Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

In view of the rapid development of edge computing and vehicular network, edge-assisted vehicular crowdsensing system has brought significant benefits to Intelligent Transportation Systems (ITS). To satisfy the requirements of ITS applications, a substantial number of sensed data are generated continuously, which incurs considerable communication and computation delays. In addition, with fast-moving vehicles and the sheer amount of data, data loss becomes an issue during the process of data uploading. To deal with these challenges, this paper aims to propose a cost-and-quality aware data collection scheme for edge-assisted vehicular crowdsensing system, so as to satisfy the accuracy and timeliness requirements of ITS applications without overloading the network. Particularly, we design a functional architecture for edge-assisted vehicular crowdsensing system, where vehicular sensing, data uploading and sensing parameter adjustment interwork to enable ITS applications. Based on the architecture, two effective algorithms are proposed, which are adaptive clustering and online sensing parameter adjustment. Adaptive clustering algorithm can support reliable and timely data uploading by taking into account the cluster stability and the communication reliability during the process of clustering vehicles. Online sensing parameter adjustment algorithm can guide the actions of intelligent vehicles based on the real-time traffic condition, which can achieve a tradeoff between sensing accuracy and communication overhead. In order to evaluate the performance of our scheme, we develop a simulation platform in which all the components in the system are built. By conducting extensive simulation, the superiority of the proposed scheme is validated.

Unmanned aerial vehicles (UAVs) can be utilized to provide communication services for the areas where the communication is unavailable due to the high mobility, agility and low cost. In this letter, we investigate the energy efficient UAVs deployment and the movement for emergency response in the multi-UAVs enabled wireless communication system. The positions of the UAVs, the users association and the transmit power of the ground users are jointly optimized to maximize the energy efficiency of all ground users. And when one UAV leaves because of the breakdown or the drained battery, how the other UAVs move with the minimum energy cost is also studied. An alternating algorithm based on successive convex approximation (SCA) technique is proposed to solve the formulated optimization problems. Numerical results show the proposed multi-UAVs deployment and movement outperform the benchmarks.

Edge-assisted mobile crowdsensing is an emerging paradigm where mobile users collect and share sensing data at the edge of networks. With the abundant on-board resources and large movement patterns of intelligent vehicles, they have become candidates to sense up-to-date and fine-grained information for large areas. The design of vehicle recruitment in edge-assisted mobile crowdsensing is challenging due to the selfishness and the uneven distribution of vehicles, as well as the spatiotemporal constraints of vehicular crowdsensing applications. To deal with these challenges, this paper proposes an incentive-aware vehicle recruitment scheme for edge-assisted mobile crowdsensing. In particular, we first design an incentive mechanism to motivate cooperation among the edge server and the intelligent vehicles, and apply the Nash bargaining theory to obtain the optimal cooperation decision. Furthermore, a practical and efficient scheme is proposed to weigh the contribution of vehicles. Then, we formulate the participant recruitment as an optimization problem, and prove that it is NP-hard. To address this problem, an effective heuristic algorithm with a guaranteed approximation ratio is proposed, by leveraging the property in submodular optimization. Finally, we conduct extensive simulations, based on a real dataset, to validate the superiority of the proposed schemes.

With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.