. Finally, simulation results demonstrate that our proposed algorithms can obtain a higher ergodic sum rate than the benchmarks.

Cooperative Non-Orthogonal Broadcast and Unicast Transmission for Integrated Satellite–Terrestrial Network

Cooperative Non-Orthogonal Broadcast and Unicast Transmission for Integrated Satellite–Terrestrial Network

Matching Game for Intelligent Resource Management in Integrated Satellite-Terrestrial Networks

Integrated satellite-terrestrial networks (ISTNs) provide a novel networking paradigm for achieving all-time, seamless and global coverage, attracting considerable research attention lately. Sixth-generation (6G) communication networks will also expect ISTNs to support countless emerging missions with different requirements. To this end, in the dynamic and complex ISTN environment, intelligent and efficient multidimensional resource management strategies (transmission and computation resources) are critical in limited resource situations. A matching game - a suitable tool to model the cooperation and strategic interaction of missions and resources - provides a potential solution to address these challenges. In this article, we first discuss the requirements and challenges of multidimensional resource management problems in ISTNs. Next, we introduce the definition, classifications and applications of the matching game. Based on the matching game, we propose an intelligent mission-resource, two-sided matching (MRTM) framework, and present a MRTM-based multidimensional resource management strategy. Finally, we provide an example to illustrate how to apply the MRTM framework. The simulation results are used to validate the effectiveness of the proposed framework.

Integrated Satellite-Terrestrial Networks With Coordinated C-NOMA and Relay Transmission

For the cooperative nonorthogonal multiple access (C-NOMA)-aided integrated satellite-terrestrial network (ISTN), an outage is inevitable if the channel from the satellite to the weak user is in deep fade. To handle this issue, this article proposes to deploy relay nodes to assist the communication, specifically, the relay and strong user forward information, which is sent from the satellite, to the weak user. After that, the weak user can boost its received signal strength by utilizing maximum ratio combining. In this context, we further study two kinds of relay protocols, i.e., decode-and-forward (DF) and amplify-and-forward (AF). For each protocol, the closed-form and asymptotic expressions of the outage probability (OP) are derived and then, based on the asymptotic OP, the diversity order is analyzed. Results indicate the DF protocol is preferable to the AF from the OP aspect although it incurs more computational load. Besides, the diversity order of the proposed ISTN is identical to that of C-NOMA and twice that of the relaying network. Simulation results clarify the superiority of the proposed scheme.

Integrated Satellite-Terrestrial Networks: Architectures, Key Techniques, and Experimental Progress

As a promising paradigm toward the sixth generation mobile communication, integrated satellite-terrestrial networks (ISTNs) can greatly extend network coverage and reduce the dependence on terrestrial infrastructure. Despite the charming potentials of ISTN, many challenges are still unsolved in terms of architectures, air interface protocol enhancement, mobility management, experimental validation, and so on. Therefore, in this article, we first clarify the four stages of ISTN architecture development and then discuss protocol enhancements as well as key techniques for ISTN. In addition, one of our previous experiments is reported in detail, where low Earth orbit satellite communication is used as the backbone connecting two private 5G networks that are 400 km away from each other. During the experiment, a remote immersive robot control service is realized with average end-to-end latency and uplink peak data rate being 30 ms and 50 Mb/s, respectively, which shows the feasibility of ISTN in supporting emerging industrial Internet applications. Meanwhile, other experimental progress is also presented, focusing on air interface protocol enhancement, transmission control protocol transmission, and network management. Finally, we point out future trends in ISTN, including programmable satellites and artificial-intelligence-based networking.

Cooperative Robust Video Multicast in Integrated Terrestrial-Satellite Networks

Integrated terrestrial-satellite networks (ITSNs) are the promising trends of future networks. However, there exist great challenges when performing video multicast in ITSNs due to the strong heterogeneity of users in comprehensive satellite coverage and the inter-system complex co-channel interference. To overcome these, we make the first attempt to propose an efficient cooperative robust video multicast (CRVM-ITSN) framework in ITSNs. The basic idea is to leverage the non-orthogonal multiple access technique in the cooperative transmission of ITSNs to achieve high-efficiency robust video multicast. The desired robust multicast performance realizes that the recovered video quality can adapt to diverse channel conditions of users. In CRVM-ITSN, to achieve the optimal cooperative transmission performance, power allocation and chunk scheduling of video data are jointly formulated as a distortion minimization problem, which is a non-convex mixed integer non-linear programming problem. To solve it, we design a provably convergent optimal algorithm by converting it to be convex. Besides, based on the theorem on optimal chunks selection of satellite cooperative transmission, a low-complexity chunk grouping algorithm is proposed to accelerate the optimal algorithm. Simulation results have demonstrated the superiority of proposed CRVM-ITSN against existing reference schemes, achieving about 4.1dB more gains in the recovered video quality.