In this paper, we propose a rate-compatible partially-coupled staircase low density parity check (PS-LDPC) coding scheme for high speed inter-satellite communications. First, we introduce the encoding process and sliding window decoding (SWD) algorithm of PS-LDPC codes, and we investigate the error floor of component codes, which validate that the PS-LDPC codes with short block-length component code can maintain the reliability, and significantly reduce the decoding latency. Then, we analyze the density evolution (DE) of PS-LDPC codes based on the multi-edge type (MET)- LDPC framework under the Gaussian approximation, and derive its decoding thresholds of SWD. Further, we propose an optimized coupling pattern (OCP) encoding algorithm that achieves the optimal coupling patterns with the minimized threshold by introducing two-stage column permutations, and modify the message exchanges in SWD algorithm according to this encoding algorithm. Moreover, we design a new decoding algorithm, named cascaded SWD (C-SWD) algorithm, which reduces the error floor and enhances decoding performance by pre-decoding, reliability enhancement, and cascading belief propagation (BP) decoder or ordered likelihood decoder (OLD) due to the error floor. Simulation results demonstrate that our PS-LDPC coding scheme outperforms the existing rate-compatible spatially coupled LDPC (SC-LDPC) coding schemes in terms of bit error rate and complexity.

Satellite networks are expected to play a key role in 6G and beyond. As demand grows, more operators are entering the market, leading to both competition and collaboration through resource sharing—similar to how the Internet developed into a network of networks. We envision a federated satellite network where operators work together for mutual benefit. To support this, we propose a cooperative beam management framework called federated satellite cooperative beam management (FSCBM). Unlike most existing approaches that focus on the Internet or simple satellite cooperation, FSCBM allocates network resources in a dynamic topology to increase capacity, using time slot and power allocation in resource-limited situations. We also develop cost models and introduce an efficient budget-constrained auction algorithm. Simulation results show that our solution significantly improves capacity and reduces cost, making it one step further toward the vision of the federated network of satellite networks.

Semantic communication exploits the inherent meaning embedded in data content and has become a key enabler in knowledge-driven image transmission frameworks. However, existing research predominantly focuses on computational methodologies, often overlooking the transmission mechanisms. In particular, quantization noise introduced during semantic compression and transmission severely impacts the fidelity and utility of the received data, which remains an unresolved issue. To address this challenge, we propose an Adversarial Learning-based Quantization Selective Hybrid Automatic Repeat reQuest (ALQS-HARQ) mechanism tailored for semantic image transmission in remote sensing satellite networks. The proposed framework dynamically configures the number of quantization bits at the semantic encoder to enhance robustness against quantization noise. Furthermore, we design a quantized bitmerging retransmission scheme, equipped with an intelligent decision-making module and a standardized packet header. A novel metric is introduced to evaluate the semantic recovery completeness, guiding efficient retransmission. Extensive experiments demonstrate that the proposed ALQS-HARQ mechanism achieves higher task success rates with reduced transmission overhead compared to conventional retransmission schemes, showcasing its superior efficiency and adaptability.

Orbital edge computing (OEC) is crucial for supporting space intelligence applications within satellite networks. However, individual satellites face resource constraints, and implementing distributed processing techniques, such as federated learning (FL), across multiple satellites introduces significant scheduling complexity. To address these challenges, we first model the key factors influencing complex satellite networks, including satellite constellations, regional resource demands, inter-satellite communication and routing, energy consumption, and battery aging—a novel aspect invoked by OEC operations. We propose an adaptive aggregation method to fundamentally improve communication efficiency in OEC-based FL. To enhance scheduling performance, we formulate a unified optimization problem that jointly considers data partitioning, resource allocation, and aggregation transmission tasks within a decentralized partially observable Markov decision process (Dec-POMDP) framework. Furthermore, we introduce an episodic-phase-recalling reward shaping (EPRS) method to correlate the influences across these phases. Inspired by multi-task learning, we propose an efficient multi-agent reinforcement learning (MARL) algorithm featuring a multi-head actor-critic (MH-AC) network structure and task-equalized adaptation (TEA) technology, designed to optimize latency, energy consumption, network traffic, and battery aging. Extensive experiments validate the effectiveness of the proposed method, showing a 29.9% reduction in total training time, an 11.5% reduction in network traffic, and superior overall performance compared to rule-based methods.

Ultra wideband (UWB) signals, with their high time resolution, large bandwidth, and low energy consumption, hold great promise as candidates for future integrated sensing and communication (ISAC) systems. In this article, we attempt to explore the tradeoff between communication and sensing by estimating the unknown parameters of transmitted symbols and channel characteristics. We utilize the Fisher Information Matrix (FIM) and Cramr-Rao Bound (CRB) to quantify the performance. Firstly, due to the coupling between channel parameters and transmitted symbols, a differential decoupling approach is proposed. Subsequently, we conducted a comparative analysis of sensing performance across various decoupling methods. We can see that the transmitted symbols have noticeable degrading effects under typical channel parameters. Numerical results are provided, to show the decouple performance degradation on different decoupling schemes.

A novel two-antenna hybrid carrier system combining Alamouti space-time block coding is proposed, in which two information symbol vectors are transmitted in single carrier (SC) and multi-carrier (MC) schemes, respectively, from different antennas at the same time. The Weighted Fractional Fourier Transform (WFRFT) is involved at the transmitter. And a new mathematical transformation termed the Weighted Fractional Negative Fourier Transform (WFRNFT) is defined for demodulation. The simulation results show that the cross-modulation system has better bit error rate (BER) performance than the existing SC and MC Alamouti systems.

Motion compensation (MOCOMP) is a key procedure in inverse synthetic aperture radar (ISAR) imaging because the accuracy of estimated parameter has a strong influence on the imaging quality. Generally, the backscattered signal of a moving target is sampled in fast time dimension, which can be approximated as the combination of multiple Chirp signals with a proper Chirp rate. Compared with the Fourier transform, the fractional Fourier transform (FrFT) performs better compression property due to its unique energy focus ability to Chirp signals. An improved Cross-correlation method based on FrFT for parameter estimation is presented in this paper. It employs the correlation between range profiles compressed by FrFT to enhance the quality of parameter estimation for ISAR applications. The method takes good balance between accuracy and complexity, and is robust to noise. Simulation results show that the proposed method outperforms the conventional Cross-correlation Method in terms of ISAR translational MOCOMP.