Lu Lv

Fractionated spacecraft is a novel technology to improve flexibility, robustness, and survivability in future satellite systems by decomposing a traditional spacecraft into multiple functional modules. This paper investigates simultaneous wireless information and power transfer (SWIPT) for a fractionated spacecraft system, where a primary satellite (PS) equipped with a large scale antenna array transmits energy signals to charge multiple energy-constrained miniaturized satellites (MSs) in the near field of the PS, and simultaneously sends information signals to serve ground information receivers (IRs) in the far field of the PS. A joint optimization problem of digital beamforming, analog beamforming, and beam scheduling at the PS is developed to maximize the achievable sum rate of the IRs subject to a requirement on MSs’ harvested energy. Due to the highly coupled optimization variables and the binary beam-scheduling variables, the formulated problem is a mixed-integer non-linear programming (MINLP) problem and hence, is difficult to solve. We first develop an alternating optimization algorithm to solve the formulated problem based on the weighted minimum meansquare error method, the manifold optimization method, and the matching theory. To get a low-complexity solution of the formulated problem, we derive the coherence between near- and far-field steering vectors. Then by exploiting the channel coherence, we propose a low-complexity algorithm that transforms the original MINLP problem to a linear programming problem. Numerical results are provided to validate the performance improvement of our proposed algorithms compared to various benchmark schemes, and reveal the practical significance of our developed beamforming and beam scheduling strategies for spectral- and energy-efficient fractionated spacecraft SWIPT.

In this correspondence, we introduce the pinching antenna (PA) into symbiotic radio (SR) systems to mitigate the double-fading effect on SR signals by leveraging PA's capability of reconfiguring the wireless channel. Specifically, we consider a time division multiple access-based PA-enhanced SR system with multiple backscatter devices (BDs), and formulate an optimization problem to maximize the sum throughput of BDs by jointly optimizing the PA positions, reflection coefficients of BDs, and time-slot durations. To solve this non-convex optimization problem, we decompose it into two subproblems: one optimizes the reflection coefficients of BDs and time-slot durations, and the other optimizes the PA positions. The former is solved by deriving the closed-form solutions, while the latter is solved by devising an improved one-dimensional search algorithm. Based on the obtained solutions to the two subproblems, we propose an alternating optimization-based iterative algorithm. Numerical results demonstrate that the proposed algorithm achieves the near-optimal BDs' sum throughput compared to the exhaustive search algorithm, while exhibiting the lowest complexity among all benchmark algorithms.

We investigate covert communications in an intelligent reflecting surface (IRS)-assisted symbiotic radio (SR) system under the parasitic SR (PSR) and the commensal SR (CSR) cases, where an IRS is exploited to create a double reflection link for legitimate users and degrade the detection performance of the warden (W). Specifically, we derive an analytical expression for the average detection error probability (ADEP) of W and design an optimal strategy to determine the transmit power and backscatter reflection coefficient. To further enhance the covert performance based on the derived results, the joint optimization of the source transmit power, backscatter device (BD) reflection coefficient, and IRS phase-shifter is formulated as an exponential-based quadratic-fractional (EQF) problem. By reformulating the original problem into a quartic polynomial (QP) problem, we further develop the phase alignment pursuit and the power leakage minimization algorithms for the PSR and the CSR cases, respectively. Numerical results confirm the accuracy of the derived results and the superiority of our proposed strategy in terms of covertness.

This paper investigates a two-stage covert enhancement beamforming design based on the extended Kalman filter (EKF) to combat the mobile eavesdropper (Willie) in the integrated sensing and covert communications. The proposed scheme employs artificial noise (AN) to interfere with and track Willie, while the beamwidth of AN is controlled by the number of antennas. In the initial stage, due to the lack of prior information, the EKF suffers from large prediction errors, preventing the jamming beam from fully aligning with Willie. Thus, a wide AN beam is adopted to guarantee the jamming effect on Willie. In the second stage, as the prediction error of the EKF converges after iterative updates, the scheme switches to a narrow AN beam to track Willie effectively. Considering the imperfect channel state information of the covert user, we jointly optimize the time allocation and transmit beamforming for the two stages, aiming to minimize transmission energy, and propose the joint wide and narrow beam optimization algorithm to address the non-convex problem. Simulation results demonstrate that the proposed scheme reduces the transmission energy by approximately 23% compared with the benchmark schemes.

This paper investigates the joint optimization of transmit beamforming and discrete antenna positions for maximally improving the sum rate of movable antenna (MA) enhanced downlink non-orthogonal multiple access (NOMA) systems. As such an optimization problem belongs to a challenging mixed-integer nonlinear programming, we first develop an equivalent problem reformulation, with which we further propose a two-sided matching assisted optimization algorithm to jointly optimize the discrete antenna positions and transmit beamforming. Theoretical analysis demonstrates that the proposed algorithm always converges to a stable matching within polynomial complexity. Simulation results show that NOMA-MA using our proposed algorithm not only significantly outperforms the NOMA-MA using sequential optimization and the reconfigurable intelligent surface aided fixed position antenna (FPA) empowered NOMA, but also achieves a better performance gain over FPA than its orthogonal multiple access counterpart.

This paper investigates energy efficient multiple access designs for a wireless-powered backscatter communication (BackCom) system. Specifically, two energy and spectrally transmission strategies, namely wireless power transfer aided orthogonal multiple access (WPT-OMA) and backscatter communication aided non-orthogonal multiple access (BAC-NOMA), are proposed to encourage energy and spectrum cooperation among devices for overall energy minimization. A unified resource allocation problem of jointly optimizing time allocation, beamforming vectors, BackCom reflection coefficients, and device transmit power is formulated and solved by an efficient optimization algorithm. Simulation results are provided to compare the performance of the proposed schemes and demonstrate their advantage in terms of overall energy minimization over the benchmark schemes.

The openness of the space environment renders satellite Internet vulnerable to external malicious attacks. As a typical approach of physical-layer authentication, radio frequency fingerprint (RFF) identification can be exploited to identify wireless user devices by extracting their unique hardware characteristics, thereby becoming a promising solution to strengthen the authentication security of satellite Internet. However, since satellite Internet is expected to provide globally seamless services to massive users, it is not only overhead-costly but also privacy-risky to construct a centralized RFF identification model by sharing wireless signals of all users in a huge dataset for RFF extraction. Fortunately, by collaborative model training among satellites, federated learning (FL) offers a viable solution for building up an on-orbit distributed RFF identification model that does not require sharing the received signals among satellites. Inspired by this fact, this article investigates the multi-satellite FL framework design for on-orbit RFF identification. In specific, we first briefly introduce the fundamentals of RFF identification. Then, following the idea of pioneering work [1], a vanilla multi-satellite FL framework is presented for on-orbit RFF identification, followed by technical challenges of its practical application, including heterogeneous local datasets, non-identical satellite resources, and dynamic link topology. To overcome these challenges, we further propose a hierarchically collaborative multi-satellite FL framework, under which three key techniques, say scheduling management, model aggregation, and model personalization are also investigated in-depth. Finally, we outline the future research directions and challenges.

We propose a drone signal out-of-distribution detection (OODD) algorithm based on the cognitive fusion of Zadoff-Chu (ZC) sequences and time-frequency images (TFI). ZC sequences are identified by analyzing the communication protocols of DJI drones, while TFI capture the time-frequency characteristics of drone signals with unknown or non-standard communication protocols. Both modalities are used jointly to enable OODD in the drone remote identification (RID) task. Specifically, ZC sequence features and TFI features are generated from the received radio frequency signals, which are then processed through dedicated feature extraction module to enhance and align them. The resultant multi-modal features undergo multi-modal feature interaction, single-modal feature fusion, and multi-modal feature fusion to produce features that integrate and complement information across modalities. Discrimination scores are computed from the fused features along both spatial and channel dimensions to capture time-frequency characteristic differences dictated by the communication protocols, and these scores will be transformed into adaptive attention weights. The weighted features are then passed through a Softmax function to produce the signal classification results. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms and achieves 1.7% and 7.5% improvements in RID and OODD metrics, respectively. The proposed algorithm also performs strong robustness under varying flight conditions and across different drone types.

We propose a drone signal out-of-distribution (OOD) detection algorithm based on discriminability-driven spatial-channel selection with a gradient norm. Time-frequency image features are adaptively weighted along both spatial and channel dimensions by quantifying inter-class similarity and variance based on protocol-specific time-frequency characteristics. Subsequently, a gradient-norm metric is introduced to measure perturbation sensitivity for capturing the inherent instability of OOD samples, which is then fused with energy-based scores for joint inference. Simulation results demonstrate that the proposed algorithm provides superior discriminative power and robust performance via SNR and various drone types.