In this paper, we investigate the spectrum-sharing for a space information network comprised of a geostationary orbit (GEO) satellite and a pair of low earth orbit (LEO) satellites, where one spectrum-sensing LEO (SLEO) is used to sense the status of spectrum occupancy of the GEO, and one data-transmission LEO (DLEO) satellite is allowed to access the shared spectrum of the GEO with the aid of the SLEO. In order to improve the throughput of the DLEO satellite, a spectrum sharing framework has been conceived relying on two stages. Differing from the conventional spectrum-sharing scheme, the data-transmission LEO satellite has the ability to access the shared spectrum both in overlay mode and in underlay mode. In overlay mode, the DLEO can perform transmission at any required transmit power. By contrast, in underlay mode, a spectrum-sensing and power allocation aided spectrum-sharing (SPA-SS) scheme is proposed to guarantee that the maximal amount of interference is imposed on the GEO caused by the DLEO. To be specific, in the first stage, the SLEO is invoked for performing spectrum-sensing, where the time intervals of spectrum-sensing have been specifically optimized to alleviate the interference received at the GEO in the second stage. Moreover, an adaptive power allocation is designed to maximal the throughput of the DLEO in the second stage, whilst maintaining the maximal interference imposed on the GEO is lower than the tolerable interference temperature of the GEO. Numerical results show that the proposed SPA-SS scheme outperforms the traditional spectrum-sharing scheme in terms of not only the average throughput, but also of the time-utilization ratio.

In this paper, we analyze the outage probability of an amplify-and-forward (AF) cooperative non-orthogonal multiple access (NOMA) model in multi-relay multiuser networks. In contrast to conventional cooperative networks, relays in the considered network have no embedded energy supply; they need to rely on the energy harvested from the signals broadcasted by the source for their cooperative NOMA transmission. Based on this structure, a new relay selection scheme is proposed, considering both channel state information (CSI) and battery status of relays. Assuming each relay has infinite or finite energy storage for accumulating energy, we use the infinite or finite Markov chain to capture the evolution of relay batteries and certain simplified assumptions to reduce computational complexity of the Markov chain analysis. The approximate closed-form expressions for the outage probability of the proposed scheme are derived therefrom. All theoretical results are validated by numerical simulations. The impacts of the system parameters, such as relay number, energy harvesting threshold and battery size, on the performance are extensively investigated.

We investigate a distributed-satellite-clusters (DSC)-system-based spectrum sensing, to enhance the ability for sensing weak signals. However, the spectrum-sensing performance may be significantly decreased by the phase deviations among different satellite clusters, where the deviations may be caused by the movement and the perturbation of satellites. To eliminate such a decrement, we propose a cooperative spectrum-sensing scheme in the presence of phase deviations, where the deviations are alleviated by a special two-stage phase synchronization. Specifically, the phase compensation is first performed relying on broadcasting reference signals and the ephemeris, to address the challenges of the deviations caused by the movement. Then, a two-bit feedback algorithm, having a dynamic disturbance step size, is further adopted for controlling and mitigating the deviations caused by the perturbation. Additionally, we provide the closed-form expression of the correct detection probability of the proposed spectrum-sensing scheme, using the specially derived probability density function of the sum of the shadowed-Rician random variables with independently identical distribution. Simulation results show that the proposed scheme can achieve the best spectrum-sensing performance, comparing with the traditional energy detection, eigenvalue ratio test and the generalized likelihood ratio test.

In this article, we consider a communication Internet of Things network where multiple low-Earth-orbit (LEO) satellites provide services to massive terminals under the grant free-random access (GF-RA) mode. Due to the high mobility of LEO satellites and the uncoordinated access characteristics of GF-RA, acquiring instantaneous channel state information (CSI) becomes particularly challenging, and the spatial configuration among satellites varies rapidly, all of which severely restricts the implementation of multibeam jointly processing (MBJP) in the uplink. To overcome these challenges, we establish a unified multisatellite cooperative MBJP (MSC-MBJP) framework through leveraging the regional statistical CSI (RsCSI) and the specially designed satellite selection scheme. Specifically, we first construct a set consisting of the specially selected satellites in real time, to meet the requirements of maximizing the resource utilization efficiency, reducing handover frequency among satellites, as well as upgrading the performance of MBJP. Then, we propose the distributed MSC-MBJP (DMSC-MBJP) and centralized MSC-MBJP (CMSC-MBJP) schemes. Different from the distributed scheme, the centralized scheme relies on collecting the data from all cooperative satellites. Subsequently, considering the intrinsic relationship between MBJP and user scheduling, we jointly optimize the beamforming vectors and the link matrix to address the optimization problems associated with the proposed two schemes. Extensive simulations demonstrate that the proposed CMSC-MBJP scheme outperforms the conventional single-satellite MBJP scheme. Moreover, the proposed DMSC-MBJP scheme can achieve a comparable user capacity when the transmit power of terminals is 35 dBmW. Especially, the proposed DMSC-MBJP scheme exhibits the superiority of the access success rate for terminals located near the area boundary, in terms of a nearby 27% increment.

Low Earth orbit (LEO) satellite systems provide ubiquitous global connectivity for massive grant-free random access Internet of Things (IoT) applications. Full frequency reuse (FFR) improves spectrum efficiency in spectrum sharing scenarios but introduces severe adjacent beam and cross-system co-channel interference. Meanwhile, the high mobility of LEO satellites hinders accurate instantaneous channel state information (iCSI) acquisition, and random direction-of-arrival (DOA) estimation errors cause statistical CSI (sCSI) mismatch, which degrades beamforming performance and makes it difficult to balance transmission robustness, user fairness, and onboard computational complexity. To address these issues, we propose a low-complexity Hybrid Optimized Robust Beamforming (HORBA) algorithm. We first construct a robust joint optimization model to characterize the coupling effects of DOA errors, outdated CSI, and multi-dimensional interference, with constraints on per-user minimum SINR and cross-system interference temperature. Then, based on the block coordinate descent framework, we decouple the original non-convex problem into two convex subproblems, which are solved via generalized eigenvalue decomposition and first-order Taylor expansion, combined with an adaptive sampling mechanism that balances accuracy and complexity. Simulation results verify that our algorithm outperforms typical benchmarks in sum rate and robustness, maintains low onboard processing complexity, and effectively alleviates edge user rate polarization.