In this paper, we propose a novel mobility-aware resource provisioning scheme for edge-assisted extended reality (XR) services. The goal is to minimize resource consumption while satisfying user quality of experience (QoE) requirement, which is measured by the weighted sum of visual quality, quality variation, and round-trip interaction latency. Specifically, we present a mobility model to capture both user spatial movements and XR content interaction features. Since user viewing distance and interaction time are key model parameters that affect the spatiotemporal service demand for XR content rendering and delivery at the edge, we estimate user-specific model parameters and adopt a sample average approximation method to model the relationship between user QoE and the consumption of both communication and edge computing resources. We design a coordinate descent algorithm to make resource provisioning decisions, where a deep neural network provides a valuable initial point to accelerate convergence. Simulation results demonstrate that our proposed scheme is more efficient to utilize network resources in comparison with benchmark schemes while satisfying user QoE requirements.

In satellite-assisted maritime communications, wave-induced rotational motions of unmanned surface vessels (USVs) cause severe beam misalignment with satellites, significantly degrading transmission performance. To overcome this challenge, we propose equipping the USV with a smart metasurface-based antenna to enable adaptive beamforming that dynamically compensates for USV rolling in harsh sea conditions. To facilitate effective beam tracking under long feedback delays, we design a transmission framework that ensures accurate channel state information (CSI) acquisition. Within this framework, a BLTNet-based model is developed to predict the instantaneous rolling angle of the USV, which is then used to infer the USV-to-satellite CSI for beamforming optimization. We further formulate a stochastic optimization problem to maximize the ergodic achievable rate of the uplink transmission and design a robust beamformer accordingly. Simulation results demonstrate the high accuracy of the proposed rolling angle prediction model under various settings and sea states, confirming that the corresponding robust beamforming design substantially enhances the USV-to-satellite transmission performance.

In this article, we investigate infrastructure-assisted environment sensing in satellite-terrestrial vehicular networks (STVN) for connected autonomous vehicles (CAVs), where satellites and roadside units (RSUs) cooperate to provide CAVs with fresh sensing data. To support satellite- and RSU-assisted environment sensing for CAVs, we formulate a long-term resource scheduling problem in STVN to satisfy sensing data freshness requirements with efficient resource usage. To deal with the challenges posed by the dynamic network environment as well as stringent data freshness requirements, we propose a cooperative satellite-terrestrial resource scheduling (CSTRS) scheme. CSTRS is a model-data co-driven approach that can jointly optimize the sensing interval and resource allocation in STVN. Specifically, benefiting from the multicast feature of the low Earth orbit satellite, coalition game, and particle swarm optimization-based algorithms are designed to partition CAVs into groups and optimize sensing intervals in large timescales. Then, a reinforcement learning-based algorithm is developed to make real-time computing and communication resource allocation decisions based on the CAV partition. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and reliability performance.

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we propose an adaptive digital twin (DT)-assisted resource slicing scheme for robust and adaptive resource management in LSN. Specifically, a slice DT, being able to capture the service demand prediction uncertainty through collected service demand data, is constructed to enhance the robustness of resource slicing decisions for dynamic service demands. In addition, the constructed DT can emulate resource slicing decisions for evaluating their performance, enabling adaptive slicing decision updates to efficiently reserve resources in LSN. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, achieving low service demand violations with efficient resource consumption.

Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we pro-pose a novel resource slicing scheme with cross-cell coordination in STIN to satisfy distinct service delay requirements and efficient resource usage. To address the challenges posed by spatiotemporal dynamics in service demands and satellite mobility, we formulate the resource slicing problem into a long-term optimization problem and propose a distributed resource slicing (DRS) scheme for scalable and flexible resource management across different cells. Specifically, a hybrid data-model co-driven approach is developed, including an asynchronous multi-agent reinforcement learning- based algorithm to determine the optimal satellite set serving each cell and a distributed optimization-based algorithm to make the resource reservation decisions for each slice. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and delay performance.

The low Earth orbit (LEO) satellite-assisted communications are envisioned as a prospective solution in next-generation networks to provide reliable, flexible, cost-effective, and globally seamless services. In this paper, we investigate satellite-terrestrial vehicular network (STVN) supporting connected autonomous vehicle (CAV) applications anytime and anywhere. We first establish a model for the LEO satellite-CAV communication system with different satellite orbital parameters. Then the LEO satellite-CAV communication performance in terms of service availability, outage probability, and system throughput is analyzed when considering practical satellite constellations. Furthermore, the impact of different terrestrial infrastructure deployment strategies on the STVN performance is investigated. Extensive numerical results are provided to validate our theoretical analysis and demonstrate the improvement of CAV network performance thanks to LEO satellites in the STVN.

To support the explosive content demands from multifarious services and applications, cache-enabled satellite-terrestrial integrated networks (STINs) are envisioned as a key enabler to reduce the content delivery delay and alleviate the backhaul pressure. In this paper, we investigate the joint optimization of cache placement and content delivery in the STIN to minimize the long-term overall content delivery delay. Considering that cache placement and content delivery are interrelated and affected by network dynamics in terms of satellite movement and random content requests, the joint optimization problem is formulated as a sequential decision making problem by leveraging a Markov decision process. We propose a hierarchical deep Q learning (HDQL) algorithm by leveraging two independent deep neural networks to learn the cache placement and content delivery policies with small action space and low time complexity. Simulation results demonstrate that the proposed HDQL algorithm outperforms the benchmark algorithms in terms of content delivery delay in the STINs.

Space-ground integrated vehicular networks (SGIVNs) are envisioned as a promising architecture to support multifarious vehicular services with enhanced network flexibility and reliability. Access mode selection (AMS) is of capital importance in the SGIVN for the ingenious cooperation among different network segments to exploit their complementary advantages. In this paper, we investigate the AMS problem for vehicles in the SGIVN by taking distinct features of satellite networks (long propagation delay) and terrestrial networks (frequent handover) into account. In light of the high vehicle/satellite mobility and dynamic data packet arrivals, we formulate a stochastic integer programming problem of sequential AMS to maximize vehicles' long-term data rate. To cope with the time-varying network dynamics, we leverage a Markov decision process framework to model the evolution of vehicle states. For the special case with known stochastic model of data packet arrivals, we transform the problem into a linear programming problem that can be solved with low complexity. For the general case without the data packet arrival model, we propose a reinforcement learning-based algorithm to make adaptive AMS decisions to keep pace with network dynamics. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of data rate under different data packet arrival patterns and vehicle velocities.

Traffic signal control for a large-scale traffic network is one challenging problem in intelligent transportation systems (ITS). High communication overheads are typically required to achieve the optimal control of the traffic signals in multiple road intersections. In this paper, in order to avoid these communication overheads among spatially distributed intersections, we propose to recover the global traffic state at each intersection in a real-time fashion by only utilizing the traffic state observed at the local intersection. Specifically, a generative adversarial network (GAN) based traffic information recovery method is presented for each intersection controller to recover the global traffic state. We also exploit a few statistics from other intersections during the training of the proposed GAN to improve the traffic state recovery accuracy. Comprehensive numerical results demonstrate the effectiveness of the proposed scheme in recovering the global traffic state.

A salient feature of the vehicular networks is the high mobility of the vehicles, which makes it a challenging issue to provide seamless handover using the conventional dedicated short range communication (DSRC) or cellular network technologies (e.g., 3G/4G). In this paper, we consider the adoption of the dual connectivity (DC) architecture in the vehicular network, which allows the user equipment (UE) to connect simultaneously to a master eNB(MeNB) and a secondary eNB(SeNB), and thus simplifies the signaling and provides enhanced mobility support. To further improve the performance, we propose a raptor codes based dual connectivity (RCDC) scheme, which can effectively address the out-of-order packet delivery problem in the DC scheme, and the coordination between the MeNB and SeNB is significantly reduced. We develop queueing models to characterize the delay performance of the DC and the RCDC schemes by taking into account the handover events in vehicular networks. Simulation results are provided to illustrate the performance of these two schemes under different vehicular network settings, which can prove that the RCDC scheme is more adaptable for the vehicle network with handover events.