Cooperative positioning with multiple low earth orbit (LEO) satellites is promising in providing location-based services and enhancing satellite-terrestrial communication. However, positioning accuracy is greatly affected by inter-beam interference and satellite-terrestrial topology geometry. To select the best combination of satellites from visible ones and suppress inter-beam interference, this paper explores the utilization of flexible beam scheduling and beamforming of multi-beam LEO satellites that can adjust beam directions toward the same earth-fixed cell to send positioning signals simultaneously. By leveraging Cramér-Rao lower bound (CRLB) to characterize user Time Difference of Arrival (TDOA) positioning accuracy, the concerned problem is formulated, aiming at optimizing user positioning accuracy under beam scheduling and beam transmission power constraints. To deal with the mixed-integer-nonconvex problem, we decompose it into an inner beamforming design problem and an outer beam scheduling problem. For the former, we first prove the monotonic relationship between user positioning accuracy and its perceived signal-to-interference-plus-noise ratio (SINR) to reformulate the problem, and then semidefinite relaxation (SDR) is adopted for beamforming design. For the outer problem, a heuristic low-complexity beam scheduling scheme is proposed, whose core idea is to schedule users with lower channel correlation to mitigate inter-beam interference while seeking a proper satellite-terrestrial topology geometry. Simulation results verify the superior positioning performance of our proposed positioning-oriented beamforming and beam scheduling scheme, and it is shown that average user positioning accuracy is improved by 17.1% and 55.9% when the beam transmission power is 20 dBw, compared to conventional beamforming and beam scheduling schemes, respectively.

Benefit from the development of satellite communication technology, low earth orbit (LEO) satellite network is poised to be a critical technology for the sixth generation (6G) communication technology. LEO satellites within the constellation and between different constellations can route data from node to node through intersatel-lite link, improving the continuous transmission of service data and space-based in-orbit service capabilities. However, traffic in the network varies over time, while the mobility of LEO satellites causes the network topology to change over time, raising a problem that requires instantaneous routing updates based on the satellite network state and topology. Motivated by this, in this paper, we propose a CNN-based real-time routing scheme with the overall goal of minimizing the service data flow delay. In the simulation scenario, experimental results show that compared with the shortest path first algorithm (SPF), the algorithm proposed in this paper can reduce the data flow completion time (FCT) by 10%.

Satellite computing represents a recent computational paradigm in the development of low Earth orbit (LEO) satellites. It aims to augment the capabilities of LEO satellites beyond their current transparent relay functions by enabling real-time processing, thereby providing low-latency computational services to end users. In LEO constellations, a significant deployment of computationally capable satellites is orchestrated to offer enhanced computational resources. Challenges arise in the optimal allocation of terminal services to the most suitable satellite due to overlapping coverage among neighboring satellites, compounded by constraints on satellite energy and computational resources. The satellite service allocation (SSA) problem is recognized as NP-hard, yet assessing allocation methods through results allows for the application of deep reinforcement learning (DRL) to obtain improved solutions, partially addressing the SSA challenge. In this paper, we introduce a satellite computing capability model to quantify satellite computational resources. A DRL model is proposed to address service demands, computational resources, and resolve service allocation conflicts, strategically placing each service on appropriate servers. Through simulation experiments, numerical results demonstrate the superiority of our proposed method over baseline approaches in service allocation and satellite resource utilization, showcasing advancements in this field.

Unmanned Aerial Vehicle (UAV) networks have evolved tremendously to provide pervasive intelligence services, handle compute-intensive and latency-sensitive tasks, and free up more network resources. However, time-varying channel gains and dynamic UAV locations hinder better offloading performance and reduce quality of service. Therefore, we propose a UAV-based computation offloading model to maximize the throughput of terrestrial users while reducing the privacy overhead. Based on an on-board battery energy harvesting technologies, we propose a multi-agent proximal policy optimization method to optimize the CPU cycle frequency and offloading decisions that further extend the UAV execution time. Then present a federated learning-based privacy protection framework to generate the optimal block size. In addition, we demonstrate the long-term energy queue constraint through massive mathematical derivations. Finally, extensive simulation results show that the proposed algorithm has better performance gains in terms of throughput and privacy protection overhead compared to some advanced benchmarks.

Integrated satellite-terrestrial networks (ISTNs) provide crucial support for ubiquitous connectivity, whereas the inherent openness poses significant security threats. Covert communication that achieves intrinsic protection by concealing transmission behavior has become an ideal solution. This work studies a downlink covert satellite communication scenario under signal overlay. Existing research mostly carries out optimizations in the power domain. However, since waveforms serve as the physical carriers of information, their design critically impacts the covert communication performance via different adjustability to channel state information (CSI). Specifically, blindly enhancing demodulation reliability does not necessarily lead to better covert capacity and may even be counterproductive. OFDM is vulnerable to carrier frequency offset, while OTFS lacks the flexibility to accommodate CSI, resulting in difficulties applying existing candidate waveforms. In light of this, we propose a nimble waveform named orthogonal fractional dual index multiplexing (OFDIM) tolerant of fractional delay and Doppler. It enables common modulation schemes through integer indices and creates a fractional domain resilient to CSI. The closed-forms of the maximum covert transmit power, reliability, and spectral efficiency are derived. Fractional indices are optimized to improve the performance based on CSI. Simulations validate the theoretical analysis and demonstrate the benefits of the fractional domain in covert communication. Additionally, OFDIM incorporates a complementary security mechanism where the indices can act as dynamic ciphers and guarantee certain security even if the communication behavior is exposed.