Ke Wang

Satellite systems have emerged as a key component of future 6G networks, where beam hopping is widely adopted to improve resource utilization under limited onboard power and bandwidth. However, due to the inherent timescale mismatch between beam scheduling and resource allocation, as well as the increased system complexity caused by wide-area coverage and flexible resource demands, efficient resource scheduling requires coordinated decision-making across multiple timescales while ensuring user-level QoS provisioning. This paper aims to address the three-dimensional coupling of resources in the beam, power, and frequency domains while satisfying heterogeneous user QoS requirements. The key idea is to establish a multitime-scale framework that decouples the original optimization problem in the temporal domain. Specifically, beam scheduling is performed over beam hopping time slots(BHTSs), while resource allocation are conducted over finer-grained user scheduling time slots. A verification approach is developed to provide theoretical justification for the proposed timescale decomposition and to ensure the consistency. Based on this framework, a weight-based search strategy is employed for beam hopping pattern (BHP) selection, and a block coordinate descent (BCD) method is adopted to iteratively optimize user-level resource allocation. The simulation results demonstrate that the proposed scheme significantly improves system throughput and service satisfaction quality compared to existing methods, while maintaining robust performance under heterogeneous traffic and delay-sensitive scenarios.

Future sixth-generation (6G) low Earth orbit (LEO) satellite systems will utilize large-scale multi-beam phased arrays operating at millimeter-wave (mmWave) frequencies, with embedded power amplifiers (PAs) at each antenna element. However, the severe path loss drives PAs near saturation, and dynamic beamforming leads to nonuniform, time-varying PA conditions, jointly resulting in complex memory nonlinearities that degrade system performance. Such in-band distortions require mitigation by PA linearization, which remains highly constrained in LEO satellite systems. Therefore, accurate modeling and quantification of these nonlinearities are critical for realistic performance evaluation and to guide the development of effective linearization strategies. Current analytical methods focus on single-PA or memoryless-array models, without addressing the frequency-dependent statistical characteristics and spatially varying radiation patterns of nonlinear distortions in multi-beam phased arrays. In this paper, we develop statistical models for the PA output and the received signals in multi-beam phased arrays, explicitly decomposing the distortion structure and deriving closed-form expressions for each distortion component’s statistical properties. Furthermore, we analyze the spatial beam-pattern distortions and derive a closed-form expression for the signal-to-distortion-plus-interference-and-noise ratio (SDINR) under nonlinear distortion. Extensive simulations, conducted measurements, and over-the-air (OTA) experiments validate that the proposed model reduces optimal input back-off estimation error by 2 dB and improves SDINR prediction accuracy by approximately 3 dB. This research establishes a robust analytical foundation for future LEO satellite system design and linearization technology development.

Satellite-based Internet of Things (S-IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While layer-coded hybrid automatic repeat request (L-HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard age of information (AoI) metric and obscuring the critical tradeoff between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross-layer optimization framework centered on a new metric, the cross-layer age of error information (C-AoEI). We derive a closed-form expression for C-AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet-level encoded L-HARQ scheme for multi-ground base station (GBS) scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the effectiveness of our proposed framework: it achieves 31.8% higher transmission efficiency and 17.2% lower C-AoEI than conventional schemes. The framework also proves robust against intercell interference and varying channel conditions, providing a foundation for designing efficient, latency-aware next-generation S-IoT protocols.

The integration of low-Earth orbit (LEO) satellites with hypersonic aircraft (HSA) presents a critical frontier for next-generation nonterrestrial networks (NTNs). However, the ultrahigh dynamics inherent in these scenarios introduce severe Doppler frequency shifts (DFSs) and time-varying delays, significantly degrading link reliability. To address these challenges, this article proposes a robust orthogonal time–frequency space (OTFS) communication framework tailored for hypersonic-LEO links. First, we establish a 3-D kinematic model based on the Earth-centered inertial (ECI) coordinate system. This model accurately characterizes the dynamic delay–Doppler (DD) channel features across the aircraft’s ascent, cruise, and descent phases. Guided by these channel insights, we develop a phase-adaptive dynamic pilot design scheme that jointly optimizes pilot position, pilot power, pilot quantity, and guard intervals to maximize spectral efficiency and estimation accuracy. Furthermore, leveraging the temporal correlation of the OTFS channel in the DD domain, we propose a multiframe joint cross correlation matching (MF-JCM) channel estimation algorithm. This algorithm aggregates multiframe channel responses to suppress noise and enhance detection probability. Simulation results demonstrate that the proposed scheme achieves a bit error rate (BER) of $10^{-4}$ at a signal-to-noise ratio (SNR) of 7.5 dB in the ascent phase and 11 dB in the descent phase. Hardware experiments based on software-defined radio (SDR) further validate the feasibility and superiority of the proposed approach in practical ultrahigh dynamic scenarios.

The application of resource allocating technology to multi-beams satellite system can effectively achieve higher throughput. This letter investigates the joint scheduling of physical layer and power resource for the Geosynchronous Earth Orbit (GEO) satellite Internet of Things (IoT). We propose the resource scheduling model aimed at maximizing system throughput by taking into account the constraints of time, frequency, and power resources and parameter optimization of the IoT Non-Terrestrial Network (NTN) channel conditions. For the multiple-choice multidimensional knapsack problem (MMKP), the non-convex optimization problem is decomposed into subproblems for each timeslot and selects the optimal set of IoT users. Specifically, we propose a joint physical layer and power resource allocation (JPPRA) algorithm based on channel conditions. Furthermore, simulation results demonstrate that the proposed approach achieves superior system capacity than other scheduling schemes.

Fully-connected (FC) hybrid beamforming (HBF) has become crucial in low Earth orbit (LEO) satellite communications. However, severe power amplifier (PA) nonlinearities arise due to high power demands. Traditional digital predistortion (DPD) techniques encounter substantial challenges within LEO environments. The constrained computational resources and codebook storage, limited RF feedback, and outdated channel state information (CSI) collectively hinder real-time DPD implementation in millisecond-scale beam hopping. To address these issues, this letter proposes a multi-element spatial DPD framework incorporating a power selection module and single-channel feedback. By storing predistorters in codebooks indexed only by power levels and enabling real-time updates at different power levels, the proposed scheme effectively decouples DPD from beam hopping, achieving robust and low-complexity nonlinear compensation in LEO satellite systems.

The 3rd Generation Partnership Project (3GPP) 5G Non-Terrestrial Networks (NTN) adopt Orthogonal Frequency Division Multiplexing (OFDM) to enable integrated space-ground networks via Low Earth Orbit (LEO) satellite global connectivity. However, the rapid movement of LEO satellites induces a significant non-uniform Doppler shift across subcarriers, resulting in the signal bandwidth changes that leads to sampling point offsets and severely impacting demodulation performance. Traditional Doppler compensation algorithms focus mainly on addressing the uniform Carrier Frequency Offset (CFO) caused by crystal oscillation. In LEO satellite broadband communication systems, the Sampling Frequency Offset (SFO), caused by the non-uniform Doppler frequency shift, can be tens of times greater than the CFO, leading to non-negligible phase rotation, inter-carrier interference (ICI), and inter-symbol interference (ISI). Consequently, a low-complexity algorithm is required to address the fast time-varying SFO — one of the core challenges in these systems. In this paper, we derive a closed-form expression for the signal distortion and propose a model-driven compensation method that leverages the predictability of satellite trajectories. The proposed method effectively removes phase rotation and mitigates ICI and ISI through fast Fourier transform (FFT) window adjustment and phase rotation compensation. The accuracy of the model is validated through extensive simulations and a real satellite communication trial. Results demonstrate that the proposed method supports higher-order modulations, leading to an average spectral efficiency improvement of approximately 50%. This pioneering research promises to ensure robust performance in dynamic LEO satellite environments.